1-Unet2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "collapsed": true,
    "executionInfo": {
     "elapsed": 299324,
     "status": "ok",
     "timestamp": 1649407648647,
     "user": {
      "displayName": "PABLO DELGADO RODRIGUEZ",
      "userId": "11999848093282927146"
     },
     "user_tz": -120
    },
    "id": "BFqqjKneHyws",
    "jupyter": {
     "outputs_hidden": true
    },
    "outputId": "da4bd4b8-cf60-4232-fcda-fb1c1a0a3ce4",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0107.tif removed because it was only backgroud\n",
      "53\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0161.tif removed because it was only backgroud\n",
      "54\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0009.tif removed because it was only backgroud\n",
      "55\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0037.tif removed because it was only backgroud\n",
      "56\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0166.tif removed because it was only backgroud\n",
      "57\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0000.tif removed because it was only backgroud\n",
      "58\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0016.tif removed because it was only backgroud\n",
      "59\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0141.tif removed because it was only backgroud\n",
      "60\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0137.tif removed because it was only backgroud\n",
      "61\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0008.tif removed because it was only backgroud\n",
      "62\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0040.tif removed because it was only backgroud\n",
      "63\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0084.tif removed because it was only backgroud\n",
      "64\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0132.tif removed because it was only backgroud\n",
      "65\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0174.tif removed because it was only backgroud\n",
      "66\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0117.tif removed because it was only backgroud\n",
      "67\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0147.tif removed because it was only backgroud\n",
      "68\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0099.tif removed because it was only backgroud\n",
      "69\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0030.tif removed because it was only backgroud\n",
      "70\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0164.tif removed because it was only backgroud\n",
      "71\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0176.tif removed because it was only backgroud\n",
      "72\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0170.tif removed because it was only backgroud\n",
      "73\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0026.tif removed because it was only backgroud\n",
      "74\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0076.tif removed because it was only backgroud\n",
      "75\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0055.tif removed because it was only backgroud\n",
      "76\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0072.tif removed because it was only backgroud\n",
      "77\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0069.tif removed because it was only backgroud\n",
      "78\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0015.tif removed because it was only backgroud\n",
      "79\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0079.tif removed because it was only backgroud\n",
      "80\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0010.tif removed because it was only backgroud\n",
      "81\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0083.tif removed because it was only backgroud\n",
      "82\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0165.tif removed because it was only backgroud\n",
      "83\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0011.tif removed because it was only backgroud\n",
      "84\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0102.tif removed because it was only backgroud\n",
      "85\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0005.tif removed because it was only backgroud\n",
      "86\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0062.tif removed because it was only backgroud\n",
      "87\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0031.tif removed because it was only backgroud\n",
      "88\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0047.tif removed because it was only backgroud\n",
      "89\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0148.tif removed because it was only backgroud\n",
      "90\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0089.tif removed because it was only backgroud\n",
      "91\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0110.tif removed because it was only backgroud\n",
      "92\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0111.tif removed because it was only backgroud\n",
      "93\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0136.tif removed because it was only backgroud\n",
      "94\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0115.tif removed because it was only backgroud\n",
      "95\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0007.tif removed because it was only backgroud\n",
      "96\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0065.tif removed because it was only backgroud\n",
      "97\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0120.tif removed because it was only backgroud\n",
      "98\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0048.tif removed because it was only backgroud\n",
      "99\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0061.tif removed because it was only backgroud\n",
      "100\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0002.tif removed because it was only backgroud\n",
      "101\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0182.tif removed because it was only backgroud\n",
      "102\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0119.tif removed because it was only backgroud\n",
      "103\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0139.tif removed because it was only backgroud\n",
      "104\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0078.tif removed because it was only backgroud\n",
      "105\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0039.tif removed because it was only backgroud\n",
      "106\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0177.tif removed because it was only backgroud\n",
      "107\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0025.tif removed because it was only backgroud\n",
      "108\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0138.tif removed because it was only backgroud\n",
      "109\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0144.tif removed because it was only backgroud\n",
      "110\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0168.tif removed because it was only backgroud\n",
      "111\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0158.tif removed because it was only backgroud\n",
      "112\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0004.tif removed because it was only backgroud\n",
      "113\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0003.tif removed because it was only backgroud\n",
      "114\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0124.tif removed because it was only backgroud\n",
      "115\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0178.tif removed because it was only backgroud\n",
      "116\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0116.tif removed because it was only backgroud\n",
      "117\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0058.tif removed because it was only backgroud\n",
      "118\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0059.tif removed because it was only backgroud\n",
      "119\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0122.tif removed because it was only backgroud\n",
      "120\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0108.tif removed because it was only backgroud\n",
      "121\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0019.tif removed because it was only backgroud\n",
      "122\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0146.tif removed because it was only backgroud\n",
      "123\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0129.tif removed because it was only backgroud\n",
      "124\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0163.tif removed because it was only backgroud\n",
      "125\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0145.tif removed because it was only backgroud\n",
      "126\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0045.tif removed because it was only backgroud\n",
      "127\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0020.tif removed because it was only backgroud\n",
      "128\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0071.tif removed because it was only backgroud\n",
      "129\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0114.tif removed because it was only backgroud\n",
      "130\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0169.tif removed because it was only backgroud\n",
      "131\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0013.tif removed because it was only backgroud\n",
      "132\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0101.tif removed because it was only backgroud\n",
      "133\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0041.tif removed because it was only backgroud\n",
      "134\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0171.tif removed because it was only backgroud\n",
      "135\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0153.tif removed because it was only backgroud\n",
      "136\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0095.tif removed because it was only backgroud\n",
      "137\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0063.tif removed because it was only backgroud\n",
      "138\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0149.tif removed because it was only backgroud\n",
      "139\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0118.tif removed because it was only backgroud\n",
      "140\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0001.tif removed because it was only backgroud\n",
      "141\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0160.tif removed because it was only backgroud\n",
      "142\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0074.tif removed because it was only backgroud\n",
      "143\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0103.tif removed because it was only backgroud\n",
      "144\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0022.tif removed because it was only backgroud\n",
      "145\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0112.tif removed because it was only backgroud\n",
      "146\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0086.tif removed because it was only backgroud\n",
      "147\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0043.tif removed because it was only backgroud\n",
      "148\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0091.tif removed because it was only backgroud\n",
      "149\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0073.tif removed because it was only backgroud\n",
      "150\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0167.tif removed because it was only backgroud\n",
      "151\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0085.tif removed because it was only backgroud\n",
      "152\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0017.tif removed because it was only backgroud\n",
      "153\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0127.tif removed because it was only backgroud\n",
      "154\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0028.tif removed because it was only backgroud\n",
      "155\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0100.tif removed because it was only backgroud\n",
      "156\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0068.tif removed because it was only backgroud\n",
      "157\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0173.tif removed because it was only backgroud\n",
      "158\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0082.tif removed because it was only backgroud\n",
      "159\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0027.tif removed because it was only backgroud\n",
      "160\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0018.tif removed because it was only backgroud\n",
      "161\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0121.tif removed because it was only backgroud\n",
      "162\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0092.tif removed because it was only backgroud\n",
      "163\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0029.tif removed because it was only backgroud\n",
      "164\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0090.tif removed because it was only backgroud\n",
      "165\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0064.tif removed because it was only backgroud\n",
      "166\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0130.tif removed because it was only backgroud\n",
      "167\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0056.tif removed because it was only backgroud\n",
      "168\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0175.tif removed because it was only backgroud\n",
      "169\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0033.tif removed because it was only backgroud\n",
      "170\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0128.tif removed because it was only backgroud\n",
      "171\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0096.tif removed because it was only backgroud\n",
      "172\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0051.tif removed because it was only backgroud\n",
      "173\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0070.tif removed because it was only backgroud\n",
      "174\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0057.tif removed because it was only backgroud\n",
      "175\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0113.tif removed because it was only backgroud\n",
      "176\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0105.tif removed because it was only backgroud\n",
      "177\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0021.tif removed because it was only backgroud\n",
      "178\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0098.tif removed because it was only backgroud\n",
      "179\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0109.tif removed because it was only backgroud\n",
      "180\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0044.tif removed because it was only backgroud\n",
      "181\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0023.tif removed because it was only backgroud\n",
      "182\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0162.tif removed because it was only backgroud\n",
      "183\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0126.tif removed because it was only backgroud\n",
      "184\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0140.tif removed because it was only backgroud\n",
      "185\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0106.tif removed because it was only backgroud\n",
      "186\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0142.tif removed because it was only backgroud\n",
      "187\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0012.tif removed because it was only backgroud\n",
      "188\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0066.tif removed because it was only backgroud\n",
      "189\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0088.tif removed because it was only backgroud\n",
      "190\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0046.tif removed because it was only backgroud\n",
      "191\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0143.tif removed because it was only backgroud\n",
      "192\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0157.tif removed because it was only backgroud\n",
      "193\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0014.tif removed because it was only backgroud\n",
      "194\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0151.tif removed because it was only backgroud\n",
      "195\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0104.tif removed because it was only backgroud\n",
      "196\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0134.tif removed because it was only backgroud\n",
      "197\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0067.tif removed because it was only backgroud\n",
      "198\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0038.tif removed because it was only backgroud\n",
      "199\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0034.tif removed because it was only backgroud\n",
      "200\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0093.tif removed because it was only backgroud\n",
      "201\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0152.tif removed because it was only backgroud\n",
      "202\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0181.tif removed because it was only backgroud\n",
      "203\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0042.tif removed because it was only backgroud\n",
      "204\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0155.tif removed because it was only backgroud\n",
      "205\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0050.tif removed because it was only backgroud\n",
      "206\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0024.tif removed because it was only backgroud\n",
      "207\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0133.tif removed because it was only backgroud\n",
      "208\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0054.tif removed because it was only backgroud\n",
      "209\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0052.tif removed because it was only backgroud\n",
      "210\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0081.tif removed because it was only backgroud\n",
      "211\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0032.tif removed because it was only backgroud\n",
      "212\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0180.tif removed because it was only backgroud\n",
      "213\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0087.tif removed because it was only backgroud\n",
      "214\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0172.tif removed because it was only backgroud\n",
      "215\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0077.tif removed because it was only backgroud\n",
      "216\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0075.tif removed because it was only backgroud\n",
      "217\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0060.tif removed because it was only backgroud\n",
      "218\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0035.tif removed because it was only backgroud\n",
      "219\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0097.tif removed because it was only backgroud\n",
      "220\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0150.tif removed because it was only backgroud\n",
      "221\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0154.tif removed because it was only backgroud\n",
      "222\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0156.tif removed because it was only backgroud\n",
      "223\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0049.tif removed because it was only backgroud\n",
      "224\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0094.tif removed because it was only backgroud\n",
      "225\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0179.tif removed because it was only backgroud\n",
      "226\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0159.tif removed because it was only backgroud\n",
      "227\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0080.tif removed because it was only backgroud\n",
      "228\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0125.tif removed because it was only backgroud\n",
      "229\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0123.tif removed because it was only backgroud\n",
      "230\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0053.tif removed because it was only backgroud\n",
      "231\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0135.tif removed because it was only backgroud\n",
      "232\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0036.tif removed because it was only backgroud\n",
      "233\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0006.tif removed because it was only backgroud\n",
      "234\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0257.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0257.tif saved (corrected name)\n",
      "235\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0198.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0198.tif saved (corrected name)\n",
      "236\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0277.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0277.tif saved (corrected name)\n",
      "237\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0250.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0250.tif saved (corrected name)\n",
      "238\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0246.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0246.tif saved (corrected name)\n",
      "239\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0295.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0295.tif saved (corrected name)\n",
      "240\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0206.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0206.tif saved (corrected name)\n",
      "241\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0260.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0260.tif saved (corrected name)\n",
      "242\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0208.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0208.tif saved (corrected name)\n",
      "243\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0286.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0286.tif saved (corrected name)\n",
      "244\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0227.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0227.tif saved (corrected name)\n",
      "245\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0258.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0258.tif saved (corrected name)\n",
      "246\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0304.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0304.tif saved (corrected name)\n",
      "247\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0240.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0240.tif saved (corrected name)\n",
      "248\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0294.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0294.tif saved (corrected name)\n",
      "249\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0247.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0247.tif saved (corrected name)\n",
      "250\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0267.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0267.tif saved (corrected name)\n",
      "251\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0209.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0209.tif saved (corrected name)\n",
      "252\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0264.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0264.tif saved (corrected name)\n",
      "253\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0303.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0303.tif saved (corrected name)\n",
      "254\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0259.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0259.tif saved (corrected name)\n",
      "255\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0192.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0192.tif saved (corrected name)\n",
      "256\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0306.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0306.tif saved (corrected name)\n",
      "257\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0242.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0242.tif saved (corrected name)\n",
      "258\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0235.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0235.tif saved (corrected name)\n",
      "259\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0185.tif removed because it was only backgroud\n",
      "260\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0263.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0263.tif saved (corrected name)\n",
      "261\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0205.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0205.tif saved (corrected name)\n",
      "262\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0269.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0269.tif saved (corrected name)\n",
      "263\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0183.tif removed because it was only backgroud\n",
      "264\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0265.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0265.tif saved (corrected name)\n",
      "265\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0224.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0224.tif saved (corrected name)\n",
      "266\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0194.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0194.tif saved (corrected name)\n",
      "267\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0272.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0272.tif saved (corrected name)\n",
      "268\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0243.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0243.tif saved (corrected name)\n",
      "269\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0296.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0296.tif saved (corrected name)\n",
      "270\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0236.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0236.tif saved (corrected name)\n",
      "271\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0285.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0285.tif saved (corrected name)\n",
      "272\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0214.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0214.tif saved (corrected name)\n",
      "273\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0280.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0280.tif saved (corrected name)\n",
      "274\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0284.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0284.tif saved (corrected name)\n",
      "275\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0190.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0190.tif saved (corrected name)\n",
      "276\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0211.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0211.tif saved (corrected name)\n",
      "277\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0278.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0278.tif saved (corrected name)\n",
      "278\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0255.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0255.tif saved (corrected name)\n",
      "279\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0293.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0293.tif saved (corrected name)\n",
      "280\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0256.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0256.tif saved (corrected name)\n",
      "281\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0233.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0233.tif saved (corrected name)\n",
      "282\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0210.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0210.tif saved (corrected name)\n",
      "283\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0245.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0245.tif saved (corrected name)\n",
      "284\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0252.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0252.tif saved (corrected name)\n",
      "285\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0191.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0191.tif saved (corrected name)\n",
      "286\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0270.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0270.tif saved (corrected name)\n",
      "287\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0201.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0201.tif saved (corrected name)\n",
      "288\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0217.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0217.tif saved (corrected name)\n",
      "289\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0203.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0203.tif saved (corrected name)\n",
      "290\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0239.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0239.tif saved (corrected name)\n",
      "291\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0219.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0219.tif saved (corrected name)\n",
      "292\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0187.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0187.tif saved (corrected name)\n",
      "293\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0189.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0189.tif saved (corrected name)\n",
      "294\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0273.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0273.tif saved (corrected name)\n",
      "295\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0274.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0274.tif saved (corrected name)\n",
      "296\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0223.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0223.tif saved (corrected name)\n",
      "297\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0237.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0237.tif saved (corrected name)\n",
      "298\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0248.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0248.tif saved (corrected name)\n",
      "299\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0292.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0292.tif saved (corrected name)\n",
      "300\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0308.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0308.tif saved (corrected name)\n",
      "301\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0290.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0290.tif saved (corrected name)\n",
      "302\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0228.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0228.tif saved (corrected name)\n",
      "303\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0244.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0244.tif saved (corrected name)\n",
      "304\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0310.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0310.tif saved (corrected name)\n",
      "305\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0268.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0268.tif saved (corrected name)\n",
      "306\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0283.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0283.tif saved (corrected name)\n",
      "307\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0251.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0251.tif saved (corrected name)\n",
      "308\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0266.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0266.tif saved (corrected name)\n",
      "309\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0300.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0300.tif saved (corrected name)\n",
      "310\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0200.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0200.tif saved (corrected name)\n",
      "311\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0279.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0279.tif saved (corrected name)\n",
      "312\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0204.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0204.tif saved (corrected name)\n",
      "313\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0221.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0221.tif saved (corrected name)\n",
      "314\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0249.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0249.tif saved (corrected name)\n",
      "315\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0199.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0199.tif saved (corrected name)\n",
      "316\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0195.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0195.tif saved (corrected name)\n",
      "317\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0202.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0202.tif saved (corrected name)\n",
      "318\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0193.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0193.tif saved (corrected name)\n",
      "319\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0216.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0216.tif saved (corrected name)\n",
      "320\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0281.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0281.tif saved (corrected name)\n",
      "321\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0254.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0254.tif saved (corrected name)\n",
      "322\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0298.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0298.tif saved (corrected name)\n",
      "323\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0220.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0220.tif saved (corrected name)\n",
      "324\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0262.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0262.tif saved (corrected name)\n",
      "325\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0271.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0271.tif saved (corrected name)\n",
      "326\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0276.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0276.tif saved (corrected name)\n",
      "327\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0196.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0196.tif saved (corrected name)\n",
      "328\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0184.tif removed because it was only backgroud\n",
      "329\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0287.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0287.tif saved (corrected name)\n",
      "330\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0186.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0186.tif saved (corrected name)\n",
      "331\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0302.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0302.tif saved (corrected name)\n",
      "332\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0299.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0299.tif saved (corrected name)\n",
      "333\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0197.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0197.tif saved (corrected name)\n",
      "334\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0225.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0225.tif saved (corrected name)\n",
      "335\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0253.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0253.tif saved (corrected name)\n",
      "336\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0215.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0215.tif saved (corrected name)\n",
      "337\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0213.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0213.tif saved (corrected name)\n",
      "338\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0297.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0297.tif saved (corrected name)\n",
      "339\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0309.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0309.tif saved (corrected name)\n",
      "340\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0288.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0288.tif saved (corrected name)\n",
      "341\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0218.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0218.tif saved (corrected name)\n",
      "342\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0232.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0232.tif saved (corrected name)\n",
      "343\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0234.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0234.tif saved (corrected name)\n",
      "344\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0231.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0231.tif saved (corrected name)\n",
      "345\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0229.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0229.tif saved (corrected name)\n",
      "346\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0207.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0207.tif saved (corrected name)\n",
      "347\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0289.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0289.tif saved (corrected name)\n",
      "348\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0261.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0261.tif saved (corrected name)\n",
      "349\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0305.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0305.tif saved (corrected name)\n",
      "350\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0222.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0222.tif saved (corrected name)\n",
      "351\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0301.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0301.tif saved (corrected name)\n",
      "352\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0230.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0230.tif saved (corrected name)\n",
      "353\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0291.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0291.tif saved (corrected name)\n",
      "354\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0212.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0212.tif saved (corrected name)\n",
      "355\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0188.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0188.tif saved (corrected name)\n",
      "356\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0275.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0275.tif saved (corrected name)\n",
      "357\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0238.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0238.tif saved (corrected name)\n",
      "358\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0307.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0307.tif saved (corrected name)\n",
      "359\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0282.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0282.tif saved (corrected name)\n",
      "360\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0226.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0226.tif saved (corrected name)\n",
      "361\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0241.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0241.tif saved (corrected name)\n",
      "362\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0339.tif removed because it was only backgroud\n",
      "363\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0441.tif removed because it was only backgroud\n",
      "364\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0336.tif removed because it was only backgroud\n",
      "365\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0426.tif removed because it was only backgroud\n",
      "366\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0347.tif removed because it was only backgroud\n",
      "367\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0372.tif removed because it was only backgroud\n",
      "368\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0465.tif removed because it was only backgroud\n",
      "369\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0439.tif removed because it was only backgroud\n",
      "370\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0427.tif removed because it was only backgroud\n",
      "371\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0382.tif removed because it was only backgroud\n",
      "372\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0436.tif removed because it was only backgroud\n",
      "373\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0453.tif removed because it was only backgroud\n",
      "374\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0352.tif removed because it was only backgroud\n",
      "375\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0396.tif removed because it was only backgroud\n",
      "376\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0406.tif removed because it was only backgroud\n",
      "377\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0428.tif removed because it was only backgroud\n",
      "378\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0330.tif removed because it was only backgroud\n",
      "379\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0443.tif removed because it was only backgroud\n",
      "380\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0383.tif removed because it was only backgroud\n",
      "381\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0371.tif removed because it was only backgroud\n",
      "382\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0357.tif removed because it was only backgroud\n",
      "383\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0359.tif removed because it was only backgroud\n",
      "384\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0314.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0314.tif saved (corrected name)\n",
      "385\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0322.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0322.tif saved (corrected name)\n",
      "386\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0327.tif removed because it was only backgroud\n",
      "387\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0407.tif removed because it was only backgroud\n",
      "388\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0424.tif removed because it was only backgroud\n",
      "389\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0346.tif removed because it was only backgroud\n",
      "390\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0356.tif removed because it was only backgroud\n",
      "391\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0432.tif removed because it was only backgroud\n",
      "392\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0438.tif removed because it was only backgroud\n",
      "393\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0374.tif removed because it was only backgroud\n",
      "394\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0431.tif removed because it was only backgroud\n",
      "395\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0325.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0325.tif saved (corrected name)\n",
      "396\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0451.tif removed because it was only backgroud\n",
      "397\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0423.tif removed because it was only backgroud\n",
      "398\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0376.tif removed because it was only backgroud\n",
      "399\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0452.tif removed because it was only backgroud\n",
      "400\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0389.tif removed because it was only backgroud\n",
      "401\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0422.tif removed because it was only backgroud\n",
      "402\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0445.tif removed because it was only backgroud\n",
      "403\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0340.tif removed because it was only backgroud\n",
      "404\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0456.tif removed because it was only backgroud\n",
      "405\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0390.tif removed because it was only backgroud\n",
      "406\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0388.tif removed because it was only backgroud\n",
      "407\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0455.tif removed because it was only backgroud\n",
      "408\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0344.tif removed because it was only backgroud\n",
      "409\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0420.tif removed because it was only backgroud\n",
      "410\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0313.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0313.tif saved (corrected name)\n",
      "411\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0363.tif removed because it was only backgroud\n",
      "412\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0471.tif removed because it was only backgroud\n",
      "413\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0468.tif removed because it was only backgroud\n",
