(3) Car Detection with OpenCV by using Trained YOLO weights.ipynb

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   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import glob\n",
    "import random\n",
    "\n",
    "\n",
    "# Load Yolo\n",
    "net = cv2.dnn.readNet(\"yolov3_training_last.weights\", \"yolov3_testing.cfg\")\n",
    "\n",
    "# Name custom object\n",
    "classes = [\"Car\"]\n",
    "\n",
    "\n",
    "\n",
    "layer_names = net.getLayerNames()\n",
    "output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]\n",
    "colors = np.random.uniform(0, 255, size=(len(classes), 3))\n"
   ]
  },
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   "source": [
    "# Image path to be used in detection. for example 'vid_5_31720.jpg' image's path\n",
    "img_path = \"testing_images/vid_5_31720.jpg\"\n",
    "\n",
    "#\n",
    "img = cv2.imread(img_path)\n",
    "img = cv2.resize(img, None, fx=0.4, fy=0.4)\n",
    "height, width, channels = img.shape\n",
    "\n",
    "# Detecting objects\n",
    "blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)\n",
    "# [blobFromImage] creates 4-dimensional blob from image. \n",
    "#Optionally resizes and crops image from center, subtract mean values,\n",
    "#scales values by scalefactor, swap Blue and Red channels\n",
    "\n",
    "net.setInput(blob)\n",
    "outs = net.forward(output_layers)\n",
    "\n",
    "# Showing informations on the screen\n",
    "class_ids = []\n",
    "confidences = []\n",
    "boxes = []\n",
    "for out in outs:\n",
    "    for detection in out:\n",
    "        scores = detection[5:]\n",
    "        class_id = np.argmax(scores)\n",
    "        confidence = scores[class_id]\n",
    "        if confidence > 0.3:\n",
    "            # Object detected\n",
    "            print(class_id)\n",
    "            center_x = int(detection[0] * width)\n",
    "            center_y = int(detection[1] * height)\n",
    "            w = int(detection[2] * width)\n",
    "            h = int(detection[3] * height)\n",
    "\n",
    "            # Rectangle coordinates\n",
    "            x = int(center_x - w / 2)\n",
    "            y = int(center_y - h / 2)\n",
    "\n",
    "            boxes.append([x, y, w, h])\n",
    "            confidences.append(float(confidence))\n",
    "            class_ids.append(class_id)\n",
    "\n",
    "indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)\n",
    "print(indexes)\n",
    "font = cv2.FONT_HERSHEY_PLAIN\n",
    "for i in range(len(boxes)):\n",
    "    if i in indexes:\n",
    "        x, y, w, h = boxes[i]\n",
    "        label = str(classes[class_ids[i]])\n",
    "        color = colors[class_ids[i]]\n",
    "        cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)\n",
    "        cv2.putText(img, label, (x, y + 30), font, 3, color, 2)\n",
    "\n",
    "\n",
    "cv2.imshow(\"Image\", img)\n",
    "key = cv2.waitKey(0)"
   ]
  }
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