Tensor_of_image.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "xCQAl0qEXHSK"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import json\n",
        "import torch\n",
        "import torchvision\n",
        "import pickle\n",
        "from PIL import Image\n",
        "import pickle\n",
        "import torchvision.transforms as transforms\n",
        "import cv2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ll4AeslXR0E",
        "outputId": "ce5d7697-d2a5-422f-8ec4-a4241e763787"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "GPU is available!\n"
          ]
        }
      ],
      "source": [
        "if torch.cuda.is_available():\n",
        "    device = torch.device(\"cuda\")\n",
        "    print(\"GPU is available!\")\n",
        "else:\n",
        "    device = torch.device(\"cpu\")\n",
        "    print(\"GPU is not available. Using CPU.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "FBS4ycZIXYsx",
        "outputId": "cd58b3c5-413c-43f9-c54d-7a4a55b56e86"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=FasterRCNN_ResNet50_FPN_Weights.COCO_V1`. You can also use `weights=FasterRCNN_ResNet50_FPN_Weights.DEFAULT` to get the most up-to-date weights.\n",
            "  warnings.warn(msg)\n",
            "Downloading: \"https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth\" to /root/.cache/torch/hub/checkpoints/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth\n",
            "100%|██████████| 160M/160M [00:02<00:00, 76.3MB/s]\n"
          ]
        },
        {
          "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",
              ")"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Define the model\n",
        "model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)\n",
        "model.eval()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "LmoRZlIDXbfd",
        "outputId": "682579d1-e6f4-43e2-dbc2-04db531286b0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2023-07-25 12:50:53--  http://images.cocodataset.org/zips/train2017.zip\n",
            "Resolving images.cocodataset.org (images.cocodataset.org)... 52.217.234.217, 54.231.192.209, 52.217.99.140, ...\n",
            "Connecting to images.cocodataset.org (images.cocodataset.org)|52.217.234.217|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 19336861798 (18G) [application/zip]\n",
            "Saving to: ‘train2017.zip’\n",
            "\n",
            "train2017.zip       100%[===================>]  18.01G  13.5MB/s    in 24m 42s \n",
            "\n",
            "2023-07-25 13:15:36 (12.4 MB/s) - ‘train2017.zip’ saved [19336861798/19336861798]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget http://images.cocodataset.org/zips/train2017.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "u8y5SBngXu-H",
        "outputId": "e22598e7-46bb-4f64-9665-4a6066b2dc70"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            " extracting: train2017/000000259556.jpg  \n",
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            " extracting: train2017/000000068565.jpg  \n",
            " extracting: train2017/000000159774.jpg  \n",
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            " extracting: train2017/000000157184.jpg  \n",
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            " extracting: train2017/000000224928.jpg  \n",
            " extracting: train2017/000000037336.jpg  \n",
            " extracting: train2017/000000178771.jpg  \n",
            " extracting: train2017/000000024577.jpg  \n",
            " extracting: train2017/000000256903.jpg  \n",
            " extracting: train2017/000000421882.jpg  \n",
            " extracting: train2017/000000318200.jpg  \n",
            " extracting: train2017/000000544691.jpg  \n",
            " extracting: train2017/000000325022.jpg  \n",
            " extracting: train2017/000000417478.jpg  \n",
            " extracting: train2017/000000000670.jpg  \n",
            " extracting: train2017/000000299115.jpg  \n",
            " extracting: train2017/000000138641.jpg  \n",
            " extracting: train2017/000000293825.jpg  \n",
            " extracting: train2017/000000483297.jpg  \n",
            " extracting: train2017/000000343821.jpg  \n",
            " extracting: train2017/000000523245.jpg  \n",
            " extracting: train2017/000000443360.jpg  \n",
            " extracting: train2017/000000005706.jpg  \n",
            " extracting: train2017/000000489983.jpg  \n",
            " extracting: train2017/000000206934.jpg  \n",
            " extracting: train2017/000000564481.jpg  \n"
          ]
        }
      ],
      "source": [
        "!unzip train2017.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "ezr7C9E5aN3V"
      },
      "outputs": [],
      "source": [
        "!rm train2017.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "cEgwGOPxa3LZ",
        "outputId": "2c29713c-89f7-4f20-8052-ebd0749101af"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Len of Train Images is:  118287\n"
          ]
        }
      ],
      "source": [
        "image_path = os.listdir(r'/content/train2017')\n",
        "print('Len of Train Images is: ',len(image_path))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "-do_oflia7Ei"
      },
      "outputs": [],
      "source": [
