OxCrest.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a2fzIx2ktysJ"
      },
      "source": [
        "# OxCrest\n",
        "\n",
        "A program to generate images of Oxford college crests based on a text prompt.\n",
        "\n",
        "The model has been trained using the college crests of the 44 Oxford colleges.  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Es76szk3wFT6"
      },
      "source": [
        "**References**\n",
        "\n",
        "[Oxford college crests by *The Oxbridge Pursuviant*](https://theoxbridgepursuivant.blogspot.com/2015/11/heraldry-cumulative-posts-by-date-of.html)\n",
        "\n",
        "[DreamBooth training example provided by *HackCambridge*](https://drive.google.com/file/d/1oGjBHkWdtJg6Vfvd9VhDF_mYPFYgWg7d/view?usp=sharing)\n",
        "\n",
        "[Blogpost for Controlnet by *lllyasviel*](https://huggingface.co/lllyasviel/sd-controlnet-scribble)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DWHs-6Lxymk0",
        "outputId": "310439e1-f9d4-4ae2-a2dd-0f361241e5e1"
      },
      "outputs": [],
      "source": [
        "!git clone https://github.com/shinben0327/CambridgeSpyderLite.git"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Z1dwbgBvS2M"
      },
      "source": [
        "# Initialise environment\n",
        "* Install necessary packages and libraries\n",
        "* Login with huggingface token and prepare accelerate configurations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FtU1XK8KH1th",
        "outputId": "96175d1d-1396-4159-b53e-147313337dec"
      },
      "outputs": [],
      "source": [
        "# Check GPU is available\n",
        "!nvidia-smi --query-gpu=name,memory.total,memory.free --format=csv,noheader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JC51-T-poNgS",
        "outputId": "d18be163-8e68-4b3b-993a-65826b6733dc"
      },
      "outputs": [],
      "source": [
        "# Install necessary packages\n",
        "!wget -q https://github.com/huggingface/diffusers/raw/main/examples/dreambooth/train_dreambooth.py\n",
        "%pip install -qq git+https://github.com/huggingface/diffusers.git\n",
        "%pip install -q -U --pre triton\n",
        "%pip install -q accelerate transformers ftfy xformers bitsandbytes==0.35.0\n",
        "\n",
        "!pip install -q diffusers==0.14.0 transformers xformers git+https://github.com/huggingface/accelerate.git\n",
        "!pip install -q opencv-contrib-python\n",
        "!pip install -q controlnet_aux"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360,
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        "id": "mSv7Ax93AnZp",
        "outputId": "50aa40af-6ff6-4fcd-da9c-8d9fc98af190"
      },
      "outputs": [],
      "source": [
        "# Enter your huggingface token for model access\n",
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xW92xLP3oqZU",
        "outputId": "71e23786-884a-493d-82f6-0fb5b9849da4"
      },
      "outputs": [],
      "source": [
        "# Create a default accelerate config\n",
        "from accelerate.utils import write_basic_config\n",
        "write_basic_config()\n",
        "\n",
        "# Ensure that your accelerate config looks ok\n",
        "!accelerate env"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "hpoUmhQ0pSHb"
      },
      "outputs": [],
      "source": [
        "# Import necessary libraries\n",
        "from diffusers import StableDiffusionPipeline\n",
        "import torch\n",
        "\n",
        "from PIL import Image\n",
        "from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler\n",
        "import torch\n",
        "from controlnet_aux import HEDdetector\n",
        "from diffusers.utils import load_image\n",
        "\n",
        "import cv2 as cv\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yA8JhH2vowAC"
      },
      "source": [
        "# Model Preparation\n",
        "* Import training image dataset\n",
        "* Train the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6A-yai8ZD3ou",
        "outputId": "ef18517f-d469-4f81-8183-418602cc3d3f"
      },
      "outputs": [],
      "source": [
        "# Save a folder with your chosen images\n",
        "!rm -r CambridgeSpyderLite\n",
        "!git clone https://github.com/shinben0327/CambridgeSpyderLite.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "Pjl_iOXYERR3"
      },
      "outputs": [],
      "source": [
        "# Define the model name and image/output paths\n",
        "MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\n",
        "INSTANCE_DIR=\"/content/CambridgeSpyderLite/crests\"\n",
        "OUTPUT_DIR=\"/content/model_weights\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TUyUzZX4Asfr",
        "outputId": "b6942e1f-dcd5-40b2-a736-fd2b2986fcb4"
