street-vision.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Mainakdeb/honeywell_hackathon/blob/main/street-vision.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TjwWHoPkErwV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e0fd7da-700f-4491-aea7-4a90e3ed1250"
      },
      "source": [
        "!git clone https://github.com/Mainakdeb/honeywell_hackathon.git\n",
        "!mv /content/honeywell_hackathon/* /content/\n",
        "!rm -r /content/honeywell_hackathon/*\n",
        "!bash /content/config/download_weights.sh\n",
        "!pip3 install filterpy pytube sk-video"
      ],
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "fatal: destination path 'honeywell_hackathon' already exists and is not an empty directory.\n",
            "mv: cannot stat '/content/honeywell_hackathon/*': No such file or directory\n",
            "rm: cannot remove '/content/honeywell_hackathon/*': No such file or directory\n",
            "--2021-02-25 09:35:12--  https://pjreddie.com/media/files/yolov3.weights\n",
            "Resolving pjreddie.com (pjreddie.com)... 128.208.4.108\n",
            "Connecting to pjreddie.com (pjreddie.com)|128.208.4.108|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 248007048 (237M) [application/octet-stream]\n",
            "Saving to: ‘yolov3.weights.1’\n",
            "\n",
            "yolov3.weights.1    100%[===================>] 236.52M   101MB/s    in 2.4s    \n",
            "\n",
            "2021-02-25 09:35:14 (101 MB/s) - ‘yolov3.weights.1’ saved [248007048/248007048]\n",
            "\n",
            "Requirement already satisfied: filterpy in /usr/local/lib/python3.7/dist-packages (1.4.5)\n",
            "Requirement already satisfied: pytube in /usr/local/lib/python3.7/dist-packages (10.5.1)\n",
            "Requirement already satisfied: sk-video in /usr/local/lib/python3.7/dist-packages (1.1.10)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from filterpy) (3.2.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from filterpy) (1.19.5)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from filterpy) (1.4.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from pytube) (3.7.4.3)\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->filterpy) (2.4.7)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->filterpy) (1.3.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->filterpy) (0.10.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->filterpy) (2.8.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from cycler>=0.10->matplotlib->filterpy) (1.15.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xrIaCOUaex3t"
      },
      "source": [
        "from models import *\n",
        "from utils import *\n",
        "from sort import *\n",
        "from IPython.display import HTML\n",
        "from base64 import b64encode\n",
        "import os, sys, time, datetime, random\n",
        "import torch\n",
        "from torch.utils.data import DataLoader\n",
        "from torchvision import datasets, transforms\n",
        "from torch.autograd import Variable\n",
        "import skvideo.io  \n",
        "from pytube import YouTube\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.patches as patches\n",
        "from PIL import Image\n",
        "import cv2\n",
        "from IPython.display import clear_output\n",
        "import pandas as pd\n",
        "from scipy.signal import lfilter\n",
        "from tqdm.notebook import tnrange, tqdm"
      ],
      "execution_count": 334,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qhDEpCUvZSSe",
        "outputId": "edbbed90-278e-42fd-d64c-63de1d4c0897"
      },
      "source": [
        "class_df=pd.read_csv(\"/content/config/coco.names\", header=None)\r\n",
        "class_list=class_df[0].to_list()\r\n",
        "#len(class_list)\r\n",
        "print(class_list[0:10])"
      ],
      "execution_count": 468,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['person', 'bicycle', 'car', 'motorbike', 'aeroplane', 'bus', 'train', 'truck', 'boat', 'traffic light']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WwY2lx7kf92y"
      },
      "source": [
        "def detect_image(img):\n",
        "    # scale and pad image\n",
        "    ratio = min(img_size/img.size[0], img_size/img.size[1])\n",
        "    imw = round(img.size[0] * ratio)\n",
        "    imh = round(img.size[1] * ratio)\n",
        "    img_transforms = transforms.Compose([ transforms.Resize((imh, imw)),\n",
        "         transforms.Pad((max(int((imh-imw)/2),0), max(int((imw-imh)/2),0), max(int((imh-imw)/2),0), max(int((imw-imh)/2),0)),\n",
        "                        (128,128,128)),\n",
        "         transforms.ToTensor(),\n",
        "         ])\n",
        "    # convert image to Tensor\n",
        "    image_tensor = img_transforms(img).float()\n",
        "    image_tensor = image_tensor.unsqueeze_(0)\n",
        "    input_img = Variable(image_tensor.type(Tensor))\n",
        "    # run inference on the model and get detections\n",
