16_Object_Detection_Selective_Search.ipynb

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    "---\n",
    "title: 17 Object Detection using Selective Search\n",
    "description: A basic object detection implementation using selective search on top of an image classifer\n",
    "---"
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    "<a href=\"https://www.kaggle.com/code/ritvik1909/object-detection-selective-search\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>"
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    "# Object Detection using Selective Search\n",
    "\n",
    "We will be using concepts of Selective Search and Non-Maxima Supression to turn almost any Image Classifier into an Object Detector\n",
    "* **Selective Search**: \n",
    "  * Selective Search algorithm over-segments an image via:\n",
    "    * Color similarity: Computing a 25-bin histogram for each channel of an image, concatenating them together, and obtaining a final descriptor that is 25×3=75-d. Color similarity of any two regions is measured by the histogram intersection distance.\n",
    "    * Texture similarity: For texture, Selective Search extracts Gaussian derivatives at 8 orientations per channel (assuming a 3-channel image). These orientations are used to compute a 10-bin histogram per channel, generating a final texture descriptor that is 8x10x=240-d. To compute texture similarity between any two regions, histogram intersection is once again used.\n",
    "    * Size similarity: The size similarity metric that Selective Search uses prefers that smaller regions be grouped earlier rather than later. By enforcing smaller regions to merge earlier, we can help prevent a large number of clusters from swallowing up all smaller regions.\n",
    "    * Shape similarity: The idea behind shape similarity in Selective Search is that they should be compatible with each other. Two regions are considered “compatible” if they “fit” into each other (thereby filling gaps in our regional proposal generation). Furthermore, shapes that do not touch should not be merged.\n",
    "    * A final meta-similarity measure: A final meta-similarity acts as a linear combination of the color similarity, texture similarity, size similarity, and shape similarity/compatibility.\n",
    "* **Non-Maxima Supression**:\n",
    "  * When performing object detection, our object detector will typically produce multiple, overlapping bounding boxes surrounding an object in an image.\n",
    "  * We somehow need to collapse/remove the extraneous bounding boxes.\n",
    "  * Non-maxima suppression (NMS) collapses weak, overlapping bounding boxes in favor of the more confident ones.\n",
    "  \n",
    "**Credits**: [PyImageSearch](https://pyimagesearch.com/2020/07/06/region-proposal-object-detection-with-opencv-keras-and-tensorflow/)"
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   "source": [
    "%%capture\n",
    "!pip install imutils huggingface_hub"
   ]
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   "source": [
    "import numpy as np\n",
    "import argparse\n",
    "import imutils\n",
    "import time\n",
    "import cv2\n",
    "\n",
    "import tensorflow as tf\n",
    "from huggingface_hub import from_pretrained_keras\n",
    "\n",
    "from imutils.object_detection import non_max_suppression\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
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     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
      "11493376/11490434 [==============================] - 0s 0us/step\n",
      "11501568/11490434 [==============================] - 0s 0us/step\n"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
    "\n",
    "orig = np.zeros((28, 112))\n",
    "orig[:, :28] = x_train[0]\n",
    "orig[:, 28:56] = x_train[1]\n",
    "orig[:, 56:84] = x_train[2]\n",
    "orig[:, 84:] = x_train[3]\n",
    "(H, W) = orig.shape[:2]\n",
    "plt.imshow(orig, cmap='gray');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06e9e075",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-01T17:59:40.224590Z",
     "iopub.status.busy": "2022-04-01T17:59:40.223536Z",
     "iopub.status.idle": "2022-04-01T17:59:40.225541Z",
     "shell.execute_reply": "2022-04-01T17:59:40.225991Z",
     "shell.execute_reply.started": "2022-04-01T17:58:17.699949Z"
    },
    "id": "06e9e075",
    "papermill": {
     "duration": 0.030648,
     "end_time": "2022-04-01T17:59:40.226160",
     "exception": false,
     "start_time": "2022-04-01T17:59:40.195512",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class SelectiveSearchObjectDetection():\n",
    "    def __init__(self, pretrained_classifier_path, **kwargs):\n",
    "        self.model = from_pretrained_keras(pretrained_classifier_path)\n",