      },
      "outputs": [],
      "source": [
        "# Train the model based on the crest image dataset\n",
        "!accelerate launch train_dreambooth.py \\\n",
        "  --pretrained_model_name_or_path=$MODEL_NAME  \\\n",
        "  --instance_data_dir=$INSTANCE_DIR \\\n",
        "  --output_dir=$OUTPUT_DIR \\\n",
        "  --instance_prompt=\"a photo of oxford college crest\" \\\n",
        "  --resolution=512 \\\n",
        "  --train_batch_size=1 \\\n",
        "  --gradient_accumulation_steps=1 --gradient_checkpointing \\\n",
        "  --use_8bit_adam \\\n",
        "  --learning_rate=5e-6 \\\n",
        "  --lr_scheduler=\"constant\" \\\n",
        "  --lr_warmup_steps=0 \\\n",
        "  --max_train_steps=10 \\\n",
        "  --enable_xformers_memory_efficient_attention"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tyNQoEbCpYj1"
      },
      "source": [
        "# Generate a crest\n",
        "* Create a stable diffusion pipeline\n",
        "* Generate the image and visualise"
      ]
    },
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      },
      "outputs": [],
      "source": [
        "# Create stable diffusion pipeline \n",
        "pipe = StableDiffusionPipeline.from_pretrained(\n",
        "    MODEL_NAME, torch_dtype=torch.float16\n",
        "    ).to(\"cuda\")"
      ]
    },
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            "8476256a07694c99b1969699565ee238",
            "965ef21302144104898b7d12f3f07ba1"
          ]
        },
        "id": "t8Q2NCSfs2uh",
        "outputId": "c29c1674-16d8-46c3-94dc-ba6bba11961c"
      },
      "outputs": [],
      "source": [
        "# Generate image based on the pipeline\n",
        "prompt = \"a photo of an oxford college crest that includes a shape of a cat\"\n",
        "guidance_scale = 55 \n",
        "generated_image = pipe(prompt, num_inference_steps=55, guidance_scale=guidance_scale).images[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "p8l9sK1w53TN",
        "outputId": "332f6439-3b7a-4b5e-e43f-7291ea41a84e"
      },
      "outputs": [],
      "source": [
        "# Show the generated image\n",
        "generated_image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "1fUD8bUEpKmv"
      },
      "outputs": [],
      "source": [
        "# Optional: Generate multiple images with varying guidance_scale settings\n",
        "\n",
        "# !mkdir result_image\n",
        "# for n in range(5, 80, 5):\n",
        "#   for i in range(5):\n",
        "#     image = pipe(prompt, num_inference_steps=50, guidance_scale=n).images[0]\n",
        "#     image.save('result_images/crest_out_' + str(n) + '_' + str(i) + '.png')\n",
        "# !zip -r /content/result_images.zip /content/result_images"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sgwts67lrBWl"
      },
      "source": [
        "# Generate a crest with border\n",
        "* Create a stable diffusion pipeline with a Controlnet to guide the shape of the generated image\n",
        "* Generate the image and visualise\n",
        "* Mask the image to fit the shield-like shape of a college crest\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 120,
          "referenced_widgets": [
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        },
        "id": "eGEQEQrC7KzX",
        "outputId": "e39a090b-626a-4484-8e4a-556ff752f98b"
      },
      "outputs": [],
      "source": [
        "# Import the shield-like shape of a college crest\n",
        "crest_outline = load_image(\"https://github.com/shinben0327/CambridgeSpyderLite/blob/main/crest_outline.png?raw=true\")\n",
        "image = crest_outline\n",
        "\n",
        "# Create stable diffusion pipeline with scribble border Controlnet\n",
        "controlnet = ControlNetModel.from_pretrained(\n",
        "    \"fusing/stable-diffusion-v1-5-controlnet-scribble\", torch_dtype=torch.float16\n",
        ")\n",
        "\n",
        "pipe = StableDiffusionControlNetPipeline.from_pretrained(\n",
        "    MODEL_NAME, controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16\n",
        ")\n",
        "pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\n",
        "pipe.enable_model_cpu_offload()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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        "id": "y8tih8nBKirF",
        "outputId": "0190ce28-9cba-4f71-9ee9-168b796d4ba4"
      },
      "outputs": [],
      "source": [
        "# Generate image\n",
        "prompt = \"a photo of an oxford college crest that includes a shape of a cat\"\n",
        "generated_image = pipe(prompt, image, num_inference_steps=20).images[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "QUNwEowr8DBw",
        "outputId": "aa6424c0-357b-49f6-9f41-c598f8481fd1"