      "414\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0381.tif removed because it was only backgroud\n",
      "415\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0418.tif removed because it was only backgroud\n",
      "416\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0417.tif removed because it was only backgroud\n",
      "417\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0351.tif removed because it was only backgroud\n",
      "418\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0411.tif removed because it was only backgroud\n",
      "419\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0447.tif removed because it was only backgroud\n",
      "420\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0318.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0318.tif saved (corrected name)\n",
      "421\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0338.tif removed because it was only backgroud\n",
      "422\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0440.tif removed because it was only backgroud\n",
      "423\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0402.tif removed because it was only backgroud\n",
      "424\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0375.tif removed because it was only backgroud\n",
      "425\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0337.tif removed because it was only backgroud\n",
      "426\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0457.tif removed because it was only backgroud\n",
      "427\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0403.tif removed because it was only backgroud\n",
      "428\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0394.tif removed because it was only backgroud\n",
      "429\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0461.tif removed because it was only backgroud\n",
      "430\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0415.tif removed because it was only backgroud\n",
      "431\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0370.tif removed because it was only backgroud\n",
      "432\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0345.tif removed because it was only backgroud\n",
      "433\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0434.tif removed because it was only backgroud\n",
      "434\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0474.tif removed because it was only backgroud\n",
      "435\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0312.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0312.tif saved (corrected name)\n",
      "436\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0362.tif removed because it was only backgroud\n",
      "437\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0317.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0317.tif saved (corrected name)\n",
      "438\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0429.tif removed because it was only backgroud\n",
      "439\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0368.tif removed because it was only backgroud\n",
      "440\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0316.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0316.tif saved (corrected name)\n",
      "441\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0399.tif removed because it was only backgroud\n",
      "442\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0341.tif removed because it was only backgroud\n",
      "443\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0319.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0319.tif saved (corrected name)\n",
      "444\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0360.tif removed because it was only backgroud\n",
      "445\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0387.tif removed because it was only backgroud\n",
      "446\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0342.tif removed because it was only backgroud\n",
      "447\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0331.tif removed because it was only backgroud\n",
      "448\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0430.tif removed because it was only backgroud\n",
      "449\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0378.tif removed because it was only backgroud\n",
      "450\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0398.tif removed because it was only backgroud\n",
      "451\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0364.tif removed because it was only backgroud\n",
      "452\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0464.tif removed because it was only backgroud\n",
      "453\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0320.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0320.tif saved (corrected name)\n",
      "454\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0413.tif removed because it was only backgroud\n",
      "455\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0333.tif removed because it was only backgroud\n",
      "456\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0442.tif removed because it was only backgroud\n",
      "457\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0397.tif removed because it was only backgroud\n",
      "458\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0354.tif removed because it was only backgroud\n",
      "459\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0405.tif removed because it was only backgroud\n",
      "460\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0361.tif removed because it was only backgroud\n",
      "461\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0460.tif removed because it was only backgroud\n",
      "462\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0335.tif removed because it was only backgroud\n",
      "463\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0343.tif removed because it was only backgroud\n",
      "464\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0355.tif removed because it was only backgroud\n",
      "465\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0462.tif removed because it was only backgroud\n",
      "466\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0449.tif removed because it was only backgroud\n",
      "467\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0404.tif removed because it was only backgroud\n",
      "468\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0358.tif removed because it was only backgroud\n",
      "469\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0367.tif removed because it was only backgroud\n",
      "470\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0326.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0326.tif saved (corrected name)\n",
      "471\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0353.tif removed because it was only backgroud\n",
      "472\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0365.tif removed because it was only backgroud\n",
      "473\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0350.tif removed because it was only backgroud\n",
      "474\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0391.tif removed because it was only backgroud\n",
      "475\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0393.tif removed because it was only backgroud\n",
      "476\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0386.tif removed because it was only backgroud\n",
      "477\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0392.tif removed because it was only backgroud\n",
      "478\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0380.tif removed because it was only backgroud\n",
      "479\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0467.tif removed because it was only backgroud\n",
      "480\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0414.tif removed because it was only backgroud\n",
      "481\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0473.tif removed because it was only backgroud\n",
      "482\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0321.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0321.tif saved (corrected name)\n",
      "483\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0385.tif removed because it was only backgroud\n",
      "484\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0444.tif removed because it was only backgroud\n",
      "485\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0448.tif removed because it was only backgroud\n",
      "486\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0472.tif removed because it was only backgroud\n",
      "487\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0450.tif removed because it was only backgroud\n",
      "488\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0348.tif removed because it was only backgroud\n",
      "489\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0334.tif removed because it was only backgroud\n",
      "490\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0425.tif removed because it was only backgroud\n",
      "491\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0369.tif removed because it was only backgroud\n",
      "492\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0463.tif removed because it was only backgroud\n",
      "493\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0332.tif removed because it was only backgroud\n",
      "494\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0435.tif removed because it was only backgroud\n",
      "495\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0459.tif removed because it was only backgroud\n",
      "496\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0437.tif removed because it was only backgroud\n",
      "497\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0324.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0324.tif saved (corrected name)\n",
      "498\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0433.tif removed because it was only backgroud\n",
      "499\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0373.tif removed because it was only backgroud\n",
      "500\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0412.tif removed because it was only backgroud\n",
      "501\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0384.tif removed because it was only backgroud\n",
      "502\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0466.tif removed because it was only backgroud\n",
      "503\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0408.tif removed because it was only backgroud\n",
      "504\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0395.tif removed because it was only backgroud\n",
      "505\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0454.tif removed because it was only backgroud\n",
      "506\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0377.tif removed because it was only backgroud\n",
      "507\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0401.tif removed because it was only backgroud\n",
      "508\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0315.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0315.tif saved (corrected name)\n",
      "509\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0323.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0323.tif saved (corrected name)\n",
      "510\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0416.tif removed because it was only backgroud\n",
      "511\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0446.tif removed because it was only backgroud\n",
      "512\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0419.tif removed because it was only backgroud\n",
      "513\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0311.tif removed due to incorrect name\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0311.tif saved (corrected name)\n",
      "514\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0421.tif removed because it was only backgroud\n",
      "515\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0379.tif removed because it was only backgroud\n",
      "516\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0458.tif removed because it was only backgroud\n",
      "517\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0349.tif removed because it was only backgroud\n",
      "518\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0400.tif removed because it was only backgroud\n",
      "519\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0410.tif removed because it was only backgroud\n",
      "520\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0329.tif removed because it was only backgroud\n",
      "521\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0409.tif removed because it was only backgroud\n",
      "522\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0469.tif removed because it was only backgroud\n",
      "523\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0470.tif removed because it was only backgroud\n",
      "524\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0328.tif removed because it was only backgroud\n",
      "525\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring-fullKidneysMask0366.tif removed because it was only backgroud\n",
      "526\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0107.tif removed because it was only backgroud\n",
      "527\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0179.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0179.tif saved (corrected name)\n",
      "528\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0066.tif removed because it was only backgroud\n",
      "529\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0108.tif removed because it was only backgroud\n",
      "530\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0111.tif removed because it was only backgroud\n",
      "531\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0037.tif removed because it was only backgroud\n",
      "532\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0199.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0199.tif saved (corrected name)\n",
      "533\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0117.tif removed because it was only backgroud\n",
      "534\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0189.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0189.tif saved (corrected name)\n",
      "535\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0100.tif removed because it was only backgroud\n",
      "536\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0154.tif removed because it was only backgroud\n",
      "537\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0175.tif removed because it was only backgroud\n",
      "538\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0127.tif removed because it was only backgroud\n",
      "539\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0098.tif removed because it was only backgroud\n",
      "540\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0114.tif removed because it was only backgroud\n",
      "541\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0163.tif removed because it was only backgroud\n",
      "542\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0122.tif removed because it was only backgroud\n",
      "543\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0081.tif removed because it was only backgroud\n",
      "544\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0020.tif removed because it was only backgroud\n",
      "545\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0165.tif removed because it was only backgroud\n",
      "546\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0126.tif removed because it was only backgroud\n",
      "547\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0003.tif removed because it was only backgroud\n",
      "548\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0023.tif removed because it was only backgroud\n",
      "549\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0082.tif removed because it was only backgroud\n",
      "550\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0078.tif removed because it was only backgroud\n",
      "551\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0150.tif removed because it was only backgroud\n",
      "552\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0181.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0181.tif saved (corrected name)\n",
      "553\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0162.tif removed because it was only backgroud\n",
      "554\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0120.tif removed because it was only backgroud\n",
      "555\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0184.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0184.tif saved (corrected name)\n",
      "556\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0051.tif removed because it was only backgroud\n",
      "557\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0204.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0204.tif saved (corrected name)\n",
      "558\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0005.tif removed because it was only backgroud\n",
      "559\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0178.tif removed because it was only backgroud\n",
      "560\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0030.tif removed because it was only backgroud\n",
      "561\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0054.tif removed because it was only backgroud\n",
      "562\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0085.tif removed because it was only backgroud\n",
      "563\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0142.tif removed because it was only backgroud\n",
      "564\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0112.tif removed because it was only backgroud\n",
      "565\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0069.tif removed because it was only backgroud\n",
      "566\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0195.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0195.tif saved (corrected name)\n",
      "567\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0027.tif removed because it was only backgroud\n",
      "568\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0169.tif removed because it was only backgroud\n",
      "569\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0124.tif removed because it was only backgroud\n",
      "570\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0133.tif removed because it was only backgroud\n",
      "571\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0044.tif removed because it was only backgroud\n",
      "572\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0144.tif removed because it was only backgroud\n",
      "573\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0045.tif removed because it was only backgroud\n",
      "574\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0206.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0206.tif saved (corrected name)\n",
      "575\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0118.tif removed because it was only backgroud\n",
      "576\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0040.tif removed because it was only backgroud\n",
      "577\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0083.tif removed because it was only backgroud\n",
      "578\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0196.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0196.tif saved (corrected name)\n",
      "579\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0131.tif removed because it was only backgroud\n",
      "580\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0036.tif removed because it was only backgroud\n",
      "581\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0018.tif removed because it was only backgroud\n",
      "582\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0080.tif removed because it was only backgroud\n",
      "583\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0022.tif removed because it was only backgroud\n",
      "584\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0009.tif removed because it was only backgroud\n",
      "585\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0067.tif removed because it was only backgroud\n",
      "586\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0125.tif removed because it was only backgroud\n",
      "587\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0153.tif removed because it was only backgroud\n",
      "588\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0086.tif removed because it was only backgroud\n",
      "589\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0192.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0192.tif saved (corrected name)\n",
      "590\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0155.tif removed because it was only backgroud\n",
      "591\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0159.tif removed because it was only backgroud\n",
      "592\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0099.tif removed because it was only backgroud\n",
      "593\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0136.tif removed because it was only backgroud\n",
      "594\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0042.tif removed because it was only backgroud\n",
      "595\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0052.tif removed because it was only backgroud\n",
      "596\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0168.tif removed because it was only backgroud\n",
      "597\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0077.tif removed because it was only backgroud\n",
      "598\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0002.tif removed because it was only backgroud\n",
      "599\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0096.tif removed because it was only backgroud\n",
      "600\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0166.tif removed because it was only backgroud\n",
      "601\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0116.tif removed because it was only backgroud\n",
      "602\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0039.tif removed because it was only backgroud\n",
      "603\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0106.tif removed because it was only backgroud\n",
      "604\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0102.tif removed because it was only backgroud\n",
      "605\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0203.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0203.tif saved (corrected name)\n",
      "606\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0200.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0200.tif saved (corrected name)\n",
      "607\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0043.tif removed because it was only backgroud\n",
      "608\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0186.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0186.tif saved (corrected name)\n",
      "609\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0065.tif removed because it was only backgroud\n",
      "610\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0024.tif removed because it was only backgroud\n",
      "611\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0146.tif removed because it was only backgroud\n",
      "612\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0143.tif removed because it was only backgroud\n",
      "613\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0014.tif removed because it was only backgroud\n",
      "614\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0128.tif removed because it was only backgroud\n",
      "615\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0089.tif removed because it was only backgroud\n",
      "616\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0137.tif removed because it was only backgroud\n",
      "617\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0060.tif removed because it was only backgroud\n",
      "618\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0193.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0193.tif saved (corrected name)\n",
      "619\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0006.tif removed because it was only backgroud\n",
      "620\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0084.tif removed because it was only backgroud\n",
      "621\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0171.tif removed because it was only backgroud\n",
      "622\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0177.tif removed because it was only backgroud\n",
      "623\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0013.tif removed because it was only backgroud\n",
      "624\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0073.tif removed because it was only backgroud\n",
      "625\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0057.tif removed because it was only backgroud\n",
      "626\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0170.tif removed because it was only backgroud\n",
      "627\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0176.tif removed because it was only backgroud\n",
      "628\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0001.tif removed because it was only backgroud\n",
      "629\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0157.tif removed because it was only backgroud\n",
      "630\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0053.tif removed because it was only backgroud\n",
      "631\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0048.tif removed because it was only backgroud\n",
      "632\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0015.tif removed because it was only backgroud\n",
      "633\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0059.tif removed because it was only backgroud\n",
      "634\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0110.tif removed because it was only backgroud\n",
      "635\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0172.tif removed because it was only backgroud\n",
      "636\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0194.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0194.tif saved (corrected name)\n",
      "637\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0134.tif removed because it was only backgroud\n",
      "638\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0115.tif removed because it was only backgroud\n",
      "639\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0055.tif removed because it was only backgroud\n",
      "640\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0028.tif removed because it was only backgroud\n",
      "641\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0191.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0191.tif saved (corrected name)\n",
      "642\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0007.tif removed because it was only backgroud\n",
      "643\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0021.tif removed because it was only backgroud\n",
      "644\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0197.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0197.tif saved (corrected name)\n",
      "645\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0101.tif removed because it was only backgroud\n",
      "646\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0063.tif removed because it was only backgroud\n",
      "647\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0038.tif removed because it was only backgroud\n",
      "648\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0050.tif removed because it was only backgroud\n",
      "649\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0202.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0202.tif saved (corrected name)\n",
      "650\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0011.tif removed because it was only backgroud\n",
      "651\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0000.tif removed because it was only backgroud\n",
      "652\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0156.tif removed because it was only backgroud\n",
      "653\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0188.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0188.tif saved (corrected name)\n",
      "654\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0091.tif removed because it was only backgroud\n",
      "655\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0008.tif removed because it was only backgroud\n",
      "656\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0061.tif removed because it was only backgroud\n",
      "657\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0123.tif removed because it was only backgroud\n",
      "658\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0010.tif removed because it was only backgroud\n",
      "659\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0173.tif removed because it was only backgroud\n",
      "660\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0180.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0180.tif saved (corrected name)\n",
      "661\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0035.tif removed because it was only backgroud\n",
      "662\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0130.tif removed because it was only backgroud\n",
      "663\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0071.tif removed because it was only backgroud\n",
      "664\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0201.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0201.tif saved (corrected name)\n",
      "665\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0149.tif removed because it was only backgroud\n",
      "666\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0105.tif removed because it was only backgroud\n",
      "667\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0025.tif removed because it was only backgroud\n",
      "668\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0104.tif removed because it was only backgroud\n",
      "669\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0097.tif removed because it was only backgroud\n",
      "670\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0095.tif removed because it was only backgroud\n",
      "671\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0113.tif removed because it was only backgroud\n",
      "672\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0147.tif removed because it was only backgroud\n",
      "673\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0164.tif removed because it was only backgroud\n",
      "674\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0029.tif removed because it was only backgroud\n",
      "675\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0160.tif removed because it was only backgroud\n",
      "676\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0152.tif removed because it was only backgroud\n",
      "677\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0132.tif removed because it was only backgroud\n",
      "678\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0092.tif removed because it was only backgroud\n",
      "679\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0109.tif removed because it was only backgroud\n",
      "680\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0041.tif removed because it was only backgroud\n",
      "681\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0141.tif removed because it was only backgroud\n",
      "682\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0145.tif removed because it was only backgroud\n",
      "683\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0026.tif removed because it was only backgroud\n",
      "684\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0129.tif removed because it was only backgroud\n",
      "685\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0138.tif removed because it was only backgroud\n",
      "686\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0033.tif removed because it was only backgroud\n",
      "687\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0090.tif removed because it was only backgroud\n",
      "688\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0119.tif removed because it was only backgroud\n",
      "689\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0012.tif removed because it was only backgroud\n",
      "690\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0072.tif removed because it was only backgroud\n",
      "691\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0062.tif removed because it was only backgroud\n",
      "692\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0135.tif removed because it was only backgroud\n",
      "693\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0075.tif removed because it was only backgroud\n",
      "694\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0058.tif removed because it was only backgroud\n",
      "695\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0139.tif removed because it was only backgroud\n",
      "696\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0070.tif removed because it was only backgroud\n",
      "697\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0174.tif removed because it was only backgroud\n",
      "698\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0161.tif removed because it was only backgroud\n",
      "699\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0140.tif removed because it was only backgroud\n",
      "700\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0087.tif removed because it was only backgroud\n",
      "701\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0182.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0182.tif saved (corrected name)\n",
      "702\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0047.tif removed because it was only backgroud\n",
      "703\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0032.tif removed because it was only backgroud\n",
      "704\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0049.tif removed because it was only backgroud\n",
      "705\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0205.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0205.tif saved (corrected name)\n",
      "706\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0185.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0185.tif saved (corrected name)\n",
      "707\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0076.tif removed because it was only backgroud\n",
      "708\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0151.tif removed because it was only backgroud\n",
      "709\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0167.tif removed because it was only backgroud\n",
      "710\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0103.tif removed because it was only backgroud\n",
      "711\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0094.tif removed because it was only backgroud\n",
      "712\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0064.tif removed because it was only backgroud\n",
      "713\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0198.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0198.tif saved (corrected name)\n",
      "714\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0088.tif removed because it was only backgroud\n",
      "715\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0121.tif removed because it was only backgroud\n",
      "716\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0093.tif removed because it was only backgroud\n",
      "717\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0158.tif removed because it was only backgroud\n",
      "718\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0183.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0183.tif saved (corrected name)\n",
      "719\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0019.tif removed because it was only backgroud\n",
      "720\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0187.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0187.tif saved (corrected name)\n",
      "721\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0068.tif removed because it was only backgroud\n",
      "722\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0056.tif removed because it was only backgroud\n",
      "723\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0004.tif removed because it was only backgroud\n",
      "724\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0016.tif removed because it was only backgroud\n",
      "725\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0031.tif removed because it was only backgroud\n",
      "726\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0190.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0190.tif saved (corrected name)\n",
      "727\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0074.tif removed because it was only backgroud\n",
      "728\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0034.tif removed because it was only backgroud\n",
      "729\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0148.tif removed because it was only backgroud\n",
      "730\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0079.tif removed because it was only backgroud\n",
      "731\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0017.tif removed because it was only backgroud\n",
      "732\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0046.tif removed because it was only backgroud\n",
      "733\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0269.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0269.tif saved (corrected name)\n",
      "734\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0271.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0271.tif saved (corrected name)\n",
      "735\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0260.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0260.tif saved (corrected name)\n",
      "736\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0216.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0216.tif saved (corrected name)\n",
      "737\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0302.tif removed because it was only backgroud\n",
      "738\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0212.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0212.tif saved (corrected name)\n",
      "739\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0249.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0249.tif saved (corrected name)\n",
      "740\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0256.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0256.tif saved (corrected name)\n",
      "741\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0234.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0234.tif saved (corrected name)\n",
      "742\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0245.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0245.tif saved (corrected name)\n",
      "743\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0313.tif removed because it was only backgroud\n",
      "744\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0270.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0270.tif saved (corrected name)\n",
      "745\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0316.tif removed because it was only backgroud\n",
      "746\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0230.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0230.tif saved (corrected name)\n",
      "747\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0273.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0273.tif saved (corrected name)\n",
      "748\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0237.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0237.tif saved (corrected name)\n",
      "749\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0238.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0238.tif saved (corrected name)\n",
      "750\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0292.tif removed because it was only backgroud\n",
      "751\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0246.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0246.tif saved (corrected name)\n",
      "752\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0295.tif removed because it was only backgroud\n",
      "753\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0225.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0225.tif saved (corrected name)\n",
      "754\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0312.tif removed because it was only backgroud\n",
      "755\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0278.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0278.tif saved (corrected name)\n",
      "756\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0305.tif removed because it was only backgroud\n",
      "757\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0297.tif removed because it was only backgroud\n",
      "758\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0284.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0284.tif saved (corrected name)\n",
      "759\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0247.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0247.tif saved (corrected name)\n",
      "760\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0218.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0218.tif saved (corrected name)\n",
      "761\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0209.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0209.tif saved (corrected name)\n",
      "762\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0227.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0227.tif saved (corrected name)\n",
      "763\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0257.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0257.tif saved (corrected name)\n",
      "764\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0207.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0207.tif saved (corrected name)\n",
      "765\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0301.tif removed because it was only backgroud\n",
      "766\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0251.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0251.tif saved (corrected name)\n",
      "767\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0220.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0220.tif saved (corrected name)\n",
      "768\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0231.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0231.tif saved (corrected name)\n",
      "769\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0303.tif removed because it was only backgroud\n",
      "770\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0274.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0274.tif saved (corrected name)\n",
      "771\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0232.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0232.tif saved (corrected name)\n",
      "772\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0298.tif removed because it was only backgroud\n",
      "773\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0276.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0276.tif saved (corrected name)\n",
      "774\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0258.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0258.tif saved (corrected name)\n",
      "775\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0275.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0275.tif saved (corrected name)\n",
      "776\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0244.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0244.tif saved (corrected name)\n",
      "777\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0210.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0210.tif saved (corrected name)\n",
      "778\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0211.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0211.tif saved (corrected name)\n",
      "779\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0288.tif removed because it was only backgroud\n",
      "780\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0229.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0229.tif saved (corrected name)\n",
      "781\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0311.tif removed because it was only backgroud\n",
      "782\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0291.tif removed because it was only backgroud\n",
      "783\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0240.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0240.tif saved (corrected name)\n",
      "784\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0282.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0282.tif saved (corrected name)\n",
      "785\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0306.tif removed because it was only backgroud\n",
      "786\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0222.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0222.tif saved (corrected name)\n",
      "787\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0261.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0261.tif saved (corrected name)\n",
      "788\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0223.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0223.tif saved (corrected name)\n",
      "789\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0304.tif removed because it was only backgroud\n",
      "790\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0287.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0287.tif saved (corrected name)\n",
      "791\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0263.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0263.tif saved (corrected name)\n",
      "792\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0213.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0213.tif saved (corrected name)\n",
      "793\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0310.tif removed because it was only backgroud\n",
      "794\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0224.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0224.tif saved (corrected name)\n",
      "795\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0265.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0265.tif saved (corrected name)\n",
