Custom Object detection live video.ipynb

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   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#auther - Jay Shankar Bhatt \n",
    "# using this code without author's permission other then leaning task is strictly prohibited"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## provide the path for testing cofing file and tained model form colab\n",
    "net = cv2.dnn.readNetFromDarknet(\"yolov3_custom.cfg\",r\"Downloads\\yolov3_custom_6000.weights\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Change here for custom classes for trained model \n",
    "\n",
    "classes = ['Elon_Musk','Barak_obama','Mark_Zuckerberg']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cap = cv2.VideoCapture(0)\n",
    "\n",
    "while 1:\n",
    "    _, img = cap.read()\n",
    "    img = cv2.resize(img,(1280,720))\n",
    "    hight,width,_ = img.shape\n",
    "    blob = cv2.dnn.blobFromImage(img, 1/255,(416,416),(0,0,0),swapRB = True,crop= False)\n",
    "\n",
    "    net.setInput(blob)\n",
    "\n",
    "    output_layers_name = net.getUnconnectedOutLayersNames()\n",
    "\n",
    "    layerOutputs = net.forward(output_layers_name)\n",
    "\n",
    "    boxes =[]\n",
    "    confidences = []\n",
    "    class_ids = []\n",
    "\n",
    "    for output in layerOutputs:\n",
    "        for detection in output:\n",
    "            score = detection[5:]\n",
    "            class_id = np.argmax(score)\n",
    "            confidence = score[class_id]\n",
    "            if confidence > 0.7:\n",
    "                center_x = int(detection[0] * width)\n",
    "                center_y = int(detection[1] * hight)\n",
    "                w = int(detection[2] * width)\n",
    "                h = int(detection[3]* hight)\n",
    "                x = int(center_x - w/2)\n",
    "                y = int(center_y - h/2)\n",
    "                boxes.append([x,y,w,h])\n",
    "                confidences.append((float(confidence)))\n",
    "                class_ids.append(class_id)\n",
    "\n",
    "\n",
    "    indexes = cv2.dnn.NMSBoxes(boxes,confidences,.5,.4)\n",
    "\n",
    "    boxes =[]\n",
    "    confidences = []\n",
    "    class_ids = []\n",
    "\n",
    "    for output in layerOutputs:\n",
    "        for detection in output:\n",
    "            score = detection[5:]\n",
    "            class_id = np.argmax(score)\n",
    "            confidence = score[class_id]\n",
    "            if confidence > 0.5:\n",
    "                center_x = int(detection[0] * width)\n",
    "                center_y = int(detection[1] * hight)\n",
    "                w = int(detection[2] * width)\n",
    "                h = int(detection[3]* hight)\n",
    "\n",
    "                x = int(center_x - w/2)\n",
    "                y = int(center_y - h/2)\n",
    "\n",
    "\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,.8,.4)\n",
    "    font = cv2.FONT_HERSHEY_PLAIN\n",
    "    colors = np.random.uniform(0,255,size =(len(boxes),3))\n",
    "    if  len(indexes)>0:\n",
    "        for i in indexes.flatten():\n",
    "            x,y,w,h = boxes[i]\n",
    "            label = str(classes[class_ids[i]])\n",
    "            confidence = str(round(confidences[i],2))\n",
    "            color = colors[i]\n",
    "            cv2.rectangle(img,(x,y),(x+w,y+h),color,2)\n",
    "            cv2.putText(img,label + \" \" + confidence, (x,y+400),font,2,color,2)\n",
    "\n",
    "    cv2.imshow('img',img)\n",
    "    if cv2.waitKey(1) == ord('q'):\n",
    "        break\n",
    "    \n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  }
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