Object_detection_Gradio_app.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "YzktWCczIeNp",
        "outputId": "da785dc8-ac11-4504-f259-a4c37a2251d5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Building wheels for collected packages: transformers\n",
            "  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for transformers: filename=transformers-4.45.0.dev0-py3-none-any.whl size=9706023 sha256=d567699fae0b89e2a7c2dc95b4c7ef2d50d326b64c60f001833368fe08f6e029\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-_i1wh4hy/wheels/c0/14/d6/6c9a5582d2ac191ec0a483be151a4495fe1eb2a6706ca49f1b\n",
            "Successfully built transformers\n",
            "Installing collected packages: transformers\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.44.2\n",
            "    Uninstalling transformers-4.44.2:\n",
            "      Successfully uninstalled transformers-4.44.2\n",
            "Successfully installed transformers-4.45.0.dev0\n"
          ]
        }
      ],
      "source": [
        "!pip install gradio\n",
        "!python -m pip install git+https://github.com/huggingface/transformers"
      ]
    },
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        "outputId": "0ff261b7-8233-4f04-c6bf-5e8353126819"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
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      ],
      "source": [
        "# Object detection\n",
        "\n",
        "import random\n",
        "import requests\n",
        "from PIL import Image, ImageDraw, ImageFont\n",
        "import numpy as np\n",
        "import torch\n",
        "from transformers import AutoProcessor, Owlv2ForObjectDetection\n",
        "from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD\n",
        "\n",
        "obj_processor = AutoProcessor.from_pretrained(\"google/owlv2-base-patch16-ensemble\")\n",
        "obj_model = Owlv2ForObjectDetection.from_pretrained(\"google/owlv2-base-patch16-ensemble\")\n",
        "\n",
        "\n",
        "def detect_objects(image, objects):\n",
        "\n",
        "    texts = [objects]\n",
        "    inputs = obj_processor(text=texts, images=image, return_tensors=\"pt\")\n",
        "\n",
        "    with torch.no_grad():\n",
        "        outputs = obj_model(**inputs)\n",
        "\n",
        "    target_sizes = torch.Tensor([image.size[::-1]])\n",
        "    results = obj_processor.post_process_object_detection(\n",
        "        outputs=outputs, threshold=0.2, target_sizes=target_sizes\n",
        "    )\n",
        "\n",
        "    i = 0\n",
        "    text = texts[i]\n",
        "    boxes, scores, labels = results[i][\"boxes\"], results[i][\"scores\"], results[i][\"labels\"]\n",
        "    return image, boxes, scores, labels\n",
        "\n",
        "\n",
        "colors = [\n",
        "    (255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 165, 0), (75, 0, 130),\n",
        "    (255, 255, 0), (0, 255, 255), (255, 105, 180), (138, 43, 226), (0, 128, 0),\n",
        "    (0, 128, 128), (255, 20, 147), (64, 224, 208), (128, 0, 128), (70, 130, 180),\n",
        "    (220, 20, 60), (255, 140, 0), (34, 139, 34), (218, 112, 214), (255, 99, 71),\n",
        "    (47, 79, 79), (186, 85, 211), (240, 230, 140), (169, 169, 169), (199, 21, 133)\n",
        "]\n",
        "\n",
        "def annotate_image(image, boxes, scores, labels, objects):\n",
        "    draw = ImageDraw.Draw(image)\n",
        "    font = ImageFont.load_default()\n",
        "\n",
        "    for i, (box, score, label) in enumerate(zip(boxes, scores, labels)):\n",
        "        box = [round(coord, 2) for coord in box.tolist()]\n",
        "        color = colors[label % len(colors)]\n",
        "        draw.rectangle(box, outline=color, width=3)\n",
        "        draw.text((box[0], box[1]), f\"{objects[label]}: {score:.2f}\", font=font, fill=color)\n",
        "\n",
        "    return image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5Rr89uxTJYUa"
      },
      "outputs": [],
      "source": [
        "import gradio as gr\n",
        "\n",
        "with gr.Blocks() as demo:\n",
        "    gr.Markdown(\"## Upload an Image\")\n",
        "    image_input = gr.Image(type=\"pil\", label=\"Upload your image here\")\n",
        "    objects_input = gr.Textbox(label=\"Enter the objects to detect (comma-separated)\", placeholder=\"e.g. 'cat, dog, car'\")\n",
        "    image_output = gr.Image(type=\"pil\", label=\"Detected Objects\")\n",
        "\n",
        "    def run_object_detection(image, objects):\n",
        "        object_list = [obj.strip() for obj in objects.split(\",\")]\n",
        "        image, boxes, scores, labels = detect_objects(image, object_list)\n",
        "        annotated_image = annotate_image(image, boxes, scores, labels, object_list)\n",
        "        return annotated_image\n",
        "\n",
        "    detect_button = gr.Button(\"Detect Objects\")\n",
        "    detect_button.click(fn=run_object_detection, inputs=[image_input, objects_input], outputs=image_output)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
          "background_save": true,
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        "outputId": "97fb79c2-af6c-4f71-a91d-b1f55d0d5f75"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
            "Running on public URL: https://29eaf3d1c65a4a15d7.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
        },
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