      "796\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0252.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0252.tif saved (corrected name)\n",
      "797\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0285.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0285.tif saved (corrected name)\n",
      "798\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0277.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0277.tif saved (corrected name)\n",
      "799\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0236.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0236.tif saved (corrected name)\n",
      "800\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0253.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0253.tif saved (corrected name)\n",
      "801\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0289.tif removed because it was only backgroud\n",
      "802\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0241.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0241.tif saved (corrected name)\n",
      "803\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0308.tif removed because it was only backgroud\n",
      "804\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0299.tif removed because it was only backgroud\n",
      "805\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0242.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0242.tif saved (corrected name)\n",
      "806\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0294.tif removed because it was only backgroud\n",
      "807\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0290.tif removed because it was only backgroud\n",
      "808\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0259.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0259.tif saved (corrected name)\n",
      "809\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0315.tif removed because it was only backgroud\n",
      "810\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0317.tif removed because it was only backgroud\n",
      "811\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0255.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0255.tif saved (corrected name)\n",
      "812\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0280.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0280.tif saved (corrected name)\n",
      "813\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0300.tif removed because it was only backgroud\n",
      "814\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0217.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0217.tif saved (corrected name)\n",
      "815\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0208.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0208.tif saved (corrected name)\n",
      "816\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0318.tif removed because it was only backgroud\n",
      "817\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0243.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0243.tif saved (corrected name)\n",
      "818\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0215.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0215.tif saved (corrected name)\n",
      "819\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0281.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0281.tif saved (corrected name)\n",
      "820\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0233.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0233.tif saved (corrected name)\n",
      "821\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0267.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0267.tif saved (corrected name)\n",
      "822\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0228.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0228.tif saved (corrected name)\n",
      "823\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0221.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0221.tif saved (corrected name)\n",
      "824\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0266.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0266.tif saved (corrected name)\n",
      "825\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0307.tif removed because it was only backgroud\n",
      "826\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0219.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0219.tif saved (corrected name)\n",
      "827\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0283.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0283.tif saved (corrected name)\n",
      "828\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0264.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0264.tif saved (corrected name)\n",
      "829\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0309.tif removed because it was only backgroud\n",
      "830\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0268.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0268.tif saved (corrected name)\n",
      "831\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0239.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0239.tif saved (corrected name)\n",
      "832\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0262.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0262.tif saved (corrected name)\n",
      "833\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0272.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0272.tif saved (corrected name)\n",
      "834\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0254.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0254.tif saved (corrected name)\n",
      "835\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0293.tif removed because it was only backgroud\n",
      "836\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0286.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0286.tif saved (corrected name)\n",
      "837\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0279.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0279.tif saved (corrected name)\n",
      "838\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0214.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0214.tif saved (corrected name)\n",
      "839\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0250.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0250.tif saved (corrected name)\n",
      "840\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0296.tif removed because it was only backgroud\n",
      "841\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0248.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0248.tif saved (corrected name)\n",
      "842\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0235.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0235.tif saved (corrected name)\n",
      "843\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0314.tif removed because it was only backgroud\n",
      "844\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0226.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0226.tif saved (corrected name)\n",
      "845\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0423.tif removed because it was only backgroud\n",
      "846\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0435.tif removed because it was only backgroud\n",
      "847\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0332.tif removed because it was only backgroud\n",
      "848\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0403.tif removed because it was only backgroud\n",
      "849\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0321.tif removed because it was only backgroud\n",
      "850\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0373.tif removed because it was only backgroud\n",
      "851\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0348.tif removed because it was only backgroud\n",
      "852\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0365.tif removed because it was only backgroud\n",
      "853\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0427.tif removed because it was only backgroud\n",
      "854\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0425.tif removed because it was only backgroud\n",
      "855\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0351.tif removed because it was only backgroud\n",
      "856\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0382.tif removed because it was only backgroud\n",
      "857\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0323.tif removed because it was only backgroud\n",
      "858\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0326.tif removed because it was only backgroud\n",
      "859\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0319.tif removed because it was only backgroud\n",
      "860\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0409.tif removed because it was only backgroud\n",
      "861\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0404.tif removed because it was only backgroud\n",
      "862\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0366.tif removed because it was only backgroud\n",
      "863\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0324.tif removed because it was only backgroud\n",
      "864\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0353.tif removed because it was only backgroud\n",
      "865\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0320.tif removed because it was only backgroud\n",
      "866\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0333.tif removed because it was only backgroud\n",
      "867\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0328.tif removed because it was only backgroud\n",
      "868\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0434.tif removed because it was only backgroud\n",
      "869\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0341.tif removed because it was only backgroud\n",
      "870\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0379.tif removed because it was only backgroud\n",
      "871\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0391.tif removed because it was only backgroud\n",
      "872\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0352.tif removed because it was only backgroud\n",
      "873\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0400.tif removed because it was only backgroud\n",
      "874\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0418.tif removed because it was only backgroud\n",
      "875\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0331.tif removed because it was only backgroud\n",
      "876\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0424.tif removed because it was only backgroud\n",
      "877\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0354.tif removed because it was only backgroud\n",
      "878\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0393.tif removed because it was only backgroud\n",
      "879\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0430.tif removed because it was only backgroud\n",
      "880\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0396.tif removed because it was only backgroud\n",
      "881\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0415.tif removed because it was only backgroud\n",
      "882\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0357.tif removed because it was only backgroud\n",
      "883\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0429.tif removed because it was only backgroud\n",
      "884\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0389.tif removed because it was only backgroud\n",
      "885\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0399.tif removed because it was only backgroud\n",
      "886\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0364.tif removed because it was only backgroud\n",
      "887\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0428.tif removed because it was only backgroud\n",
      "888\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0376.tif removed because it was only backgroud\n",
      "889\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0411.tif removed because it was only backgroud\n",
      "890\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0420.tif removed because it was only backgroud\n",
      "891\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0334.tif removed because it was only backgroud\n",
      "892\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0397.tif removed because it was only backgroud\n",
      "893\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0330.tif removed because it was only backgroud\n",
      "894\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0408.tif removed because it was only backgroud\n",
      "895\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0358.tif removed because it was only backgroud\n",
      "896\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0395.tif removed because it was only backgroud\n",
      "897\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0335.tif removed because it was only backgroud\n",
      "898\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0421.tif removed because it was only backgroud\n",
      "899\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0346.tif removed because it was only backgroud\n",
      "900\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0431.tif removed because it was only backgroud\n",
      "901\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0412.tif removed because it was only backgroud\n",
      "902\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0406.tif removed because it was only backgroud\n",
      "903\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0372.tif removed because it was only backgroud\n",
      "904\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0374.tif removed because it was only backgroud\n",
      "905\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0380.tif removed because it was only backgroud\n",
      "906\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0422.tif removed because it was only backgroud\n",
      "907\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0368.tif removed because it was only backgroud\n",
      "908\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0322.tif removed because it was only backgroud\n",
      "909\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0350.tif removed because it was only backgroud\n",
      "910\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0414.tif removed because it was only backgroud\n",
      "911\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0407.tif removed because it was only backgroud\n",
      "912\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0345.tif removed because it was only backgroud\n",
      "913\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0349.tif removed because it was only backgroud\n",
      "914\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0378.tif removed because it was only backgroud\n",
      "915\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0343.tif removed because it was only backgroud\n",
      "916\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0375.tif removed because it was only backgroud\n",
      "917\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0325.tif removed because it was only backgroud\n",
      "918\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0371.tif removed because it was only backgroud\n",
      "919\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0361.tif removed because it was only backgroud\n",
      "920\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0426.tif removed because it was only backgroud\n",
      "921\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0363.tif removed because it was only backgroud\n",
      "922\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0419.tif removed because it was only backgroud\n",
      "923\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0383.tif removed because it was only backgroud\n",
      "924\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0367.tif removed because it was only backgroud\n",
      "925\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0386.tif removed because it was only backgroud\n",
      "926\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0417.tif removed because it was only backgroud\n",
      "927\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0387.tif removed because it was only backgroud\n",
      "928\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0370.tif removed because it was only backgroud\n",
      "929\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0329.tif removed because it was only backgroud\n",
      "930\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0416.tif removed because it was only backgroud\n",
      "931\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0413.tif removed because it was only backgroud\n",
      "932\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0401.tif removed because it was only backgroud\n",
      "933\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0381.tif removed because it was only backgroud\n",
      "934\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0385.tif removed because it was only backgroud\n",
      "935\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0355.tif removed because it was only backgroud\n",
      "936\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0394.tif removed because it was only backgroud\n",
      "937\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0362.tif removed because it was only backgroud\n",
      "938\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0356.tif removed because it was only backgroud\n",
      "939\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0337.tif removed because it was only backgroud\n",
      "940\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0410.tif removed because it was only backgroud\n",
      "941\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0390.tif removed because it was only backgroud\n",
      "942\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0340.tif removed because it was only backgroud\n",
      "943\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0338.tif removed because it was only backgroud\n",
      "944\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0432.tif removed because it was only backgroud\n",
      "945\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0384.tif removed because it was only backgroud\n",
      "946\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0436.tif removed because it was only backgroud\n",
      "947\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0405.tif removed because it was only backgroud\n",
      "948\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0433.tif removed because it was only backgroud\n",
      "949\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0347.tif removed because it was only backgroud\n",
      "950\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0359.tif removed because it was only backgroud\n",
      "951\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0388.tif removed because it was only backgroud\n",
      "952\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0377.tif removed because it was only backgroud\n",
      "953\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0344.tif removed because it was only backgroud\n",
      "954\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0360.tif removed because it was only backgroud\n",
      "955\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0369.tif removed because it was only backgroud\n",
      "956\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0342.tif removed because it was only backgroud\n",
      "957\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0339.tif removed because it was only backgroud\n",
      "958\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0327.tif removed because it was only backgroud\n",
      "959\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0398.tif removed because it was only backgroud\n",
      "960\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0392.tif removed because it was only backgroud\n",
      "961\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0402.tif removed because it was only backgroud\n",
      "962\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0336.tif removed because it was only backgroud\n",
      "963\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0449.tif removed because it was only backgroud\n",
      "964\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0453.tif removed because it was only backgroud\n",
      "965\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0466.tif removed because it was only backgroud\n",
      "966\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0440.tif removed because it was only backgroud\n",
      "967\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0454.tif removed because it was only backgroud\n",
      "968\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0474.tif removed because it was only backgroud\n",
      "969\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0471.tif removed because it was only backgroud\n",
      "970\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0465.tif removed because it was only backgroud\n",
      "971\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0459.tif removed because it was only backgroud\n",
      "972\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0438.tif removed because it was only backgroud\n",
      "973\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0463.tif removed because it was only backgroud\n",
      "974\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0443.tif removed because it was only backgroud\n",
      "975\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0472.tif removed because it was only backgroud\n",
      "976\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0469.tif removed because it was only backgroud\n",
      "977\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0464.tif removed because it was only backgroud\n",
      "978\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0437.tif removed because it was only backgroud\n",
      "979\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0473.tif removed because it was only backgroud\n",
      "980\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0448.tif removed because it was only backgroud\n",
      "981\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0461.tif removed because it was only backgroud\n",
      "982\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0446.tif removed because it was only backgroud\n",
      "983\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0462.tif removed because it was only backgroud\n",
      "984\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0441.tif removed because it was only backgroud\n",
      "985\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0468.tif removed because it was only backgroud\n",
      "986\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0455.tif removed because it was only backgroud\n",
      "987\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0451.tif removed because it was only backgroud\n",
      "988\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0470.tif removed because it was only backgroud\n",
      "989\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0467.tif removed because it was only backgroud\n",
      "990\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0439.tif removed because it was only backgroud\n",
      "991\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0456.tif removed because it was only backgroud\n",
      "992\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0457.tif removed because it was only backgroud\n",
      "993\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0460.tif removed because it was only backgroud\n",
      "994\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0447.tif removed because it was only backgroud\n",
      "995\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0442.tif removed because it was only backgroud\n",
      "996\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0450.tif removed because it was only backgroud\n",
      "997\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0458.tif removed because it was only backgroud\n",
      "998\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0445.tif removed because it was only backgroud\n",
      "999\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0444.tif removed because it was only backgroud\n",
      "1000\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0452.tif removed because it was only backgroud\n",
      "1001\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0296.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0296.tif saved (corrected name)\n",
      "1002\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0414.tif removed because it was only backgroud\n",
      "1003\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0130.tif removed because it was only backgroud\n",
      "1004\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0105.tif removed because it was only backgroud\n",
      "1005\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0430.tif removed because it was only backgroud\n",
      "1006\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0008.tif removed because it was only backgroud\n",
      "1007\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0027.tif removed because it was only backgroud\n",
      "1008\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0006.tif removed because it was only backgroud\n",
      "1009\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0429.tif removed because it was only backgroud\n",
      "1010\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0024.tif removed because it was only backgroud\n",
      "1011\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0409.tif removed because it was only backgroud\n",
      "1012\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0236.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0236.tif saved (corrected name)\n",
      "1013\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0136.tif removed because it was only backgroud\n",
      "1014\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0416.tif removed because it was only backgroud\n",
      "1015\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0175.tif removed because it was only backgroud\n",
      "1016\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0379.tif removed because it was only backgroud\n",
      "1017\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0377.tif removed because it was only backgroud\n",
      "1018\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0255.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0255.tif saved (corrected name)\n",
      "1019\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0115.tif removed because it was only backgroud\n",
      "1020\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0140.tif removed because it was only backgroud\n",
      "1021\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0034.tif removed because it was only backgroud\n",
      "1022\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0038.tif removed because it was only backgroud\n",
      "1023\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0238.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0238.tif saved (corrected name)\n",
      "1024\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0112.tif removed because it was only backgroud\n",
      "1025\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0297.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0297.tif saved (corrected name)\n",
      "1026\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0299.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0299.tif saved (corrected name)\n",
      "1027\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0440.tif removed because it was only backgroud\n",
      "1028\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0316.tif removed because it was only backgroud\n",
      "1029\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0014.tif removed because it was only backgroud\n",
      "1030\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0196.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0196.tif saved (corrected name)\n",
      "1031\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0417.tif removed because it was only backgroud\n",
      "1032\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0373.tif removed because it was only backgroud\n",
      "1033\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0375.tif removed because it was only backgroud\n",
      "1034\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0319.tif removed because it was only backgroud\n",
      "1035\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0114.tif removed because it was only backgroud\n",
      "1036\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0389.tif removed because it was only backgroud\n",
      "1037\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0039.tif removed because it was only backgroud\n",
      "1038\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0071.tif removed because it was only backgroud\n",
      "1039\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0355.tif removed because it was only backgroud\n",
      "1040\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0230.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0230.tif saved (corrected name)\n",
      "1041\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0251.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0251.tif saved (corrected name)\n",
      "1042\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0387.tif removed because it was only backgroud\n",
      "1043\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0351.tif removed because it was only backgroud\n",
      "1044\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0203.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0203.tif saved (corrected name)\n",
      "1045\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0083.tif removed because it was only backgroud\n",
      "1046\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0028.tif removed because it was only backgroud\n",
      "1047\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0189.tif removed because it was only backgroud\n",
      "1048\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0017.tif removed because it was only backgroud\n",
      "1049\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0288.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0288.tif saved (corrected name)\n",
      "1050\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0087.tif removed because it was only backgroud\n",
      "1051\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0151.tif removed because it was only backgroud\n",
      "1052\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0148.tif removed because it was only backgroud\n",
      "1053\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0385.tif removed because it was only backgroud\n",
      "1054\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0124.tif removed because it was only backgroud\n",
      "1055\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0042.tif removed because it was only backgroud\n",
      "1056\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0157.tif removed because it was only backgroud\n",
      "1057\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0106.tif removed because it was only backgroud\n",
      "1058\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0133.tif removed because it was only backgroud\n",
      "1059\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0121.tif removed because it was only backgroud\n",
      "1060\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0052.tif removed because it was only backgroud\n",
      "1061\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0356.tif removed because it was only backgroud\n",
      "1062\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0134.tif removed because it was only backgroud\n",
      "1063\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0205.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0205.tif saved (corrected name)\n",
      "1064\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0257.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0257.tif saved (corrected name)\n",
      "1065\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0176.tif removed because it was only backgroud\n",
      "1066\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0026.tif removed because it was only backgroud\n",
      "1067\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0318.tif removed because it was only backgroud\n",
      "1068\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0271.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0271.tif saved (corrected name)\n",
      "1069\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0286.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0286.tif saved (corrected name)\n",
      "1070\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0233.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0233.tif saved (corrected name)\n",
      "1071\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0352.tif removed because it was only backgroud\n",
      "1072\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0294.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0294.tif saved (corrected name)\n",
      "1073\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0412.tif removed because it was only backgroud\n",
      "1074\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0079.tif removed because it was only backgroud\n",
      "1075\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0098.tif removed because it was only backgroud\n",
      "1076\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0211.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0211.tif saved (corrected name)\n",
      "1077\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0247.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0247.tif saved (corrected name)\n",
      "1078\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0048.tif removed because it was only backgroud\n",
      "1079\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0322.tif removed because it was only backgroud\n",
      "1080\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0420.tif removed because it was only backgroud\n",
      "1081\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0446.tif removed because it was only backgroud\n",
      "1082\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0404.tif removed because it was only backgroud\n",
      "1083\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0016.tif removed because it was only backgroud\n",
      "1084\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0244.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0244.tif saved (corrected name)\n",
      "1085\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0365.tif removed because it was only backgroud\n",
      "1086\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0031.tif removed because it was only backgroud\n",
      "1087\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0279.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0279.tif saved (corrected name)\n",
      "1088\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0259.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0259.tif saved (corrected name)\n",
      "1089\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0361.tif removed because it was only backgroud\n",
      "1090\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0137.tif removed because it was only backgroud\n",
      "1091\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0241.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0241.tif saved (corrected name)\n",
      "1092\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0129.tif removed because it was only backgroud\n",
      "1093\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0057.tif removed because it was only backgroud\n",
      "1094\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0030.tif removed because it was only backgroud\n",
      "1095\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0088.tif removed because it was only backgroud\n",
      "1096\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0165.tif removed because it was only backgroud\n",
      "1097\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0320.tif removed because it was only backgroud\n",
      "1098\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0062.tif removed because it was only backgroud\n",
      "1099\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0214.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0214.tif saved (corrected name)\n",
      "1100\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0188.tif removed because it was only backgroud\n",
      "1101\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0408.tif removed because it was only backgroud\n",
      "1102\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0336.tif removed because it was only backgroud\n",
      "1103\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0147.tif removed because it was only backgroud\n",
      "1104\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0362.tif removed because it was only backgroud\n",
      "1105\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0307.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0307.tif saved (corrected name)\n",
      "1106\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0250.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0250.tif saved (corrected name)\n",
      "1107\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0078.tif removed because it was only backgroud\n",
      "1108\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0390.tif removed because it was only backgroud\n",
      "1109\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0354.tif removed because it was only backgroud\n",
      "1110\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0213.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0213.tif saved (corrected name)\n",
      "1111\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0302.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0302.tif saved (corrected name)\n",
      "1112\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0331.tif removed because it was only backgroud\n",
      "1113\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0005.tif removed because it was only backgroud\n",
      "1114\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0097.tif removed because it was only backgroud\n",
      "1115\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0149.tif removed because it was only backgroud\n",
      "1116\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0275.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0275.tif saved (corrected name)\n",
      "1117\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0256.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0256.tif saved (corrected name)\n",
      "1118\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0117.tif removed because it was only backgroud\n",
      "1119\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0367.tif removed because it was only backgroud\n",
      "1120\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0380.tif removed because it was only backgroud\n",
      "1121\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0424.tif removed because it was only backgroud\n",
      "1122\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0237.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0237.tif saved (corrected name)\n",
      "1123\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0329.tif removed because it was only backgroud\n",
      "1124\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0172.tif removed because it was only backgroud\n",
      "1125\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0202.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0202.tif saved (corrected name)\n",
      "1126\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0350.tif removed because it was only backgroud\n",
      "1127\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0273.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0273.tif saved (corrected name)\n",
      "1128\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0413.tif removed because it was only backgroud\n",
      "1129\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0270.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0270.tif saved (corrected name)\n",
      "1130\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0267.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0267.tif saved (corrected name)\n",
      "1131\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0291.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0291.tif saved (corrected name)\n",
      "1132\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0376.tif removed because it was only backgroud\n",
      "1133\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0290.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0290.tif saved (corrected name)\n",
      "1134\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0158.tif removed because it was only backgroud\n",
      "1135\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0439.tif removed because it was only backgroud\n",
      "1136\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0138.tif removed because it was only backgroud\n",
      "1137\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0074.tif removed because it was only backgroud\n",
      "1138\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0010.tif removed because it was only backgroud\n",
      "1139\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0096.tif removed because it was only backgroud\n",
      "1140\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0070.tif removed because it was only backgroud\n",
      "1141\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0152.tif removed because it was only backgroud\n",
      "1142\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0109.tif removed because it was only backgroud\n",
      "1143\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0025.tif removed because it was only backgroud\n",
      "1144\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0166.tif removed because it was only backgroud\n",
      "1145\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0089.tif removed because it was only backgroud\n",
      "1146\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0040.tif removed because it was only backgroud\n",
      "1147\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0168.tif removed because it was only backgroud\n",
      "1148\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0337.tif removed because it was only backgroud\n",
      "1149\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0358.tif removed because it was only backgroud\n",
      "1150\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0029.tif removed because it was only backgroud\n",
      "1151\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0402.tif removed because it was only backgroud\n",
      "1152\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0381.tif removed because it was only backgroud\n",
      "1153\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0253.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0253.tif saved (corrected name)\n",
      "1154\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0059.tif removed because it was only backgroud\n",
      "1155\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0284.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0284.tif saved (corrected name)\n",
      "1156\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0266.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0266.tif saved (corrected name)\n",
      "1157\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0276.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0276.tif saved (corrected name)\n",
      "1158\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0035.tif removed because it was only backgroud\n",
      "1159\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0033.tif removed because it was only backgroud\n",
      "1160\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0308.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0308.tif saved (corrected name)\n",
      "1161\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0194.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0194.tif saved (corrected name)\n",
      "1162\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0227.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0227.tif saved (corrected name)\n",
      "1163\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0447.tif removed because it was only backgroud\n",
      "1164\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0295.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0295.tif saved (corrected name)\n",
      "1165\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0304.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0304.tif saved (corrected name)\n",
      "1166\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0208.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0208.tif saved (corrected name)\n",
      "1167\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0415.tif removed because it was only backgroud\n",
      "1168\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0226.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0226.tif saved (corrected name)\n",
      "1169\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0101.tif removed because it was only backgroud\n",
      "1170\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0289.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0289.tif saved (corrected name)\n",
      "1171\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0221.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0221.tif saved (corrected name)\n",
      "1172\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0187.tif removed because it was only backgroud\n",
      "1173\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0346.tif removed because it was only backgroud\n",
      "1174\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0210.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0210.tif saved (corrected name)\n",
      "1175\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0428.tif removed because it was only backgroud\n",