        "    with torch.no_grad():\n",
        "        detections = model(input_img)\n",
        "        detections = utils.non_max_suppression(detections, 80, conf_thres, nms_thres)\n",
        "    return detections[0]"
      ],
      "execution_count": 470,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QYiKdwE4gz3H"
      },
      "source": [
        "def download_from_yt(video_link):\n",
        "    videopath=YouTube(video_link).streams.first().download(\"yt_downloads/\")\n",
        "    return(videopath)\n",
        "def detector(input_video_path, num_frames, save_video, save_video_path, df):\n",
        "  cmap = plt.get_cmap('tab20b')\n",
        "  colors = [cmap(i)[:3] for i in np.linspace(0, 1, 20)]\n",
        "  size = 720*16//9, 720\n",
        "  duration = 2\n",
        "  fps = 25\n",
        "\n",
        "  # initialize Sort object and video capture\n",
        "  vid = cv2.VideoCapture(input_video_path)\n",
        "\n",
        "  if num_frames==-1:\n",
        "    videodata = skvideo.io.vread(input_video_path)\n",
        "    num_frames=videodata.shape[0]\n",
        "\n",
        "  mot_tracker = Sort() \n",
        "  frame_arr=[]\n",
        "  #while(True):\n",
        "  \n",
        "  for ii in tnrange(num_frames):\n",
        "      ret, frame = vid.read()\n",
        "      frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
        "      pilimg = Image.fromarray(frame)\n",
        "      detections = detect_image(pilimg)\n",
        "      #print(detections)##\n",
        "      img = np.array(pilimg)\n",
        "      pad_x = max(img.shape[0] - img.shape[1], 0) * (img_size / max(img.shape))\n",
        "      pad_y = max(img.shape[1] - img.shape[0], 0) * (img_size / max(img.shape))\n",
        "      unpad_h = img_size - pad_y\n",
        "      unpad_w = img_size - pad_x\n",
        "      unique_obj_ids=[]\n",
        "      unique_obj_labels=[]\n",
        "      if detections is not None:\n",
        "          tracked_objects = mot_tracker.update(detections.cpu())\n",
        "          #rint(tracked_objects)\n",
        "          unique_labels = detections[:, -1].cpu().unique()\n",
        "          #print(unique_labels)##\n",
        "          n_cls_preds = len(unique_labels)\n",
        "          for x1, y1, x2, y2, obj_id, cls_pred in tracked_objects:\n",
        "              box_h = int(((y2 - y1) / unpad_h) * img.shape[0])\n",
        "              box_w = int(((x2 - x1) / unpad_w) * img.shape[1])\n",
        "              y1 = int(((y1 - pad_y // 2) / unpad_h) * img.shape[0])\n",
        "              x1 = int(((x1 - pad_x // 2) / unpad_w) * img.shape[1])\n",
        "\n",
        "              color = colors[int(obj_id) % len(colors)]\n",
        "              color = [i * 255 for i in color]\n",
        "              cls = classes[int(cls_pred)]\n",
        "              cv2.rectangle(frame, (x1, y1), (x1+box_w, y1+box_h), color, 4)\n",
        "              cv2.rectangle(frame, (x1, y1-35), (x1+len(cls)*19+60, y1), color, -1)\n",
        "              cv2.putText(frame, cls  + str(int(obj_id)), (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 3)\n",
        "\n",
        "              unique_obj_ids.append(obj_id)\n",
        "              unique_obj_labels.append(cls_pred)\n",
        "\n",
        "      counts_list=[] #refresh for each frame\n",
        "      for j in range(0,len(df.columns)):\n",
        "          counts_list.append(unique_obj_labels.count(j))\n",
        "\n",
        "      frame_arr.append(frame)\n",
        "      df.loc[len(df)] = counts_list\n",
        "\n",
        "  if save_video:\n",
        "    skvideo.io.vwrite(save_video_path, frame_arr)\n",
        "\n",
        "  return(save_video_path)\n",
        "          "
      ],
      "execution_count": 657,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s9jeyF9wiuLR"
      },
      "source": [
        "def player(vid_path):\n",
        "  mp4 = open(vid_path,'rb').read()\n",
        "  data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n",
        "  return HTML(\"\"\"\n",
        "  <video width=400 controls>\n",
        "        <source src=\"%s\" type=\"video/mp4\">\n",
        "  </video>\n",
        "  \"\"\" % data_url)"
      ],
      "execution_count": 658,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iQQ8oLQGf6yA",
        "outputId": "72f3d44c-57e2-4235-c50d-309908381da2"
      },
      "source": [
        "# Initialise config paths\n",
        "config_path='/content/config/yolov3.cfg'\n",
        "weights_path='/content/yolov3.weights'\n",
        "class_path='/content/config/coco.names'\n",
        "img_size=416\n",
        "conf_thres=0.8\n",
        "nms_thres=0.4\n",
        "\n",
        "# Load model and weights\n",
        "model = Darknet(config_path, img_size=img_size)\n",
        "model.load_weights(weights_path)\n",
        "model.cuda()\n",
        "model.eval()\n",
        "classes = utils.load_classes(class_path)\n",
        "Tensor = torch.cuda.FloatTensor"
