{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M1D2lpUcGw5h"
      },
      "source": [
        "# Set up environment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MI-dYn9_7dLR",
        "outputId": "8551fc6d-9f07-477b-e133-1bd2ad91bf52"
      },
      "outputs": [],
      "source": [
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qT7r3OZBIw7T"
      },
      "outputs": [],
      "source": [
        "from IPython.display import clear_output\n",
        "# !pip3 install transformers\n",
        "clear_output()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HfZ_GJZwJmFB"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn.functional as F\n",
        "from torchvision import transforms\n",
        "from transformers import pipeline, SegformerImageProcessor, SegformerForSemanticSegmentation\n",
        "import requests\n",
        "from PIL import Image\n",
        "import urllib.parse as parse\n",
        "import os"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iE1u564q7yPB"
      },
      "outputs": [],
      "source": [
        "# a function to determine whether a string is a URL or not\n",
        "def is_url(string):\n",
        "    try:\n",
        "        result = parse.urlparse(string)\n",
        "        return all([result.scheme, result.netloc, result.path])\n",
        "    except:\n",
        "        return False\n",
        "\n",
        "# a function to load an image\n",
        "def load_image(image_path):\n",
        "    \"\"\"Helper function to load images from their URLs or paths.\"\"\"\n",
        "    if is_url(image_path):\n",
        "        return Image.open(requests.get(image_path, stream=True).raw)\n",
        "    elif os.path.exists(image_path):\n",
        "        return Image.open(image_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y1SmhZoYOrvy"
      },
      "source": [
        "# Load Image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "29ZtvebWB9_b"
      },
      "outputs": [],
      "source": [
        "img_path = \"https://shorthaircatbreeds.com/wp-content/uploads/2020/06/Urban-cat-crossing-a-road-300x180.jpg\"\n",
        "image = load_image(img_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "OJklTNORvBXR",
        "outputId": "0eb0d627-31cd-42bb-c8a6-a1e2a48aa119"
      },
      "outputs": [],
      "source": [
        "image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "87AL5K_BFYYR",
        "outputId": "f20f1494-8c1a-4bb4-88e1-c246fa67500c"
      },
      "outputs": [],
      "source": [
        "# convert PIL Image to pytorch tensors\n",
        "transform = transforms.ToTensor()\n",
        "image_tensor = image.convert(\"RGB\")\n",
        "image_tensor = transform(image_tensor)\n",
        "image_tensor.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "moDfKkvUOuRo"
      },
      "source": [
        "# Helper functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "grSPZVnO3lNf"
      },
      "outputs": [],
      "source": [
        "def color_palette():\n",
        "  \"\"\"Color palette to map each class to its corresponding color.\"\"\"\n",
        "  return [[0, 128, 128],\n",
        "          [255, 170, 0],\n",
        "          [161, 19, 46],\n",
        "          [118, 171, 47],\n",
        "          [255, 255, 0],\n",
        "          [84, 170, 127],\n",
        "          [170, 84, 127],\n",
        "          [33, 138, 200],\n",
        "          [255, 84, 0],\n",
        "          [255, 140, 208]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L2DFsquBZyum"
      },
      "outputs": [],
      "source": [
        "def overlay_segments(image, seg_mask):\n",
        "  \"\"\"Return different segments predicted by the model overlaid on image.\"\"\"\n",
        "  H, W = seg_mask.shape\n",
        "  image_mask = np.zeros((H, W, 3), dtype=np.uint8)\n",
        "  colors = np.array(color_palette())\n",
        "\n",
        "  # convert to a pytorch tensor if seg_mask is not one already\n",
        "  seg_mask = seg_mask if torch.is_tensor(seg_mask) else torch.tensor(seg_mask)\n",
        "  unique_labels = torch.unique(seg_mask)\n",
        "\n",
        "  # map each segment label to a unique color\n",
        "  for i, label in enumerate(unique_labels):\n",
        "    image_mask[seg_mask == label.item(), :] = colors[i]\n",
        "\n",
        "  image = np.array(image)\n",
        "  # percentage of original image in the final overlaid iamge\n",
        "  img_weight = 0.5 \n",
        "\n",
        "  # overlay input image and the generated segment mask\n",
        "  img = img_weight * np.array(image) * 255 + (1 - img_weight) * image_mask\n",
        "\n",
        "  return img.astype(np.uint8)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pxbZmVr2FBY7"
      },
      "outputs": [],
      "source": [
        "def replace_label(mask, label):\n",
        "  \"\"\"Replace the segment masks values with label.\"\"\"\n",
        "  mask = np.array(mask)\n",
        "  mask[mask == 255] = label\n",
        "  return mask"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7LL5Mt9FG4FW"
      },
      "source": [
        "# Image segmentation using Hugging Face Pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202,
