Object_Detection_(DL,_Difficulty_5).ipynb

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      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import warnings"
      ],
      "metadata": {
        "id": "4IP35_bFe9fa"
      },
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "nL1N66XdfAho"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nAZeepEDe8j0"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nZRib3PTe8Mp"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install dash dash-bootstrap-components"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OLJ-aPogevK2",
        "outputId": "1a57ca31-d1f1-4832-ea37-fbcf81edea21"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: dash in /usr/local/lib/python3.11/dist-packages (2.18.2)\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UUCb03gwezx9"
      },
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install gdown"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z-cmbGOFez3C",
        "outputId": "971a7ace-2d0d-4a24-9b54-ca807f91becb"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: gdown in /usr/local/lib/python3.11/dist-packages (5.2.0)\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.11/dist-packages (from gdown) (4.13.3)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from gdown) (3.17.0)\n",
            "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.11/dist-packages (from gdown) (2.32.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (from gdown) (4.67.1)\n",
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            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests[socks]->gdown) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests[socks]->gdown) (2.3.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests[socks]->gdown) (2025.1.31)\n",
            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.11/dist-packages (from requests[socks]->gdown) (1.7.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1V8kfyEme25r"
      },
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!gdown --id FILE_ID --output val2017.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BllGmUyIe2_M",
        "outputId": "66650861-1981-4020-f826-3be93d583c0b"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/gdown/__main__.py:140: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Failed to retrieve file url:\n",
            "\n",
            "\tCannot retrieve the public link of the file. You may need to change\n",
            "\tthe permission to 'Anyone with the link', or have had many accesses.\n",
            "\tCheck FAQ in https://github.com/wkentaro/gdown?tab=readme-ov-file#faq.\n",
            "\n",
            "You may still be able to access the file from the browser:\n",
            "\n",
            "\thttps://drive.google.com/uc?id=FILE_ID\n",
            "\n",
            "but Gdown can't. Please check connections and permissions.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PLYMm6Kwe5I0"
      },
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip val2017.zip -d ./coco"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vDHBOa0oe5O0",
        "outputId": "46b0a8d5-0a82-4a0a-b465-86e17e5131fb"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "unzip:  cannot find or open val2017.zip, val2017.zip.zip or val2017.zip.ZIP.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cznhBIsFevFZ"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GO-LORsTeu-k"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uB1eCHYGeum7"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch"
      ],
      "metadata": {
        "id": "ifMWEHAiWOro"
      },
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torchvision.transforms as transforms"
      ],
      "metadata": {
        "id": "Bh_ayyFBWOuL"
      },
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torchvision.models.detection import fasterrcnn_resnet50_fpn"
      ],
      "metadata": {
        "id": "pcLBCqOeWYz1"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pycocotools.coco import COCO"
      ],
      "metadata": {
        "id": "DQrMpsH2WY2z"
      },
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import cv2"
      ],
      "metadata": {
        "id": "YDhQc-L-WY5d"
      },
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "QPxK9WQMWY8A"
      },
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "auAEyk1zWY-k"
      },
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os"
      ],
      "metadata": {
        "id": "-CDfmkUmWZBM"
      },
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import requests"
      ],
      "metadata": {
        "id": "nGuFUIyiWZDW"
      },
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import zipfile"
      ],
      "metadata": {
        "id": "qjovJUNSWZF1"
      },
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import DataLoader, Dataset"
      ],
      "metadata": {
        "id": "1nLWTaycWOwo"
      },
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_score, recall_score, average_precision_score"
      ],
      "metadata": {
        "id": "3NsygvQxWm9V"
      },
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import dash"
      ],
      "metadata": {
        "id": "q6l9D_pGWnAG"
      },
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from dash import dcc, html, Input, Output"
      ],
      "metadata": {
        "id": "AOvRRLR9fOZh"
      },
      "execution_count": 123,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import dash_bootstrap_components as dbc"
      ],
      "metadata": {
        "id": "sXNagKRGfOgC"
      },