      ],
      "execution_count": 659,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/nn/_reduction.py:44: UserWarning: size_average and reduce args will be deprecated, please use reduction='mean' instead.\n",
            "  warnings.warn(warning.format(ret))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gfbd_AqMdnD2",
        "outputId": "509ba7f8-53a9-44cd-82a5-3a0bcb8b0079"
      },
      "source": [
        "video_link = input('Enter the youtube video link: ')\r\n",
        "local_path = download_from_yt(video_link)\r\n",
        "\r\n",
        "# https://www.youtube.com/watch?v=MNn9qKG2UFI&list=PLcQZGj9lFR7y5WikozDSrdk6UCtAnM9mB&index=2&ab_channel=KarolMajek\r\n",
        "# https://www.youtube.com/watch?v=GJNjaRJWVP8&ab_channel=CCTVPeople\r\n",
        "# https://www.youtube.com/watch?v=jjlBnrzSGjc&ab_channel=Panasonicsecurity"
      ],
      "execution_count": 660,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Enter the youtube video link: https://www.youtube.com/watch?v=jjlBnrzSGjc&ab_channel=Panasonicsecurity\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KNHCVaN0mfZh"
      },
      "source": [
        "insight_df = pd.DataFrame([], columns=class_list[0:9])\n",
        "len(insight_df.columns)\n",
        "\n",
        "output_vid_path = detector(local_path,\n",
        "                         num_frames=-1,  # set as -1 for all frames\n",
        "                         save_video=True, \n",
        "                         save_video_path=\"output_1.mp4\", \n",
        "                         df=insight_df)\n",
        "\n",
        "player(output_vid_path)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "9LbDVKnby7R8",
        "outputId": "37babd04-0edb-4394-f970-b6a38edf3edb"
      },
      "source": [
        "insight_df"
      ],
      "execution_count": 662,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>person</th>\n",
              "      <th>bicycle</th>\n",
              "      <th>car</th>\n",
              "      <th>motorbike</th>\n",
              "      <th>aeroplane</th>\n",
              "      <th>bus</th>\n",
              "      <th>train</th>\n",
              "      <th>truck</th>\n",
              "      <th>boat</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2643</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2644</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2645</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2646</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2647</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2648 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     person bicycle car motorbike aeroplane bus train truck boat\n",
              "0         0       0   0         0         0   0     0     0    0\n",
              "1         0       0   0         0         0   0     0     0    0\n",
              "2         0       0   0         0         0   0     0     0    0\n",
              "3         0       0   0         0         0   0     0     0    0\n",
              "4         0       0   0         0         0   0     0     0    0\n",
              "...     ...     ...  ..       ...       ...  ..   ...   ...  ...\n",
              "2643      0       0  15         0         0   0     0     1    0\n",
              "2644      0       0  15         0         0   0     0     1    0\n",
              "2645      0       0  15         0         0   0     0     1    0\n",
              "2646      0       0  16         0         0   0     0     1    0\n",
              "2647      0       0  16         0         0   0     0     1    0\n",
              "\n",
              "[2648 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 662
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zhc9zRYwis16"
      },
      "source": [
        "def count_total_unique_instances(df, class_list):\r\n",
        "    tot_counts=[]\r\n",
        "    ret_dict={}\r\n",
        "\r\n",
        "    for i in class_list: \r\n",
        "        ret_dict[\"total unique \"+i+\"s\"]=None\r\n",
        "\r\n",
        "    for c in class_list:\r\n",
        "        all_instances=list(insight_df[c])\r\n",
        "        count=0\r\n",
        "        for i in range(1,len(all_instances)):\r\n",
        "            if all_instances[i]>all_instances[i-1]:\r\n",
        "                count+=1\r\n",
        "        tot_counts.append(count)\r\n",
        "\r\n",
        "    for i in range(len(tot_counts)):\r\n",
        "        ret_dict[list(ret_dict.keys())[i]]=tot_counts[i]\r\n",
        "\r\n",
        "    return(ret_dict)"
      ],
      "execution_count": 663,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E8iZ7AEx_F7y"
      },
      "source": [
        "def visualize_classes(df, class_list, denoise, save_image, show, image_name): \r\n",
        "    n = 20 # the larger n is, the smoother curve will be in case denoise = True\r\n",
        "    b = [1.0 / n] * n\r\n",
        "    a = 1\r\n",
        "\r\n",
        "    plt.rcParams['figure.figsize'] =10, 5\r\n",