      },
      "outputs": [],
      "source": [
        "# Show the generated image\n",
        "generated_image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "235tCER0N-6g",
        "outputId": "df1833f4-bf48-431b-f165-9257b3fb8ba0"
      },
      "outputs": [],
      "source": [
        "# Mask the image to remove the background of the image\n",
        "mask = load_image(\"https://github.com/shinben0327/CambridgeSpyderLite/blob/main/crest_mask.png?raw=true\")\n",
        "mask.save('mask.png')\n",
        "generated_image.save('image.png')\n",
        "\n",
        "cv_mask = cv.imread('mask.png')\n",
        "cv_image = cv.imread('image.png')\n",
        "\n",
        "masked = cv.bitwise_and(cv_image, cv_mask)\n",
        "\n",
        "cv.imwrite('masked.png', masked)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "q4Pb8543fuKU"
      },
      "outputs": [],
      "source": [
        "# Optional: Generate multiple images\n",
        "\n",
        "# !mkdir masked_batch\n",
        "# for n in range(30):\n",
        "#   generated_image = pipe(\"a photo of an oxford college crest that includes a symbol of a cat\", image, num_inference_steps=20).images[0]\n",
        "#   generated_image.save('image.png')\n",
        "#   cv_image = cv.imread('image.png')\n",
        "#   masked = cv.bitwise_and(cv_image, cv_mask)\n",
        "#   filename_masked = 'masked_batch/' + 'masked' + str(n).zfill(2) + '.png'\n",
        "#   cv.imwrite(filename_masked, masked)\n",
        "# !zip -r /content/file.zip /content/masked_batch"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0gaBeywZxR1r"
      },
      "source": [
        "# Additional trials..."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qqzXT_7Cxbf0"
      },
      "source": [
        "In addition to the crest generation above, an attempt wa made to automate the detection and replacement of Oxford college crests in photos. Specifically, this attmept was focused on pictures of college puffers, which feature each colleges' crest on the left chest region."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ey8tOCcox19j",
        "outputId": "89a09015-bfb7-490b-aefe-4e878b72d00e"
      },
      "outputs": [],
      "source": [
        "!pip install controlnet_aux\n",
        "!pip install xformers\n",
        "!pip install diffusers transformers git+https://github.com/huggingface/accelerate.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "gHPqdpDWx217"
      },
      "outputs": [],
      "source": [
        "from PIL import Image\n",
        "import os\n",
        "import numpy as np\n",
        "import cv2 as cv\n",
        "from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler\n",
        "import torch\n",
        "from controlnet_aux import HEDdetector\n",
        "from diffusers.utils import load_image\n",
        "from google.colab.patches import cv2_imshow"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nZQkhGxGzBQy"
      },
      "source": [
        "# Methodologies\n",
        "The following methods were used in an attempt to pinpoint the location and orientation of the college crest on a photograph.\n",
        "\n",
        "## HED detector\n",
        "\n",
        "The HED detector, originally written for the scribble version of the Stable Diffusion Controlnet, takes an input image and outputs a 'scribble' version that vaguely matches the outlines of the photo.\n",
        "\n",
        "The location of the college crest was manually entered for a first iteration (see cropped image for *puffer1*), as we expected to automate this by future design iterations. This unfortunately did not happen within the time limit of the event."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        },
        "id": "hxLmo-sSx5Fr",
        "outputId": "2ff584dd-ba9a-4397-bc2d-a4f382b52f86"
      },
      "outputs": [],
      "source": [
        "# Declaring HED detector\n",
        "hed = HEDdetector.from_pretrained('lllyasviel/ControlNet')\n",
        "\n",
        "image_dir = '/content/CambridgeSpyderLite/images/test/'\n",
        "\n",
        "for file in os.listdir(image_dir):\n",
        "  filenamelist = os.path.splitext(file)\n",
        "  filename = filenamelist[0]\n",
        "  \n",
        "  print(filename)\n",
        "  \n",
        "  # get image_original(PIL.Image.image)\n",
        "  image_original = load_image(image_dir+filename+'.jpg')\n",
        "\n",
        "  # HED Stable Diffusion Control Net\n",
        "  image = hed(image_original, scribble=True)\n",
        "  image_crop = image.crop((300,180,370,247))\n",
        "  \n",
        "  image.save(os.path.join('/content/CambridgeSpyderLite/images/',filename+'_output.jpeg'))\n",
