15_Object_Detection_Sliding_Window.ipynb

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    "---\n",
    "title: 16 Object Detection using Sliding Window and Image Pyramid\n",
    "description: Object Detection using Sliding Window and Image Pyramid\n",
    "---"
   ]
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    "<a href=\"https://colab.research.google.com/drive/1CiLN6g7puBheHq-tfk4YB4X-Lgypur8M?usp=sharing#scrollTo=93c3dbee\" target=\"_blank\"><img align=\"left\" alt=\"Colab\" title=\"Open in Colab\" src=\"https://colab.research.google.com/assets/colab-badge.svg\"></a>"
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    "<a href=\"https://www.kaggle.com/code/ritvik1909/object-detection-sliding-window\" 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 Sliding Window\n",
    "\n",
    "We will be using concepts of Image Pyramid, Sliding Window and Non-Maxima Supression to turn almost any Image Classifier into an Object Detector\n",
    "* **Image Pyramid**: \n",
    "  * An image pyramid is a multi-scale representation of an image, allowing us to find objects in images at different scales of an image.\n",
    "  * At the bottom of the pyramid, we have the original image at its original size (in terms of width and height).\n",
    "  * And at each subsequent layer, the image is resized (subsampled) and optionally smoothed (usually via Gaussian blurring).\n",
    "  * The image is progressively subsampled until some stopping criterion is met, which is normally when a minimum size has been reached and no further subsampling needs to take place.\n",
    "* **Sliding Window**:\n",
    "  * A sliding window is a fixed-size rectangle that slides from left-to-right and top-to-bottom within an image.\n",
    "  * At each stop of the window we would:\n",
    "    * Extract the ROI\n",
    "    * Pass it through our image classifier\n",
    "    * Obtain the output predictions\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/2020/06/22/turning-any-cnn-image-classifier-into-an-object-detector-with-keras-tensorflow-and-opencv/)"
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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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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
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     "output_type": "display_data"
    }
   ],
   "source": [
    "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
    "orig = np.zeros((28, 84))\n",
    "orig[:, :28] = x_train[0]\n",
    "orig[:, 28:56] = x_train[2]\n",
    "orig[:, 56:] = x_train[3]\n",
    "(H, W) = orig.shape[:2]\n",
    "plt.imshow(orig, cmap='gray');"
   ]
  },
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   "source": [
    "class SlidingWindowObjectDetection():\n",
    "    def __init__(self, pretrained_classifier_path, **kwargs):\n",
    "        self.model = from_pretrained_keras(pretrained_classifier_path)\n",
    "        self.kwargs = kwargs\n",
    "    \n",
    "    def sliding_window(self, image, step, ws):\n",
    "        for y in range(0, image.shape[0] - ws[1], step):\n",
    "            for x in range(0, image.shape[1] - ws[0], step):\n",
    "                yield (x, y, image[y:y + ws[1], x:x + ws[0]])\n",
    "\n",
    "    def image_pyramid(self, image, scale=1.5, minSize=(28, 28)):\n",
    "        yield image\n",
    "        while True:\n",
    "            w = int(image.shape[1] / scale)\n",
    "            image = imutils.resize(image, width=w)\n",
    "            if image.shape[0] < minSize[1] or image.shape[1] < minSize[0]:\n",
    "                break\n",
    "            yield image   \n",
    "            \n",
    "    def get_rois_and_locs(self, pyramid):\n",
    "        rois = []\n",
    "        locs = []\n",
    "        for image in pyramid:\n",
    "            scale = W / float(image.shape[1])\n",
    "            for (x, y, roiOrig) in self.sliding_window(image, self.kwargs['WIN_STEP'], self.kwargs['ROI_SIZE']):\n",
    "                x = int(x * scale)\n",
    "                y = int(y * scale)\n",
    "                w = int(self.kwargs['ROI_SIZE'][0] * scale)\n",
    "                h = int(self.kwargs['ROI_SIZE'][1] * scale)\n",
    "\n",
    "                roi = cv2.resize(roiOrig, self.kwargs['INPUT_SIZE'])\n",
    "\n",
    "                rois.append(roi)\n",
    "                locs.append((x, y, x + w, y + 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, endX, endY) in boxes:\n",
    "                cv2.rectangle(clone, (startX, startY), (endX, endY), (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",
    "            ax.imshow(clone, cmap='gray')\n",
    "    \n",
    "    def __call__(self, img):\n",
    "        pyramid = self.image_pyramid(img, scale=self.kwargs['PYR_SCALE'], minSize=self.kwargs['ROI_SIZE'])\n",
    "        rois, locs = self.get_rois_and_locs(pyramid)\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",
    "        \n",
    "        return nms_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf632d44",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-01T17:43:05.992169Z",
     "iopub.status.busy": "2022-04-01T17:43:05.991490Z",
     "iopub.status.idle": "2022-04-01T17:43:05.995449Z",
     "shell.execute_reply": "2022-04-01T17:43:05.996010Z",
     "shell.execute_reply.started": "2022-04-01T17:41:40.911708Z"
    },
    "id": "bf632d44",
    "papermill": {
     "duration": 0.019499,
     "end_time": "2022-04-01T17:43:05.996167",
     "exception": false,
     "start_time": "2022-04-01T17:43:05.976668",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "kwargs = dict(\n",
    "    PYR_SCALE=1.25,\n",
    "    WIN_STEP=7,\n",
    "    ROI_SIZE=(21, 21),\n",
    "    INPUT_SIZE=(28, 28),\n",
    "    VISUALIZE=True,\n",
    "    MIN_CONF=0.8,\n",
    "    VIZ_ROIS=True\n",
    ")"
   ]
  },
  {
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   "execution_count": null,
   "id": "a1624499",
   "metadata": {
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     "height": 1000,
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    },
    "execution": {
     "iopub.execute_input": "2022-04-01T17:43:06.024209Z",
     "iopub.status.busy": "2022-04-01T17:43:06.023594Z",
     "iopub.status.idle": "2022-04-01T17:43:12.634753Z",
     "shell.execute_reply": "2022-04-01T17:43:12.635280Z",
     "shell.execute_reply.started": "2022-04-01T17:41:40.929206Z"
    },
    "id": "a1624499",
    "outputId": "555d1bc8-ca9c-49f7-f026-24dc2eb8c211",
    "papermill": {
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "config.json not found in HuggingFace Hub\n"
     ]
    },
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       "Downloading:   0%|          | 0.00/4.83M [00:00<?, ?B/s]"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions:  {1: [[52, 0, 78, 26]], 2: [[21, 0, 42, 21]], 3: [[8, 0, 34, 26]], 4: [[26, 0, 52, 26]], 5: [[0, 0, 26, 26]], 7: [[14, 0, 35, 21]]}\n"
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\n",
      "text/plain": [
       "<Figure size 1440x432 with 16 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"
    },
    {
     "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 = SlidingWindowObjectDetection('Ritvik19/mnist-net', **kwargs)\n",
    "preds = model(orig)\n",
    "print(\"Predictions: \", preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b52a7e6",
   "metadata": {
    "id": "8b52a7e6",
    "papermill": {
     "duration": 0.017854,
     "end_time": "2022-04-01T17:43:12.671513",
     "exception": false,
     "start_time": "2022-04-01T17:43:12.653659",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Object Detection Sliding Window.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 39.563539,
   "end_time": "2022-04-01T17:43:15.733024",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2022-04-01T17:42:36.169485",
   "version": "2.3.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}