      "1176\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0126.tif removed because it was only backgroud\n",
      "1177\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0448.tif removed because it was only backgroud\n",
      "1178\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0280.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0280.tif saved (corrected name)\n",
      "1179\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0113.tif removed because it was only backgroud\n",
      "1180\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0374.tif removed because it was only backgroud\n",
      "1181\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0222.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0222.tif saved (corrected name)\n",
      "1182\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0153.tif removed because it was only backgroud\n",
      "1183\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0200.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0200.tif saved (corrected name)\n",
      "1184\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0163.tif removed because it was only backgroud\n",
      "1185\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0357.tif removed because it was only backgroud\n",
      "1186\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0410.tif removed because it was only backgroud\n",
      "1187\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0324.tif removed because it was only backgroud\n",
      "1188\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0229.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0229.tif saved (corrected name)\n",
      "1189\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0298.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0298.tif saved (corrected name)\n",
      "1190\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0422.tif removed because it was only backgroud\n",
      "1191\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0323.tif removed because it was only backgroud\n",
      "1192\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0315.tif removed because it was only backgroud\n",
      "1193\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0072.tif removed because it was only backgroud\n",
      "1194\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0327.tif removed because it was only backgroud\n",
      "1195\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0119.tif removed because it was only backgroud\n",
      "1196\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0224.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0224.tif saved (corrected name)\n",
      "1197\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0161.tif removed because it was only backgroud\n",
      "1198\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0046.tif removed because it was only backgroud\n",
      "1199\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0004.tif removed because it was only backgroud\n",
      "1200\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0333.tif removed because it was only backgroud\n",
      "1201\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0242.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0242.tif saved (corrected name)\n",
      "1202\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0021.tif removed because it was only backgroud\n",
      "1203\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0111.tif removed because it was only backgroud\n",
      "1204\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0032.tif removed because it was only backgroud\n",
      "1205\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0332.tif removed because it was only backgroud\n",
      "1206\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0015.tif removed because it was only backgroud\n",
      "1207\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0309.tif removed because it was only backgroud\n",
      "1208\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0131.tif removed because it was only backgroud\n",
      "1209\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0392.tif removed because it was only backgroud\n",
      "1210\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0345.tif removed because it was only backgroud\n",
      "1211\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0239.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0239.tif saved (corrected name)\n",
      "1212\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0058.tif removed because it was only backgroud\n",
      "1213\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0169.tif removed because it was only backgroud\n",
      "1214\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0181.tif removed because it was only backgroud\n",
      "1215\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0397.tif removed because it was only backgroud\n",
      "1216\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0183.tif removed because it was only backgroud\n",
      "1217\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0091.tif removed because it was only backgroud\n",
      "1218\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0195.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0195.tif saved (corrected name)\n",
      "1219\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0144.tif removed because it was only backgroud\n",
      "1220\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0060.tif removed because it was only backgroud\n",
      "1221\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0235.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0235.tif saved (corrected name)\n",
      "1222\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0421.tif removed because it was only backgroud\n",
      "1223\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0343.tif removed because it was only backgroud\n",
      "1224\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0360.tif removed because it was only backgroud\n",
      "1225\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0394.tif removed because it was only backgroud\n",
      "1226\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0139.tif removed because it was only backgroud\n",
      "1227\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0003.tif removed because it was only backgroud\n",
      "1228\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0234.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0234.tif saved (corrected name)\n",
      "1229\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0216.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0216.tif saved (corrected name)\n",
      "1230\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0023.tif removed because it was only backgroud\n",
      "1231\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0444.tif removed because it was only backgroud\n",
      "1232\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0437.tif removed because it was only backgroud\n",
      "1233\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0300.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0300.tif saved (corrected name)\n",
      "1234\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0143.tif removed because it was only backgroud\n",
      "1235\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0103.tif removed because it was only backgroud\n",
      "1236\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0425.tif removed because it was only backgroud\n",
      "1237\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0218.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0218.tif saved (corrected name)\n",
      "1238\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0145.tif removed because it was only backgroud\n",
      "1239\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0383.tif removed because it was only backgroud\n",
      "1240\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0399.tif removed because it was only backgroud\n",
      "1241\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0041.tif removed because it was only backgroud\n",
      "1242\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0001.tif removed because it was only backgroud\n",
      "1243\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0076.tif removed because it was only backgroud\n",
      "1244\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0179.tif removed because it was only backgroud\n",
      "1245\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0180.tif removed because it was only backgroud\n",
      "1246\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0123.tif removed because it was only backgroud\n",
      "1247\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0085.tif removed because it was only backgroud\n",
      "1248\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0262.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0262.tif saved (corrected name)\n",
      "1249\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0341.tif removed because it was only backgroud\n",
      "1250\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0265.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0265.tif saved (corrected name)\n",
      "1251\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0002.tif removed because it was only backgroud\n",
      "1252\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0442.tif removed because it was only backgroud\n",
      "1253\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0182.tif removed because it was only backgroud\n",
      "1254\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0441.tif removed because it was only backgroud\n",
      "1255\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0193.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0193.tif saved (corrected name)\n",
      "1256\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0338.tif removed because it was only backgroud\n",
      "1257\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0217.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0217.tif saved (corrected name)\n",
      "1258\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0232.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0232.tif saved (corrected name)\n",
      "1259\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0198.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0198.tif saved (corrected name)\n",
      "1260\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0150.tif removed because it was only backgroud\n",
      "1261\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0171.tif removed because it was only backgroud\n",
      "1262\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0011.tif removed because it was only backgroud\n",
      "1263\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0261.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0261.tif saved (corrected name)\n",
      "1264\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0334.tif removed because it was only backgroud\n",
      "1265\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0339.tif removed because it was only backgroud\n",
      "1266\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0092.tif removed because it was only backgroud\n",
      "1267\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0278.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0278.tif saved (corrected name)\n",
      "1268\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0204.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0204.tif saved (corrected name)\n",
      "1269\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0220.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0220.tif saved (corrected name)\n",
      "1270\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0301.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0301.tif saved (corrected name)\n",
      "1271\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0386.tif removed because it was only backgroud\n",
      "1272\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0411.tif removed because it was only backgroud\n",
      "1273\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0110.tif removed because it was only backgroud\n",
      "1274\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0326.tif removed because it was only backgroud\n",
      "1275\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0047.tif removed because it was only backgroud\n",
      "1276\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0159.tif removed because it was only backgroud\n",
      "1277\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0248.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0248.tif saved (corrected name)\n",
      "1278\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0007.tif removed because it was only backgroud\n",
      "1279\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0249.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0249.tif saved (corrected name)\n",
      "1280\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0209.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0209.tif saved (corrected name)\n",
      "1281\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0368.tif removed because it was only backgroud\n",
      "1282\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0177.tif removed because it was only backgroud\n",
      "1283\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0263.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0263.tif saved (corrected name)\n",
      "1284\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0065.tif removed because it was only backgroud\n",
      "1285\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0066.tif removed because it was only backgroud\n",
      "1286\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0426.tif removed because it was only backgroud\n",
      "1287\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0104.tif removed because it was only backgroud\n",
      "1288\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0313.tif removed because it was only backgroud\n",
      "1289\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0310.tif removed because it was only backgroud\n",
      "1290\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0037.tif removed because it was only backgroud\n",
      "1291\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0225.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0225.tif saved (corrected name)\n",
      "1292\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0353.tif removed because it was only backgroud\n",
      "1293\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0128.tif removed because it was only backgroud\n",
      "1294\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0191.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0191.tif saved (corrected name)\n",
      "1295\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0311.tif removed because it was only backgroud\n",
      "1296\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0056.tif removed because it was only backgroud\n",
      "1297\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0156.tif removed because it was only backgroud\n",
      "1298\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0184.tif removed because it was only backgroud\n",
      "1299\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0342.tif removed because it was only backgroud\n",
      "1300\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0201.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0201.tif saved (corrected name)\n",
      "1301\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0443.tif removed because it was only backgroud\n",
      "1302\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0281.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0281.tif saved (corrected name)\n",
      "1303\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0432.tif removed because it was only backgroud\n",
      "1304\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0107.tif removed because it was only backgroud\n",
      "1305\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0423.tif removed because it was only backgroud\n",
      "1306\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0215.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0215.tif saved (corrected name)\n",
      "1307\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0185.tif removed because it was only backgroud\n",
      "1308\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0303.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0303.tif saved (corrected name)\n",
      "1309\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0445.tif removed because it was only backgroud\n",
      "1310\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0199.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0199.tif saved (corrected name)\n",
      "1311\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0366.tif removed because it was only backgroud\n",
      "1312\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0328.tif removed because it was only backgroud\n",
      "1313\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0321.tif removed because it was only backgroud\n",
      "1314\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0347.tif removed because it was only backgroud\n",
      "1315\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0044.tif removed because it was only backgroud\n",
      "1316\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0045.tif removed because it was only backgroud\n",
      "1317\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0019.tif removed because it was only backgroud\n",
      "1318\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0192.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0192.tif saved (corrected name)\n",
      "1319\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0164.tif removed because it was only backgroud\n",
      "1320\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0260.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0260.tif saved (corrected name)\n",
      "1321\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0116.tif removed because it was only backgroud\n",
      "1322\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0269.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0269.tif saved (corrected name)\n",
      "1323\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0359.tif removed because it was only backgroud\n",
      "1324\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0340.tif removed because it was only backgroud\n",
      "1325\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0141.tif removed because it was only backgroud\n",
      "1326\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0173.tif removed because it was only backgroud\n",
      "1327\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0090.tif removed because it was only backgroud\n",
      "1328\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0335.tif removed because it was only backgroud\n",
      "1329\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0167.tif removed because it was only backgroud\n",
      "1330\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0407.tif removed because it was only backgroud\n",
      "1331\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0372.tif removed because it was only backgroud\n",
      "1332\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0349.tif removed because it was only backgroud\n",
      "1333\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0287.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0287.tif saved (corrected name)\n",
      "1334\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0125.tif removed because it was only backgroud\n",
      "1335\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0009.tif removed because it was only backgroud\n",
      "1336\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0382.tif removed because it was only backgroud\n",
      "1337\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0254.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0254.tif saved (corrected name)\n",
      "1338\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0245.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0245.tif saved (corrected name)\n",
      "1339\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0274.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0274.tif saved (corrected name)\n",
      "1340\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0400.tif removed because it was only backgroud\n",
      "1341\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0068.tif removed because it was only backgroud\n",
      "1342\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0395.tif removed because it was only backgroud\n",
      "1343\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0061.tif removed because it was only backgroud\n",
      "1344\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0438.tif removed because it was only backgroud\n",
      "1345\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0418.tif removed because it was only backgroud\n",
      "1346\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0344.tif removed because it was only backgroud\n",
      "1347\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0431.tif removed because it was only backgroud\n",
      "1348\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0054.tif removed because it was only backgroud\n",
      "1349\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0406.tif removed because it was only backgroud\n",
      "1350\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0363.tif removed because it was only backgroud\n",
      "1351\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0049.tif removed because it was only backgroud\n",
      "1352\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0118.tif removed because it was only backgroud\n",
      "1353\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0063.tif removed because it was only backgroud\n",
      "1354\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0093.tif removed because it was only backgroud\n",
      "1355\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0405.tif removed because it was only backgroud\n",
      "1356\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0398.tif removed because it was only backgroud\n",
      "1357\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0207.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0207.tif saved (corrected name)\n",
      "1358\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0231.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0231.tif saved (corrected name)\n",
      "1359\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0228.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0228.tif saved (corrected name)\n",
      "1360\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0314.tif removed because it was only backgroud\n",
      "1361\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0080.tif removed because it was only backgroud\n",
      "1362\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0127.tif removed because it was only backgroud\n",
      "1363\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0043.tif removed because it was only backgroud\n",
      "1364\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0369.tif removed because it was only backgroud\n",
      "1365\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0186.tif removed because it was only backgroud\n",
      "1366\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0306.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0306.tif saved (corrected name)\n",
      "1367\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0053.tif removed because it was only backgroud\n",
      "1368\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0100.tif removed because it was only backgroud\n",
      "1369\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0120.tif removed because it was only backgroud\n",
      "1370\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0162.tif removed because it was only backgroud\n",
      "1371\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0427.tif removed because it was only backgroud\n",
      "1372\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0190.tif removed because it was only backgroud\n",
      "1373\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0434.tif removed because it was only backgroud\n",
      "1374\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0364.tif removed because it was only backgroud\n",
      "1375\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0388.tif removed because it was only backgroud\n",
      "1376\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0022.tif removed because it was only backgroud\n",
      "1377\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0064.tif removed because it was only backgroud\n",
      "1378\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0212.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0212.tif saved (corrected name)\n",
      "1379\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0067.tif removed because it was only backgroud\n",
      "1380\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0378.tif removed because it was only backgroud\n",
      "1381\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0082.tif removed because it was only backgroud\n",
      "1382\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0174.tif removed because it was only backgroud\n",
      "1383\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0055.tif removed because it was only backgroud\n",
      "1384\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0077.tif removed because it was only backgroud\n",
      "1385\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0036.tif removed because it was only backgroud\n",
      "1386\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0102.tif removed because it was only backgroud\n",
      "1387\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0403.tif removed because it was only backgroud\n",
      "1388\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0146.tif removed because it was only backgroud\n",
      "1389\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0135.tif removed because it was only backgroud\n",
      "1390\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0206.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0206.tif saved (corrected name)\n",
      "1391\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0069.tif removed because it was only backgroud\n",
      "1392\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0000.tif removed because it was only backgroud\n",
      "1393\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0142.tif removed because it was only backgroud\n",
      "1394\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0370.tif removed because it was only backgroud\n",
      "1395\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0160.tif removed because it was only backgroud\n",
      "1396\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0095.tif removed because it was only backgroud\n",
      "1397\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0293.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0293.tif saved (corrected name)\n",
      "1398\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0155.tif removed because it was only backgroud\n",
      "1399\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0283.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0283.tif saved (corrected name)\n",
      "1400\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0277.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0277.tif saved (corrected name)\n",
      "1401\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0012.tif removed because it was only backgroud\n",
      "1402\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0099.tif removed because it was only backgroud\n",
      "1403\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0108.tif removed because it was only backgroud\n",
      "1404\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0219.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0219.tif saved (corrected name)\n",
      "1405\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0122.tif removed because it was only backgroud\n",
      "1406\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0264.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0264.tif saved (corrected name)\n",
      "1407\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0075.tif removed because it was only backgroud\n",
      "1408\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0268.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0268.tif saved (corrected name)\n",
      "1409\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0419.tif removed because it was only backgroud\n",
      "1410\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0170.tif removed because it was only backgroud\n",
      "1411\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0154.tif removed because it was only backgroud\n",
      "1412\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0197.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0197.tif saved (corrected name)\n",
      "1413\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0020.tif removed because it was only backgroud\n",
      "1414\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0084.tif removed because it was only backgroud\n",
      "1415\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0013.tif removed because it was only backgroud\n",
      "1416\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0282.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0282.tif saved (corrected name)\n",
      "1417\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0292.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0292.tif saved (corrected name)\n",
      "1418\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0384.tif removed because it was only backgroud\n",
      "1419\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0325.tif removed because it was only backgroud\n",
      "1420\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0258.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0258.tif saved (corrected name)\n",
      "1421\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0223.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0223.tif saved (corrected name)\n",
      "1422\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0435.tif removed because it was only backgroud\n",
      "1423\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0330.tif removed because it was only backgroud\n",
      "1424\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0391.tif removed because it was only backgroud\n",
      "1425\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring-fullKidneysMask0252.tif removed due to incorrect name\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0252.tif saved (corrected name)\n"
     ]
    }
   ],
   "source": [
    "#Only necessary to run this cell once\n",
    "\n",
    "#Removing '-fulKidneysMask1' and '-fullKidneysMask' from all mask names, keeping\n",
    "#only images with some intensity greater than 0. \n",
    "\n",
    "trainImFolder = '/content/drive/MyDrive/Pablo Delgado/Doctorado-FPU/AAV para enfermedades renales/CT/2DUnet/train/images'\n",
    "trainMaskFolder = '/content/drive/MyDrive/Pablo Delgado/Doctorado-FPU/AAV para enfermedades renales/CT/2DUnet/train/masks'\n",
    "\n",
    "\n",
    "#trainImFolder = '/content/drive/Shareddrives/GR.INV. BIIG-QMIC/AVV_Renal_2017/Pablo/Segmentacion quistes CT y MRI 24-01-2022/2DUnet/train/images'\n",
    "#trainMaskFolder = '/content/drive/Shareddrives/GR.INV. BIIG-QMIC/AVV_Renal_2017/Pablo/Segmentacion quistes CT y MRI 24-01-2022/2DUnet/train/masks'\n",
    "\n",
    "import os\n",
    "import tifffile as tiff\n",
    "from glob import glob as glob\n",
    "\n",
    "i=0\n",
    "for fullMaskName in glob(os.path.join(trainMaskFolder,'*.tif')):\n",
    "  i+=1\n",
    "  print(i)\n",
    "  maskName = os.path.basename(fullMaskName)\n",
    "\n",
    "  mask = tiff.imread(os.path.join(trainMaskFolder,maskName))\n",
    "  \n",
    "  newName = maskName.replace('-fullKidneysMask','')\n",
    "\n",
    "  #Delete the images if they only have background\n",
    "  if np.max(mask)==0:\n",
    "    print(maskName+' removed because it was only backgroud')\n",
    "    os.remove(os.path.join(trainMaskFolder,maskName))\n",
    "    os.remove(os.path.join(trainImFolder,newName))\n",
    "\n",
    "  else:\n",
    "\n",
    "    print(maskName+' removed due to incorrect name')\n",
    "    #Remove the previous file\n",
    "    os.remove(os.path.join(trainMaskFolder,maskName))\n",
    "\n",
    "    print(newName+' saved (corrected name)')\n",
    "    #Store the new one (with the correct name)\n",
    "    tiff.imsave(os.path.join(trainMaskFolder,newName),mask)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IkSguVy8Xv83"
   },
   "source": [
    "# **U-Net (2D)**\n",
    "---\n",
    "\n",
    "<font size = 4>U-Net is an encoder-decoder network architecture originally used for image segmentation, first published by [Ronneberger *et al.*](https://arxiv.org/abs/1505.04597). The first half of the U-Net architecture is a downsampling convolutional neural network which acts as a feature extractor from input images. The other half upsamples these results and restores an image by combining results from downsampling with the upsampled images.\n",
    "\n",
    "<font size = 4> **This particular notebook enables image segmentation of 2D dataset. If you are interested in 3D dataset, you should use the 3D U-Net notebook instead.**\n",
    "\n",
    "---\n",
    "<font size = 4>*Disclaimer*:\n",
    "\n",
    "<font size = 4>This notebook is part of the Zero-Cost Deep-Learning to Enhance Microscopy project (https://github.com/HenriquesLab/DeepLearning_Collab/wiki). Jointly developed by the Jacquemet (link to https://cellmig.org/) and Henriques (https://henriqueslab.github.io/) laboratories.\n",
    "\n",
    "<font size = 4>This notebook is largely based on the papers: \n",
    "\n",
    "<font size = 4>**U-Net: Convolutional Networks for Biomedical Image Segmentation** by Ronneberger *et al.*  published on arXiv in 2015 (https://arxiv.org/abs/1505.04597)\n",
    "\n",
    "<font size = 4>and \n",
    "\n",
    "<font size = 4>**U-Net: deep learning for cell counting, detection, and morphometry** by Thorsten Falk *et al.* in Nature Methods 2019\n",
    "(https://www.nature.com/articles/s41592-018-0261-2)\n",
    "And source code found in: https://github.com/zhixuhao/unet by *Zhixuhao*\n",
    "\n",
    "<font size = 4>**Please also cite this original paper when using or developing this notebook.** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jWAz2i7RdxUV"
   },
   "source": [
    "# **How to use this notebook?**\n",
    "\n",
    "---\n",
    "\n",
    "<font size = 4>Video describing how to use our notebooks are available on youtube:\n",
    "  - [**Video 1**](https://www.youtube.com/watch?v=GzD2gamVNHI&feature=youtu.be): Full run through of the workflow to obtain the notebooks and the provided test datasets as well as a common use of the notebook\n",
    "  - [**Video 2**](https://www.youtube.com/watch?v=PUuQfP5SsqM&feature=youtu.be): Detailed description of the different sections of the notebook\n",
    "\n",
    "\n",
    "\n",
    "---\n",
    "###**Structure of a notebook**\n",
    "\n",
    "<font size = 4>The notebook contains two types of cell:  \n",
    "\n",
    "<font size = 4>**Text cells** provide information and can be modified by douple-clicking the cell. You are currently reading the text cell. You can create a new text by clicking `+ Text`.\n",
    "\n",
    "<font size = 4>**Code cells** contain code and the code can be modfied by selecting the cell. To execute the cell, move your cursor on the `[ ]`-mark on the left side of the cell (play button appears). Click to execute the cell. After execution is done the animation of play button stops. You can create a new coding cell by clicking `+ Code`.\n",
    "\n",
    "---\n",
    "###**Table of contents, Code snippets** and **Files**\n",
    "\n",
    "<font size = 4>On the top left side of the notebook you find three tabs which contain from top to bottom:\n",
    "\n",
    "<font size = 4>*Table of contents* = contains structure of the notebook. Click the content to move quickly between sections.\n",
    "\n",
    "<font size = 4>*Code snippets* = contain examples how to code certain tasks. You can ignore this when using this notebook.\n",
    "\n",
    "<font size = 4>*Files* = contain all available files. After mounting your google drive (see section 1.) you will find your files and folders here. \n",
    "\n",
    "<font size = 4>**Remember that all uploaded files are purged after changing the runtime.** All files saved in Google Drive will remain. You do not need to use the Mount Drive-button; your Google Drive is connected in section 1.2.\n",
    "\n",
    "<font size = 4>**Note:** The \"sample data\" in \"Files\" contains default files. Do not upload anything in here!\n",
    "\n",
    "---\n",
    "###**Making changes to the notebook**\n",
    "\n",
    "<font size = 4>**You can make a copy** of the notebook and save it to your Google Drive. To do this click file -> save a copy in drive.\n",
    "\n",
    "<font size = 4>To **edit a cell**, double click on the text. This will show you either the source code (in code cells) or the source text (in text cells).\n",
    "You can use the `#`-mark in code cells to comment out parts of the code. This allows you to keep the original code piece in the cell as a comment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gKDLkLWUd-YX"
   },
   "source": [
    "# **0. Before getting started**\n",
    "---\n",
    "\n",
    "<font size = 4>Before you run the notebook, please ensure that you are logged into your Google account and have the training and/or data to process in your Google Drive.\n",
    "\n",
    "<font size = 4>For U-Net to train, **it needs to have access to a paired training dataset corresponding to images and their corresponding masks**. Information on how to generate a training dataset is available in our Wiki page: https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki\n",
    "\n",
    "<font size = 4>**We strongly recommend that you generate extra paired images. These images can be used to assess the quality of your trained model (Quality control dataset)**. The quality control assessment can be done directly in this notebook.\n",
    "\n",
    "<font size = 4>Additionally, the corresponding Training_source and Training_target files need to have **the same name**.\n",
    "\n",
    "<font size = 4>Here's a common data structure that can work:\n",
    "*   Experiment A\n",
    "    - **Training dataset**\n",
    "      - Training_source\n",
    "        - img_1.tif, img_2.tif, ...\n",
    "      - Training_target\n",
    "        - img_1.tif, img_2.tif, ...\n",
    "    - **Quality control dataset**\n",
    "     - Training_source\n",
    "        - img_1.tif, img_2.tif\n",
    "      - Training_target \n",
    "        - img_1.tif, img_2.tif\n",
    "    - **Data to be predicted**\n",
    "    - **Results**\n",
    "\n",
    "---\n",
    "<font size = 4>**Important note**\n",
    "\n",
    "<font size = 4>- If you wish to **Train a network from scratch** using your own dataset (and we encourage everyone to do that), you will need to run **sections 1 - 4**, then use **section 5** to assess the quality of your model and **section 6** to run predictions using the model that you trained.\n",
    "\n",
    "<font size = 4>- If you wish to **Evaluate your model** using a model previously generated and saved on your Google Drive, you will only need to run **sections 1 and 2** to set up the notebook, then use **section 5** to assess the quality of your model.\n",
    "\n",
    "<font size = 4>- If you only wish to **run predictions** using a model previously generated and saved on your Google Drive, you will only need to run **sections 1 and 2** to set up the notebook, then use **section 6** to run the predictions on the desired model.\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AdN8B91xZO0x"
   },
   "source": [
    "# **1. Install U-Net dependencies**\n",
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UGWnGOFsf07b"
   },
   "source": [
    "## **1.1. Install key dependencies**\n",
    "---\n",
    "<font size = 4> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 36295,
     "status": "ok",
     "timestamp": 1649775766939,
     "user": {
      "displayName": "PABLO DELGADO RODRIGUEZ",
      "userId": "11999848093282927146"
     },
     "user_tz": -120
    },
    "id": "uc0haIa-fZiG",
    "outputId": "e9ed9728-5c00-4028-a424-9da0376db2c3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting data\n",
      "  Downloading data-0.4.tar.gz (7.0 kB)\n",
      "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from data) (1.15.0)\n",
      "Requirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from data) (4.4.2)\n",
      "Collecting funcsigs\n",
      "  Downloading funcsigs-1.0.2-py2.py3-none-any.whl (17 kB)\n",
      "Building wheels for collected packages: data\n",
      "  Building wheel for data (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for data: filename=data-0.4-py3-none-any.whl size=7247 sha256=162356bd66ee0536ae498b44bd9454e78abc16e3a3543795c909a14918fc7729\n",
      "  Stored in directory: /root/.cache/pip/wheels/28/76/4b/7db27e02c1de4d2181559d108fcdfed1680ff7d3534553d460\n",
      "Successfully built data\n",
      "Installing collected packages: funcsigs, data\n",
      "Successfully installed data-0.4 funcsigs-1.0.2\n",
      "Collecting fpdf\n",
      "  Downloading fpdf-1.7.2.tar.gz (39 kB)\n",
      "Building wheels for collected packages: fpdf\n",
      "  Building wheel for fpdf (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for fpdf: filename=fpdf-1.7.2-py2.py3-none-any.whl size=40725 sha256=5c4a12bbbe59e9bd0c096cd4cfeaaeb82c975cb4a28846c356072dae24c1e5f1\n",
      "  Stored in directory: /root/.cache/pip/wheels/d7/ca/c8/86467e7957bbbcbdf4cf4870fc7dc95e9a16404b2e3c3a98c3\n",
      "Successfully built fpdf\n",
      "Installing collected packages: fpdf\n",
      "Successfully installed fpdf-1.7.2\n",
      "Collecting h5py==2.10\n",
      "  Downloading h5py-2.10.0-cp37-cp37m-manylinux1_x86_64.whl (2.9 MB)\n",
      "\u001b[K     |████████████████████████████████| 2.9 MB 3.8 MB/s \n",
      "\u001b[?25hRequirement already satisfied: numpy>=1.7 in /usr/local/lib/python3.7/dist-packages (from h5py==2.10) (1.21.5)\n",
      "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from h5py==2.10) (1.15.0)\n",
      "Installing collected packages: h5py\n",
      "  Attempting uninstall: h5py\n",
      "    Found existing installation: h5py 3.1.0\n",
      "    Uninstalling h5py-3.1.0:\n",
      "      Successfully uninstalled h5py-3.1.0\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "tensorflow 2.8.0 requires tf-estimator-nightly==2.8.0.dev2021122109, which is not installed.\u001b[0m\n",
      "Successfully installed h5py-2.10.0\n",
      "Collecting segmentation_models\n",
      "  Downloading segmentation_models-1.0.1-py3-none-any.whl (33 kB)\n",
      "Collecting efficientnet==1.0.0\n",
      "  Downloading efficientnet-1.0.0-py3-none-any.whl (17 kB)\n",
      "Collecting keras-applications<=1.0.8,>=1.0.7\n",
      "  Downloading Keras_Applications-1.0.8-py3-none-any.whl (50 kB)\n",
      "\u001b[K     |████████████████████████████████| 50 kB 5.3 MB/s \n",
      "\u001b[?25hCollecting image-classifiers==1.0.0\n",
      "  Downloading image_classifiers-1.0.0-py3-none-any.whl (19 kB)\n",
      "Requirement already satisfied: scikit-image in /usr/local/lib/python3.7/dist-packages (from efficientnet==1.0.0->segmentation_models) (0.18.3)\n",
      "Requirement already satisfied: h5py in /usr/local/lib/python3.7/dist-packages (from keras-applications<=1.0.8,>=1.0.7->segmentation_models) (2.10.0)\n",
      "Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.7/dist-packages (from keras-applications<=1.0.8,>=1.0.7->segmentation_models) (1.21.5)\n",
      "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from h5py->keras-applications<=1.0.8,>=1.0.7->segmentation_models) (1.15.0)\n",
      "Requirement already satisfied: pillow!=7.1.0,!=7.1.1,>=4.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->efficientnet==1.0.0->segmentation_models) (7.1.2)\n",
      "Requirement already satisfied: matplotlib!=3.0.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->efficientnet==1.0.0->segmentation_models) (3.2.2)\n",
      "Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->efficientnet==1.0.0->segmentation_models) (2.6.3)\n",
      "Requirement already satisfied: tifffile>=2019.7.26 in /usr/local/lib/python3.7/dist-packages (from scikit-image->efficientnet==1.0.0->segmentation_models) (2021.11.2)\n",
      "Requirement already satisfied: scipy>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from scikit-image->efficientnet==1.0.0->segmentation_models) (1.4.1)\n",
      "Requirement already satisfied: imageio>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->efficientnet==1.0.0->segmentation_models) (2.4.1)\n",
      "Requirement already satisfied: PyWavelets>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from scikit-image->efficientnet==1.0.0->segmentation_models) (1.3.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet==1.0.0->segmentation_models) (2.8.2)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet==1.0.0->segmentation_models) (3.0.7)\n",
      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet==1.0.0->segmentation_models) (0.11.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet==1.0.0->segmentation_models) (1.4.0)\n",
      "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet==1.0.0->segmentation_models) (3.10.0.2)\n",
      "Installing collected packages: keras-applications, image-classifiers, efficientnet, segmentation-models\n",
      "Successfully installed efficientnet-1.0.0 image-classifiers-1.0.0 keras-applications-1.0.8 segmentation-models-1.0.1\n"
     ]
    }
   ],
   "source": [
    "#@markdown ##Play to install U-Net dependencies\n",
    "\n",
    "!pip install data\n",
    "!pip install fpdf\n",
    "!pip install h5py==2.10\n",
    "\n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
    "!pip install segmentation_models\n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
    "\n",
    "#Force session restart\n",
    "exit(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I4O5zctbf4Gb"
   },
   "source": [
    "\n",
    "## **1.2. Restart your runtime**\n",
    "---\n",
    "<font size = 4>\n",