      "execution_count": 124,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px"
      ],
      "metadata": {
        "id": "7o7bDC-NfT5l"
      },
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import base64"
      ],
      "metadata": {
        "id": "P5n8rZ64fT-u"
      },
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rKWIiDC_fOmC"
      },
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1Nu_ELO2WnCc"
      },
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "969DtI_uWnFG"
      },
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 1: Data Loading and Preprocessing"
      ],
      "metadata": {
        "id": "MISrm8dcWnHn"
      },
      "execution_count": 127,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def download_coco(root_dir='./coco'):\n",
        "    urls = [\n",
        "        'http://images.cocodataset.org/zips/val2017.zip',\n",
        "        'http://images.cocodataset.org/annotations/annotations_trainval2017.zip'\n",
        "    ]\n",
        "    os.makedirs(root_dir, exist_ok=True)\n",
        "    for url in urls:\n",
        "        filename = os.path.join(root_dir, url.split('/')[-1])\n",
        "        if not os.path.exists(filename):\n",
        "            print(f\"Downloading {filename}...\")\n",
        "            with requests.get(url, stream=True) as r, open(filename, 'wb') as f:\n",
        "                for chunk in r.iter_content(chunk_size=4096):\n",
        "                    f.write(chunk)\n",
        "            print(f\"{filename} downloaded.\")\n",
        "\n",
        "    for file in os.listdir(root_dir):\n",
        "        if file.endswith('.zip'):\n",
        "            zip_path = os.path.join(root_dir, file)\n",
        "            print(f\"Extracting {zip_path}...\")\n",
        "            with zipfile.ZipFile(zip_path, 'r') as zip_ref:\n",
        "                zip_ref.extractall(root_dir)\n",
        "            print(f\"{zip_path} extracted.\")\n"
      ],
      "metadata": {
        "id": "qzhT7NNTWnLA"
      },
      "execution_count": 128,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uYPMpTJDWnNb"
      },
      "execution_count": 128,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def load_coco_dataset(dataset_dir='coco'):\n",
        "    annotation_file = os.path.join(dataset_dir, 'annotations', 'instances_val2017.json')\n",
        "    if not os.path.exists(annotation_file):\n",
        "        print(\"COCO dataset not found. Please download it manually or use the download_coco function.\")\n",
        "    return COCO(annotation_file)\n"
      ],
      "metadata": {
        "id": "AFmLWN4SWnQH"
      },
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **OR**"
      ],
      "metadata": {
        "id": "_TtW5HOUa44R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def load_coco_dataset(dataset_dir='coco'):\n",
        "    annotation_file = os.path.join(dataset_dir, 'annotations', 'instances_val2017.json')\n",
        "    if not os.path.exists(annotation_file):\n",
        "        print(\"COCO dataset not found. Please download it manually or use the download_coco function.\")\n",
        "    coco = COCO(annotation_file)\n",
        "\n",
        "    # Exploratory Data Analysis (EDA)\n",
        "    print(\"Number of Images:\", len(coco.getImgIds()))\n",
        "    print(\"Number of Annotations:\", len(coco.getAnnIds()))\n",
        "    categories = coco.loadCats(coco.getCatIds())\n",
        "    category_names = [cat['name'] for cat in categories]\n",
        "    print(\"Categories:\", category_names)\n",
        "\n",
        "    img_ids = coco.getImgIds()[:5]\n",
        "    for img_id in img_ids:\n",
        "        img_info = coco.loadImgs(img_id)[0]\n",
        "        print(f\"Image ID: {img_id}, File: {img_info['file_name']}, Width: {img_info['width']}, Height: {img_info['height']}\")\n",
        "\n",
        "    return coco\n"
      ],
      "metadata": {
        "id": "FHADUuhwa7cs"
      },
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SVJMGtrkWnSj"
      },
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "AKHXMlQ7WOzW"
      },
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "E-A6CNThWO2P"
      },
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 2: YOLOv3 Model Setup"
      ],
      "metadata": {
        "id": "gzwI17vFWO5Q"
      },
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def load_model():\n",
        "    model = fasterrcnn_resnet50_fpn(weights=\"DEFAULT\")\n",
        "    model.eval()\n",
        "    return model.to(torch.device('cuda' if torch.cuda.is_available() else 'cpu'))\n"
      ],
      "metadata": {
        "id": "m5OmpsS2WMMp"
      },
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BqQmplqPWMPO"
      },
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zFo9J6YcWMRh"
      },
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4aASFrliWMUA"
      },
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 3: Image Preprocessing"
      ],
      "metadata": {
        "id": "AOz2iNSYWMWp"
      },
      "execution_count": 133,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocess_image(img_path):\n",
        "    img = cv2.imread(img_path)\n",
        "    if img is None:\n",
        "        raise FileNotFoundError(f\"Image not found at {img_path}\")\n",
        "    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "    transform = transforms.Compose([\n",
        "        transforms.ToPILImage(),\n",
        "        transforms.Resize((416, 416)),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
        "    ])\n",
        "    return transform(img).unsqueeze(0)"