    "        self.kwargs = kwargs\n",
    "        \n",
    "    def selective_search(self, image, method=\"fast\"):\n",
    "        ss = cv2.ximgproc.segmentation.createSelectiveSearchSegmentation()\n",
    "        ss.setBaseImage(image)\n",
    "        if method == \"fast\":\n",
    "            ss.switchToSelectiveSearchFast()\n",
    "        else:\n",
    "            ss.switchToSelectiveSearchQuality()\n",
    "        return ss.process()\n",
    "    \n",
    "    def get_rois_and_locs(self, rects):\n",
    "        rois = []\n",
    "        locs = []\n",
    "        for (x, y, w, h) in rects:\n",
    "            if w / float(W) < 0.1 or h / float(H) < 0.1:\n",
    "                continue\n",
    "            roi = orig[y:y + h, x:x + w]\n",
    "            roi = cv2.resize(roi, self.kwargs['INPUT_SIZE'])\n",
    "            rois.append(roi)\n",
    "            locs.append((x, y, w, h))\n",
    "        return rois, locs\n",
    "    \n",
    "    def visualize_rois(self, rois):\n",
    "        fig, axes = plt.subplots(1, len(rois), figsize=(20, 6))\n",
    "        for ax, roi in zip(axes, rois):\n",
    "            ax.imshow(roi, cmap='gray')\n",
    "            \n",
    "    def get_preds(self, rois, locs):\n",
    "        rois = np.array(rois, dtype=\"float32\")\n",
    "        preds = self.model.predict(rois)\n",
    "        preds = list(zip(preds.argmax(axis=1).tolist(), preds.max(axis=1).tolist()))\n",
    "        labels = {}\n",
    "\n",
    "        for (i, p) in enumerate(preds):\n",
    "            (label, prob) = p\n",
    "            if prob >= self.kwargs['MIN_CONF']:\n",
    "                box = locs[i]\n",
    "                L = labels.get(label, [])\n",
    "                L.append((box, prob))\n",
    "                labels[label] = L\n",
    "        return preds, labels\n",
    "    \n",
    "    def apply_nms(self, labels):\n",
    "        nms_labels = {}\n",
    "        for label in sorted(labels.keys()):\n",
    "            boxes = np.array([p[0] for p in labels[label]])\n",
    "            proba = np.array([p[1] for p in labels[label]])\n",
    "            boxes = non_max_suppression(boxes, proba)\n",
    "            nms_labels[label] = boxes.tolist()\n",
    "        return nms_labels\n",
    "            \n",
    "    def visualize_preds(self, img, nms_labels):\n",
    "        for label in sorted(nms_labels.keys()):\n",
    "            clone = img.copy()\n",
    "            fig, ax = plt.subplots(figsize=(20, 6))\n",
    "            boxes = nms_labels[label]\n",
    "            for (startX, startY, width, height) in boxes:\n",
    "                cv2.rectangle(clone, (startX, startY), (startX+width, startY+height), (255, 255, 255), 1)\n",
    "                y = startY - 10 if startY - 10 > 10 else startY + 10\n",
    "                cv2.putText(clone, str(label), (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (255, 255, 255), 1)\n",
    "\n",
    "            ax.imshow(clone, cmap='gray')\n",
    "            ax.set_title(\"After\")\n",
    "    \n",
    "    def __call__(self, img):\n",
    "        rects = self.selective_search(np.dstack([img, img, img]).astype(np.float32))\n",
    "        rois, locs = self.get_rois_and_locs(rects)\n",
    "        if self.kwargs['VIZ_ROIS']:\n",
    "            self.visualize_rois(rois)\n",
    "        preds, labels = self.get_preds(rois, locs)\n",
    "        nms_labels = self.apply_nms(labels)\n",
    "        \n",
    "        if self.kwargs['VISUALIZE']:\n",
    "            self.visualize_preds(img, nms_labels)\n",
    "        return nms_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2a5b43d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-01T17:59:40.248487Z",
     "iopub.status.busy": "2022-04-01T17:59:40.247927Z",
     "iopub.status.idle": "2022-04-01T17:59:40.250911Z",
     "shell.execute_reply": "2022-04-01T17:59:40.251362Z",
     "shell.execute_reply.started": "2022-04-01T17:58:18.899820Z"
    },
    "id": "c2a5b43d",
    "papermill": {
     "duration": 0.015914,
     "end_time": "2022-04-01T17:59:40.251516",
     "exception": false,
     "start_time": "2022-04-01T17:59:40.235602",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "kwargs = dict(INPUT_SIZE=(28, 28), MIN_CONF=0.9, VISUALIZE=True, VIZ_ROIS=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f92e6df0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
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      "3788681052c348318758fffcfb9f4505",
      "c8eccb9a76ce4dbda3d06f923241ef0f",
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      "fce78648334846a3a4b93e13f85f1249",
      "ecd322dcbb5c4bad89ce5ccd9b5102a1",