        "    plt.xlabel(\"frame number\", fontsize = 15)\r\n",
        "    plt.ylabel(\"count\", fontsize = 15)\r\n",
        "\r\n",
        "    all_arr=[]\r\n",
        "    for c in class_list:\r\n",
        "        arr=list(df[c])\r\n",
        "        all_arr.append(arr)\r\n",
        "        \r\n",
        "    for i in range(len(all_arr)):\r\n",
        "        if denoise:\r\n",
        "            plt.plot(lfilter(b,a,all_arr[i]), label=\"count - \"+class_list[i])\r\n",
        "        else:\r\n",
        "            plt.plot(all_arr[i], label=\"count - \"+class_list[i])\r\n",
        "\r\n",
        "    plt.grid()\r\n",
        "    plt.legend(loc=\"upper left\", numpoints=1)\r\n",
        "    #plt.legend(loc=\"best\")\r\n",
        "    if show:\r\n",
        "        plt.show()\r\n",
        "    if save_image:\r\n",
        "        plt.savefig(image_name)\r\n",
        "        plt.clf()\r\n",
        "\r\n",
        "def plot_total_instances(df, class_list, save_image, image_name):\r\n",
        "    dict_tots=count_total_unique_instances(insight_df, class_list)\r\n",
        "\r\n",
        "    plt.rcParams['figure.figsize'] =10, 5\r\n",
        "    width = 0.50\r\n",
        "    labels=list(dict_tots.keys())\r\n",
        "    ind = np.arange(len(list(dict_tots.keys())))\r\n",
        "    plt.bar(ind, list(dict_tots.values()), width, label=\"Total unique instances\", color='green', alpha=0.5)\r\n",
        "    plt.xticks(ind, labels, rotation='vertical', fontsize=10)\r\n",
        "    plt.ylabel('Number of unique instances', fontsize=15)\r\n",
        "    plt.xlabel(\"Classes\", fontsize=15)\r\n",
        "    plt.grid(True)\r\n",
        "    plt.legend(loc='best')\r\n",
        "    if save_image:\r\n",
        "        plt.savefig(image_name)\r\n",
        "        \r\n",
        "    else:\r\n",
        "        plt.show()"
      ],
      "execution_count": 744,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "3ErrNv-TAGSG",
        "outputId": "c3bf1d62-a34e-4ee3-99ce-753e74c1e43b"
      },
      "source": [
        "visualize_classes(insight_df,\r\n",
        "                  [\"car\", \"truck\", \"person\", \"motorbike\"], \r\n",
        "                  denoise=True, \r\n",
        "                  save_image=True,\r\n",
        "                  show=True,\r\n",
        "                  image_name=\"count_vs_frame_plot.png\")"
      ],
      "execution_count": 745,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "OiNHV-1qWzes",
        "outputId": "a5e79023-d133-43a0-d85e-e985003feb91"
      },
      "source": [
        "plot_total_instances(insight_df,\r\n",
        "                     [\"car\", \"truck\", \"person\", \"motorbike\"],\r\n",
        "                     save_image=True,\r\n",
        "                     image_name=\"bar_plot.png\")"
      ],
      "execution_count": 746,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aClyBy9S81tC"
      },
      "source": [
        "!mkdir plots"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85,
          "referenced_widgets": [
            "c5690496c92640e38cba26d420b21fec",
            "63b38b4f920e4d6ebaa5c661d2e1ad1e",
            "059dde0150de4c2cbee19112e5637cf4",
            "8bc2226825224178a56137e814deb8d4",
            "60a5d94c4e8e484189d8982e3a0840c6",
            "43e788a7e376449196dab4ad17ba1636",
            "dd1217f6322b4b4e9debb7425c8f0b8a",
            "08180ca96a7848f3a736239dbc56fe1a"
          ]
        },
        "id": "g1RBpIe6xRjl",
        "outputId": "886a1f50-756a-4981-bcd9-115c88a9deee"
      },
      "source": [
        "#save progressive plots\r\n",
        "!rm -r /content/plots/*\r\n",
        "\r\n",
        "for i in tnrange(1,len(insight_df)):\r\n",
        "    visualize_classes(insight_df[0:i],\r\n",
        "                  [\"car\", \"truck\", \"person\", \"motorbike\"], \r\n",
        "                  denoise=True, \r\n",
        "                  save_image=True,\r\n",
        "                  show=False,\r\n",
        "                  image_name=\"/content/plots/count_vs_frame_plot_\"+str(i)+\".png\")"
      ],
      "execution_count": 756,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c5690496c92640e38cba26d420b21fec",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=2647.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kf8QNVmX8oAw"
      },
      "source": [
        "!zip -r plots_1 /content/plots"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3odnQ3j4NYwZ",
        "outputId": "afcb641a-d11d-4b97-af72-afced1eb2058",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount=True)"
      ],
      "execution_count": 769,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HcJVIMrJNZWa"
      },
      "source": [
        "!cp -r /content/plots_1.zip /content/drive/MyDrive/mydata/"
      ],
      "execution_count": 772,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4fgeSjliNu68"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}