      ],
      "metadata": {
        "id": "2BM5jX3EVY_L"
      },
      "execution_count": 134,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Z28KvY5nW_8P"
      },
      "execution_count": 134,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LU4ug_t-W_-x"
      },
      "execution_count": 134,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Nf01UNHxXAC0"
      },
      "execution_count": 134,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 4: Object Detection"
      ],
      "metadata": {
        "id": "hEvGD_7OXAHb"
      },
      "execution_count": 135,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def detect_objects(model, img_tensor, threshold=0.6):\n",
        "    with torch.no_grad():\n",
        "        predictions = model(img_tensor)[0]\n",
        "    boxes, labels, scores = predictions['boxes'], predictions['labels'], predictions['scores']\n",
        "    mask = scores > threshold\n",
        "    return boxes[mask], labels[mask], scores[mask]\n"
      ],
      "metadata": {
        "id": "L_qgq9tJXAKa"
      },
      "execution_count": 136,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9u83d5BhXLJ1"
      },
      "execution_count": 136,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0Xt0KK-1XLM8"
      },
      "execution_count": 136,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zb2ZhZX7XLPm"
      },
      "execution_count": 136,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 5: Visualization"
      ],
      "metadata": {
        "id": "SXKGQl2OXLTn"
      },
      "execution_count": 137,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def visualize_detections(img_path, boxes, labels, scores, coco):\n",
        "    img = cv2.imread(img_path)\n",
        "    if img is None:\n",
        "        raise FileNotFoundError(f\"Image not found at {img_path}\")\n",
        "    for box, label, score in zip(boxes, labels, scores):\n",
        "        x1, y1, x2, y2 = map(int, box.cpu().numpy())\n",
        "        class_name = coco.loadCats(int(label))[0]['name']\n",
        "        cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)\n",
        "        cv2.putText(img, f'{class_name} {score:.2f}', (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)\n",
        "    plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))\n",
        "    plt.axis('off')\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "2fExzFBmXLWd"
      },
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jGn8BHfMXLZY"
      },
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "slEkIVR3XLcZ"
      },
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "64TT9vTAXLfE"
      },
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 6: Model Evaluation"
      ],
      "metadata": {
        "id": "VW0Cu9gLXAND"
      },
      "execution_count": 139,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate_model(model, coco, image_dir='coco/val2017'):\n",
        "    y_true, y_pred = [], []\n",
        "    img_ids = coco.getImgIds()[:20]\n",
        "    for img_id in img_ids:\n",
        "        img_info = coco.loadImgs(img_id)[0]\n",
        "        img_path = os.path.join(image_dir, img_info['file_name'])\n",
        "        try:\n",
        "            img_tensor = preprocess_image(img_path).to(torch.device('cuda' if torch.cuda.is_available() else 'cpu'))\n",
        "            boxes, labels, scores = detect_objects(model, img_tensor)\n",
        "            true_ann = coco.loadAnns(coco.getAnnIds(imgIds=img_id))\n",
        "            true_labels = [ann['category_id'] for ann in true_ann]\n",
        "            pred_labels = labels.cpu().numpy().tolist()\n",
        "            y_true.extend(true_labels)\n",
        "            y_pred.extend(pred_labels)\n",
        "        except Exception as e:\n",
        "            print(f\"Skipping image {img_path}: {e}\")\n",
        "    print(\"Precision:\", precision_score(y_true, y_pred, average='macro', zero_division=0))\n",
        "    print(\"Recall:\", recall_score(y_true, y_pred, average='macro', zero_division=0))\n",
        "    print(\"Average Precision:\", average_precision_score(y_true, y_pred, average='macro'))\n"
      ],
      "metadata": {
        "id": "72OB6Kn8VZCB"
      },
      "execution_count": 140,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uDaLv01xXS5X"
      },
      "execution_count": 140,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wfMz3UGYXS8F"
      },
      "execution_count": 140,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LlpIRGN5XS_H"
      },
      "execution_count": 140,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Main workflow"
      ],
      "metadata": {
        "id": "qjW_zLKrXTCF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "download_coco()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1NakYb2bXTFL",
        "outputId": "273dffed-593b-4a35-9e3f-8fbb58d9cc3e"
      },
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./coco/annotations_trainval2017.zip...\n",
            "./coco/annotations_trainval2017.zip extracted.\n",
            "Extracting ./coco/val2017.zip...\n",
            "./coco/val2017.zip extracted.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Z05NYKNcbXpB"
      },
      "execution_count": 141,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "coco = load_coco_dataset()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NKPKcZcHXTIK",
        "outputId": "dab15a94-dc5b-4b54-a78a-c362508942c4"
      },
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loading annotations into memory...\n",