    "\n",
    "\n",
    "**<font size = 4> Ignore the following message error message. Your Runtime has automatically restarted. This is normal.**\n",
    "\n",
    "<img width=\"40%\" alt =\"\" src=\"https://github.com/HenriquesLab/ZeroCostDL4Mic/raw/master/Wiki_files/session_crash.png\"><figcaption>  </figcaption>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iiX3Ly-7gA5h"
   },
   "source": [
    "## **1.3. Load key dependencies**\n",
    "---\n",
    "<font size = 4> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 12280,
     "status": "ok",
     "timestamp": 1649775946489,
     "user": {
      "displayName": "PABLO DELGADO RODRIGUEZ",
      "userId": "11999848093282927146"
     },
     "user_tz": -120
    },
    "id": "fq21zJVFNASx",
    "outputId": "1a8a55a2-b25f-489a-9529-1560390dc4c8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorFlow 1.x selected.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Segmentation Models: using `keras` framework.\n",
      "-------------------\n",
      "U-Net and dependencies installed.\n",
      "Notebook version: 1.13\n",
      "Latest notebook version: 1.13\n",
      "This notebook is up-to-date.\n"
     ]
    }
   ],
   "source": [
    "Notebook_version = '1.13'\n",
    "Network = 'U-Net (2D)'\n",
    "\n",
    "\n",
    "from builtins import any as b_any\n",
    "\n",
    "def get_requirements_path():\n",
    "    # Store requirements file in 'contents' directory \n",
    "    current_dir = os.getcwd()\n",
    "    dir_count = current_dir.count('/') - 1\n",
    "    path = '../' * (dir_count) + 'requirements.txt'\n",
    "    return path\n",
    "\n",
    "def filter_files(file_list, filter_list):\n",
    "    filtered_list = []\n",
    "    for fname in file_list:\n",
    "        if b_any(fname.split('==')[0] in s for s in filter_list):\n",
    "            filtered_list.append(fname)\n",
    "    return filtered_list\n",
    "\n",
    "def build_requirements_file(before, after):\n",
    "    path = get_requirements_path()\n",
    "\n",
    "    # Exporting requirements.txt for local run\n",
    "    !pip freeze > $path\n",
    "\n",
    "    # Get minimum requirements file\n",
    "    df = pd.read_csv(path, delimiter = \"\\n\")\n",
    "    mod_list = [m.split('.')[0] for m in after if not m in before]\n",
    "    req_list_temp = df.values.tolist()\n",
    "    req_list = [x[0] for x in req_list_temp]\n",
    "\n",
    "    # Replace with package name and handle cases where import name is different to module name\n",
    "    mod_name_list = [['sklearn', 'scikit-learn'], ['skimage', 'scikit-image']]\n",
    "    mod_replace_list = [[x[1] for x in mod_name_list] if s in [x[0] for x in mod_name_list] else s for s in mod_list] \n",
    "    filtered_list = filter_files(req_list, mod_replace_list)\n",
    "\n",
    "    file=open(path,'w')\n",
    "    for item in filtered_list:\n",
    "        file.writelines(item + '\\n')\n",
    "\n",
    "    file.close()\n",
    "\n",
    "import sys\n",
    "before = [str(m) for m in sys.modules]\n",
    "\n",
    "#@markdown ##Load key U-Net dependencies\n",
    "\n",
    "#As this notebokk depends mostly on keras which runs a tensorflow backend (which in turn is pre-installed in colab)\n",
    "#only the data library needs to be additionally installed.\n",
    "%tensorflow_version 1.x\n",
    "import tensorflow as tf\n",
    "# print(tensorflow.__version__)\n",
    "# print(\"Tensorflow enabled.\")\n",
    "\n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
    "import segmentation_models\n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
    "\n",
    "# Keras imports\n",
    "from keras import models\n",
    "from keras.models import Model, load_model\n",
    "from keras.layers import Input, Conv2D, MaxPooling2D, Dropout, concatenate, UpSampling2D\n",
    "from keras.optimizers import Adam\n",
    "# from keras.callbacks import ModelCheckpoint, LearningRateScheduler, CSVLogger # we currently don't use any other callbacks from ModelCheckpoints\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras.callbacks import ReduceLROnPlateau\n",
    "from keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img\n",
    "from keras import backend as keras\n",
    "\n",
    "# General import\n",
    "from __future__ import print_function\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import glob\n",
    "from skimage import img_as_ubyte, io, transform\n",
    "import matplotlib as mpl\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imread\n",
    "from pathlib import Path\n",
    "import shutil\n",
    "import random\n",
    "import time\n",
    "import csv\n",
    "import sys\n",
    "from math import ceil\n",
    "from fpdf import FPDF, HTMLMixin\n",
    "from pip._internal.operations.freeze import freeze\n",
    "import subprocess\n",
    "# Imports for QC\n",
    "from PIL import Image\n",
    "from scipy import signal\n",
    "from scipy import ndimage\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from skimage.util import img_as_uint\n",
    "from skimage.metrics import structural_similarity\n",
    "from skimage.metrics import peak_signal_noise_ratio as psnr\n",
    "\n",
    "# For sliders and dropdown menu and progress bar\n",
    "from ipywidgets import interact\n",
    "import ipywidgets as widgets\n",
    "# from tqdm import tqdm\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "from sklearn.feature_extraction import image\n",
    "from skimage import img_as_ubyte, io, transform\n",
    "from skimage.util.shape import view_as_windows\n",
    "\n",
    "from datetime import datetime\n",
    "\n",
    "\n",
    "# Suppressing some warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def create_patches(Training_source, Training_target, patch_width, patch_height, min_fraction):\n",
    "  \"\"\"\n",
    "  Function creates patches from the Training_source and Training_target images. \n",
    "  The steps parameter indicates the offset between patches and, if integer, is the same in x and y.\n",
    "  Saves all created patches in two new directories in the /content folder.\n",
    "\n",
    "  min_fraction is the minimum fraction of pixels that need to be foreground to be considered as a valid patch\n",
    "\n",
    "  Returns: - Two paths to where the patches are now saved\n",
    "  \"\"\"\n",
    "  DEBUG = False\n",
    "\n",
    "  Patch_source = os.path.join('/content','img_patches')\n",
    "  Patch_target = os.path.join('/content','mask_patches')\n",
    "  Patch_rejected = os.path.join('/content','rejected')\n",
    "  \n",
    "\n",
    "  #Here we save the patches, in the /content directory as they will not usually be needed after training\n",
    "  if os.path.exists(Patch_source):\n",
    "    shutil.rmtree(Patch_source)\n",
    "  if os.path.exists(Patch_target):\n",
    "    shutil.rmtree(Patch_target)\n",
    "  if os.path.exists(Patch_rejected):\n",
    "    shutil.rmtree(Patch_rejected)\n",
    "\n",
    "  os.mkdir(Patch_source)\n",
    "  os.mkdir(Patch_target)\n",
    "  os.mkdir(Patch_rejected) #This directory will contain the images that have too little signal.\n",
    "  \n",
    "  patch_num = 0\n",
    "\n",
    "  for file in tqdm(os.listdir(Training_source)):\n",
    "\n",
    "    img = io.imread(os.path.join(Training_source, file))\n",
    "    mask = io.imread(os.path.join(Training_target, file),as_gray=True)\n",
    "\n",
    "    if DEBUG:\n",
    "      print(file)\n",
    "      print(img.dtype)\n",
    "\n",
    "    # Using view_as_windows with step size equal to the patch size to ensure there is no overlap\n",
    "    patches_img = view_as_windows(img, (patch_width, patch_height), (patch_width, patch_height))\n",
    "    patches_mask = view_as_windows(mask, (patch_width, patch_height), (patch_width, patch_height))\n",
    "\n",
    "    patches_img = patches_img.reshape(patches_img.shape[0]*patches_img.shape[1], patch_width,patch_height)\n",
    "    patches_mask = patches_mask.reshape(patches_mask.shape[0]*patches_mask.shape[1], patch_width,patch_height)\n",
    "\n",
    "    if DEBUG:\n",
    "      print(all_patches_img.shape)\n",
    "      print(all_patches_img.dtype)\n",
    "\n",
    "    for i in range(patches_img.shape[0]):\n",
    "      img_save_path = os.path.join(Patch_source,'patch_'+str(patch_num)+'.tif')\n",
    "      mask_save_path = os.path.join(Patch_target,'patch_'+str(patch_num)+'.tif')\n",
    "      patch_num += 1\n",
    "\n",
    "      # if the mask conatins at least 2% of its total number pixels as mask, then go ahead and save the images\n",
    "      pixel_threshold_array = sorted(patches_mask[i].flatten())\n",
    "      if pixel_threshold_array[int(round(len(pixel_threshold_array)*(1-min_fraction)))]>0:\n",
    "        io.imsave(img_save_path, img_as_ubyte(normalizeMinMax(patches_img[i])))\n",
    "        io.imsave(mask_save_path, convert2Mask(normalizeMinMax(patches_mask[i]),0))\n",
    "      else:\n",
    "        io.imsave(Patch_rejected+'/patch_'+str(patch_num)+'_image.tif', img_as_ubyte(normalizeMinMax(patches_img[i])))\n",
    "        io.imsave(Patch_rejected+'/patch_'+str(patch_num)+'_mask.tif', convert2Mask(normalizeMinMax(patches_mask[i]),0))\n",
    "\n",
    "  return Patch_source, Patch_target\n",
    "\n",
    "\n",
    "def estimatePatchSize(data_path, max_width = 512, max_height = 512):\n",
    "\n",
    "  files = os.listdir(data_path)\n",
    "  \n",
    "  # Get the size of the first image found in the folder and initialise the variables to that\n",
    "  n = 0 \n",
    "  while os.path.isdir(os.path.join(data_path, files[n])):\n",
    "    n += 1\n",
    "  (height_min, width_min) = Image.open(os.path.join(data_path, files[n])).size\n",
    "\n",
    "  # Screen the size of all dataset to find the minimum image size\n",
    "  for file in files:\n",
    "    if not os.path.isdir(os.path.join(data_path, file)):\n",
    "      (height, width) = Image.open(os.path.join(data_path, file)).size\n",
    "      if width < width_min:\n",
    "        width_min = width\n",
    "      if height < height_min:\n",
    "        height_min = height\n",
    "  \n",
    "  # Find the power of patches that will fit within the smallest dataset\n",
    "  width_min, height_min = (fittingPowerOfTwo(width_min), fittingPowerOfTwo(height_min))\n",
    "\n",
    "  # Clip values at maximum permissible values\n",
    "  if width_min > max_width:\n",
    "    width_min = max_width\n",
    "\n",
    "  if height_min > max_height:\n",
    "    height_min = max_height\n",
    "  \n",
    "  return (width_min, height_min)\n",
    "\n",
    "def fittingPowerOfTwo(number):\n",
    "  n = 0\n",
    "  while 2**n <= number:\n",
    "    n += 1 \n",
    "  return 2**(n-1)\n",
    "\n",
    "\n",
    "def getClassWeights(Training_target_path):\n",
    "\n",
    "  Mask_dir_list = os.listdir(Training_target_path)\n",
    "  number_of_dataset = len(Mask_dir_list)\n",
    "\n",
    "  class_count = np.zeros(2, dtype=int)\n",
    "  for i in tqdm(range(number_of_dataset)):\n",
    "    print(Mask_dir_list[i])\n",
    "    mask = io.imread(os.path.join(Training_target_path, Mask_dir_list[i]))\n",
    "    mask = normalizeMinMax(mask)\n",
    "    class_count[0] += mask.shape[0]*mask.shape[1] - mask.sum()\n",
    "    class_count[1] += mask.sum()\n",
    "\n",
    "  n_samples = class_count.sum()\n",
    "  n_classes = 2\n",
    "\n",
    "  class_weights = n_samples / (n_classes * class_count)\n",
    "  return class_weights\n",
    "\n",
    "def weighted_binary_crossentropy(class_weights):\n",
    "\n",
    "    def _weighted_binary_crossentropy(y_true, y_pred):\n",
    "        binary_crossentropy = keras.binary_crossentropy(y_true, y_pred)\n",
    "        weight_vector = y_true * class_weights[1] + (1. - y_true) * class_weights[0]\n",
    "        weighted_binary_crossentropy = weight_vector * binary_crossentropy\n",
    "\n",
    "        return keras.mean(weighted_binary_crossentropy)\n",
    "\n",
    "    return _weighted_binary_crossentropy\n",
    "\n",
    "\n",
    "def save_augment(datagen,orig_img,dir_augmented_data=\"/content/augment\"):\n",
    "  \"\"\"\n",
    "  Saves a subset of the augmented data for visualisation, by default in /content.\n",
    "\n",
    "  This is adapted from: https://fairyonice.github.io/Learn-about-ImageDataGenerator.html\n",
    "  \n",
    "  \"\"\"\n",
    "  try:\n",
    "    os.mkdir(dir_augmented_data)\n",
    "  except:\n",
    "        ## if the preview folder exists, then remove\n",
    "        ## the contents (pictures) in the folder\n",
    "    for item in os.listdir(dir_augmented_data):\n",
    "      os.remove(dir_augmented_data + \"/\" + item)\n",
    "\n",
    "    ## convert the original image to array\n",
    "  x = img_to_array(orig_img)\n",
    "    ## reshape (Sampke, Nrow, Ncol, 3) 3 = R, G or B\n",
    "    #print(x.shape)\n",
    "  x = x.reshape((1,) + x.shape)\n",
    "    #print(x.shape)\n",
    "    ## -------------------------- ##\n",
    "    ## randomly generate pictures\n",
    "    ## -------------------------- ##\n",
    "  i = 0\n",
    "    #We will just save 5 images,\n",
    "    #but this can be changed, but note the visualisation in 3. currently uses 5.\n",
    "  Nplot = 5\n",
    "  for batch in datagen.flow(x,batch_size=1,\n",
    "                            save_to_dir=dir_augmented_data,\n",
    "                            save_format='tif',\n",
    "                            seed=42):\n",
    "    i += 1\n",
    "    if i > Nplot - 1:\n",
    "      break\n",
    "\n",
    "# Generators\n",
    "def buildDoubleGenerator(image_datagen, mask_datagen, image_folder_path, mask_folder_path, subset, batch_size, target_size):\n",
    "  '''\n",
    "  Can generate image and mask at the same time use the same seed for image_datagen and mask_datagen to ensure the transformation for image and mask is the same\n",
    "  \n",
    "  datagen: ImageDataGenerator \n",
    "  subset: can take either 'training' or 'validation'\n",
    "  '''\n",
    "  seed = 1\n",
    "  image_generator = image_datagen.flow_from_directory(\n",
    "      os.path.dirname(image_folder_path),\n",
    "      classes = [os.path.basename(image_folder_path)],\n",
    "      class_mode = None,\n",
    "      color_mode = \"grayscale\",\n",
    "      target_size = target_size,\n",
    "      batch_size = batch_size,\n",
    "      subset = subset,\n",
    "      interpolation = \"bicubic\",\n",
    "      seed = seed)\n",
    "  \n",
    "  mask_generator = mask_datagen.flow_from_directory(\n",
    "      os.path.dirname(mask_folder_path),\n",
    "      classes = [os.path.basename(mask_folder_path)],\n",
    "      class_mode = None,\n",
    "      color_mode = \"grayscale\",\n",
    "      target_size = target_size,\n",
    "      batch_size = batch_size,\n",
    "      subset = subset,\n",
    "      interpolation = \"nearest\",\n",
    "      seed = seed)\n",
    "  \n",
    "  this_generator = zip(image_generator, mask_generator)\n",
    "  for (img,mask) in this_generator:\n",
    "      # img,mask = adjustData(img,mask)\n",
    "      yield (img,mask)\n",
    "\n",
    "\n",
    "def prepareGenerators(image_folder_path, mask_folder_path, datagen_parameters, batch_size = 4, target_size = (512, 512)):\n",
    "  image_datagen = ImageDataGenerator(**datagen_parameters, preprocessing_function = normalizePercentile)\n",
    "  mask_datagen = ImageDataGenerator(**datagen_parameters, preprocessing_function = normalizeMinMax)\n",
    "\n",
    "  train_datagen = buildDoubleGenerator(image_datagen, mask_datagen, image_folder_path, mask_folder_path, 'training', batch_size, target_size)\n",
    "  validation_datagen = buildDoubleGenerator(image_datagen, mask_datagen, image_folder_path, mask_folder_path, 'validation', batch_size, target_size)\n",
    "\n",
    "  return (train_datagen, validation_datagen)\n",
    "\n",
    "\n",
    "# Normalization functions from Martin Weigert\n",
    "def normalizePercentile(x, pmin=1, pmax=99.8, axis=None, clip=False, eps=1e-20, dtype=np.float32):\n",
    "    \"\"\"This function is adapted from Martin Weigert\"\"\"\n",
    "    \"\"\"Percentile-based image normalization.\"\"\"\n",
    "\n",
    "    mi = np.percentile(x,pmin,axis=axis,keepdims=True)\n",
    "    ma = np.percentile(x,pmax,axis=axis,keepdims=True)\n",
    "    return normalize_mi_ma(x, mi, ma, clip=clip, eps=eps, dtype=dtype)\n",
    "\n",
    "\n",
    "def normalize_mi_ma(x, mi, ma, clip=False, eps=1e-20, dtype=np.float32):#dtype=np.float32\n",
    "    \"\"\"This function is adapted from Martin Weigert\"\"\"\n",
    "    if dtype is not None:\n",
    "        x   = x.astype(dtype,copy=False)\n",
    "        mi  = dtype(mi) if np.isscalar(mi) else mi.astype(dtype,copy=False)\n",
    "        ma  = dtype(ma) if np.isscalar(ma) else ma.astype(dtype,copy=False)\n",
    "        eps = dtype(eps)\n",
    "\n",
    "    try:\n",
    "        import numexpr\n",
    "        x = numexpr.evaluate(\"(x - mi) / ( ma - mi + eps )\")\n",
    "    except ImportError:\n",
    "        x =                   (x - mi) / ( ma - mi + eps )\n",
    "\n",
    "    if clip:\n",
    "        x = np.clip(x,0,1)\n",
    "\n",
    "    return x\n",
    "\n",
    "\n",
    "\n",
    "# Simple normalization to min/max fir the Mask\n",
    "def normalizeMinMax(x, dtype=np.float32):\n",
    "  x = x.astype(dtype,copy=False)\n",
    "  x = (x - np.amin(x)) / (np.amax(x) - np.amin(x))\n",
    "  return x\n",
    "\n",
    "\n",
    "# This is code outlines the architecture of U-net. The choice of pooling steps decides the depth of the network. \n",
    "\n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
    "def unet(pretrained_weights = None, input_size = (256,256,1), pooling_steps = 4, learning_rate = 1e-4, verbose=True, class_weights=np.ones(2)):\n",
    "    \n",
    "    print('----Unet Definition----')\n",
    "\n",
    "    base_model = segmentation_models.Unet('resnet34',encoder_weights='imagenet')\n",
    "\n",
    "    print('base model.summary()')\n",
    "    base_model.summary()\n",
    "\n",
    "    inp = Input(shape=input_size)\n",
    "    print('inp: ',inp)\n",
    "    l1 = Conv2D(3, (1, 1))(inp) # map N channels data to 3 channels\n",
    "    print('l1: ',l1)\n",
    "    out = base_model(l1)\n",
    "    print('out: ',out)\n",
    "    model = Model(inp, out, name=base_model.name)\n",
    "\n",
    "    # model.compile(optimizer = Adam(lr = learning_rate), loss = 'binary_crossentropy', metrics = ['acc'])\n",
    "    model.compile(optimizer = Adam(lr = learning_rate), loss = weighted_binary_crossentropy(class_weights))\n",
    "\n",
    "\n",
    "    if verbose:\n",
    "      model.summary()\n",
    "\n",
    "    if(pretrained_weights):\n",
    "    \tmodel.load_weights(pretrained_weights);\n",
    "\n",
    "    return model\n",
    "\n",
    "\"\"\"\n",
    "def unet(pretrained_weights = None, input_size = (256,256,1), pooling_steps = 4, learning_rate = 1e-4, verbose=True, class_weights=np.ones(2)):\n",
    "    inputs = Input(input_size)\n",
    "    conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)\n",
    "    conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)\n",
    "    # Downsampling steps\n",
    "    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)\n",
    "    conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)\n",
    "    conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)\n",
    "    \n",
    "    if pooling_steps > 1:\n",
    "      pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)\n",
    "      conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)\n",
    "      conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)\n",
    "\n",
    "      if pooling_steps > 2:\n",
    "        pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)\n",
    "        conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)\n",
    "        conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)\n",
    "        drop4 = Dropout(0.5)(conv4)\n",
    "      \n",
    "        if pooling_steps > 3:\n",
    "          pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)\n",
    "          conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)\n",
    "          conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)\n",
    "          drop5 = Dropout(0.5)(conv5)\n",
    "\n",
    "          #Upsampling steps\n",
    "          up6 = Conv2D(512, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop5))\n",
    "          merge6 = concatenate([drop4,up6], axis = 3)\n",
    "          conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)\n",
    "          conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)\n",
    "          \n",
    "    if pooling_steps > 2:\n",
    "      up7 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop4))\n",
    "      if pooling_steps > 3:\n",
    "        up7 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv6))\n",
    "      merge7 = concatenate([conv3,up7], axis = 3)\n",
    "      conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge7)\n",
    "      conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)\n",
    "\n",
    "    if pooling_steps > 1:\n",
    "      up8 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv3))\n",
    "      if pooling_steps > 2:\n",
    "        up8 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv7))\n",
    "      merge8 = concatenate([conv2,up8], axis = 3)\n",
    "      conv8 = Conv2D(128, 3, activation= 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge8)\n",
    "      conv8 = Conv2D(128, 3, activation= 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)\n",
    "      \n",
    "    if pooling_steps == 1:\n",
    "      up9 = Conv2D(64, 2, padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv2))\n",
    "    else:\n",
    "      up9 = Conv2D(64, 2, padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv8)) #activation = 'relu'\n",
    "    \n",
    "    merge9 = concatenate([conv1,up9], axis = 3)\n",
    "    conv9 = Conv2D(64, 3, padding = 'same', kernel_initializer = 'he_normal')(merge9) #activation = 'relu'\n",
    "    conv9 = Conv2D(64, 3, padding = 'same', kernel_initializer = 'he_normal')(conv9) #activation = 'relu'\n",
    "    conv9 = Conv2D(2, 3, padding = 'same', kernel_initializer = 'he_normal')(conv9) #activation = 'relu'\n",
    "    conv10 = Conv2D(1, 1, activation = 'sigmoid')(conv9)\n",
    "\n",
    "    model = Model(inputs = inputs, outputs = conv10)\n",
    "\n",
    "    # model.compile(optimizer = Adam(lr = learning_rate), loss = 'binary_crossentropy', metrics = ['acc'])\n",
    "    model.compile(optimizer = Adam(lr = learning_rate), loss = weighted_binary_crossentropy(class_weights))\n",
    "\n",
    "\n",
    "    if verbose:\n",
    "      model.summary()\n",
    "\n",
    "    if(pretrained_weights):\n",
    "    \tmodel.load_weights(pretrained_weights);\n",
    "\n",
    "    return model\n",
    "\"\"\"\n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
    "\n",
    "def predict_as_tiles(Image_path, model):\n",
    "\n",
    "  # Read the data in and normalize\n",
    "  Image_raw = io.imread(Image_path, as_gray = True)\n",
    "  Image_raw = normalizePercentile(Image_raw)\n",
    "\n",
    "  # Get the patch size from the input layer of the model\n",
    "  patch_size = model.layers[0].output_shape[1:3]\n",
    "\n",
    "  # Pad the image with zeros if any of its dimensions is smaller than the patch size\n",
    "  if Image_raw.shape[0] < patch_size[0] or Image_raw.shape[1] < patch_size[1]:\n",
    "    Image = np.zeros((max(Image_raw.shape[0], patch_size[0]), max(Image_raw.shape[1], patch_size[1])))\n",
    "    Image[0:Image_raw.shape[0], 0: Image_raw.shape[1]] = Image_raw\n",
    "  else:\n",
    "    Image = Image_raw\n",
    "\n",
    "  # Calculate the number of patches in each dimension\n",
    "  n_patch_in_width = ceil(Image.shape[0]/patch_size[0])\n",
    "  n_patch_in_height = ceil(Image.shape[1]/patch_size[1])\n",
    "\n",
    "  prediction = np.zeros(Image.shape)\n",
    "\n",
    "  for x in range(n_patch_in_width):\n",
    "    for y in range(n_patch_in_height):\n",
    "      xi = patch_size[0]*x\n",
    "      yi = patch_size[1]*y\n",
    "\n",
    "      # If the patch exceeds the edge of the image shift it back \n",
    "      if xi+patch_size[0] >= Image.shape[0]:\n",
    "        xi = Image.shape[0]-patch_size[0]\n",
    "\n",
    "      if yi+patch_size[1] >= Image.shape[1]:\n",
    "        yi = Image.shape[1]-patch_size[1]\n",
    "      \n",
    "      # Extract and reshape the patch\n",
    "      patch = Image[xi:xi+patch_size[0], yi:yi+patch_size[1]]\n",
    "      patch = np.reshape(patch,patch.shape+(1,))\n",
    "      patch = np.reshape(patch,(1,)+patch.shape)\n",
    "\n",
    "      # Get the prediction from the patch and paste it in the prediction in the right place\n",
    "      predicted_patch = model.predict(patch, batch_size = 1)\n",
    "      prediction[xi:xi+patch_size[0], yi:yi+patch_size[1]] = np.squeeze(predicted_patch)\n",
    "\n",
    "\n",
    "  return prediction[0:Image_raw.shape[0], 0: Image_raw.shape[1]]\n",
    "  \n",
    "\n",
    "\n",
    "\n",
    "def saveResult(save_path, nparray, source_dir_list, prefix='', threshold=None):\n",
    "  for (filename, image) in zip(source_dir_list, nparray):\n",
    "      io.imsave(os.path.join(save_path, prefix+os.path.splitext(filename)[0]+'.tif'), img_as_ubyte(image)) # saving as unsigned 8-bit image\n",
    "      \n",
    "      # For masks, threshold the images and return 8 bit image\n",
    "      if threshold is not None:\n",
    "        mask = convert2Mask(image, threshold)\n",
    "        io.imsave(os.path.join(save_path, prefix+'mask_'+os.path.splitext(filename)[0]+'.tif'), mask)\n",
    "\n",
    "\n",
    "def convert2Mask(image, threshold):\n",
    "  mask = img_as_ubyte(image, force_copy=True)\n",
    "  mask[mask > threshold] = 255\n",
    "  mask[mask <= threshold] = 0\n",
    "  return mask\n",
    "\n",
    "\n",
    "def getIoUvsThreshold(prediction_filepath, groud_truth_filepath):\n",
    "  prediction = io.imread(prediction_filepath)\n",
    "  ground_truth_image = img_as_ubyte(io.imread(groud_truth_filepath, as_gray=True), force_copy=True)\n",
    "\n",
    "  threshold_list = []\n",
    "  IoU_scores_list = []\n",
    "\n",
    "  for threshold in range(0,256): \n",
    "    # Convert to 8-bit for calculating the IoU\n",
    "    mask = img_as_ubyte(prediction, force_copy=True)\n",
    "    mask[mask > threshold] = 255\n",
    "    mask[mask <= threshold] = 0\n",
    "\n",
    "    # Intersection over Union metric\n",
    "    intersection = np.logical_and(ground_truth_image, np.squeeze(mask))\n",
    "    union = np.logical_or(ground_truth_image, np.squeeze(mask))\n",
    "    iou_score = np.sum(intersection) / np.sum(union)\n",
    "\n",
    "    threshold_list.append(threshold)\n",
    "    IoU_scores_list.append(iou_score)\n",
    "\n",
    "  return (threshold_list, IoU_scores_list)\n",
    "\n",
    "\n",
    "\n",
    "# -------------- Other definitions -----------\n",
    "W  = '\\033[0m'  # white (normal)\n",
    "R  = '\\033[31m' # red\n",
    "prediction_prefix = 'Predicted_'\n",
    "\n",
    "\n",
    "print('-------------------')\n",
    "print('U-Net and dependencies installed.')\n",
    "\n",
    "# Colors for the warning messages\n",
    "class bcolors:\n",
    "  WARNING = '\\033[31m'\n",
    "\n",
    "# Check if this is the latest version of the notebook\n",
    "\n",
    "All_notebook_versions = pd.read_csv(\"https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/Latest_Notebook_versions.csv\", dtype=str)\n",
    "print('Notebook version: '+Notebook_version)\n",
    "Latest_Notebook_version = All_notebook_versions[All_notebook_versions[\"Notebook\"] == Network]['Version'].iloc[0]\n",
    "print('Latest notebook version: '+Latest_Notebook_version)\n",
    "if Notebook_version == Latest_Notebook_version:\n",
    "  print(\"This notebook is up-to-date.\")\n",
    "else:\n",
    "  print(bcolors.WARNING +\"A new version of this notebook has been released. We recommend that you download it at https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki\")\n",
    "\n",
    "\n",
    "def pdf_export(trained = False, augmentation = False, pretrained_model = False):\n",
    "  class MyFPDF(FPDF, HTMLMixin):\n",
    "    pass\n",
    "\n",
    "  pdf = MyFPDF()\n",
    "  pdf.add_page()\n",
    "  pdf.set_right_margin(-1)\n",
    "  pdf.set_font(\"Arial\", size = 11, style='B') \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "  day = datetime.now()\n",
    "  datetime_str = str(day)[0:10]\n",
    "\n",
    "  Header = 'Training report for '+Network+' model ('+model_name+')\\nDate: '+datetime_str\n",
    "  pdf.multi_cell(180, 5, txt = Header, align = 'L') \n",
    "    \n",
    "  # add another cell \n",
    "  if trained:\n",
    "    training_time = \"Training time: \"+str(hour)+ \"hour(s) \"+str(mins)+\"min(s) \"+str(round(sec))+\"sec(s)\"\n",
    "    pdf.cell(190, 5, txt = training_time, ln = 1, align='L')\n",
    "  pdf.ln(1)\n",
    "\n",
    "  Header_2 = 'Information for your materials and method:'\n",
    "  pdf.cell(190, 5, txt=Header_2, ln=1, align='L')\n",
    "\n",
    "  all_packages = ''\n",
    "  for requirement in freeze(local_only=True):\n",
    "    all_packages = all_packages+requirement+', '\n",
    "  #print(all_packages)\n",
    "\n",
    "  #Main Packages\n",
    "  main_packages = ''\n",
    "  version_numbers = []\n",
    "  for name in ['tensorflow','numpy','Keras']:\n",
    "    find_name=all_packages.find(name)\n",
    "    main_packages = main_packages+all_packages[find_name:all_packages.find(',',find_name)]+', '\n",
    "    #Version numbers only here:\n",
    "    version_numbers.append(all_packages[find_name+len(name)+2:all_packages.find(',',find_name)])\n",
    "\n",
    "  cuda_version = subprocess.run('nvcc --version',stdout=subprocess.PIPE, shell=True)\n",
    "  cuda_version = cuda_version.stdout.decode('utf-8')\n",
    "  cuda_version = cuda_version[cuda_version.find(', V')+3:-1]\n",
    "  gpu_name = subprocess.run('nvidia-smi',stdout=subprocess.PIPE, shell=True)\n",
    "  gpu_name = gpu_name.stdout.decode('utf-8')\n",
    "  gpu_name = gpu_name[gpu_name.find('Tesla'):gpu_name.find('Tesla')+10]\n",
    "  #print(cuda_version[cuda_version.find(', V')+3:-1])\n",
    "  #print(gpu_name)\n",
    "  loss = str(model.loss)[str(model.loss).find('function')+len('function'):str(model.loss).find('.<')]\n",
    "  shape = io.imread(Training_source+'/'+os.listdir(Training_source)[1]).shape\n",
    "  dataset_size = len(os.listdir(Training_source))\n",
    "\n",
    "  text = 'The '+Network+' model was trained from scratch for '+str(number_of_epochs)+' epochs on '+str(number_of_training_dataset)+' paired image patches (image dimensions: '+str(shape)+', patch size: ('+str(patch_width)+','+str(patch_height)+')) with a batch size of '+str(batch_size)+' and a'+loss+' loss function,'+' using the '+Network+' ZeroCostDL4Mic notebook (v '+Notebook_version[0]+') (von Chamier & Laine et al., 2020). Key python packages used include tensorflow (v '+version_numbers[0]+'), Keras (v '+version_numbers[2]+'), numpy (v '+version_numbers[1]+'), cuda (v '+cuda_version+'). The training was accelerated using a '+gpu_name+'GPU.'\n",
    "\n",
    "  if pretrained_model:\n",
    "    text = 'The '+Network+' model was trained for '+str(number_of_epochs)+' epochs on '+str(number_of_training_dataset)+' paired image patches (image dimensions: '+str(shape)+', patch size: ('+str(patch_width)+','+str(patch_height)+')) with a batch size of '+str(batch_size)+'  and a'+loss+' loss function,'+' using the '+Network+' ZeroCostDL4Mic notebook (v '+Notebook_version[0]+') (von Chamier & Laine et al., 2020). The model was re-trained from a pretrained model. Key python packages used include tensorflow (v '+version_numbers[0]+'), Keras (v '+version_numbers[2]+'), numpy (v '+version_numbers[1]+'), cuda (v '+cuda_version+'). The training was accelerated using a '+gpu_name+'GPU.'\n",
    "\n",
    "  pdf.set_font('')\n",
    "  pdf.set_font_size(10.)\n",
    "  pdf.multi_cell(180, 5, txt = text, align='L')\n",
    "  pdf.set_font('')\n",
    "  pdf.set_font('Arial', size = 10, style = 'B')\n",
    "  pdf.ln(1)\n",
    "  pdf.cell(28, 5, txt='Augmentation: ', ln=1)\n",
    "  pdf.set_font('')\n",
    "  if augmentation:\n",
    "    aug_text = 'The dataset was augmented by'\n",
    "    if rotation_range != 0:\n",
    "      aug_text = aug_text+'\\n- rotation'\n",
    "    if horizontal_flip == True or vertical_flip == True:\n",
    "      aug_text = aug_text+'\\n- flipping'\n",
    "    if zoom_range != 0:\n",
    "      aug_text = aug_text+'\\n- random zoom magnification'\n",
    "    if horizontal_shift != 0 or vertical_shift != 0:\n",
    "      aug_text = aug_text+'\\n- shifting'\n",
    "    if shear_range != 0:\n",
    "      aug_text = aug_text+'\\n- image shearing'\n",
    "  else:\n",
    "    aug_text = 'No augmentation was used for training.'\n",
    "  pdf.multi_cell(190, 5, txt=aug_text, align='L')\n",
    "  pdf.set_font('Arial', size = 11, style = 'B')\n",
    "  pdf.ln(1)\n",
    "  pdf.cell(180, 5, txt = 'Parameters', align='L', ln=1)\n",
    "  pdf.set_font('')\n",
    "  pdf.set_font_size(10.)\n",
    "  if Use_Default_Advanced_Parameters:\n",
    "    pdf.cell(200, 5, txt='Default Advanced Parameters were enabled')\n",
    "  pdf.cell(200, 5, txt='The following parameters were used for training:')\n",
    "  pdf.ln(1)\n",
    "  html = \"\"\" \n",
    "  <table width=40% style=\"margin-left:0px;\">\n",
    "    <tr>\n",
    "      <th width = 50% align=\"left\">Parameter</th>\n",
    "      <th width = 50% align=\"left\">Value</th>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td width = 50%>number_of_epochs</td>\n",
    "      <td width = 50%>{0}</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td width = 50%>patch_size</td>\n",
    "      <td width = 50%>{1}</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td width = 50%>batch_size</td>\n",
    "      <td width = 50%>{2}</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td width = 50%>number_of_steps</td>\n",
    "      <td width = 50%>{3}</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td width = 50%>percentage_validation</td>\n",
    "      <td width = 50%>{4}</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td width = 50%>initial_learning_rate</td>\n",
    "      <td width = 50%>{5}</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td width = 50%>pooling_steps</td>\n",
    "      <td width = 50%>{6}</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td width = 50%>min_fraction</td>\n",
    "      <td width = 50%>{7}</td>\n",
    "  </table>\n",
    "  \"\"\".format(number_of_epochs, str(patch_width)+'x'+str(patch_height), batch_size, number_of_steps, percentage_validation, initial_learning_rate, pooling_steps, min_fraction)\n",
    "  pdf.write_html(html)\n",
    "\n",
    "  #pdf.multi_cell(190, 5, txt = text_2, align='L')\n",
    "  pdf.set_font(\"Arial\", size = 11, style='B')\n",
    "  pdf.ln(1)\n",
    "  pdf.cell(190, 5, txt = 'Training Dataset', align='L', ln=1)\n",
    "  pdf.set_font('')\n",
    "  pdf.set_font('Arial', size = 10, style = 'B')\n",
    "  pdf.cell(29, 5, txt= 'Training_source:', align = 'L', ln=0)\n",
    "  pdf.set_font('')\n",
    "  pdf.multi_cell(170, 5, txt = Training_source, align = 'L')\n",
    "  pdf.set_font('')\n",
    "  pdf.set_font('Arial', size = 10, style = 'B')\n",
    "  pdf.cell(28, 5, txt= 'Training_target:', align = 'L', ln=0)\n",
    "  pdf.set_font('')\n",
    "  pdf.multi_cell(170, 5, txt = Training_target, align = 'L')\n",
    "  #pdf.cell(190, 5, txt=aug_text, align='L', ln=1)\n",
    "  pdf.ln(1)\n",
    "  pdf.set_font('')\n",
    "  pdf.set_font('Arial', size = 10, style = 'B')\n",
    "  pdf.cell(21, 5, txt= 'Model Path:', align = 'L', ln=0)\n",
    "  pdf.set_font('')\n",
    "  pdf.multi_cell(170, 5, txt = model_path+'/'+model_name, align = 'L')\n",
    "  pdf.ln(1)\n",
    "  pdf.cell(60, 5, txt = 'Example Training pair', ln=1)\n",
    "  pdf.ln(1)\n",
    "  exp_size = io.imread('/content/TrainingDataExample_Unet2D.png').shape\n",
    "  pdf.image('/content/TrainingDataExample_Unet2D.png', x = 11, y = None, w = round(exp_size[1]/8), h = round(exp_size[0]/8))\n",
    "  pdf.ln(1)\n",
    "  ref_1 = 'References:\\n - ZeroCostDL4Mic: von Chamier, Lucas & Laine, Romain, et al. \"Democratising deep learning for microscopy with ZeroCostDL4Mic.\" Nature Communications (2021).'\n",
    "  pdf.multi_cell(190, 5, txt = ref_1, align='L')\n",
    "  ref_2 = '- Unet: Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. \"U-net: Convolutional networks for biomedical image segmentation.\" International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015.'\n",
    "  pdf.multi_cell(190, 5, txt = ref_2, align='L')\n",
    "  # if Use_Data_augmentation:\n",
    "  #   ref_3 = '- Augmentor: Bloice, Marcus D., Christof Stocker, and Andreas Holzinger. \"Augmentor: an image augmentation library for machine learning.\" arXiv preprint arXiv:1708.04680 (2017).'\n",
    "  #   pdf.multi_cell(190, 5, txt = ref_3, align='L')\n",
    "  pdf.ln(3)\n",
    "  reminder = 'Important:\\nRemember to perform the quality control step on all newly trained models\\nPlease consider depositing your training dataset on Zenodo'\n",
    "  pdf.set_font('Arial', size = 11, style='B')\n",
    "  pdf.multi_cell(190, 5, txt=reminder, align='C')\n",
    "\n",
    "  pdf.output(model_path+'/'+model_name+'/'+model_name+'_training_report.pdf')\n",
    "\n",
    "  print('------------------------------')\n",
    "  print('PDF report exported in '+model_path+'/'+model_name+'/')\n",
    "\n",
    "def qc_pdf_export():\n",
    "  class MyFPDF(FPDF, HTMLMixin):\n",
    "    pass\n",
    "\n",
    "  pdf = MyFPDF()\n",
    "  pdf.add_page()\n",
    "  pdf.set_right_margin(-1)\n",
    "  pdf.set_font(\"Arial\", size = 11, style='B') \n",
    "\n",
    "  Network = 'Unet 2D'\n",
    "\n",
    "  day = datetime.now()\n",
    "  datetime_str = str(day)[0:10]\n",
    "\n",
    "  Header = 'Quality Control report for '+Network+' model ('+QC_model_name+')\\nDate: '+datetime_str\n",
    "  pdf.multi_cell(180, 5, txt = Header, align = 'L') \n",
    "\n",
    "  all_packages = ''\n",
    "  for requirement in freeze(local_only=True):\n",
    "    all_packages = all_packages+requirement+', '\n",
    "\n",
    "  pdf.set_font('')\n",
    "  pdf.set_font('Arial', size = 11, style = 'B')\n",
    "  pdf.ln(2)\n",
    "  pdf.cell(190, 5, txt = 'Loss curves', ln=1, align='L')\n",
    "  pdf.ln(1)\n",
    "  exp_size = io.imread(full_QC_model_path+'/Quality Control/QC_example_data.png').shape\n",
    "  if os.path.exists(full_QC_model_path+'/Quality Control/lossCurvePlots.png'):\n",
    "    pdf.image(full_QC_model_path+'/Quality Control/lossCurvePlots.png', x = 11, y = None, w = round(exp_size[1]/12), h = round(exp_size[0]/3))\n",
    "  else:\n",
    "    pdf.set_font('')\n",
    "    pdf.set_font('Arial', size=10)\n",
    "    pdf.multi_cell(190, 5, txt='If you would like to see the evolution of the loss function during training please play the first cell of the QC section in the notebook.',align='L')\n",
    "  pdf.ln(2)\n",
    "  pdf.set_font('')\n",
    "  pdf.set_font('Arial', size = 11, style = 'B')\n",
    "  pdf.ln(2)\n",
    "  pdf.cell(190, 5, txt = 'Threshold Optimisation', ln=1, align='L')\n",
    "  #pdf.ln(1)\n",
    "  exp_size = io.imread(full_QC_model_path+'/Quality Control/'+QC_model_name+'_IoUvsThresholdPlot.png').shape\n",
    "  pdf.image(full_QC_model_path+'/Quality Control/'+QC_model_name+'_IoUvsThresholdPlot.png', x = 11, y = None, w = round(exp_size[1]/6), h = round(exp_size[0]/7))\n",
    "  pdf.set_font('')\n",
    "  pdf.set_font('Arial', size = 10, style = 'B')\n",
    "  pdf.ln(3)\n",
    "  pdf.cell(80, 5, txt = 'Example Quality Control Visualisation', ln=1)\n",
    "  pdf.ln(1)\n",
    "  exp_size = io.imread(full_QC_model_path+'/Quality Control/QC_example_data.png').shape\n",
    "  pdf.image(full_QC_model_path+'/Quality Control/QC_example_data.png', x = 16, y = None, w = round(exp_size[1]/8), h = round(exp_size[0]/8))\n",
    "  pdf.ln(1)\n",
    "  pdf.set_font('')\n",
    "  pdf.set_font('Arial', size = 11, style = 'B')\n",
    "  pdf.ln(1)\n",
    "  pdf.cell(180, 5, txt = 'Quality Control Metrics', align='L', ln=1)\n",
    "  pdf.set_font('')\n",
    "  pdf.set_font_size(10.)\n",
    "\n",
    "  pdf.ln(1)\n",
    "  html = \"\"\"\n",
    "  <body>\n",
    "  <font size=\"10\" face=\"Courier New\" >\n",
    "  <table width=60% style=\"margin-left:0px;\">\"\"\"\n",
    "  with open(full_QC_model_path+'/Quality Control/QC_metrics_'+QC_model_name+'.csv', 'r') as csvfile:\n",
    "    metrics = csv.reader(csvfile)\n",
    "    header = next(metrics)\n",
    "    image = header[0]\n",
    "    IoU = header[1]\n",
    "    IoU_OptThresh = header[2]\n",
    "    header = \"\"\"\n",
    "    <tr>\n",
    "    <th width = 33% align=\"center\">{0}</th>\n",
    "    <th width = 33% align=\"center\">{1}</th>\n",
    "    <th width = 33% align=\"center\">{2}</th>\n",
    "    </tr>\"\"\".format(image,IoU,IoU_OptThresh)\n",
    "    html = html+header\n",
    "    i=0\n",
    "    for row in metrics:\n",
    "      i+=1\n",
    "      image = row[0]\n",
    "      IoU = row[1]\n",
    "      IoU_OptThresh = row[2]\n",
    "      cells = \"\"\"\n",
    "        <tr>\n",
    "          <td width = 33% align=\"center\">{0}</td>\n",
    "          <td width = 33% align=\"center\">{1}</td>\n",
    "          <td width = 33% align=\"center\">{2}</td>\n",
    "        </tr>\"\"\".format(image,str(round(float(IoU),3)),str(round(float(IoU_OptThresh),3)))\n",
    "      html = html+cells\n",
    "    html = html+\"\"\"</body></table>\"\"\"\n",
    "    \n",
    "  pdf.write_html(html)\n",
    "\n",
    "  pdf.ln(1)\n",
    "  pdf.set_font('')\n",
    "  pdf.set_font_size(10.)\n",
    "  ref_1 = 'References:\\n - ZeroCostDL4Mic: von Chamier, Lucas & Laine, Romain, et al. \"Democratising deep learning for microscopy with ZeroCostDL4Mic.\" Nature Communications (2021).'\n",
    "  pdf.multi_cell(190, 5, txt = ref_1, align='L')\n",
    "  ref_2 = '- Unet: Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. \"U-net: Convolutional networks for biomedical image segmentation.\" International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015.'\n",
    "  pdf.multi_cell(190, 5, txt = ref_2, align='L')\n",
    "\n",
    "  pdf.ln(3)\n",
    "  reminder = 'To find the parameters and other information about how this model was trained, go to the training_report.pdf of this model which should be in the folder of the same name.'\n",
    "\n",
    "  pdf.set_font('Arial', size = 11, style='B')\n",
    "  pdf.multi_cell(190, 5, txt=reminder, align='C')\n",
    "\n",
    "  pdf.output(full_QC_model_path+'/Quality Control/'+QC_model_name+'_QC_report.pdf')\n",
    "\n",
    "  print('------------------------------')\n",
    "  print('QC PDF report exported as '+full_QC_model_path+'/Quality Control/'+QC_model_name+'_QC_report.pdf')\n",
    "\n",
    "# Build requirements file for local run\n",
    "after = [str(m) for m in sys.modules]\n",
    "build_requirements_file(before, after)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "n4yWFoJNnoin"
   },
   "source": [
    "# **2. Complete the Colab session**\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form",
    "id": "qOGVm1Ihg98M"
   },
   "outputs": [],
   "source": [
    "#@markdown ##Play the cell to connect your Google Drive to Colab\n",
    "\n",
    "#@markdown * Click on the URL. \n",
    "\n",
    "#@markdown * Sign in your Google Account. \n",
    "\n",
    "#@markdown * Copy the authorization code. \n",
    "\n",
    "#@markdown * Enter the authorization code. \n",
    "\n",
    "#@markdown * Click on \"Files\" site on the right. Refresh the site. Your Google Drive folder should now be available here as \"drive\". \n",
    "\n",
    "# mount user's Google Drive to Google Colab.\n",
    "from google.colab import drive\n",
    "drive.mount('/content/gdrive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DMNHVZfHmbKb"
   },
   "source": [
    "\n",
    "## **2.1. Check for GPU access**\n",
    "---\n",
    "\n",
    "By default, the session should be using Python 3 and GPU acceleration, but it is possible to ensure that these are set properly by doing the following:\n",
    "\n",
    "<font size = 4>Go to **Runtime -> Change the Runtime type**\n",
    "\n",
    "<font size = 4>**Runtime type: Python 3** *(Python 3 is programming language in which this program is written)*\n",
    "\n",
    "<font size = 4>**Accelerator: GPU** *(Graphics processing unit)*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1362,
     "status": "ok",
     "timestamp": 1648476349225,
     "user": {
      "displayName": "PABLO DELGADO RODRIGUEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "11999848093282927146"
     },
     "user_tz": -120
    },
    "id": "zCvebubeSaGY",
    "outputId": "0fdc03b3-4694-4308-e998-09213b07037b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You have GPU access\n",
      "Mon Mar 28 14:05:49 2022       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                               |                      |               MIG M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  Tesla K80           Off  | 00000000:00:04.0 Off |                    0 |\n",
      "| N/A   59C    P0    73W / 149W |     60MiB / 11441MiB |      0%      Default |\n",
      "|                               |                      |                  N/A |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                                  |\n",
      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
      "|        ID   ID                                                   Usage      |\n",
      "|=============================================================================|\n",
      "+-----------------------------------------------------------------------------+\n",
      "Tensorflow version is 1.15.2\n"
     ]
    }
   ],
   "source": [
    "#@markdown ##Run this cell to check if you have GPU access\n",
    "\n",
    "if tf.test.gpu_device_name()=='':\n",
    "  print('You do not have GPU access.') \n",
    "  print('Did you change your runtime ?') \n",
    "  print('If the runtime setting is correct then Google did not allocate a GPU for your session')\n",
    "  print('Expect slow performance. To access GPU try reconnecting later')\n",
    "\n",
    "else:\n",
    "  print('You have GPU access')\n",
    "  !nvidia-smi\n",
    "\n",
    "# from tensorflow.python.client import device_lib \n",
    "# device_lib.list_local_devices()\n",
    "\n",
    "# print the tensorflow version\n",
    "print('Tensorflow version is ' + str(tf.__version__))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HLYcZR9gMv42"
   },
   "source": [
    "# **3. Select your parameters and paths**\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FQ_QxtSWQ7CL"
   },
   "source": [
    "## **3.1. Setting main training parameters**\n",
    "---\n",
    "<font size = 4> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AuESFimvMv43"
   },