        "os.chdir(r'/content/train2017')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "mp9IFGxCbONV"
      },
      "outputs": [],
      "source": [
        "def resize_img(img, scale_precent):\n",
        "    width = int(img.shape[1]*scale_precent/100)\n",
        "    hight = int(img.shape[0]*scale_precent/100)\n",
        "    dim = (width, hight)\n",
        "\n",
        "    resized = cv2.resize(img, dim)\n",
        "    return resized"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "Z3Mo-qhWbQG-"
      },
      "outputs": [],
      "source": [
        "\n",
        "# Create a dictionary with label numbers as keys and tensors as values\n",
        "tensor_dict = {}\n",
        "def preprocess_image5(image_path, model):\n",
        "    # Open the image\n",
        "    image = cv2.imread(image_path)\n",
        "    image = resize_img(image, 50)\n",
        "\n",
        "    # Transform the image to a tensor\n",
        "    transform = transforms.Compose([\n",
        "        transforms.ToTensor(),\n",
        "    ])\n",
        "    image_tensor = transform(image)\n",
        "\n",
        "    # Add a batch dimension\n",
        "    image_tensor = image_tensor.unsqueeze(0)\n",
        "\n",
        "    # Register the forward hook to capture intermediate outputs\n",
        "    outputs = []\n",
        "    hook = model.backbone.register_forward_hook(lambda self, image_tensor, output: outputs.append(output))\n",
        "\n",
        "    # Perform inference on the preprocessed image\n",
        "    res = model(image_tensor)\n",
        "\n",
        "    # Remove the forward hook\n",
        "    hook.remove()\n",
        "\n",
        "    # Extract selected ROIs using the intermediate outputs\n",
        "    selected_rois = model.roi_heads.box_roi_pool(outputs[0], [r['boxes'] for r in res], [i.shape[-2:] for i in image_tensor])\n",
        "\n",
        "    # Define the threshold value\n",
        "    threshold = 0.5\n",
        "\n",
        "    # Get the box features and scores\n",
        "    box_features = res[0]\n",
        "    scores = box_features.get('scores')  # Assuming the last column contains the scores\n",
        "\n",
        "    # Find indices of scores above the threshold\n",
        "    above_threshold_indices = torch.where(scores > threshold)[0]\n",
        "\n",
        "    # Get scores above the threshold\n",
        "    above_threshold_scores = scores[above_threshold_indices]\n",
        "\n",
        "    # Find the box features above the threshold\n",
        "    above_threshold_box_features = selected_rois[above_threshold_indices]\n",
        "\n",
        "    #print(above_threshold_box_features.shape)\n",
        "\n",
        "\n",
        "    for i, label in enumerate(res[0]['labels'][above_threshold_indices]):\n",
        "        label_number = label.item()\n",
        "        if image_path in tensor_dict:\n",
        "            tensor_dict[image_path][label_number] = above_threshold_box_features[i]\n",
        "        else:\n",
        "            tensor_dict[image_path] = {label_number: above_threshold_box_features[i]}\n",
        "\n",
        "\n",
        "\n",
        "    return tensor_dict\n",
        "    # return image_tensor, above_threshold_box_features\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rcSTKQqjbTIh"
      },
      "outputs": [],
      "source": [
        "batch_size = 10\n",
        "num_images = len(image_path)\n",
        "num_batches = num_images // batch_size\n",
        "\n",
        "# Specify the directory path to save the pickle files\n",
        "save_directory = r\"/content/drive/MyDrive/PKL files\"\n",
        "\n",
        "# Loop over each batch\n",
        "for batch_num in range(num_batches):\n",
        "    # Initialize an empty dictionary to store the tensors for this batch\n",
        "    tensor_dict = {}\n",
        "\n",
        "    # Loop over images in the current batch\n",
        "    for i in range(batch_num * batch_size, (batch_num + 1) * batch_size):\n",
        "        if i >= num_images:\n",
        "            break\n",
        "        path = image_path[i]\n",
        "        # Assuming preprocess_image5 returns the tensor for each image\n",
        "        tensor = preprocess_image5(path, model)\n",
        "\n",
        "        # Save the tensor in the dictionary with a key corresponding to the image index\n",
        "        tensor_dict[i] = tensor\n",
        "\n",
        "    # Save the tensors for this batch in a separate file with specified path\n",
        "    file_path = os.path.join(save_directory, f'tensor_dict_batch_{batch_num}.pkl')\n",
        "    with open(file_path, 'wb') as f:\n",
        "        pickle.dump(tensor_dict, f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "V1opobrZccKD"
      },
      "outputs": [],
      "source": [
        "save_directory = r\"/content/drive/MyDrive/PKL files\"\n",
        "file_path = os.path.join(save_directory, f'tensor_dict_batch_1000.pkl')\n",
        "for i in image_path[:1000]:\n",
        "  preprocess_image5(i,model)\n",
        "  with open('tensor_dict_1000.pkl', 'wb') as f:\n",
        "    pickle.dump(tensor_dict, f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZLB_K5tZfsmq"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}