            "Done (t=2.55s)\n",
            "creating index...\n",
            "index created!\n",
            "Number of Images: 5000\n",
            "Number of Annotations: 36781\n",
            "Categories: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush']\n",
            "Image ID: 397133, File: 000000397133.jpg, Width: 640, Height: 427\n",
            "Image ID: 37777, File: 000000037777.jpg, Width: 352, Height: 230\n",
            "Image ID: 252219, File: 000000252219.jpg, Width: 640, Height: 428\n",
            "Image ID: 87038, File: 000000087038.jpg, Width: 640, Height: 480\n",
            "Image ID: 174482, File: 000000174482.jpg, Width: 640, Height: 388\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uxMjZhVPbYVB"
      },
      "execution_count": 142,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = load_model()"
      ],
      "metadata": {
        "id": "L0qa25cybVPX"
      },
      "execution_count": 143,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "img_path = 'coco/val2017/000000000139.jpg'\n"
      ],
      "metadata": {
        "id": "WyE8hIDnbbWW"
      },
      "execution_count": 144,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "img_tensor = preprocess_image(img_path).to(torch.device('cuda' if torch.cuda.is_available() else 'cpu'))\n"
      ],
      "metadata": {
        "id": "b9zQ6AvTbbal"
      },
      "execution_count": 145,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "boxes, labels, scores = detect_objects(model, img_tensor)"
      ],
      "metadata": {
        "id": "2z15ozBWbVTH"
      },
      "execution_count": 146,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "visualize_detections(img_path, boxes, labels, scores, coco)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "UIw_JXcrbVW6",
        "outputId": "80eaf4fc-e7fd-4d50-c7a0-f9de49642acb"
      },
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KR4Y2ZO9bVbC"
      },
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uqC-aiMZbKss"
      },
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tkZNgQ1gcVsc"
      },
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "0-zVXmvacTKk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **INTERACTIVE DASHBOARD**"
      ],
      "metadata": {
        "id": "5F3gEWoAdnXD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize Dash app"
      ],
      "metadata": {
        "id": "Mb0NY3sKdnbb"
      },
      "execution_count": 148,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])"
      ],
      "metadata": {
        "id": "HM7nu_jtdnfu"
      },
      "execution_count": 149,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8BYKuBFOdnjd"
      },
      "execution_count": 149,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load model"
      ],
      "metadata": {
        "id": "TjngNTmWdnoD"
      },
      "execution_count": 150,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = fasterrcnn_resnet50_fpn(weights=\"DEFAULT\")"
      ],
      "metadata": {
        "id": "Cbws4D8qdnsA"
      },
      "execution_count": 151,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.eval()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yNtjxKJidxV6",
        "outputId": "988ca07c-225b-44b4-e35a-e18f96c02a7a"
      },
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FasterRCNN(\n",
              "  (transform): GeneralizedRCNNTransform(\n",
              "      Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
              "      Resize(min_size=(800,), max_size=1333, mode='bilinear')\n",
              "  )\n",
              "  (backbone): BackboneWithFPN(\n",
              "    (body): IntermediateLayerGetter(\n",
              "      (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
              "      (bn1): FrozenBatchNorm2d(64, eps=0.0)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
              "      (layer1): Sequential(\n",
              "        (0): Bottleneck(\n",
              "          (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(64, eps=0.0)\n",
              "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(64, eps=0.0)\n",
              "          (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "          (downsample): Sequential(\n",
              "            (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (1): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          )\n",
              "        )\n",
              "        (1): Bottleneck(\n",
              "          (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(64, eps=0.0)\n",
              "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(64, eps=0.0)\n",
              "          (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (2): Bottleneck(\n",
              "          (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(64, eps=0.0)\n",
              "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(64, eps=0.0)\n",
              "          (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "      (layer2): Sequential(\n",
              "        (0): Bottleneck(\n",
              "          (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(128, eps=0.0)\n",
              "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(128, eps=0.0)\n",
              "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(512, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "          (downsample): Sequential(\n",
              "            (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "            (1): FrozenBatchNorm2d(512, eps=0.0)\n",
              "          )\n",
              "        )\n",