      "0f4d47898748410f9256b198249f018c",
      "bee937739d434856be31a0ad574e45c8",
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      "6b0856aeb06c432a93fd27f5dca288fb",
      "375a139157894ba3a8d7da4dd218fcc7",
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      "ae6a74c2e023427db28603436a2cac2a",
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      "6464f8c5c4bf4230bb349ba0675368a7",
      "0ea9422cc9e144158b30b490e0e22ff9",
      "85e64bc8846a4f50822199934eb442c7",
      "d01407ec1c5e4548b553676ff7c3059f",
      "7875457ade384e97a3c63eb086387f24",
      "7121a4533aa04021a725a61564e088d7",
      "b1c46dbcb9de490299ca9aad831a2948",
      "d278ed188eca43ef836966abaeb3044b",
      "7a6abc817e84489ebf5123d0c81cb0e3",
      "9306b0ed01844203a86f24c988371ea7",
      "67a8f1b771fe41488fa5c384f6f08993",
      "bcbef9e603e7432a8ac6d5650d8fb3fd",
      "427e9b10f0b1436b944014168fe4d0d8",
      "ed0af336b5f54b72a7eea332cd0de4fc"
     ]
    },
    "execution": {
     "iopub.execute_input": "2022-04-01T17:59:40.272816Z",
     "iopub.status.busy": "2022-04-01T17:59:40.272251Z",
     "iopub.status.idle": "2022-04-01T17:59:47.406903Z",
     "shell.execute_reply": "2022-04-01T17:59:47.406292Z",
     "shell.execute_reply.started": "2022-04-01T17:58:19.755854Z"
    },
    "id": "f92e6df0",
    "outputId": "d3596abb-5e33-4b15-e795-d212664f4f45",
    "papermill": {
     "duration": 7.146192,
     "end_time": "2022-04-01T17:59:47.407041",
     "exception": false,
     "start_time": "2022-04-01T17:59:40.260849",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "config.json not found in HuggingFace Hub\n"
     ]
    },
    {
     "data": {
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       "model_id": "c83b3a4e54c3414e89060cf2b8a776eb",
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      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/1.29k [00:00<?, ?B/s]"
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     "output_type": "display_data"
    },
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       "Downloading:   0%|          | 0.00/373 [00:00<?, ?B/s]"
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     },
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    },
    {
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      "text/plain": [
       "Downloading:   0%|          | 0.00/530k [00:00<?, ?B/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "version_major": 2,
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      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/4.83M [00:00<?, ?B/s]"
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     },
     "metadata": {},
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    },
    {
     "data": {
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      "text/plain": [
       "Downloading:   0%|          | 0.00/27.1M [00:00<?, ?B/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "Downloading:   0%|          | 0.00/38.2k [00:00<?, ?B/s]"
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions:  {0: [[32, 2, 21, 24], [34, 3, 18, 22], [33, 3, 19, 22], [34, 4, 17, 20], [35, 5, 16, 18], [35, 4, 16, 19]], 1: [[90, 3, 18, 24], [91, 4, 16, 22]], 4: [[57, 3, 24, 24], [0, 0, 112, 28], [59, 5, 20, 20], [59, 5, 19, 21]], 5: [[3, 4, 22, 22]]}\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x432 with 19 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ritvik19/mnist-net is a convnet trained on mnist data read more: https://huggingface.co/Ritvik19/mnist-net\n",
    "model = SelectiveSearchObjectDetection('Ritvik19/mnist-net', **kwargs)\n",
    "preds = model(orig)\n",
    "print(\"Predictions: \", preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b143ea92",
   "metadata": {
    "id": "b143ea92",
    "papermill": {
     "duration": 0.015668,
     "end_time": "2022-04-01T17:59:47.438879",
     "exception": false,
     "start_time": "2022-04-01T17:59:47.423211",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Ending Notes\n",
    "* The above approach simplifies what happens in an RCNN\n",
    "* We can build a Fast RCNN using the same approach, with few additional steps:\n",
    "  * Data Preparation:\n",
    "      * Use selective search to detect objects in your image dataset\n",
    "      * Label those images with appropriate classes and Bounding boxes\n",
    "  * Modelling:\n",
    "      * Use a pretrained CNN to extract feature maps from the input image\n",
    "      * Use selective search to identify ROIs in the input image\n",
    "      * Use ROI Pooling to project the ROIs into the feature maps\n",
    "      * Train a downstream classifier to classify the images, and a bounding box regression model"
   ]
  }
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