   "source": [
    "\n",
    "<font size = 5> **Paths for training data and models**\n",
    "\n",
    "<font size = 4>**`Training_source`, `Training_target`:** These are the folders containing your source (e.g. EM images) and target files (segmentation masks). Enter the path to the source and target images for training. **These should be located in the same parent folder.**\n",
    "\n",
    "<font size = 4>**`model_name`:** Use only my_model -style, not my-model. If you want to use a previously trained model, enter the name of the pretrained model (which should be contained in the trained_model -folder after training).\n",
    "\n",
    "<font size = 4>**`model_path`**: Enter the path of the folder where you want to save your model.\n",
    "\n",
    "<font size = 4>**`visual_validation_after_training`**: If you select this option, a random image pair will be set aside from your training set and will be used to display a predicted image of the trained network next to the input and the ground-truth. This can aid in visually assessing the performance of your network after training. **Note: Your training set size will decrease by 1 if you select this option.**\n",
    "\n",
    "<font size = 5> **Select training parameters**\n",
    "\n",
    "<font size = 4>**`number_of_epochs`**: Choose more epochs for larger training sets. Observing how much the loss reduces between epochs during training may help determine the optimal value. **Default: 200**\n",
    "\n",
    "<font size = 5>**Advanced parameters - experienced users only**\n",
    "\n",
    "<font size = 4>**`batch_size`**: This parameter describes the amount of images that are loaded into the network per step. Smaller batchsizes may improve training performance slightly but may increase training time. If the notebook crashes while loading the dataset this can be due to a too large batch size. Decrease the number in this case. **Default: 4**\n",
    "\n",
    "<font size = 4>**`number_of_steps`**: This number should be equivalent to the number of samples in the training set divided by the batch size, to ensure the training iterates through the entire training set. The default value is calculated to ensure this. This behaviour can also be obtained by setting it to 0. Other values can be used for testing.\n",
    "\n",
    "<font size = 4> **`pooling_steps`**: Choosing a different number of pooling layers can affect the performance of the network. Each additional pooling step will also two additional convolutions. The network can learn more complex information but is also more likely to overfit. Achieving best performance may require testing different values here. **Default: 2**\n",
    "\n",
    "<font size = 4>**`percentage_validation`:**  Input the percentage of your training dataset you want to use to validate the network during training. **Default value: 10** \n",
    "\n",
    "<font size = 4>**`initial_learning_rate`:**  Input the initial value to be used as learning rate. **Default value: 0.0003**\n",
    "\n",
    "<font size = 4>**`patch_width` and `patch_height`:** The notebook crops the data in patches of fixed size prior to training. The dimensions of the patches can be defined here. When `Use_Default_Advanced_Parameters` is selected, the largest 2^n x 2^n patch that fits in the smallest dataset is chosen. Larger patches than 512x512 should **NOT** be selected for network stability.\n",
    "\n",
    "<font size = 4>**`min_fraction`:** Minimum fraction of pixels being foreground for a slected patch to be considered valid. It should be between 0 and 1.**Default value: 0.02** (2%)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 482,
     "referenced_widgets": [
      "20a2d7cff5d341d0a5039dd5ba1fc26a",
      "c7d56ffd3f2d48e387cd09937532c944",
      "bd4aa7c3451e4009ab4d14ce50240f18",
      "d0802e1ac8224673aa47498ef1680c3a",
      "55f5eb5dd7d9467785aa65f5ceae9cc1",
      "4bf1f029fa4c4631820faa2514abea18",
      "d647d1e45b66496481b6ef96587d8dd0",
      "47403ae7e98a4142b43014365135766d",
      "475cc221bf19412987fe20aa00b56835",
      "db03f0fc76f447ebb755912df6b95848",
      "4b15308c583646739385bc7d9c74fd37"
     ]
    },
    "executionInfo": {
     "elapsed": 145915,
     "status": "error",
     "timestamp": 1649776249315,
     "user": {
      "displayName": "PABLO DELGADO RODRIGUEZ",
      "userId": "11999848093282927146"
     },
     "user_tz": -120
    },
    "id": "ewpNJ_I0Mv47",
    "outputId": "7aeb7b62-c457-4cbf-92a2-7af1b60c3201"
   },
   "outputs": [],
   "source": [
    "# ------------- Initial user input ------------\n",
    "#@markdown ###Path to training images:\n",
    "Training_source = '/content/drive/MyDrive/Pablo Delgado/Doctorado-FPU/AAV para enfermedades renales/CT/2DUnet/train/images' #@param {type:\"string\"}\n",
    "Training_target = '/content/drive/MyDrive/Pablo Delgado/Doctorado-FPU/AAV para enfermedades renales/CT/2DUnet/train/masks' #@param {type:\"string\"}\n",
    "\n",
    "model_name = 'cysts4-Imagenet-pretrained' #@param {type:\"string\"}\n",
    "model_path = '/content/drive/MyDrive/Pablo Delgado/Doctorado-FPU/AAV para enfermedades renales/CT/2DUnet/models' #@param {type:\"string\"}\n",
    "\n",
    "#@markdown ###Training parameters:\n",
    "#@markdown Number of epochs\n",
    "number_of_epochs =  400#@param {type:\"number\"}\n",
    "\n",
    "#@markdown ###Advanced parameters:\n",
    "Use_Default_Advanced_Parameters = False #@param {type:\"boolean\"}\n",
    "\n",
    "#@markdown ###If not, please input:\n",
    "batch_size =  4#@param {type:\"integer\"}\n",
    "number_of_steps =  0#@param {type:\"number\"}\n",
    "pooling_steps = 2 #@param [1,2,3,4]{type:\"raw\"}\n",
    "percentage_validation =  10#@param{type:\"number\"}\n",
    "initial_learning_rate = 0.0003 #@param {type:\"number\"}\n",
    "\n",
    "patch_width =  512#@param{type:\"number\"}\n",
    "patch_height =  512#@param{type:\"number\"}\n",
    "min_fraction = 0.000005#@param{type:\"number\"}\n",
    "\n",
    "\n",
    "# ------------- Initialising folder, variables and failsafes ------------\n",
    "#  Create the folders where to save the model and the QC\n",
    "full_model_path = os.path.join(model_path, model_name)\n",
    "if os.path.exists(full_model_path):\n",
    "  print(R+'!! WARNING: Folder already exists and will be overwritten !!'+W)\n",
    "\n",
    "if (Use_Default_Advanced_Parameters): \n",
    "  print(\"Default advanced parameters enabled\")\n",
    "  batch_size = 4\n",
    "  pooling_steps = 2\n",
    "  percentage_validation = 10\n",
    "  initial_learning_rate = 0.0003\n",
    "  patch_width, patch_height = estimatePatchSize(Training_source)\n",
    "  min_fraction = 0.02\n",
    "\n",
    "\n",
    "#The create_patches function will create the two folders below\n",
    "# Patch_source = '/content/img_patches'\n",
    "# Patch_target = '/content/mask_patches'\n",
    "print('Training on patches of size (x,y): ('+str(patch_width)+','+str(patch_height)+')')\n",
    "\n",
    "#Create patches\n",
    "print('Creating patches...')\n",
    "Patch_source, Patch_target = create_patches(Training_source, Training_target, patch_width, patch_height, min_fraction)\n",
    "\n",
    "number_of_training_dataset = len(os.listdir(Patch_source))\n",
    "print('Total number of valid patches: '+str(number_of_training_dataset))\n",
    "\n",
    "if Use_Default_Advanced_Parameters or number_of_steps == 0:\n",
    "  number_of_steps = ceil((100-percentage_validation)/100*number_of_training_dataset/batch_size)\n",
    "print('Number of steps: '+str(number_of_steps))\n",
    "\n",
    "# Calculate the number of steps to use for validation\n",
    "validation_steps = max(1, ceil(percentage_validation/100*number_of_training_dataset/batch_size))\n",
    "\n",
    "\n",
    "# Here we disable pre-trained model by default (in case the next cell is not ran)\n",
    "Use_pretrained_model = False\n",
    "# Here we disable data augmentation by default (in case the cell is not ran)\n",
    "Use_Data_augmentation = False\n",
    "# Build the default dict for the ImageDataGenerator\n",
    "data_gen_args = dict(width_shift_range = 0.,\n",
    "                     height_shift_range = 0.,\n",
    "                     rotation_range = 0., #90\n",
    "                     zoom_range = 0.,\n",
    "                     shear_range = 0.,\n",
    "                     horizontal_flip = False,\n",
    "                     vertical_flip = False,\n",
    "                     validation_split = percentage_validation/100,\n",
    "                     fill_mode = 'reflect')\n",
    "\n",
    "# ------------- Display ------------\n",
    "\n",
    "#if not os.path.exists('/content/img_patches/'):\n",
    "random_choice = random.choice(os.listdir(Patch_source))\n",
    "x = io.imread(os.path.join(Patch_source, random_choice))\n",
    "\n",
    "#os.chdir(Training_target)\n",
    "y = io.imread(os.path.join(Patch_target, random_choice), as_gray=True)\n",
    "\n",
    "f=plt.figure(figsize=(16,8))\n",
    "plt.subplot(1,2,1)\n",
    "plt.imshow(x, interpolation='nearest',cmap='gray')\n",
    "plt.title('Training image patch')\n",
    "plt.axis('off');\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.imshow(y, interpolation='nearest',cmap='gray')\n",
    "plt.title('Training mask patch')\n",
    "plt.axis('off');\n",
    "\n",
    "plt.savefig('/content/TrainingDataExample_Unet2D.png',bbox_inches='tight',pad_inches=0)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "w_jCy7xOx2g3"
   },
   "source": [
    "##**3.2. Data augmentation**\n",
    "\n",
    "---\n",
    "\n",
    "<font size = 4> Data augmentation can improve training progress by amplifying differences in the dataset. This can be useful if the available dataset is small since, in this case, it is possible that a network could quickly learn every example in the dataset (overfitting), without augmentation. Augmentation is not necessary for training and if the dataset is large the values can be set to 0.\n",
    "\n",
    "<font size = 4> The augmentation options below are to be used as follows:\n",
    "\n",
    "* <font size = 4> **shift**: a translation of the image by a fraction of the image size (width or height), **default: 10%**\n",
    "* **zoom_range**: Increasing or decreasing the field of view. E.g. 10% will result in a zoom range of (0.9 to 1.1), with pixels added or interpolated, depending on the transformation, **default: 10%**\n",
    "* **shear_range**: Shear angle in counter-clockwise direction, **default: 10%**\n",
    "* **flip**: creating a mirror image along specified axis (horizontal or vertical), **default: True**\n",
    "* **rotation_range**: range of allowed rotation angles in degrees (from 0 to *value*), **default: 180**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 174,
     "status": "ok",
     "timestamp": 1649412708030,
     "user": {
      "displayName": "PABLO DELGADO RODRIGUEZ",
      "userId": "11999848093282927146"
     },
     "user_tz": -120
    },
    "id": "DMqWq5-AxnFU",
    "outputId": "9427a74f-321e-4c41-9abd-efe6f6e83833"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No augmentation will be used\n"
     ]
    }
   ],
   "source": [
    "#@markdown ##**Augmentation options**\n",
    "\n",
    "Use_Data_augmentation = False #@param {type:\"boolean\"}\n",
    "Use_Default_Augmentation_Parameters = True #@param {type:\"boolean\"}\n",
    "\n",
    "if Use_Data_augmentation:\n",
    "  if Use_Default_Augmentation_Parameters:\n",
    "    horizontal_shift =  10 \n",
    "    vertical_shift =  20 \n",
    "    zoom_range =  10\n",
    "    shear_range =  10\n",
    "    horizontal_flip = True\n",
    "    vertical_flip = True\n",
    "    rotation_range =  180\n",
    "#@markdown ###If you are not using the default settings, please provide the values below:\n",
    "\n",
    "#@markdown ###**Image shift, zoom, shear and flip (%)**\n",
    "  else:\n",
    "    horizontal_shift =  10 #@param {type:\"slider\", min:0, max:100, step:1}\n",
    "    vertical_shift =  10 #@param {type:\"slider\", min:0, max:100, step:1}\n",
    "    zoom_range =  10 #@param {type:\"slider\", min:0, max:100, step:1}\n",
    "    shear_range =  10 #@param {type:\"slider\", min:0, max:100, step:1}\n",
    "    horizontal_flip = True #@param {type:\"boolean\"}\n",
    "    vertical_flip = True #@param {type:\"boolean\"}\n",
    "\n",
    "#@markdown ###**Rotate image within angle range (degrees):**\n",
    "    rotation_range =  180 #@param {type:\"slider\", min:0, max:180, step:1}\n",
    "\n",
    "#given behind the # are the default values for each parameter.\n",
    "\n",
    "else:\n",
    "  horizontal_shift =  0 \n",
    "  vertical_shift =  0 \n",
    "  zoom_range =  0\n",
    "  shear_range =  0\n",
    "  horizontal_flip = False\n",
    "  vertical_flip = False\n",
    "  rotation_range =  0\n",
    "\n",
    "\n",
    "# Build the dict for the ImageDataGenerator\n",
    "data_gen_args = dict(width_shift_range = horizontal_shift/100.,\n",
    "                     height_shift_range = vertical_shift/100.,\n",
    "                     rotation_range = rotation_range, #90\n",
    "                     zoom_range = zoom_range/100.,\n",
    "                     shear_range = shear_range/100.,\n",
    "                     horizontal_flip = horizontal_flip,\n",
    "                     vertical_flip = vertical_flip,\n",
    "                     validation_split = percentage_validation/100,\n",
    "                     fill_mode = 'reflect')\n",
    "\n",
    "\n",
    "\n",
    "# ------------- Display ------------\n",
    "dir_augmented_data_imgs=\"/content/augment_img\"\n",
    "dir_augmented_data_masks=\"/content/augment_mask\"\n",
    "random_choice = random.choice(os.listdir(Patch_source))\n",
    "orig_img = load_img(os.path.join(Patch_source,random_choice))\n",
    "orig_mask = load_img(os.path.join(Patch_target,random_choice))\n",
    "\n",
    "augment_view = ImageDataGenerator(**data_gen_args)\n",
    "\n",
    "if Use_Data_augmentation:\n",
    "  print(\"Parameters enabled\")\n",
    "  print(\"Here is what a subset of your augmentations looks like:\")\n",
    "  save_augment(augment_view, orig_img, dir_augmented_data=dir_augmented_data_imgs)\n",
    "  save_augment(augment_view, orig_mask, dir_augmented_data=dir_augmented_data_masks)\n",
    "\n",
    "  fig = plt.figure(figsize=(15, 7))\n",
    "  fig.subplots_adjust(hspace=0.0,wspace=0.1,left=0,right=1.1,bottom=0, top=0.8)\n",
    "\n",
    " \n",
    "  ax = fig.add_subplot(2, 6, 1,xticks=[],yticks=[])        \n",
    "  new_img=img_as_ubyte(normalizeMinMax(img_to_array(orig_img)))\n",
    "  ax.imshow(new_img)\n",
    "  ax.set_title('Original Image')\n",
    "  i = 2\n",
    "  for imgnm in os.listdir(dir_augmented_data_imgs):\n",
    "    ax = fig.add_subplot(2, 6, i,xticks=[],yticks=[]) \n",
    "    img = load_img(dir_augmented_data_imgs + \"/\" + imgnm)\n",
    "    ax.imshow(img)\n",
    "    i += 1\n",
    "\n",
    "  ax = fig.add_subplot(2, 6, 7,xticks=[],yticks=[])        \n",
    "  new_mask=img_as_ubyte(normalizeMinMax(img_to_array(orig_mask)))\n",
    "  ax.imshow(new_mask)\n",
    "  ax.set_title('Original Mask')\n",
    "  j=2\n",
    "  for imgnm in os.listdir(dir_augmented_data_masks):\n",
    "    ax = fig.add_subplot(2, 6, j+6,xticks=[],yticks=[]) \n",
    "    mask = load_img(dir_augmented_data_masks + \"/\" + imgnm)\n",
    "    ax.imshow(mask)\n",
    "    j += 1\n",
    "  plt.show()\n",
    "\n",
    "else:\n",
    "  print(\"No augmentation will be used\")\n",
    "\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3L9zSGtORKYI"
   },
   "source": [
    "\n",
    "## **3.3. Using weights from a pre-trained model as initial weights**\n",
    "---\n",
    "<font size = 4>  Here, you can set the the path to a pre-trained model from which the weights can be extracted and used as a starting point for this training session. **This pre-trained model needs to be a U-Net model**. \n",
    "\n",
    "<font size = 4> This option allows you to perform training over multiple Colab runtimes or to do transfer learning using models trained outside of ZeroCostDL4Mic. **You do not need to run this section if you want to train a network from scratch**.\n",
    "\n",
    "<font size = 4> In order to continue training from the point where the pre-trained model left off, it is adviseable to also **load the learning rate** that was used when the training ended. This is automatically saved for models trained with ZeroCostDL4Mic and will be loaded here. If no learning rate can be found in the model folder provided, the default learning rate will be used. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 182,
     "status": "ok",
     "timestamp": 1649412711570,
     "user": {
      "displayName": "PABLO DELGADO RODRIGUEZ",
      "userId": "11999848093282927146"
     },
     "user_tz": -120
    },
    "id": "9vC2n-HeLdiJ",
    "outputId": "59c78faa-ee1b-4156-eced-a7c133f4e9a0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31mNo pretrained network will be used.\n"
     ]
    }
   ],
   "source": [
    "# @markdown ##Loading weights from a pre-trained network\n",
    "\n",
    "Use_pretrained_model = False #@param {type:\"boolean\"}\n",
    "pretrained_model_choice = \"Model_from_file\" #@param [\"Model_from_file\"]\n",
    "Weights_choice = \"last\" #@param [\"last\", \"best\"]\n",
    "\n",
    "\n",
    "#@markdown ###If you chose \"Model_from_file\", please provide the path to the model folder:\n",
    "pretrained_model_path = \"\" #@param {type:\"string\"}\n",
    "\n",
    "# --------------------- Check if we load a previously trained model ------------------------\n",
    "if Use_pretrained_model:\n",
    "\n",
    "# --------------------- Load the model from the choosen path ------------------------\n",
    "  if pretrained_model_choice == \"Model_from_file\":\n",
    "    h5_file_path = os.path.join(pretrained_model_path, \"weights_\"+Weights_choice+\".hdf5\")\n",
    "\n",
    "\n",
    "# --------------------- Download the a model provided in the XXX ------------------------\n",
    "\n",
    "  if pretrained_model_choice == \"Model_name\":\n",
    "    pretrained_model_name = \"Model_name\"\n",
    "    pretrained_model_path = \"/content/\"+pretrained_model_name\n",
    "    print(\"Downloading the UNET_Model_from_\")\n",
    "    if os.path.exists(pretrained_model_path):\n",
    "      shutil.rmtree(pretrained_model_path)\n",
    "    os.makedirs(pretrained_model_path)\n",
    "    wget.download(\"\", pretrained_model_path)\n",
    "    wget.download(\"\", pretrained_model_path)\n",
    "    wget.download(\"\", pretrained_model_path)    \n",
    "    wget.download(\"\", pretrained_model_path)\n",
    "    h5_file_path = os.path.join(pretrained_model_path, \"weights_\"+Weights_choice+\".hdf5\")\n",
    "\n",
    "# --------------------- Add additional pre-trained models here ------------------------\n",
    "\n",
    "\n",
    "\n",
    "# --------------------- Check the model exist ------------------------\n",
    "# If the model path chosen does not contain a pretrain model then use_pretrained_model is disabled, \n",
    "  if not os.path.exists(h5_file_path):\n",
    "    print(R+'WARNING: pretrained model does not exist')\n",
    "    Use_pretrained_model = False\n",
    "    \n",
    "\n",
    "# If the model path contains a pretrain model, we load the training rate, \n",
    "  if os.path.exists(h5_file_path):\n",
    "#Here we check if the learning rate can be loaded from the quality control folder\n",
    "    if os.path.exists(os.path.join(pretrained_model_path, 'Quality Control', 'training_evaluation.csv')):\n",
    "\n",
    "      with open(os.path.join(pretrained_model_path, 'Quality Control', 'training_evaluation.csv'),'r') as csvfile:\n",
    "        csvRead = pd.read_csv(csvfile, sep=',')\n",
    "        #print(csvRead)\n",
    "    \n",
    "        if \"learning rate\" in csvRead.columns: #Here we check that the learning rate column exist (compatibility with model trained un ZeroCostDL4Mic bellow 1.4)\n",
    "          print(\"pretrained network learning rate found\")\n",
    "          #find the last learning rate\n",
    "          lastLearningRate = csvRead[\"learning rate\"].iloc[-1]\n",
    "          #Find the learning rate corresponding to the lowest validation loss\n",
    "          min_val_loss = csvRead[csvRead['val_loss'] == min(csvRead['val_loss'])]\n",
    "          #print(min_val_loss)\n",
    "          bestLearningRate = min_val_loss['learning rate'].iloc[-1]\n",
    "\n",
    "          if Weights_choice == \"last\":\n",
    "            print('Last learning rate: '+str(lastLearningRate))\n",
    "\n",
    "          if Weights_choice == \"best\":\n",
    "            print('Learning rate of best validation loss: '+str(bestLearningRate))\n",
    "\n",
    "        if not \"learning rate\" in csvRead.columns: #if the column does not exist, then initial learning rate is used instead\n",
    "          bestLearningRate = initial_learning_rate\n",
    "          lastLearningRate = initial_learning_rate\n",
    "          print(bcolors.WARNING+'WARNING: The learning rate cannot be identified from the pretrained network. Default learning rate of '+str(bestLearningRate)+' will be used instead' + W)\n",
    "\n",
    "#Compatibility with models trained outside ZeroCostDL4Mic but default learning rate will be used\n",
    "    if not os.path.exists(os.path.join(pretrained_model_path, 'Quality Control', 'training_evaluation.csv')):\n",
    "      print(bcolors.WARNING+'WARNING: The learning rate cannot be identified from the pretrained network. Default learning rate of '+str(initial_learning_rate)+' will be used instead'+ W)\n",
    "      bestLearningRate = initial_learning_rate\n",
    "      lastLearningRate = initial_learning_rate\n",
    "\n",
    "\n",
    "# Display info about the pretrained model to be loaded (or not)\n",
    "if Use_pretrained_model:\n",
    "  print('Weights found in:')\n",
    "  print(h5_file_path)\n",
    "  print('will be loaded prior to training.')\n",
    "\n",
    "else:\n",
    "  print(R+'No pretrained network will be used.')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MCGklf1vZf2M"
   },
   "source": [
    "# **4. Train the network**\n",
    "---\n",
    "####**Troubleshooting:** If you receive a time-out or exhausted error, try reducing the batchsize of your training set. This reduces the amount of data loaded into the model at one point in time. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1KYOuygETJkT"
   },
   "source": [
    "## **4.1. Prepare the training data and model for training**\n",
    "---\n",
    "<font size = 4>Here, we use the information from 3. to build the model and convert the training data into a suitable format for training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
      "1a4ca1b57ad947569257a824d0c052bd",
      "6ec549bf4ede454a884db89b94c8037d",
      "a5d3bc46d5134296b9fd18d5dfc38203",
      "618d31fe6af24660a27c9b14168b1593",
      "2414c2e40e1f41869882d7910d295fcf",
      "6e298eeabf974040bd08d0b971458796",
      "526f717b4134431b8ad2ea76ff158d77",
      "a451411be89c435384fc8e6da45f69bc",
      "c822f7ab9a5a4361b5b0f350e16a6d6f",
      "902391fa63a54fe6b983e58deaa9c341",
      "9102133b5c944757941f87879f1b31c3"
     ]
    },
    "collapsed": true,
    "executionInfo": {
     "elapsed": 18513,
     "status": "ok",
     "timestamp": 1649413747781,
     "user": {
      "displayName": "PABLO DELGADO RODRIGUEZ",
      "userId": "11999848093282927146"
     },
     "user_tz": -120
    },
    "id": "lIUAOJ_LMv5E",
    "jupyter": {
     "outputs_hidden": true
    },
    "outputId": "898f0e03-c5f0-4ed0-aed2-51c10687b740",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting class weights...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1a4ca1b57ad947569257a824d0c052bd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/368 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0243.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0246.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0240.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0285.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0272.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0305.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0257.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0198.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0277.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0250.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0246.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0295.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0206.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0260.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0208.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0286.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0227.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0258.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0304.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0240.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0294.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0247.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0267.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0209.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0264.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0303.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0259.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0192.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0306.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0242.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0235.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0263.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0205.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0269.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0265.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0224.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0194.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0272.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0243.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0296.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0236.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0285.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0214.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0280.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0284.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0190.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0211.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0278.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0255.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0293.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0256.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0233.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0210.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0245.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0252.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0191.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0270.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0201.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0217.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0203.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0239.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0219.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0187.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0189.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0273.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0274.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0223.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0237.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0248.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0292.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0308.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0290.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0228.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0244.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0310.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0268.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0283.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0251.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0266.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0300.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0200.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0279.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0204.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0221.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0249.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0199.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0195.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0202.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0193.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0216.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0281.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0254.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0298.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0220.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0262.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0271.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0276.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0196.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0287.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0186.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0302.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0299.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0197.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0225.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0253.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0215.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0213.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0297.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0309.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0288.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0218.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0232.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0234.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0231.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0229.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0207.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0289.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0261.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0305.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0222.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0301.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0230.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0291.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0212.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0188.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0275.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0238.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0307.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0282.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0226.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0241.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0314.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0322.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0325.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0313.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0318.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0312.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0317.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0316.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0319.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0320.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0326.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0321.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0324.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0315.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0323.tif\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring0311.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0179.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0199.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0189.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0181.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0184.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0204.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0195.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0206.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0196.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0192.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0203.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0200.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0186.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0193.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0194.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0191.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0197.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0202.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0188.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0180.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0201.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0182.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0205.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0185.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0198.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0183.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0187.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0190.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0269.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0271.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0260.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0216.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0212.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0249.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0256.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0234.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0245.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0270.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0230.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0273.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0237.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0238.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0246.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0225.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0278.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0284.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0247.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0218.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0209.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0227.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0257.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0207.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0251.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0220.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0231.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0274.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0232.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0276.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0258.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0275.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0244.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0210.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0211.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0229.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0240.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0282.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0222.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0261.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0223.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0287.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0263.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0213.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0224.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0265.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0252.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0285.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0277.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0236.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0253.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0241.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0242.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0259.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0255.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0280.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0217.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0208.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0243.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0215.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0281.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0233.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0267.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0228.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0221.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0266.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0219.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0283.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0264.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0268.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0239.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0262.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0272.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0254.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0286.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0279.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0214.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0250.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0248.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0235.tif\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring0226.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0296.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0236.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0255.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0238.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0297.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0299.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0196.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0230.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0251.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0203.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0288.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0205.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0257.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0271.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0286.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0233.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0294.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0211.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0247.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0244.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0279.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0259.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0241.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0214.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0307.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0250.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0213.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0302.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0275.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0256.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0237.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0202.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0273.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0270.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0267.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0291.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0290.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0253.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0284.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0266.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0276.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0308.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0194.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0227.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0295.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0304.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0208.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0226.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0289.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0221.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0210.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0280.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0222.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0200.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0229.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0298.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0224.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0242.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0239.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0195.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0235.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0234.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0216.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0300.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0218.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0262.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0265.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0193.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0217.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0232.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0198.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0261.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0278.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0204.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0220.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0301.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0248.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0249.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0209.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0263.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0225.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0191.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0201.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0281.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0215.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0303.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0199.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0192.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0260.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0269.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0287.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0254.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0245.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0274.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0207.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0231.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0228.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0306.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0212.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0206.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0293.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0283.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0277.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0219.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0264.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0268.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0197.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0282.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0292.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0258.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0223.tif\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring0252.tif\n",
      "----Unet Definition----\n",
      "base model.summary()\n",
      "Model: \"model_6\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "data (InputLayer)               (None, None, None, 3 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "bn_data (BatchNormalization)    (None, None, None, 3 9           data[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_69 (ZeroPadding2 (None, None, None, 3 0           bn_data[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv0 (Conv2D)                  (None, None, None, 6 9408        zero_padding2d_69[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "bn0 (BatchNormalization)        (None, None, None, 6 256         conv0[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "relu0 (Activation)              (None, None, None, 6 0           bn0[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_70 (ZeroPadding2 (None, None, None, 6 0           relu0[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "pooling0 (MaxPooling2D)         (None, None, None, 6 0           zero_padding2d_70[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "stage1_unit1_bn1 (BatchNormaliz (None, None, None, 6 256         pooling0[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "stage1_unit1_relu1 (Activation) (None, None, None, 6 0           stage1_unit1_bn1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_71 (ZeroPadding2 (None, None, None, 6 0           stage1_unit1_relu1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage1_unit1_conv1 (Conv2D)     (None, None, None, 6 36864       zero_padding2d_71[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "stage1_unit1_bn2 (BatchNormaliz (None, None, None, 6 256         stage1_unit1_conv1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage1_unit1_relu2 (Activation) (None, None, None, 6 0           stage1_unit1_bn2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_72 (ZeroPadding2 (None, None, None, 6 0           stage1_unit1_relu2[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage1_unit1_conv2 (Conv2D)     (None, None, None, 6 36864       zero_padding2d_72[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "stage1_unit1_sc (Conv2D)        (None, None, None, 6 4096        stage1_unit1_relu1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "add_33 (Add)                    (None, None, None, 6 0           stage1_unit1_conv2[0][0]         \n",