              "        (1): Bottleneck(\n",
              "          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(128, eps=0.0)\n",
              "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(128, eps=0.0)\n",
              "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(512, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (2): Bottleneck(\n",
              "          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(128, eps=0.0)\n",
              "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(128, eps=0.0)\n",
              "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(512, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (3): Bottleneck(\n",
              "          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(128, eps=0.0)\n",
              "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(128, eps=0.0)\n",
              "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(512, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "      (layer3): Sequential(\n",
              "        (0): Bottleneck(\n",
              "          (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(1024, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "          (downsample): Sequential(\n",
              "            (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "            (1): FrozenBatchNorm2d(1024, eps=0.0)\n",
              "          )\n",
              "        )\n",
              "        (1): Bottleneck(\n",
              "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(1024, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (2): Bottleneck(\n",
              "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(1024, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (3): Bottleneck(\n",
              "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(1024, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (4): Bottleneck(\n",
              "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(1024, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (5): Bottleneck(\n",
              "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(1024, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "      (layer4): Sequential(\n",
              "        (0): Bottleneck(\n",
              "          (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(512, eps=0.0)\n",
              "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(512, eps=0.0)\n",
              "          (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(2048, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "          (downsample): Sequential(\n",
              "            (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "            (1): FrozenBatchNorm2d(2048, eps=0.0)\n",
              "          )\n",
              "        )\n",
              "        (1): Bottleneck(\n",
              "          (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(512, eps=0.0)\n",
              "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(512, eps=0.0)\n",
              "          (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(2048, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (2): Bottleneck(\n",
              "          (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(512, eps=0.0)\n",
              "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(512, eps=0.0)\n",
              "          (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(2048, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (fpn): FeaturePyramidNetwork(\n",
              "      (inner_blocks): ModuleList(\n",
              "        (0): Conv2dNormActivation(\n",
              "          (0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (1): Conv2dNormActivation(\n",
              "          (0): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (2): Conv2dNormActivation(\n",
              "          (0): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (3): Conv2dNormActivation(\n",
              "          (0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "      )\n",
              "      (layer_blocks): ModuleList(\n",
              "        (0-3): 4 x Conv2dNormActivation(\n",
              "          (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        )\n",
              "      )\n",
              "      (extra_blocks): LastLevelMaxPool()\n",
              "    )\n",
              "  )\n",
              "  (rpn): RegionProposalNetwork(\n",
              "    (anchor_generator): AnchorGenerator()\n",
              "    (head): RPNHead(\n",
              "      (conv): Sequential(\n",
              "        (0): Conv2dNormActivation(\n",
              "          (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (1): ReLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "      (cls_logits): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))\n",
              "      (bbox_pred): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1))\n",
              "    )\n",
              "  )\n",
              "  (roi_heads): RoIHeads(\n",
              "    (box_roi_pool): MultiScaleRoIAlign(featmap_names=['0', '1', '2', '3'], output_size=(7, 7), sampling_ratio=2)\n",
              "    (box_head): TwoMLPHead(\n",
              "      (fc6): Linear(in_features=12544, out_features=1024, bias=True)\n",
              "      (fc7): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "    )\n",
              "    (box_predictor): FastRCNNPredictor(\n",
              "      (cls_score): Linear(in_features=1024, out_features=91, bias=True)\n",