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        },
        "id": "C3c1JHC6FuEU",
        "outputId": "129e69e9-5d0c-46b3-ab24-ad7c90f86b7b"
      },
      "outputs": [],
      "source": [
        "# load the entire image segmentation pipeline\n",
        "img_segmentation_pipeline = pipeline('image-segmentation', \n",
        "                                     model=\"nvidia/segformer-b5-finetuned-ade-640-640\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C8tbGPylZywm",
        "outputId": "8f32fffe-3281-4d89-b90f-00e47b7edb4f"
      },
      "outputs": [],
      "source": [
        "output = img_segmentation_pipeline(image)\n",
        "output"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "3GO1nJBQUn1g",
        "outputId": "c9d21459-f65e-4b3a-caed-52dadc514b9c"
      },
      "outputs": [],
      "source": [
        "output[0]['mask']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "CEL1NkYP8P0J",
        "outputId": "6b50e3e8-9e8c-4f94-8730-74987610db68"
      },
      "outputs": [],
      "source": [
        "output[2]['mask']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "voaoIa8kh1Yk"
      },
      "outputs": [],
      "source": [
        "# load the feature extractor (to preprocess images) and the model (to get outputs)\n",
        "W, H = image.size\n",
        "segmentation_mask = np.zeros((H, W), dtype=np.uint8)\n",
        "\n",
        "for i in range(len(output)):\n",
        "  segmentation_mask += replace_label(output[i]['mask'], i)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "0XLcHLqN7JnE",
        "outputId": "afb65063-c882-490c-d04e-c3be05481001"
      },
      "outputs": [],
      "source": [
        "# overlay the predicted segmentation masks on the original image\n",
        "segmented_img = overlay_segments(image_tensor.permute(1, 2, 0), segmentation_mask)\n",
        "\n",
        "# convert to PIL Image\n",
        "Image.fromarray(segmented_img)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MRtEM9s5G-Jm"
      },
      "source": [
        "# Image segmentation using custom Hugging Face models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o0IbIPmm-VNp"
      },
      "outputs": [],
      "source": [
        "# load the feature extractor (to preprocess images) and the model (to get outputs)\n",
        "feature_extractor = SegformerImageProcessor.from_pretrained(\"nvidia/segformer-b5-finetuned-ade-640-640\")\n",
        "model = SegformerForSemanticSegmentation.from_pretrained(\"nvidia/segformer-b5-finetuned-ade-640-640\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2mVAr5UX8xLp"
      },
      "outputs": [],
      "source": [
        "def to_tensor(image):\n",
        "  \"\"\"Convert PIL Image to pytorch tensor.\"\"\"\n",
        "  transform = transforms.ToTensor()\n",
        "  image_tensor = image.convert(\"RGB\")\n",
        "  image_tensor = transform(image_tensor)\n",
        "  return image_tensor\n",
        "\n",
        "# a function that takes an image and return the segmented image\n",
        "def get_segmented_image(model, feature_extractor, image_path):\n",
        "  \"\"\"Return the predicted segmentation mask for the input image.\"\"\"\n",
        "  # load the image\n",
        "  image = load_image(image_path)\n",
        "  # preprocess input\n",
        "  inputs = feature_extractor(images=image, return_tensors=\"pt\")\n",
        "  # convert to pytorch tensor\n",
        "  image_tensor = to_tensor(image)\n",
        "  # pass the processed input to the model\n",
        "  outputs = model(**inputs)\n",
        "  print(\"outputs.logits.shape:\", outputs.logits.shape)\n",
        "  # interpolate output logits to the same shape as the input image\n",
        "  upsampled_logits = F.interpolate(\n",
        "      outputs.logits, # tensor to be interpolated\n",
        "      size=image_tensor.shape[1:], # output size we want\n",
        "      mode='bilinear', # do bilinear interpolation\n",
        "      align_corners=False)\n",
        "\n",
        "  # get the class with max probabilities\n",
        "  segmentation_mask = upsampled_logits.argmax(dim=1)[0]\n",
        "  print(f\"{segmentation_mask.shape=}\")\n",
        "  # get the segmented image\n",
        "  segmented_img = overlay_segments(image_tensor.permute(1, 2, 0), segmentation_mask)\n",
        "  # convert to PIL Image\n",
        "  return Image.fromarray(segmented_img)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        },
        "id": "ADSXrFO4ADon",
        "outputId": "e5435612-ccdf-4f97-996b-b6ed196ec24a"
      },
      "outputs": [],
      "source": [
        "get_segmented_image(model, feature_extractor, \"https://shorthaircatbreeds.com/wp-content/uploads/2020/06/Urban-cat-crossing-a-road-300x180.jpg\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 532
        },
        "id": "f0v_SEfjAGER",
        "outputId": "c88ec523-8c72-4efd-c8bc-60816fb54b96"
      },
      "outputs": [],
      "source": [
        "get_segmented_image(model, feature_extractor, \"http://images.cocodataset.org/test-stuff2017/000000000001.jpg\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CDd7PM8wHz-Y"
      },
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    }
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