      "                                                                 stage1_unit1_sc[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "stage1_unit2_bn1 (BatchNormaliz (None, None, None, 6 256         add_33[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "stage1_unit2_relu1 (Activation) (None, None, None, 6 0           stage1_unit2_bn1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_73 (ZeroPadding2 (None, None, None, 6 0           stage1_unit2_relu1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage1_unit2_conv1 (Conv2D)     (None, None, None, 6 36864       zero_padding2d_73[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "stage1_unit2_bn2 (BatchNormaliz (None, None, None, 6 256         stage1_unit2_conv1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage1_unit2_relu2 (Activation) (None, None, None, 6 0           stage1_unit2_bn2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_74 (ZeroPadding2 (None, None, None, 6 0           stage1_unit2_relu2[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage1_unit2_conv2 (Conv2D)     (None, None, None, 6 36864       zero_padding2d_74[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "add_34 (Add)                    (None, None, None, 6 0           stage1_unit2_conv2[0][0]         \n",
      "                                                                 add_33[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "stage1_unit3_bn1 (BatchNormaliz (None, None, None, 6 256         add_34[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "stage1_unit3_relu1 (Activation) (None, None, None, 6 0           stage1_unit3_bn1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_75 (ZeroPadding2 (None, None, None, 6 0           stage1_unit3_relu1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage1_unit3_conv1 (Conv2D)     (None, None, None, 6 36864       zero_padding2d_75[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "stage1_unit3_bn2 (BatchNormaliz (None, None, None, 6 256         stage1_unit3_conv1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage1_unit3_relu2 (Activation) (None, None, None, 6 0           stage1_unit3_bn2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_76 (ZeroPadding2 (None, None, None, 6 0           stage1_unit3_relu2[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage1_unit3_conv2 (Conv2D)     (None, None, None, 6 36864       zero_padding2d_76[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "add_35 (Add)                    (None, None, None, 6 0           stage1_unit3_conv2[0][0]         \n",
      "                                                                 add_34[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "stage2_unit1_bn1 (BatchNormaliz (None, None, None, 6 256         add_35[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "stage2_unit1_relu1 (Activation) (None, None, None, 6 0           stage2_unit1_bn1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_77 (ZeroPadding2 (None, None, None, 6 0           stage2_unit1_relu1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage2_unit1_conv1 (Conv2D)     (None, None, None, 1 73728       zero_padding2d_77[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "stage2_unit1_bn2 (BatchNormaliz (None, None, None, 1 512         stage2_unit1_conv1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage2_unit1_relu2 (Activation) (None, None, None, 1 0           stage2_unit1_bn2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_78 (ZeroPadding2 (None, None, None, 1 0           stage2_unit1_relu2[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage2_unit1_conv2 (Conv2D)     (None, None, None, 1 147456      zero_padding2d_78[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "stage2_unit1_sc (Conv2D)        (None, None, None, 1 8192        stage2_unit1_relu1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "add_36 (Add)                    (None, None, None, 1 0           stage2_unit1_conv2[0][0]         \n",
      "                                                                 stage2_unit1_sc[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "stage2_unit2_bn1 (BatchNormaliz (None, None, None, 1 512         add_36[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "stage2_unit2_relu1 (Activation) (None, None, None, 1 0           stage2_unit2_bn1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_79 (ZeroPadding2 (None, None, None, 1 0           stage2_unit2_relu1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage2_unit2_conv1 (Conv2D)     (None, None, None, 1 147456      zero_padding2d_79[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "stage2_unit2_bn2 (BatchNormaliz (None, None, None, 1 512         stage2_unit2_conv1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage2_unit2_relu2 (Activation) (None, None, None, 1 0           stage2_unit2_bn2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_80 (ZeroPadding2 (None, None, None, 1 0           stage2_unit2_relu2[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage2_unit2_conv2 (Conv2D)     (None, None, None, 1 147456      zero_padding2d_80[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "add_37 (Add)                    (None, None, None, 1 0           stage2_unit2_conv2[0][0]         \n",
      "                                                                 add_36[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "stage2_unit3_bn1 (BatchNormaliz (None, None, None, 1 512         add_37[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "stage2_unit3_relu1 (Activation) (None, None, None, 1 0           stage2_unit3_bn1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_81 (ZeroPadding2 (None, None, None, 1 0           stage2_unit3_relu1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage2_unit3_conv1 (Conv2D)     (None, None, None, 1 147456      zero_padding2d_81[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "stage2_unit3_bn2 (BatchNormaliz (None, None, None, 1 512         stage2_unit3_conv1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage2_unit3_relu2 (Activation) (None, None, None, 1 0           stage2_unit3_bn2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_82 (ZeroPadding2 (None, None, None, 1 0           stage2_unit3_relu2[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage2_unit3_conv2 (Conv2D)     (None, None, None, 1 147456      zero_padding2d_82[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "add_38 (Add)                    (None, None, None, 1 0           stage2_unit3_conv2[0][0]         \n",
      "                                                                 add_37[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "stage2_unit4_bn1 (BatchNormaliz (None, None, None, 1 512         add_38[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "stage2_unit4_relu1 (Activation) (None, None, None, 1 0           stage2_unit4_bn1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_83 (ZeroPadding2 (None, None, None, 1 0           stage2_unit4_relu1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage2_unit4_conv1 (Conv2D)     (None, None, None, 1 147456      zero_padding2d_83[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "stage2_unit4_bn2 (BatchNormaliz (None, None, None, 1 512         stage2_unit4_conv1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage2_unit4_relu2 (Activation) (None, None, None, 1 0           stage2_unit4_bn2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_84 (ZeroPadding2 (None, None, None, 1 0           stage2_unit4_relu2[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage2_unit4_conv2 (Conv2D)     (None, None, None, 1 147456      zero_padding2d_84[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "add_39 (Add)                    (None, None, None, 1 0           stage2_unit4_conv2[0][0]         \n",
      "                                                                 add_38[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "stage3_unit1_bn1 (BatchNormaliz (None, None, None, 1 512         add_39[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "stage3_unit1_relu1 (Activation) (None, None, None, 1 0           stage3_unit1_bn1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_85 (ZeroPadding2 (None, None, None, 1 0           stage3_unit1_relu1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage3_unit1_conv1 (Conv2D)     (None, None, None, 2 294912      zero_padding2d_85[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "stage3_unit1_bn2 (BatchNormaliz (None, None, None, 2 1024        stage3_unit1_conv1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage3_unit1_relu2 (Activation) (None, None, None, 2 0           stage3_unit1_bn2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_86 (ZeroPadding2 (None, None, None, 2 0           stage3_unit1_relu2[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage3_unit1_conv2 (Conv2D)     (None, None, None, 2 589824      zero_padding2d_86[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "stage3_unit1_sc (Conv2D)        (None, None, None, 2 32768       stage3_unit1_relu1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "add_40 (Add)                    (None, None, None, 2 0           stage3_unit1_conv2[0][0]         \n",
      "                                                                 stage3_unit1_sc[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "stage3_unit2_bn1 (BatchNormaliz (None, None, None, 2 1024        add_40[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "stage3_unit2_relu1 (Activation) (None, None, None, 2 0           stage3_unit2_bn1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_87 (ZeroPadding2 (None, None, None, 2 0           stage3_unit2_relu1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage3_unit2_conv1 (Conv2D)     (None, None, None, 2 589824      zero_padding2d_87[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "stage3_unit2_bn2 (BatchNormaliz (None, None, None, 2 1024        stage3_unit2_conv1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage3_unit2_relu2 (Activation) (None, None, None, 2 0           stage3_unit2_bn2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_88 (ZeroPadding2 (None, None, None, 2 0           stage3_unit2_relu2[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage3_unit2_conv2 (Conv2D)     (None, None, None, 2 589824      zero_padding2d_88[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "add_41 (Add)                    (None, None, None, 2 0           stage3_unit2_conv2[0][0]         \n",
      "                                                                 add_40[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "stage3_unit3_bn1 (BatchNormaliz (None, None, None, 2 1024        add_41[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "stage3_unit3_relu1 (Activation) (None, None, None, 2 0           stage3_unit3_bn1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_89 (ZeroPadding2 (None, None, None, 2 0           stage3_unit3_relu1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage3_unit3_conv1 (Conv2D)     (None, None, None, 2 589824      zero_padding2d_89[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "stage3_unit3_bn2 (BatchNormaliz (None, None, None, 2 1024        stage3_unit3_conv1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage3_unit3_relu2 (Activation) (None, None, None, 2 0           stage3_unit3_bn2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_90 (ZeroPadding2 (None, None, None, 2 0           stage3_unit3_relu2[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage3_unit3_conv2 (Conv2D)     (None, None, None, 2 589824      zero_padding2d_90[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "add_42 (Add)                    (None, None, None, 2 0           stage3_unit3_conv2[0][0]         \n",
      "                                                                 add_41[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "stage3_unit4_bn1 (BatchNormaliz (None, None, None, 2 1024        add_42[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "stage3_unit4_relu1 (Activation) (None, None, None, 2 0           stage3_unit4_bn1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_91 (ZeroPadding2 (None, None, None, 2 0           stage3_unit4_relu1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage3_unit4_conv1 (Conv2D)     (None, None, None, 2 589824      zero_padding2d_91[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "stage3_unit4_bn2 (BatchNormaliz (None, None, None, 2 1024        stage3_unit4_conv1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage3_unit4_relu2 (Activation) (None, None, None, 2 0           stage3_unit4_bn2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_92 (ZeroPadding2 (None, None, None, 2 0           stage3_unit4_relu2[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage3_unit4_conv2 (Conv2D)     (None, None, None, 2 589824      zero_padding2d_92[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "add_43 (Add)                    (None, None, None, 2 0           stage3_unit4_conv2[0][0]         \n",
      "                                                                 add_42[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "stage3_unit5_bn1 (BatchNormaliz (None, None, None, 2 1024        add_43[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "stage3_unit5_relu1 (Activation) (None, None, None, 2 0           stage3_unit5_bn1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_93 (ZeroPadding2 (None, None, None, 2 0           stage3_unit5_relu1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage3_unit5_conv1 (Conv2D)     (None, None, None, 2 589824      zero_padding2d_93[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "stage3_unit5_bn2 (BatchNormaliz (None, None, None, 2 1024        stage3_unit5_conv1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage3_unit5_relu2 (Activation) (None, None, None, 2 0           stage3_unit5_bn2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_94 (ZeroPadding2 (None, None, None, 2 0           stage3_unit5_relu2[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage3_unit5_conv2 (Conv2D)     (None, None, None, 2 589824      zero_padding2d_94[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "add_44 (Add)                    (None, None, None, 2 0           stage3_unit5_conv2[0][0]         \n",
      "                                                                 add_43[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "stage3_unit6_bn1 (BatchNormaliz (None, None, None, 2 1024        add_44[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "stage3_unit6_relu1 (Activation) (None, None, None, 2 0           stage3_unit6_bn1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_95 (ZeroPadding2 (None, None, None, 2 0           stage3_unit6_relu1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage3_unit6_conv1 (Conv2D)     (None, None, None, 2 589824      zero_padding2d_95[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "stage3_unit6_bn2 (BatchNormaliz (None, None, None, 2 1024        stage3_unit6_conv1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage3_unit6_relu2 (Activation) (None, None, None, 2 0           stage3_unit6_bn2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_96 (ZeroPadding2 (None, None, None, 2 0           stage3_unit6_relu2[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage3_unit6_conv2 (Conv2D)     (None, None, None, 2 589824      zero_padding2d_96[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "add_45 (Add)                    (None, None, None, 2 0           stage3_unit6_conv2[0][0]         \n",
      "                                                                 add_44[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "stage4_unit1_bn1 (BatchNormaliz (None, None, None, 2 1024        add_45[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "stage4_unit1_relu1 (Activation) (None, None, None, 2 0           stage4_unit1_bn1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_97 (ZeroPadding2 (None, None, None, 2 0           stage4_unit1_relu1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage4_unit1_conv1 (Conv2D)     (None, None, None, 5 1179648     zero_padding2d_97[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "stage4_unit1_bn2 (BatchNormaliz (None, None, None, 5 2048        stage4_unit1_conv1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage4_unit1_relu2 (Activation) (None, None, None, 5 0           stage4_unit1_bn2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_98 (ZeroPadding2 (None, None, None, 5 0           stage4_unit1_relu2[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage4_unit1_conv2 (Conv2D)     (None, None, None, 5 2359296     zero_padding2d_98[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "stage4_unit1_sc (Conv2D)        (None, None, None, 5 131072      stage4_unit1_relu1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "add_46 (Add)                    (None, None, None, 5 0           stage4_unit1_conv2[0][0]         \n",
      "                                                                 stage4_unit1_sc[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "stage4_unit2_bn1 (BatchNormaliz (None, None, None, 5 2048        add_46[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "stage4_unit2_relu1 (Activation) (None, None, None, 5 0           stage4_unit2_bn1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_99 (ZeroPadding2 (None, None, None, 5 0           stage4_unit2_relu1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage4_unit2_conv1 (Conv2D)     (None, None, None, 5 2359296     zero_padding2d_99[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "stage4_unit2_bn2 (BatchNormaliz (None, None, None, 5 2048        stage4_unit2_conv1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage4_unit2_relu2 (Activation) (None, None, None, 5 0           stage4_unit2_bn2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_100 (ZeroPadding (None, None, None, 5 0           stage4_unit2_relu2[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage4_unit2_conv2 (Conv2D)     (None, None, None, 5 2359296     zero_padding2d_100[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "add_47 (Add)                    (None, None, None, 5 0           stage4_unit2_conv2[0][0]         \n",
      "                                                                 add_46[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "stage4_unit3_bn1 (BatchNormaliz (None, None, None, 5 2048        add_47[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "stage4_unit3_relu1 (Activation) (None, None, None, 5 0           stage4_unit3_bn1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_101 (ZeroPadding (None, None, None, 5 0           stage4_unit3_relu1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage4_unit3_conv1 (Conv2D)     (None, None, None, 5 2359296     zero_padding2d_101[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage4_unit3_bn2 (BatchNormaliz (None, None, None, 5 2048        stage4_unit3_conv1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage4_unit3_relu2 (Activation) (None, None, None, 5 0           stage4_unit3_bn2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_102 (ZeroPadding (None, None, None, 5 0           stage4_unit3_relu2[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "stage4_unit3_conv2 (Conv2D)     (None, None, None, 5 2359296     zero_padding2d_102[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "add_48 (Add)                    (None, None, None, 5 0           stage4_unit3_conv2[0][0]         \n",
      "                                                                 add_47[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bn1 (BatchNormalization)        (None, None, None, 5 2048        add_48[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "relu1 (Activation)              (None, None, None, 5 0           bn1[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage0_upsampling (UpSa (None, None, None, 5 0           relu1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage0_concat (Concaten (None, None, None, 7 0           decoder_stage0_upsampling[0][0]  \n",
      "                                                                 stage4_unit1_relu1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage0a_conv (Conv2D)   (None, None, None, 2 1769472     decoder_stage0_concat[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage0a_bn (BatchNormal (None, None, None, 2 1024        decoder_stage0a_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage0a_relu (Activatio (None, None, None, 2 0           decoder_stage0a_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage0b_conv (Conv2D)   (None, None, None, 2 589824      decoder_stage0a_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage0b_bn (BatchNormal (None, None, None, 2 1024        decoder_stage0b_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage0b_relu (Activatio (None, None, None, 2 0           decoder_stage0b_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage1_upsampling (UpSa (None, None, None, 2 0           decoder_stage0b_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage1_concat (Concaten (None, None, None, 3 0           decoder_stage1_upsampling[0][0]  \n",
      "                                                                 stage3_unit1_relu1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage1a_conv (Conv2D)   (None, None, None, 1 442368      decoder_stage1_concat[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage1a_bn (BatchNormal (None, None, None, 1 512         decoder_stage1a_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage1a_relu (Activatio (None, None, None, 1 0           decoder_stage1a_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage1b_conv (Conv2D)   (None, None, None, 1 147456      decoder_stage1a_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage1b_bn (BatchNormal (None, None, None, 1 512         decoder_stage1b_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage1b_relu (Activatio (None, None, None, 1 0           decoder_stage1b_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage2_upsampling (UpSa (None, None, None, 1 0           decoder_stage1b_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage2_concat (Concaten (None, None, None, 1 0           decoder_stage2_upsampling[0][0]  \n",
      "                                                                 stage2_unit1_relu1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage2a_conv (Conv2D)   (None, None, None, 6 110592      decoder_stage2_concat[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage2a_bn (BatchNormal (None, None, None, 6 256         decoder_stage2a_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage2a_relu (Activatio (None, None, None, 6 0           decoder_stage2a_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage2b_conv (Conv2D)   (None, None, None, 6 36864       decoder_stage2a_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage2b_bn (BatchNormal (None, None, None, 6 256         decoder_stage2b_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage2b_relu (Activatio (None, None, None, 6 0           decoder_stage2b_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage3_upsampling (UpSa (None, None, None, 6 0           decoder_stage2b_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage3_concat (Concaten (None, None, None, 1 0           decoder_stage3_upsampling[0][0]  \n",
      "                                                                 relu0[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage3a_conv (Conv2D)   (None, None, None, 3 36864       decoder_stage3_concat[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage3a_bn (BatchNormal (None, None, None, 3 128         decoder_stage3a_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage3a_relu (Activatio (None, None, None, 3 0           decoder_stage3a_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage3b_conv (Conv2D)   (None, None, None, 3 9216        decoder_stage3a_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage3b_bn (BatchNormal (None, None, None, 3 128         decoder_stage3b_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage3b_relu (Activatio (None, None, None, 3 0           decoder_stage3b_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage4_upsampling (UpSa (None, None, None, 3 0           decoder_stage3b_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage4a_conv (Conv2D)   (None, None, None, 1 4608        decoder_stage4_upsampling[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage4a_bn (BatchNormal (None, None, None, 1 64          decoder_stage4a_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage4a_relu (Activatio (None, None, None, 1 0           decoder_stage4a_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage4b_conv (Conv2D)   (None, None, None, 1 2304        decoder_stage4a_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage4b_bn (BatchNormal (None, None, None, 1 64          decoder_stage4b_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage4b_relu (Activatio (None, None, None, 1 0           decoder_stage4b_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "final_conv (Conv2D)             (None, None, None, 1 145         decoder_stage4b_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "sigmoid (Activation)            (None, None, None, 1 0           final_conv[0][0]                 \n",
      "==================================================================================================\n",
      "Total params: 24,456,154\n",
      "Trainable params: 24,438,804\n",
      "Non-trainable params: 17,350\n",
      "__________________________________________________________________________________________________\n",
      "inp:  Tensor(\"input_3:0\", shape=(?, 512, 512, 1), dtype=float32)\n",
      "l1:  Tensor(\"conv2d_2/BiasAdd:0\", shape=(?, 512, 512, 3), dtype=float32)\n",
      "out:  Tensor(\"model_6/sigmoid/Sigmoid:0\", shape=(?, 512, 512, 1), dtype=float32)\n",
      "WARNING:tensorflow:From /tensorflow-1.15.2/python3.7/tensorflow_core/python/ops/nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "Model: \"model_6\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_3 (InputLayer)         (None, 512, 512, 1)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 512, 512, 3)       6         \n",
      "_________________________________________________________________\n",
      "model_6 (Model)              multiple                  24456154  \n",
      "=================================================================\n",
      "Total params: 24,456,160\n",
      "Trainable params: 24,438,810\n",
      "Non-trainable params: 17,350\n",
      "_________________________________________________________________\n",
      "{'learning_rate': 0.0003000000142492354, 'beta_1': 0.8999999761581421, 'beta_2': 0.9990000128746033, 'decay': 0.0, 'epsilon': 1e-07, 'amsgrad': False}\n",
      "---------------------------- Main training parameters ----------------------------\n",
      "Number of epochs: 400\n",
      "Batch size: 4\n",
      "Number of training dataset: 368\n",
      "Number of training steps: 83\n",
      "Number of validation steps: 10\n",
      "---------------------------- ------------------------ ----------------------------\n",
      "------------------------------\n",
      "PDF report exported in /content/drive/MyDrive/Pablo Delgado/Doctorado-FPU/AAV para enfermedades renales/CT/2DUnet/models/cysts3-Imagenet-pretrained/\n"
     ]
    }
   ],
   "source": [
    "#@markdown ##Play this cell to prepare the model for training\n",
    "\n",
    "\n",
    "# ------------------ Set the generators, model and logger ------------------\n",
    "# This will take the image size and set that as a patch size (arguable...)\n",
    "# Read image size (without actuall reading the data)\n",
    "\n",
    "(train_datagen, validation_datagen) = prepareGenerators(Patch_source, Patch_target, data_gen_args, batch_size, target_size = (patch_width, patch_height))\n",
    "\n",
    "\n",
    "# This modelcheckpoint will only save the best model from the validation loss point of view\n",
    "model_checkpoint = ModelCheckpoint(os.path.join(full_model_path, 'weights_best.hdf5'), monitor='val_loss',verbose=1, save_best_only=True)\n",
    "\n",
    "print('Getting class weights...')\n",
    "class_weights = getClassWeights(Training_target)\n",
    "\n",
    "# --------------------- Using pretrained model ------------------------\n",
    "#Here we ensure that the learning rate set correctly when using pre-trained models\n",
    "if Use_pretrained_model:\n",
    "  if Weights_choice == \"last\":\n",
    "    initial_learning_rate = lastLearningRate\n",
    "\n",
    "  if Weights_choice == \"best\":            \n",
    "    initial_learning_rate = bestLearningRate\n",
    "else:\n",
    "  h5_file_path = None\n",
    "\n",
    "# --------------------- ---------------------- ------------------------\n",
    "\n",
    "# --------------------- Reduce learning rate on plateau ------------------------\n",
    "\n",
    "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, verbose=1, mode='auto',\n",
    "                              patience=10, min_lr=0)\n",
    "# --------------------- ---------------------- ------------------------\n",
    "\n",
    "\n",
    "# Define the model\n",
    "\n",
    "\n",
    "model = unet(pretrained_weights = h5_file_path, \n",
    "             input_size = (patch_width,patch_height,1), \n",
    "             pooling_steps = pooling_steps, \n",
    "             learning_rate = initial_learning_rate, \n",
    "             class_weights = class_weights)\n",
    "\n",
    "\n",
    "config_model= model.optimizer.get_config()\n",
    "print(config_model)\n",
    "\n",
    "\n",
    "# ------------------ Failsafes ------------------\n",
    "if os.path.exists(full_model_path):\n",
    "  print(R+'!! WARNING: Model folder already existed and has been removed !!'+W)\n",
    "  shutil.rmtree(full_model_path)\n",
    "\n",
    "os.makedirs(full_model_path)\n",
    "os.makedirs(os.path.join(full_model_path,'Quality Control'))\n",
    "\n",
    "\n",
    "# ------------------ Display ------------------\n",
    "print('---------------------------- Main training parameters ----------------------------')\n",
    "print('Number of epochs: '+str(number_of_epochs))\n",
    "print('Batch size: '+str(batch_size))\n",
    "print('Number of training dataset: '+str(number_of_training_dataset))\n",
    "print('Number of training steps: '+str(number_of_steps))\n",
    "print('Number of validation steps: '+str(validation_steps))\n",
    "print('---------------------------- ------------------------ ----------------------------')\n",
    "\n",
    "pdf_export(augmentation = Use_Data_augmentation, pretrained_model = Use_pretrained_model)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0Dfn8ZsEMv5d"
   },
   "source": [
    "## **4.2. Start Training**\n",
    "---\n",
    "<font size = 4>When playing the cell below you should see updates after each epoch (round). Network training can take some time.\n",
    "\n",
    "<font size = 4>* **CRITICAL NOTE:** Google Colab has a time limit for processing (to prevent using GPU power for datamining). Training time must be less than 12 hours! If training takes longer than 12 hours, please decrease the number of epochs or number of patches. Another way circumvent this is to save the parameters of the model after training and start training again from this point.\n",
    "\n",
    "<font size = 4>Once training is complete, the trained model is automatically saved on your Google Drive, in the **model_path** folder that was selected in Section 3. It is however wise to download the folder from Google Drive as all data can be erased at the next training if using the same folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iwNmp1PUzRDQ",
    "outputId": "679722b4-a8aa-46f2-e3aa-a44135a9b558",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /tensorflow-1.15.2/python3.7/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "Epoch 1/400\n",
      "Found 36 images belonging to 1 classes.\n",
      "Found 332 images belonging to 1 classes.\n",
      "Found 36 images belonging to 1 classes.\n",
      "Found 332 images belonging to 1 classes.\n",
      "83/83 [==============================] - 2262s 27s/step - loss: 0.2477 - val_loss: 0.5753\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.57525, saving model to /content/drive/MyDrive/Pablo Delgado/Doctorado-FPU/AAV para enfermedades renales/CT/2DUnet/models/cysts3-Imagenet-pretrained/weights_best.hdf5\n",
      "Epoch 2/400\n",
      "83/83 [==============================] - 2268s 27s/step - loss: 0.0996 - val_loss: 0.6858\n",
      "\n",
      "Epoch 00002: val_loss did not improve from 0.57525\n",
      "Epoch 3/400\n",
      "83/83 [==============================] - 2283s 28s/step - loss: 0.0530 - val_loss: 0.8710\n",
      "\n",
      "Epoch 00003: val_loss did not improve from 0.57525\n",
      "Epoch 4/400\n",
      "83/83 [==============================] - 2288s 28s/step - loss: 0.0314 - val_loss: 1.5895\n",
      "\n",
      "Epoch 00004: val_loss did not improve from 0.57525\n",
      "Epoch 5/400\n",
      "83/83 [==============================] - 2303s 28s/step - loss: 0.0214 - val_loss: 1.3726\n",
      "\n",
      "Epoch 00005: val_loss did not improve from 0.57525\n",
      "Epoch 6/400\n",
      "83/83 [==============================] - 2328s 28s/step - loss: 0.0157 - val_loss: 2.5871\n",
      "\n",
      "Epoch 00006: val_loss did not improve from 0.57525\n",
      "Epoch 7/400\n",
      "83/83 [==============================] - 2348s 28s/step - loss: 0.0122 - val_loss: 2.3679\n",
      "\n",
      "Epoch 00007: val_loss did not improve from 0.57525\n",
      "Epoch 8/400\n",
      "83/83 [==============================] - 2382s 29s/step - loss: 0.0101 - val_loss: 2.5920\n",
      "\n",
      "Epoch 00008: val_loss did not improve from 0.57525\n",
      "Epoch 9/400\n",
      "83/83 [==============================] - 2415s 29s/step - loss: 0.0084 - val_loss: 1.9445\n",
      "\n",
      "Epoch 00009: val_loss did not improve from 0.57525\n",
      "Epoch 10/400\n",
      "83/83 [==============================] - 2451s 30s/step - loss: 0.0073 - val_loss: 1.5545\n",
      "\n",
      "Epoch 00010: val_loss did not improve from 0.57525\n",
      "Epoch 11/400\n",
      "83/83 [==============================] - 2403s 29s/step - loss: 0.0065 - val_loss: 0.7028\n",
      "\n",
      "Epoch 00011: val_loss did not improve from 0.57525\n",
      "\n",
      "Epoch 00011: ReduceLROnPlateau reducing learning rate to 3.000000142492354e-05.\n",
      "Epoch 12/400\n",
      "83/83 [==============================] - 2408s 29s/step - loss: 0.0059 - val_loss: 0.1218\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.57525 to 0.12182, saving model to /content/drive/MyDrive/Pablo Delgado/Doctorado-FPU/AAV para enfermedades renales/CT/2DUnet/models/cysts3-Imagenet-pretrained/weights_best.hdf5\n",
      "Epoch 13/400\n",
      "83/83 [==============================] - 2419s 29s/step - loss: 0.0057 - val_loss: 0.0852\n",
      "\n",
      "Epoch 00013: val_loss improved from 0.12182 to 0.08517, saving model to /content/drive/MyDrive/Pablo Delgado/Doctorado-FPU/AAV para enfermedades renales/CT/2DUnet/models/cysts3-Imagenet-pretrained/weights_best.hdf5\n",
      "Epoch 14/400\n",
      "83/83 [==============================] - 2468s 30s/step - loss: 0.0056 - val_loss: 0.0149\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.08517 to 0.01486, saving model to /content/drive/MyDrive/Pablo Delgado/Doctorado-FPU/AAV para enfermedades renales/CT/2DUnet/models/cysts3-Imagenet-pretrained/weights_best.hdf5\n",
      "Epoch 15/400\n",
      "83/83 [==============================] - 2468s 30s/step - loss: 0.0055 - val_loss: 0.0127\n",
      "\n",
      "Epoch 00015: val_loss improved from 0.01486 to 0.01268, saving model to /content/drive/MyDrive/Pablo Delgado/Doctorado-FPU/AAV para enfermedades renales/CT/2DUnet/models/cysts3-Imagenet-pretrained/weights_best.hdf5\n",
      "Epoch 16/400\n",
      "31/83 [==========>...................] - ETA: 24:30 - loss: 0.0053"
     ]
    }
   ],
   "source": [
    "#@markdown ##Start training\n",
    "\n",
    "start = time.time()\n",
    "# history = model.fit_generator(train_datagen, steps_per_epoch = number_of_steps, epochs=epochs, callbacks=[model_checkpoint,csv_log], validation_data = validation_datagen, validation_steps = validation_steps, shuffle=True, verbose=1)\n",
    "history = model.fit_generator(train_datagen, steps_per_epoch = number_of_steps, epochs = number_of_epochs, callbacks=[model_checkpoint, reduce_lr], validation_data = validation_datagen, validation_steps = validation_steps, shuffle=True, verbose=1)\n",
    "\n",
    "# Save the last model\n",
    "model.save(os.path.join(full_model_path, 'weights_last.hdf5'))\n",
    "\n",
    "\n",
    "# convert the history.history dict to a pandas DataFrame:     \n",
    "lossData = pd.DataFrame(history.history) \n",
    "\n",
    "# The training evaluation.csv is saved (overwrites the Files if needed). \n",
    "lossDataCSVpath = os.path.join(full_model_path,'Quality Control/training_evaluation.csv')\n",
    "with open(lossDataCSVpath, 'w') as f:\n",
    "  writer = csv.writer(f)\n",
    "  writer.writerow(['loss','val_loss', 'learning rate'])\n",
    "  for i in range(len(history.history['loss'])):\n",
    "    writer.writerow([history.history['loss'][i], history.history['val_loss'][i], history.history['lr'][i]])\n",
    "    \n",
    "\n",
    "\n",
    "# Displaying the time elapsed for training\n",
    "print(\"------------------------------------------\")\n",
    "dt = time.time() - start\n",
    "mins, sec = divmod(dt, 60) \n",
    "hour, mins = divmod(mins, 60) \n",
    "print(\"Time elapsed:\", hour, \"hour(s)\", mins,\"min(s)\",round(sec),\"sec(s)\")\n",
    "print(\"------------------------------------------\")\n",
    "\n",
    "#Create a pdf document with training summary\n",
    "\n",
    "pdf_export(trained = True, augmentation = Use_Data_augmentation, pretrained_model = Use_pretrained_model)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_0Hynw3-xHp1"
   },
   "source": [
    "# **5. Evaluate your model**\n",
    "---\n",
    "\n",
    "<font size = 4>This section allows the user to perform important quality checks on the validity and generalisability of the trained model. \n",
    "\n",
    "<font size = 4>**We highly recommend to perform quality control on all newly trained models.**\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 290,
     "status": "ok",
     "timestamp": 1648538393104,
     "user": {
      "displayName": "PABLO DELGADO RODRIGUEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "11999848093282927146"
     },
     "user_tz": -120
    },
    "id": "eAJzMwPA6tlH",
    "outputId": "f823ca1d-e9a6-4ac3-8934-ddc45b8de68f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cysts1 network will be evaluated\n"
     ]
    }
   ],
   "source": [
    "#@markdown ###Do you want to assess the model you just trained ?\n",
    "\n",
    "Use_the_current_trained_model = False #@param {type:\"boolean\"}\n",
    "\n",
    "#@markdown ###If not, please provide the path to the model folder:\n",
    "\n",
    "QC_model_folder = \"/content/drive/Shareddrives/GR.INV. BIIG-QMIC/AVV_Renal_2017/Pablo/Segmentacion quistes CT y MRI 24-01-2022/2DUnet/models/cysts1\" #@param {type:\"string\"}\n",
    "\n",
    "#Here we define the loaded model name and path\n",
    "QC_model_name = os.path.basename(QC_model_folder)\n",
    "QC_model_path = os.path.dirname(QC_model_folder)\n",
    "\n",
    "\n",
    "if (Use_the_current_trained_model): \n",
    "  print(\"Using current trained network\")\n",
    "  QC_model_name = model_name\n",
    "  QC_model_path = model_path\n",
    "\n",
    "\n",
    "full_QC_model_path = os.path.join(QC_model_path, QC_model_name)\n",
    "if os.path.exists(os.path.join(full_QC_model_path, 'weights_best.hdf5')):\n",
    "  print(\"The \"+QC_model_name+\" network will be evaluated\")\n",
    "else:\n",
    "  print(R+'!! WARNING: The chosen model does not exist !!'+W)\n",
    "  print('Please make sure you provide a valid model path and model name before proceeding further.')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dhJROwlAMv5o"
   },
   "source": [
    "## **5.1. Inspection of the loss function**\n",
    "---\n",
    "\n",
    "<font size = 4>First, it is good practice to evaluate the training progress by comparing the training loss with the validation loss. The latter is a metric which shows how well the network performs on a subset of unseen data which is set aside from the training dataset. For more information on this, see for example [this review](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6381354/) by Nichols *et al.*\n",
    "\n",
    "<font size = 4>**Training loss** describes an error value after each epoch for the difference between the model's prediction and its ground-truth target.\n",
    "\n",
    "<font size = 4>**Validation loss** describes the same error value between the model's prediction on a validation image and compared to it's target.\n",
    "\n",
    "<font size = 4>During training both values should decrease before reaching a minimal value which does not decrease further even after more training. Comparing the development of the validation loss with the training loss can give insights into the model's performance.\n",
    "\n",
    "<font size = 4>Decreasing **Training loss** and **Validation loss** indicates that training is still necessary and increasing the `number_of_epochs` is recommended. Note that the curves can look flat towards the right side, just because of the y-axis scaling. The network has reached convergence once the curves flatten out. After this point no further training is required. If the **Validation loss** suddenly increases again an the **Training loss** simultaneously goes towards zero, it means that the network is overfitting to the training data. In other words the network is remembering the exact patterns from the training data and no longer generalizes well to unseen data. In this case the training dataset has to be increased."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 621
    },
    "executionInfo": {
     "elapsed": 2996,
     "status": "ok",
     "timestamp": 1648538400694,
     "user": {
      "displayName": "PABLO DELGADO RODRIGUEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "11999848093282927146"
     },
     "user_tz": -120
    },
    "id": "vMzSP50kMv5p",
    "outputId": "19815b43-fca7-4416-cb49-a99f98754607"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#@markdown ##Play the cell to show a plot of training errors vs. epoch number\n",
    "\n",
    "epochNumber = []\n",
    "lossDataFromCSV = []\n",
    "vallossDataFromCSV = []\n",
    "\n",
    "with open(os.path.join(full_QC_model_path, 'Quality Control', 'training_evaluation.csv'),'r') as csvfile:\n",
    "    csvRead = csv.reader(csvfile, delimiter=',')\n",
    "    next(csvRead)\n",
    "    for row in csvRead:\n",
    "        lossDataFromCSV.append(float(row[0]))\n",
    "        vallossDataFromCSV.append(float(row[1]))\n",
    "\n",
    "epochNumber = range(len(lossDataFromCSV))\n",
    "\n",
    "plt.figure(figsize=(15,10))\n",
    "\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(epochNumber,lossDataFromCSV, label='Training loss')\n",
    "plt.plot(epochNumber,vallossDataFromCSV, label='Validation loss')\n",
    "plt.title('Training loss and validation loss vs. epoch number (linear scale)')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch number')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "plt.semilogy(epochNumber,lossDataFromCSV, label='Training loss')\n",
    "plt.semilogy(epochNumber,vallossDataFromCSV, label='Validation loss')\n",
    "plt.title('Training loss and validation loss vs. epoch number (log scale)')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch number')\n",
    "plt.legend()\n",
    "plt.savefig(os.path.join(full_QC_model_path, 'Quality Control', 'lossCurvePlots.png'),bbox_inches='tight',pad_inches=0)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X5_92nL2xdP6"
   },
   "source": [
    "## **5.2. Error mapping and quality metrics estimation**\n",
    "---\n",
    "<font size = 4>This section will calculate the Intersection over Union score for all the images provided in the Source_QC_folder and Target_QC_folder. The result for one of the image will also be displayed.\n",
    "\n",
    "<font size = 4>The **Intersection over Union** metric is a method that can be used to quantify the percent overlap between the target mask and your prediction output. **Therefore, the closer to 1, the better the performance.** This metric can be used to assess the quality of your model to accurately predict nuclei. \n",
    "\n",
    "<font size = 4>The Input, Ground Truth, Prediction and IoU maps are shown below for the last example in the QC set.\n",
    "\n",
    "<font size = 4> The results for all QC examples can be found in the \"*Quality Control*\" folder which is located inside your \"model_folder\".\n",
    "\n",
    "### **Thresholds for image masks**\n",
    "\n",
    "<font size = 4> Since the output from Unet is not a binary mask, the output images are converted to binary masks using thresholding. This section will test different thresholds (from 0 to 255) to find the one yielding the best IoU score compared with the ground truth. The best threshold for each image and the average of these thresholds will be displayed below. **These values can be a guideline when creating masks for unseen data in section 6.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form",
    "id": "w90MdriMxhjD"
   },
   "outputs": [],
   "source": [
    "# ------------- User input ------------\n",
    "#@markdown ##Choose the folders that contain your Quality Control dataset\n",
    "Source_QC_folder = \"\" #@param{type:\"string\"}\n",
    "Target_QC_folder = \"\" #@param{type:\"string\"}\n",
    "\n",
    "\n",
    "# ------------- Initialise folders ------------\n",
    "# Create a quality control/Prediction Folder\n",
    "prediction_QC_folder = os.path.join(full_QC_model_path, 'Quality Control', 'Prediction')\n",
    "if os.path.exists(prediction_QC_folder):\n",
    "  shutil.rmtree(prediction_QC_folder)\n",
    "\n",
    "os.makedirs(prediction_QC_folder)\n",
    "\n",
    "\n",
    "# ------------- Prepare the model and run predictions ------------\n",
    "\n",
    "# Load the model\n",
    "unet = load_model(os.path.join(full_QC_model_path, 'weights_best.hdf5'), custom_objects={'_weighted_binary_crossentropy': weighted_binary_crossentropy(np.ones(2))})\n",
    "Input_size = unet.layers[0].output_shape[1:3]\n",
    "print('Model input size: '+str(Input_size[0])+'x'+str(Input_size[1]))\n",
    "\n",
    "# Create a list of sources\n",
    "source_dir_list = os.listdir(Source_QC_folder)\n",
    "number_of_dataset = len(source_dir_list)\n",