              "      (bbox_pred): Linear(in_features=1024, out_features=364, bias=True)\n",
              "    )\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
      ],
      "metadata": {
        "id": "KCeEIqXgdxau"
      },
      "execution_count": 153,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q3oe1lRZdxel",
        "outputId": "ebfc9799-a72c-4b30-a185-e8f9cb68e5c8"
      },
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FasterRCNN(\n",
              "  (transform): GeneralizedRCNNTransform(\n",
              "      Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
              "      Resize(min_size=(800,), max_size=1333, mode='bilinear')\n",
              "  )\n",
              "  (backbone): BackboneWithFPN(\n",
              "    (body): IntermediateLayerGetter(\n",
              "      (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
              "      (bn1): FrozenBatchNorm2d(64, eps=0.0)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
              "      (layer1): Sequential(\n",
              "        (0): Bottleneck(\n",
              "          (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(64, eps=0.0)\n",
              "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(64, eps=0.0)\n",
              "          (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "          (downsample): Sequential(\n",
              "            (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (1): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          )\n",
              "        )\n",
              "        (1): Bottleneck(\n",
              "          (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(64, eps=0.0)\n",
              "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(64, eps=0.0)\n",
              "          (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (2): Bottleneck(\n",
              "          (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(64, eps=0.0)\n",
              "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(64, eps=0.0)\n",
              "          (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "      (layer2): Sequential(\n",
              "        (0): Bottleneck(\n",
              "          (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(128, eps=0.0)\n",
              "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(128, eps=0.0)\n",
              "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(512, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "          (downsample): Sequential(\n",
              "            (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "            (1): FrozenBatchNorm2d(512, eps=0.0)\n",
              "          )\n",
              "        )\n",
              "        (1): Bottleneck(\n",
              "          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(128, eps=0.0)\n",
              "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(128, eps=0.0)\n",
              "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(512, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (2): Bottleneck(\n",
              "          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(128, eps=0.0)\n",
              "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(128, eps=0.0)\n",
              "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(512, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (3): Bottleneck(\n",
              "          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(128, eps=0.0)\n",
              "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(128, eps=0.0)\n",
              "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(512, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "      (layer3): Sequential(\n",
              "        (0): Bottleneck(\n",
              "          (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(1024, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "          (downsample): Sequential(\n",
              "            (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "            (1): FrozenBatchNorm2d(1024, eps=0.0)\n",
              "          )\n",
              "        )\n",
              "        (1): Bottleneck(\n",
              "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(1024, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (2): Bottleneck(\n",
              "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(1024, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (3): Bottleneck(\n",
              "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(1024, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (4): Bottleneck(\n",
              "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(1024, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (5): Bottleneck(\n",
              "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(256, eps=0.0)\n",
              "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(1024, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "      (layer4): Sequential(\n",
              "        (0): Bottleneck(\n",
              "          (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(512, eps=0.0)\n",
              "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(512, eps=0.0)\n",
              "          (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(2048, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "          (downsample): Sequential(\n",
              "            (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "            (1): FrozenBatchNorm2d(2048, eps=0.0)\n",