        "  image_crop.save(os.path.join('/content/CambridgeSpyderLite/images/',filename+'_crop.jpeg'))\n",
        "\n",
        "  cv2_imshow(np.asarray(image_original))\n",
        "  cv2_imshow(np.asarray(image))\n",
        "  cv2_imshow(np.asarray(image_crop))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0ggoiAJA0f-d"
      },
      "source": [
        "## OpenCV Harris Corner Detection\n",
        "\n",
        "To automatically find the location and orientation of the college crest, the Harris Corner Detection algorithm was used (using OpenCV), as we initially reasoned that the corner coordinates of the college crest would allow us to define two representative lines and apply a transformation matrix to our generated crest.\n",
        "\n",
        "After implementing the corner detection algorithm, we found that it was not reliable enough in its current state to define two clear lines.\n",
        "\n",
        "We also attempted to apply the corner detection algorithm to the output of the HED detector, which gave more promising results (see second output photo of each puffer; the red dots are more representative of the outlines). While we were unable to further pursue this method, this could be a continuing point of interest for further development.**bold text**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "0m1jZWSLz81f",
        "outputId": "17a8a63d-04fe-4d8c-8d2b-7af99efecf33"
      },
      "outputs": [],
      "source": [
        "for file in os.listdir(image_dir):\n",
        "  filenamelist = os.path.splitext(file)\n",
        "  filename = filenamelist[0]\n",
        "\n",
        "  print(filename)\n",
        "  \n",
        "  image_original = load_image(image_dir+filename+'.jpg')\n",
        "  image = hed(image_original, scribble=True)\n",
        "  image_crop = image.crop((300,180,370,247))\n",
        "\n",
        "  # OpenCV Harris Corner Detection\n",
        "  image_cv = np.asarray(image_original)\n",
        "  image_crop_cv = np.asarray(image_crop)\n",
        "\n",
        "  image_cv_gray = cv.cvtColor(image_cv,cv.COLOR_BGR2GRAY)\n",
        "  image_crop_cv_gray = cv.cvtColor(image_crop_cv,cv.COLOR_BGR2GRAY)\n",
        "\n",
        "  dst = cv.cornerHarris(image_cv_gray,2,3,0.04)\n",
        "  dst_crop = cv.cornerHarris(image_crop_cv_gray,2,3,0.04)\n",
        "\n",
        "  image_cv[dst>0.01*dst.max()]=[0,0,255]\n",
        "  cv2_imshow(image_cv)\n",
        "\n",
        "  image_crop_cv[dst_crop>0.01*dst_crop.max()]=[0,0,255]\n",
        "  cv2_imshow(image_crop_cv)\n",
        "\n",
        "  final_output = Image.fromarray(np.uint8(image_crop_cv)).convert('RGB')\n",
        "  final_output.save('/content/CambridgeSpyderLite/'+filename+'_final.jpeg')\n",
        "  \n",
        "  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b_gbTaHW0hFQ"
      },
      "source": [
        "## Template Matching (attempted)\n",
        "\n",
        "This method was a last-ditch attempt for a easy way to locate the location and orientation of the college crest. This method involves dilation and template matching of the HED output in order to define the crest location. This method was unfortunately dysfunctional by the end of the allocated time period."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "KCYZ7Qfw0QNw",
        "outputId": "47a9dfe8-95a1-4a14-f351-37c1f9707949"
      },
      "outputs": [],
      "source": [
        "for file in os.listdir(image_dir):\n",
        "  filenamelist = os.path.splitext(file)\n",
        "  filename = filenamelist[0]\n",
        "\n",
        "  print(filename)\n",
        "  \n",
        "  image = hed(image_original, scribble=True)\n",
        "\n",
        "  # (attempted) Template Matching\n",
        "  kernel = np.ones((5, 5), np.uint8)\n",
        "  dst_no_dilate = cv.cvtColor(np.asarray(image),cv.COLOR_BGR2GRAY)\n",
        "  dst_dilate_rgb = cv.dilate(np.asarray(image), kernel, iterations=1)\n",
        "  dst_dilate = cv.cvtColor(dst_dilate_rgb,cv.COLOR_BGR2GRAY)\n",
        "  cv2_imshow(dst_dilate)\n",
        "\n",
        "  template_rgb = cv.imread('/content/CambridgeSpyderLite/images/template/crest_outline.jpg',)\n",
        "  template = cv.cvtColor(template_rgb, cv.COLOR_BGR2GRAY)\n",
        "  print(type(template))\n",
        "  width, height = template.shape\n",
        "  \n",
        "  res = cv.matchTemplate(dst_no_dilate, template, cv.TM_CCOEFF_NORMED)\n",
        "  threshold = 0.5\n",
        "\n",
        "  loc = np.where(res >= threshold)\n",
        "  for pt in zip(*loc[::-1]):\n",
        "    cv.rectangle(dst_dilate, pt, (pt[0] + width, pt[1] + height), (0, 255, 255), 2)\n",
        "  cv2_imshow(dst_dilate)\n"
      ]
    }
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