    "print('Number of dataset found in the folder: '+str(number_of_dataset))\n",
    "\n",
    "predictions = []\n",
    "for i in tqdm(range(number_of_dataset)):\n",
    "  predictions.append(predict_as_tiles(os.path.join(Source_QC_folder, source_dir_list[i]), unet))\n",
    "\n",
    "\n",
    "# Save the results in the folder along with the masks according to the set threshold\n",
    "saveResult(prediction_QC_folder, predictions, source_dir_list, prefix=prediction_prefix, threshold=None)\n",
    "\n",
    "#-----------------------------Calculate Metrics----------------------------------------#\n",
    "\n",
    "f = plt.figure(figsize=((5,5)))\n",
    "\n",
    "with open(os.path.join(full_QC_model_path,'Quality Control', 'QC_metrics_'+QC_model_name+'.csv'), \"w\", newline='') as file:\n",
    "  writer = csv.writer(file)\n",
    "  writer.writerow([\"File name\",\"IoU\", \"IoU-optimised threshold\"])  \n",
    "\n",
    "  # Initialise the lists \n",
    "  filename_list = []\n",
    "  best_threshold_list = []\n",
    "  best_IoU_score_list = []\n",
    "\n",
    "  for filename in os.listdir(Source_QC_folder):\n",
    "\n",
    "    if not os.path.isdir(os.path.join(Source_QC_folder, filename)):\n",
    "      print('Running QC on: '+filename)\n",
    "      test_input = io.imread(os.path.join(Source_QC_folder, filename), as_gray=True)\n",
    "      test_ground_truth_image = io.imread(os.path.join(Target_QC_folder, filename), as_gray=True)\n",
    "\n",
    "      (threshold_list, iou_scores_per_threshold) = getIoUvsThreshold(os.path.join(prediction_QC_folder, prediction_prefix+filename), os.path.join(Target_QC_folder, filename))\n",
    "      plt.plot(threshold_list,iou_scores_per_threshold, label=filename)\n",
    "\n",
    "      # Here we find which threshold yielded the highest IoU score for image n.\n",
    "      best_IoU_score = max(iou_scores_per_threshold)\n",
    "      best_threshold = iou_scores_per_threshold.index(best_IoU_score)\n",
    "\n",
    "      # Write the results in the CSV file\n",
    "      writer.writerow([filename, str(best_IoU_score), str(best_threshold)])\n",
    "\n",
    "      # Here we append the best threshold and score to the lists\n",
    "      filename_list.append(filename)\n",
    "      best_IoU_score_list.append(best_IoU_score)\n",
    "      best_threshold_list.append(best_threshold)\n",
    "\n",
    "# Display the IoV vs Threshold plot\n",
    "plt.title('IoU vs. Threshold')\n",
    "plt.ylabel('IoU')\n",
    "plt.xlabel('Threshold value')\n",
    "plt.legend()\n",
    "plt.savefig(full_QC_model_path+'/Quality Control/'+QC_model_name+'_IoUvsThresholdPlot.png',bbox_inches='tight',pad_inches=0)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# Table with metrics as dataframe output\n",
    "pdResults = pd.DataFrame(index = filename_list)\n",
    "pdResults[\"IoU\"] = best_IoU_score_list\n",
    "pdResults[\"IoU-optimised threshold\"] = best_threshold_list\n",
    "\n",
    "average_best_threshold = sum(best_threshold_list)/len(best_threshold_list)\n",
    "\n",
    "\n",
    "# ------------- For display ------------\n",
    "print('--------------------------------------------------------------')\n",
    "@interact\n",
    "def show_QC_results(file=os.listdir(Source_QC_folder)):\n",
    "  \n",
    "  plt.figure(figsize=(25,5))\n",
    "  #Input\n",
    "  plt.subplot(1,4,1)\n",
    "  plt.axis('off')\n",
    "  plt.imshow(plt.imread(os.path.join(Source_QC_folder, file)), aspect='equal', cmap='gray', interpolation='nearest')\n",
    "  plt.title('Input')\n",
    "\n",
    "  #Ground-truth\n",
    "  plt.subplot(1,4,2)\n",
    "  plt.axis('off')\n",
    "  test_ground_truth_image = io.imread(os.path.join(Target_QC_folder, file),as_gray=True)\n",
    "  plt.imshow(test_ground_truth_image, aspect='equal', cmap='Greens')\n",
    "  plt.title('Ground Truth')\n",
    "\n",
    "  #Prediction\n",
    "  plt.subplot(1,4,3)\n",
    "  plt.axis('off')\n",
    "  test_prediction = plt.imread(os.path.join(prediction_QC_folder, prediction_prefix+file))\n",
    "  test_prediction_mask = np.empty_like(test_prediction)\n",
    "  test_prediction_mask[test_prediction > average_best_threshold] = 255\n",
    "  test_prediction_mask[test_prediction <= average_best_threshold] = 0\n",
    "  plt.imshow(test_prediction_mask, aspect='equal', cmap='Purples')\n",
    "  plt.title('Prediction')\n",
    "\n",
    "  #Overlay\n",
    "  plt.subplot(1,4,4)\n",
    "  plt.axis('off')\n",
    "  plt.imshow(test_ground_truth_image, cmap='Greens')\n",
    "  plt.imshow(test_prediction_mask, alpha=0.5, cmap='Purples')\n",
    "  metrics_title = 'Overlay (IoU: ' + str(round(pdResults.loc[file][\"IoU\"],3)) + ' T: ' + str(round(pdResults.loc[file][\"IoU-optimised threshold\"])) + ')'\n",
    "  plt.title(metrics_title)\n",
    "  plt.savefig(full_QC_model_path+'/Quality Control/QC_example_data.png',bbox_inches='tight',pad_inches=0)\n",
    "\n",
    "\n",
    "print('--------------------------------------------------------------')\n",
    "print('Best average threshold is: '+str(round(average_best_threshold)))\n",
    "print('--------------------------------------------------------------')\n",
    "\n",
    "pdResults.head()\n",
    "\n",
    "qc_pdf_export()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-tJeeJjLnRkP"
   },
   "source": [
    "# **6. Using the trained model**\n",
    "\n",
    "---\n",
    "<font size = 4>In this section the unseen data is processed using the trained model (in section 4). First, your unseen images are uploaded and prepared for prediction. After that your trained model from section 4 is activated and finally saved into your Google Drive."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d8wuQGjoq6eN"
   },
   "source": [
    "## **6.1 Generate prediction(s) from unseen dataset**\n",
    "---\n",
    "\n",
    "<font size = 4>The current trained model (from section 4.1) can now be used to process images. If you want to use an older model, untick the **Use_the_current_trained_model** box and enter the name and path of the model to use. Predicted output images are saved in your **Result_folder** folder.\n",
    "\n",
    "<font size = 4>**`Data_folder`:** This folder should contain the images that you want to use your trained network on for processing.\n",
    "\n",
    "<font size = 4>**`Result_folder`:** This folder will contain the predicted output images.\n",
    "\n",
    "<font size = 4> Once the predictions are complete the cell will display a random example prediction beside the input image and the calculated mask for visual inspection.\n",
    "\n",
    "<font size = 4> **Troubleshooting:** If there is a low contrast image warning when saving the images, this may be due to overfitting of the model to the data. It may result in images containing only a single colour. Train the network again with different network hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 720,
     "referenced_widgets": [
      "7feaf712a7ee483ca428f466d676e85a",
      "39fe8e4a7bcc40e0b292486343b32ead",
      "77ea6cb62c49455292493edd4a37725b",
      "5a5be8f2b8e64d44b1995d1954307260",
      "6557015bbdc2492e99b27b1ffdfde3b1",
      "bd68118ef9204e189f3018d7ce5fc790",
      "83b45ec7a4a8447786f49454432d4eb7",
      "9193c787838449cab1f82c4c7a7df6c7",
      "ecb0a22846c4451a9cc1885f9da8c82b",
      "c406119536a74b77a79c2ddaea32f861",
      "a6683d79f59c421b8a7853e8c3275434",
      "1b6312f025224c0cbf2b40330f5ae993",
      "30ad74ce9c074bfca17143b29a5816bd",
      "5b04ad3f7db84bcab3d555a942a9cf85",
      "319b88533eed40afbac771b65fb9c298",
      "6465726886954000aba96ef05a7abd5f",
      "437c41fc10a447e8be5497c24c349606",
      "02a90cbfdc654f509c2e14e092ac598d",
      "7588fc79cb5548299d8766ade46ed43b",
      "b15b07d5244743d895f5cd9f38c0c38a",
      "a602bb8fb82c464db7ced88b9e457b4e"
     ]
    },
    "collapsed": true,
    "executionInfo": {
     "elapsed": 599470,
     "status": "ok",
     "timestamp": 1648565451305,
     "user": {
      "displayName": "PABLO DELGADO RODRIGUEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "11999848093282927146"
     },
     "user_tz": -120
    },
    "id": "y2TD5p7MZrEb",
    "jupyter": {
     "outputs_hidden": true
    },
    "outputId": "8330621e-5043-413f-9b6b-89f3457ba6f4",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cysts1 network will be used.\n",
      "WARNING:tensorflow:From /tensorflow-1.15.2/python3.7/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "WARNING:tensorflow:From /tensorflow-1.15.2/python3.7/keras/backend/tensorflow_backend.py:4070: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n",
      "WARNING:tensorflow:From /tensorflow-1.15.2/python3.7/tensorflow_core/python/ops/nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "WARNING:tensorflow:From /tensorflow-1.15.2/python3.7/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "Model input size: 128x128\n",
      "['TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0007.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0005.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0004.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0009.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0000.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0008.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0001.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0006.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0010.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0003.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0002.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0011.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0292.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0261.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0297.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0210.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0298.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0048.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0016.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0286.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0178.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0216.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0080.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0047.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0105.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0063.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0220.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0098.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0205.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0194.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0226.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0265.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0135.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0081.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0249.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0066.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0074.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0104.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0316.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0102.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0022.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0181.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0199.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0209.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0232.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0326.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0049.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0240.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0044.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0277.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0124.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0096.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0076.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0323.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0013.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0189.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0025.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0100.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0173.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0327.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0320.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0263.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0131.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0140.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0085.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0246.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0219.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0070.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0275.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0032.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0160.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0187.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0071.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0309.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0116.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0157.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0313.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0302.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0273.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0200.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0068.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0141.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0029.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0177.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0167.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0017.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0097.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0258.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0257.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0053.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0184.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0021.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0301.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0314.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0253.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0252.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0057.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0099.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0046.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0039.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0139.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0193.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0090.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0317.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0312.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0170.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0089.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0165.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0161.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0086.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0198.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0031.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0117.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0311.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0023.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0290.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0233.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0075.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0190.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0145.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0239.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0112.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0150.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0241.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0281.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0015.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0168.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0293.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0040.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0223.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0155.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0322.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0107.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0045.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0056.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0179.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0142.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0291.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0196.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0128.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0156.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0307.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0051.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0280.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0136.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0120.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0237.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0138.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0041.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0012.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0033.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0242.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0256.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0176.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0254.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0094.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0279.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0108.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0153.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0204.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0151.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0072.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0114.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0274.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0213.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0164.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0182.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0192.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0271.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0064.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0288.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0269.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0180.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0169.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0230.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0276.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0227.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0091.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0234.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0212.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0225.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0294.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0143.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0130.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0296.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0147.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0121.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0264.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0038.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0185.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0014.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0171.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0082.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0152.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0186.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0283.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0077.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0163.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0214.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0295.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0244.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0175.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0262.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0195.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0255.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0159.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0113.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0217.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0027.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0300.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0118.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0079.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0019.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0073.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0174.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0299.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0247.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0197.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0050.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0267.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0266.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0224.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0218.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0158.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0259.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0282.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0324.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0028.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0134.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0148.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0154.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0119.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0211.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0095.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0034.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0123.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0018.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0035.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0122.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0235.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0062.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0202.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0231.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0110.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0149.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0084.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0061.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0101.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0026.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0087.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0060.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0201.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0052.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0238.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0222.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0318.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0188.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0172.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0058.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0030.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0059.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0260.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0284.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0191.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0111.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0236.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0303.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0221.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0162.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0106.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0129.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0270.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0325.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0054.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0203.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0306.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0308.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0243.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0132.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0137.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0103.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0245.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0146.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0310.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0278.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0109.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0083.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0251.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0305.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0319.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0268.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0125.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0208.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0287.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0206.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0088.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0144.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0092.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0115.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0328.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0207.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0042.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0315.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0285.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0024.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0037.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0166.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0127.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0020.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0215.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0043.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0183.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0093.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0065.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0228.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0289.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0126.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0036.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0133.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0250.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0321.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0078.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0229.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0304.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0069.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0248.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0055.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0067.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0272.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0332.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0347.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0423.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0414.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0417.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0371.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0430.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0429.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0369.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0373.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0422.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0416.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0376.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0405.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0330.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0368.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0432.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0395.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0384.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0403.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0399.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0351.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0407.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0396.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0349.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0412.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0427.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0366.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0361.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0415.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0340.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0381.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0338.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0410.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0357.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0420.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0421.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0341.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0372.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0342.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0424.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0418.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0350.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0331.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0367.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0335.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0334.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0408.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0343.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0352.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0378.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0388.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0346.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0344.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0391.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0389.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0431.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0411.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0401.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0353.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0426.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0393.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0337.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0386.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0397.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0402.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0409.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0377.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0398.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0363.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0364.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0385.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0355.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0383.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0382.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0400.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0356.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0370.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0425.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0428.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0333.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0392.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0360.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0365.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0359.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0404.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0348.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0413.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0375.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0380.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0394.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0345.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0419.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0379.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0329.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0339.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0390.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0354.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0374.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0336.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0358.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0406.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0387.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0362.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0443.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0468.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0435.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0464.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0449.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0466.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0434.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0472.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0461.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0459.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0448.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0467.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0455.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0433.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0452.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0454.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0471.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0439.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0450.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0446.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0447.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0462.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0463.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0474.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0437.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0442.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0465.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0457.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0473.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0469.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0444.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0436.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0456.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0451.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0441.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0460.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0445.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0470.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0458.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0453.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0440.tif', 'TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring0438.tif']\n",
      "Number of dataset found in the folder: 475\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7feaf712a7ee483ca428f466d676e85a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/475 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1b6312f025224c0cbf2b40330f5ae993",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='file', options=('TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq00…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# ------------- Initial user input ------------\n",
    "#@markdown ###Provide the path to your dataset and to the folder where the prediction will be saved (Result folder), then play the cell to predict output on your unseen images.\n",
    "Data_folder = '/content/drive/Shareddrives/GR.INV. BIIG-QMIC/AVV_Renal_2017/Pablo/Segmentacion quistes CT y MRI 24-01-2022/2DUnet/inference/TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring' #@param {type:\"string\"}\n",
    "Results_folder = '/content/drive/Shareddrives/GR.INV. BIIG-QMIC/AVV_Renal_2017/Pablo/Segmentacion quistes CT y MRI 24-01-2022/2DUnet/inference/TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring-Results' #@param {type:\"string\"}\n",
    "\n",
    "#@markdown ###Do you want to use the current trained model?\n",
    "Use_the_current_trained_model = False #@param {type:\"boolean\"}\n",
    "\n",
    "#@markdown ###If not, please provide the path to the model folder:\n",
    "\n",
    "Prediction_model_folder = \"/content/drive/Shareddrives/GR.INV. BIIG-QMIC/AVV_Renal_2017/Pablo/Segmentacion quistes CT y MRI 24-01-2022/2DUnet/models/cysts1\" #@param {type:\"string\"}\n",
    "\n",
    "#Here we find the loaded model name and parent path\n",
    "Prediction_model_name = os.path.basename(Prediction_model_folder)\n",
    "Prediction_model_path = os.path.dirname(Prediction_model_folder)\n",
    "\n",
    "\n",
    "# ------------- Failsafes ------------\n",
    "if (Use_the_current_trained_model): \n",
    "  print(\"Using current trained network\")\n",
    "  Prediction_model_name = model_name\n",
    "  Prediction_model_path = model_path\n",
    "\n",
    "full_Prediction_model_path = os.path.join(Prediction_model_path, Prediction_model_name)\n",
    "if os.path.exists(full_Prediction_model_path):\n",
    "  print(\"The \"+Prediction_model_name+\" network will be used.\")\n",
    "else:\n",
    "  print(R+'!! WARNING: The chosen model does not exist !!'+W)\n",
    "  print('Please make sure you provide a valid model path and model name before proceeding further.')\n",
    "\n",
    "\n",
    "# ------------- Prepare the model and run predictions ------------\n",
    "\n",
    "# Load the model and prepare generator\n",
    "\n",
    "\n",
    "\n",
    "unet = load_model(os.path.join(Prediction_model_path, Prediction_model_name, 'weights_best.hdf5'), custom_objects={'_weighted_binary_crossentropy': weighted_binary_crossentropy(np.ones(2))})\n",
    "Input_size = unet.layers[0].output_shape[1:3]\n",
    "print('Model input size: '+str(Input_size[0])+'x'+str(Input_size[1]))\n",
    "\n",
    "# Create a list of sources\n",
    "source_dir_list = os.listdir(Data_folder)\n",
    "print(source_dir_list)\n",
    "number_of_dataset = len(source_dir_list)\n",
    "print('Number of dataset found in the folder: '+str(number_of_dataset))\n",
    "\n",
    "predictions = []\n",
    "for i in tqdm(range(number_of_dataset)):\n",
    "  predictions.append(predict_as_tiles(os.path.join(Data_folder, source_dir_list[i]), unet))\n",
    "  # predictions.append(prediction(os.path.join(Data_folder, source_dir_list[i]), os.path.join(Prediction_model_path, Prediction_model_name)))\n",
    "\n",
    "\n",
    "# Save the results in the folder along with the masks according to the set threshold\n",
    "saveResult(Results_folder, predictions, source_dir_list, prefix=prediction_prefix, threshold=None)\n",
    "\n",
    "\n",
    "# ------------- For display ------------\n",
    "print('--------------------------------------------------------------')\n",
    "\n",
    "\n",
    "def show_prediction_mask(file=os.listdir(Data_folder), threshold=(0,255,1)):\n",
    "\n",
    "  plt.figure(figsize=(18,6))\n",
    "  # Wide-field\n",
    "  plt.subplot(1,3,1)\n",
    "  plt.axis('off')\n",
    "  img_Source = plt.imread(os.path.join(Data_folder, file))\n",
    "  plt.imshow(img_Source, cmap='gray')\n",
    "  plt.title('Source image',fontsize=15)\n",
    "  # Prediction\n",
    "  plt.subplot(1,3,2)\n",
    "  plt.axis('off')\n",
    "  img_Prediction = plt.imread(os.path.join(Results_folder, prediction_prefix+file))\n",
    "  plt.imshow(img_Prediction, cmap='gray')\n",
    "  plt.title('Prediction',fontsize=15)\n",
    "\n",
    "  # Thresholded mask\n",
    "  plt.subplot(1,3,3)\n",
    "  plt.axis('off')\n",
    "  img_Mask = convert2Mask(img_Prediction, threshold)\n",
    "  plt.imshow(img_Mask, cmap='gray')\n",
    "  plt.title('Mask (Threshold: '+str(round(threshold))+')',fontsize=15)\n",
    "\n",
    "\n",
    "interact(show_prediction_mask, continuous_update=True);\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "stS96mFZLMOU"
   },
   "source": [
    "## **6.2. Export results as masks**\n",
    "---\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 442132,
     "status": "ok",
     "timestamp": 1648566208353,
     "user": {
      "displayName": "PABLO DELGADO RODRIGUEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "11999848093282927146"
     },
     "user_tz": -120
    },
    "id": "qb5ZmFstLNbR",
    "outputId": "e6ed5117-bb5c-48cb-d2a2-fdf38b246d53"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------\n",
      "Masks were saved in: /content/drive/Shareddrives/GR.INV. BIIG-QMIC/AVV_Renal_2017/Pablo/Segmentacion quistes CT y MRI 24-01-2022/2DUnet/inference/TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring-Results\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# @markdown #Play this cell to save results as masks with the chosen threshold\n",
    "threshold =  254#@param {type:\"number\"}\n",
    "\n",
    "saveResult(Results_folder, predictions, source_dir_list, prefix=prediction_prefix, threshold=threshold)\n",
    "print('-------------------')\n",
    "print('Masks were saved in: '+Results_folder)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hvkd66PldsXB"
   },
   "source": [
    "## **6.3. Download your predictions**\n",
    "---\n",
    "\n",
    "<font size = 4>**Store your data** and ALL its results elsewhere by downloading it from Google Drive and after that clean the original folder tree (datasets, results, trained model etc.) if you plan to train or use new networks. Please note that the notebook will otherwise **OVERWRITE** all files which have the same name."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BphZ0wBrC2Zw"
   },
   "source": [
    "# **7. Version log**\n",
    "\n",
    "---\n",
    "<font size = 4>**v1.13**: \n",
    "* This version now includes an automatic restart allowing to set the h5py library to v2.10. \n",
    "* The section 1 and 2 are now swapped for better export of *requirements.txt*. \n",
    "* This version also now includes built-in version check and the version log that you're reading now.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UvSlTaH14s3t"
   },
   "source": [
    "#**Thank you for using 2D U-Net!**\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyNHeAUjQqSHaNs2AemYRe7d",
   "collapsed_sections": [],
   "mount_file_id": "1cjnyXmApz0cencdW-RcFsv1zPXgZ5j_Q",
   "name": "Unet2.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "1a4ca1b57ad947569257a824d0c052bd": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_6ec549bf4ede454a884db89b94c8037d",
       "IPY_MODEL_a5d3bc46d5134296b9fd18d5dfc38203",
       "IPY_MODEL_618d31fe6af24660a27c9b14168b1593"
      ],
      "layout": "IPY_MODEL_2414c2e40e1f41869882d7910d295fcf"
     }
    },
    "20a2d7cff5d341d0a5039dd5ba1fc26a": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_c7d56ffd3f2d48e387cd09937532c944",
       "IPY_MODEL_bd4aa7c3451e4009ab4d14ce50240f18",
       "IPY_MODEL_d0802e1ac8224673aa47498ef1680c3a"
      ],
      "layout": "IPY_MODEL_55f5eb5dd7d9467785aa65f5ceae9cc1"
     }
    },
    "2414c2e40e1f41869882d7910d295fcf": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "47403ae7e98a4142b43014365135766d": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "475cc221bf19412987fe20aa00b56835": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "4b15308c583646739385bc7d9c74fd37": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "4bf1f029fa4c4631820faa2514abea18": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "526f717b4134431b8ad2ea76ff158d77": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "55f5eb5dd7d9467785aa65f5ceae9cc1": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "618d31fe6af24660a27c9b14168b1593": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_902391fa63a54fe6b983e58deaa9c341",
      "placeholder": "​",
      "style": "IPY_MODEL_9102133b5c944757941f87879f1b31c3",
      "value": " 368/368 [00:04&lt;00:00, 87.25it/s]"
     }
    },
    "6e298eeabf974040bd08d0b971458796": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "6ec549bf4ede454a884db89b94c8037d": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_6e298eeabf974040bd08d0b971458796",
      "placeholder": "​",
      "style": "IPY_MODEL_526f717b4134431b8ad2ea76ff158d77",
      "value": "100%"
     }
    },
    "902391fa63a54fe6b983e58deaa9c341": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "9102133b5c944757941f87879f1b31c3": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "a451411be89c435384fc8e6da45f69bc": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "a5d3bc46d5134296b9fd18d5dfc38203": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_a451411be89c435384fc8e6da45f69bc",
      "max": 368,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_c822f7ab9a5a4361b5b0f350e16a6d6f",
      "value": 368
     }
    },
    "bd4aa7c3451e4009ab4d14ce50240f18": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "danger",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_47403ae7e98a4142b43014365135766d",
      "max": 843,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_475cc221bf19412987fe20aa00b56835",
      "value": 368
     }
    },
    "c7d56ffd3f2d48e387cd09937532c944": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_4bf1f029fa4c4631820faa2514abea18",
      "placeholder": "​",
      "style": "IPY_MODEL_d647d1e45b66496481b6ef96587d8dd0",
      "value": " 44%"
     }
    },
    "c822f7ab9a5a4361b5b0f350e16a6d6f": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "d0802e1ac8224673aa47498ef1680c3a": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_db03f0fc76f447ebb755912df6b95848",
      "placeholder": "​",
      "style": "IPY_MODEL_4b15308c583646739385bc7d9c74fd37",
      "value": " 368/843 [02:15&lt;02:31,  3.14it/s]"
     }
    },
    "d647d1e45b66496481b6ef96587d8dd0": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "db03f0fc76f447ebb755912df6b95848": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}