              "          )\n",
              "        )\n",
              "        (1): Bottleneck(\n",
              "          (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(512, eps=0.0)\n",
              "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(512, eps=0.0)\n",
              "          (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(2048, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (2): Bottleneck(\n",
              "          (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(512, eps=0.0)\n",
              "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(512, eps=0.0)\n",
              "          (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(2048, eps=0.0)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (fpn): FeaturePyramidNetwork(\n",
              "      (inner_blocks): ModuleList(\n",
              "        (0): Conv2dNormActivation(\n",
              "          (0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (1): Conv2dNormActivation(\n",
              "          (0): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (2): Conv2dNormActivation(\n",
              "          (0): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (3): Conv2dNormActivation(\n",
              "          (0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "      )\n",
              "      (layer_blocks): ModuleList(\n",
              "        (0-3): 4 x Conv2dNormActivation(\n",
              "          (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        )\n",
              "      )\n",
              "      (extra_blocks): LastLevelMaxPool()\n",
              "    )\n",
              "  )\n",
              "  (rpn): RegionProposalNetwork(\n",
              "    (anchor_generator): AnchorGenerator()\n",
              "    (head): RPNHead(\n",
              "      (conv): Sequential(\n",
              "        (0): Conv2dNormActivation(\n",
              "          (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (1): ReLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "      (cls_logits): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))\n",
              "      (bbox_pred): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1))\n",
              "    )\n",
              "  )\n",
              "  (roi_heads): RoIHeads(\n",
              "    (box_roi_pool): MultiScaleRoIAlign(featmap_names=['0', '1', '2', '3'], output_size=(7, 7), sampling_ratio=2)\n",
              "    (box_head): TwoMLPHead(\n",
              "      (fc6): Linear(in_features=12544, out_features=1024, bias=True)\n",
              "      (fc7): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "    )\n",
              "    (box_predictor): FastRCNNPredictor(\n",
              "      (cls_score): Linear(in_features=1024, out_features=91, bias=True)\n",
              "      (bbox_pred): Linear(in_features=1024, out_features=364, bias=True)\n",
              "    )\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "eZAuqI2Hd3vt"
      },
      "execution_count": 154,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4TVtSnqcd3z4"
      },
      "execution_count": 154,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load COCO dataset"
      ],
      "metadata": {
        "id": "NbuqFMh2d34E"
      },
      "execution_count": 155,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "coco = COCO('coco/annotations/instances_val2017.json')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h_XTnH8Hd38A",
        "outputId": "62eccaa9-7f66-4e68-bbf9-94fd512aa1de"
      },
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loading annotations into memory...\n",
            "Done (t=1.00s)\n",
            "creating index...\n",
            "index created!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Qyk2AsK7d4Az"
      },
      "execution_count": 156,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Z_twFl6kd4Fz"
      },
      "execution_count": 156,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Image preprocessing"
      ],
      "metadata": {
        "id": "okqfGquUdxiz"
      },
      "execution_count": 157,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocess_image(img_path):\n",
        "    img = cv2.imread(img_path)\n",
        "    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "    transform = transforms.Compose([\n",
        "        transforms.ToPILImage(),\n",
        "        transforms.Resize((416, 416)),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
        "    ])\n",
        "    return transform(img).unsqueeze(0)"
      ],
      "metadata": {
        "id": "LXgEs39kdnwj"
      },
      "execution_count": 158,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Tt49a0CUeGCQ"
      },
      "execution_count": 158,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pzlo4LrmeIWX"
      },
      "execution_count": 158,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Object detection"
      ],
      "metadata": {
        "id": "rRCNpZq_eGIR"
      },
      "execution_count": 159,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def detect_objects(model, img_tensor, threshold=0.6):\n",
        "    with torch.no_grad():\n",
        "        predictions = model(img_tensor)[0]\n",
        "    boxes, labels, scores = predictions['boxes'], predictions['labels'], predictions['scores']\n",
        "    mask = scores > threshold\n",
        "    return boxes[mask], labels[mask], scores[mask]"
      ],
      "metadata": {
        "id": "pdrOrPQYeGOx"
      },
