1  {
   2   "cells": [
   3    {
   4     "cell_type": "code",
   5     "execution_count": 7,
   6     "id": "f810581d",
   7     "metadata": {},
   8     "outputs": [],
   9     "source": [
  10      "import cv2\n",
  11      "import numpy as np\n",
  12      "\n",
  13      "# Load Yolo\n",
  14      "net = cv2.dnn.readNet(\"yolov3-tiny.weights\", \"yolov3-tiny.cfg\")\n",
  15      "classes = []\n",
  16      "with open(\"coco.names\", \"r\") as f:\n",
  17      "    classes = [line.strip() for line in f.readlines()]\n",
  18      "layer_names = net.getLayerNames()\n",
  19      "output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]\n",
  20      "colors = np.random.uniform(0, 255, size=(len(classes), 3))\n",
  21      "\n",
  22      "cap = cv2.VideoCapture('data/sample3.mp4')\n",
  23      "# Loading image\n",
  24      "while True:\n",
  25      "    ret, img = cap.read()\n",
  26      "    key = cv2.waitKey(1)\n",
  27      "    if key == ord('q') or ret == False:\n",
  28      "            break\n",
  29      "    else:\n",
  30      "        img = cv2.resize(img, None, fx=0.7, fy=0.7)\n",
  31      "        height, width, channels = img.shape\n",
  32      "\n",
  33      "        # Detecting objects\n",
  34      "        blob = cv2.dnn.blobFromImage(img, 1/255, (width, height), (0, 0, 0), True, crop=False)\n",
  35      "\n",
  36      "        net.setInput(blob)\n",
  37      "        outs = net.forward(output_layers)\n",
  38      "\n",
  39      "        # Showing informations on the screen\n",
  40      "        class_ids = []\n",
  41      "        confidences = []\n",
  42      "        boxes = []\n",
  43      "        for out in outs:\n",
  44      "            for detection in out:\n",
  45      "                scores = detection[5:]\n",
  46      "                class_id = np.argmax(scores)\n",
  47      "                confidence = scores[class_id]\n",
  48      "                if confidence > 0.2:\n",
  49      "                    # Object detected\n",
  50      "                    center_x = int(detection[0] * width)\n",
  51      "                    center_y = int(detection[1] * height)\n",
  52      "                    w = int(detection[2] * width)\n",
  53      "                    h = int(detection[3] * height)\n",
  54      "\n",
  55      "                    # Rectangle coordinates\n",
  56      "                    x = int(center_x - w / 2)\n",
  57      "                    y = int(center_y - h / 2)\n",
  58      "\n",
  59      "                    boxes.append([x, y, w, h])\n",
  60      "                    confidences.append(float(confidence))\n",
  61      "                    class_ids.append(class_id)\n",
  62      "\n",
  63      "        indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)\n",
  64      "        #print(indexes)\n",
  65      "        font = cv2.FONT_HERSHEY_PLAIN\n",
  66      "        for i in range(len(boxes)):\n",
  67      "            if i in indexes:\n",
  68      "                x, y, w, h = boxes[i]\n",
  69      "                label = str(classes[class_ids[i]])\n",
  70      "                color = colors[class_ids[i]]\n",
  71      "                cv2.rectangle(img, (x, y), (x + w, y + h), (0,255,0) , 2)\n",
  72      "                cv2.putText(img, 'p', (x, y + 10), font, 3, color, 2)\n",
  73      "\n",
  74      "\n",
  75      "        cv2.imshow(\"Image\", img)\n",
  76      "cv2.destroyAllWindows()"
  77     ]
  78    },
  79    {
  80     "cell_type": "code",
  81     "execution_count": null,
  82     "id": "7c2e776a",
  83     "metadata": {},
  84     "outputs": [],
  85     "source": []
  86    }
  87   ],
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  89    "kernelspec": {
  90     "display_name": "Python 3",
  91     "language": "python",
  92     "name": "python3"
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  94    "language_info": {
  95     "codemirror_mode": {
  96      "name": "ipython",
  97      "version": 3
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  99     "file_extension": ".py",
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