      "execution_count": 160,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Ha5q7yOVeGXh"
      },
      "execution_count": 160,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vbbkiLTzeGcH"
      },
      "execution_count": 160,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualization"
      ],
      "metadata": {
        "id": "f6wGALCbeGhe"
      },
      "execution_count": 161,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def visualize_detections(img_path, boxes, labels, scores):\n",
        "    img = cv2.imread(img_path)\n",
        "    for box, label, score in zip(boxes, labels, scores):\n",
        "        x1, y1, x2, y2 = map(int, box.cpu().numpy())\n",
        "        class_name = coco.loadCats(int(label))[0]['name']\n",
        "        cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)\n",
        "        cv2.putText(img, f'{class_name} {score:.2f}', (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)\n",
        "    _, img_encoded = cv2.imencode('.jpg', img)\n",
        "    return base64.b64encode(img_encoded).decode('utf-8')\n"
      ],
      "metadata": {
        "id": "tuc8Yij7dn0h"
      },
      "execution_count": 162,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JA5MVEr3eP8-"
      },
      "execution_count": 162,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "v7sr0kF8eQBY"
      },
      "execution_count": 162,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Layout"
      ],
      "metadata": {
        "id": "gvSyke11eQFv"
      },
      "execution_count": 163,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "app.layout = dbc.Container([\n",
        "    dbc.Row([html.H2(\"Faster R-CNN Object Detection Dashboard\")]),\n",
        "    dbc.Row([\n",
        "        dcc.Upload(\n",
        "            id='upload-image',\n",
        "            children=html.Div(['Drag and Drop or ', html.A('Select an Image')]),\n",
        "            style={\n",
        "                'width': '100%', 'height': '60px', 'lineHeight': '60px',\n",
        "                'borderWidth': '1px', 'borderStyle': 'dashed', 'borderRadius': '5px',\n",
        "                'textAlign': 'center', 'margin': '10px'\n",
        "            },\n",
        "            multiple=False\n",
        "        )\n",
        "    ]),\n",
        "    dbc.Row([html.Div(id='output-image')]),\n",
        "    dbc.Row([html.Div(id='detection-results')]),\n",
        "])\n",
        "\n",
        "@app.callback(\n",
        "    [Output('output-image', 'children'), Output('detection-results', 'children')],\n",
        "    [Input('upload-image', 'contents')]\n",
        ")\n",
        "def update_output(contents):\n",
        "    if contents is None:\n",
        "        return \"\", \"\"\n",
        "    # Decode and save the uploaded image\n",
        "    content_type, content_string = contents.split(',')\n",
        "    img_data = base64.b64decode(content_string)\n",
        "    img_path = \"uploaded_image.jpg\"\n",
        "    with open(img_path, \"wb\") as f:\n",
        "        f.write(img_data)\n",
        "\n",
        "    img_tensor = preprocess_image(img_path).to(device)\n",
        "    boxes, labels, scores = detect_objects(model, img_tensor)\n",
        "    img_base64 = visualize_detections(img_path, boxes, labels, scores)\n",
        "\n",
        "    img_element = html.Img(src='data:image/jpeg;base64,' + img_base64, style={'width': '100%'})\n",
        "\n",
        "    results = [\n",
        "        html.Ul([\n",
        "            html.Li(f\"Label: {coco.loadCats(int(label))[0]['name']}, Score: {score:.2f}\")\n",
        "            for label, score in zip(labels, scores)\n",
        "        ])\n",
        "    ]\n",
        "    return img_element, results"
      ],
      "metadata": {
        "id": "EaPZHFYGeUCj"
      },
      "execution_count": 164,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3jqcI407eQYy"
      },
      "execution_count": 164,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if __name__ == '__main__':\n",
        "    app.run_server(debug=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "id": "REzEZjXRdn4a",
        "outputId": "1a426a5b-b223-4a3d-cef0-03851be36e24"
      },
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "(async (port, path, width, height, cache, element) => {\n",
              "    if (!google.colab.kernel.accessAllowed && !cache) {\n",
              "      return;\n",
              "    }\n",
              "    element.appendChild(document.createTextNode(''));\n",
              "    const url = await google.colab.kernel.proxyPort(port, {cache});\n",
              "    const iframe = document.createElement('iframe');\n",
              "    iframe.src = new URL(path, url).toString();\n",
              "    iframe.height = height;\n",
              "    iframe.width = width;\n",
              "    iframe.style.border = 0;\n",
              "    iframe.allow = [\n",
              "        'accelerometer',\n",
              "        'autoplay',\n",
              "        'camera',\n",
              "        'clipboard-read',\n",
              "        'clipboard-write',\n",
              "        'gyroscope',\n",
              "        'magnetometer',\n",
              "        'microphone',\n",
              "        'serial',\n",
              "        'usb',\n",
              "        'xr-spatial-tracking',\n",
              "    ].join('; ');\n",
              "    element.appendChild(iframe);\n",
              "  })(8050, \"/\", \"100%\", 650, false, window.element)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "UCcQNU8Cf8T5"
      }
    }
  ]
}