Sheep_Detection.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
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     "text": [
      "4.5.1\n"
     ]
    }
   ],
   "source": [
    "#Importing libraries\n",
    "import cv2\n",
    "import numpy as np\n",
    "print(cv2.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading Yolo Weights & Configuration\n",
    "net = cv2.dnn.readNet('yolov3.weights', 'yolov3.cfg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extracting the object names from the coco file & put everything into a array\n",
    "classes = []\n",
    "with open('coco.names','r') as f:\n",
    "    classes = f.read().splitlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['person', 'bicycle', 'car', 'motorbike', 'aeroplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'sofa', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tvmonitor', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush']\n"
     ]
    }
   ],
   "source": [
    "print(classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reading and scaling image\n",
    "img = cv2.imread('image.jpg')\n",
    "height, width, _ = img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reading and scaling video\n",
    "#while True:\n",
    "#cap = cv2.VideoCapture('test.mp4')\n",
    "#height, width, _ = img.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What is Mean Substraction\n",
    "Mean subtraction is used to help combat illumination changes in the input images in our dataset. We can therefore view mean subtraction as a technique used to aid our Convolutional Neural Networks.\n",
    "## SwapRB parameter \n",
    "OpenCV assumes images are in BGR channel order; however, the `mean` value assumes we are using RGB order. To resolve this discrepancy we can swap the R and B channels in image  by setting this value to `True`. By default OpenCV performs this channel swapping for us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalizing by dividing the pixel value 255 & indicating image size / Mean subtraction / swapRB converting the RB our image \n",
    "# Mean Subsraction \n",
    "blob = cv2.dnn.blobFromImage(img, 1/255, (416,416), (0,0,0), swapRB=True, crop=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting Input\n",
    "net.setInput(blob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#OutputLayerNames Connection\n",
    "output_layers_names = net.getUnconnectedOutLayersNames()\n",
    "layerOutputs = net.forward(output_layers_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extracting bounding boxes array\n",
    "boxes = []\n",
    "#Storing confidences\n",
    "confidences = []\n",
    "#Storing Class_Id\n",
    "class_ids = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating 2 for loops\n",
    "# First loop for extracting all the informations from the layers outputs\n",
    "# Second for loop is used to extract the information from the each of the output \n",
    "for output in layerOutputs:\n",
    "    for detection in output:\n",
    "        scores = detection[5:]\n",
    "        class_id = np.argmax(scores)\n",
    "        confidence = scores[class_id]\n",
    "        if confidence > 0.5:\n",
    "#Center size of the object\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",
    "            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",
    "\n",
    "            \n",
    "indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5,0.4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "font = cv2.FONT_HERSHEY_PLAIN\n",
    "colors = np.random.uniform(0,255, size=(len(boxes),3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Identify each of the objects detected\n",
    "for i in indexes.flatten():\n",
    "#Extracting each of the boxes(identify locations)   \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+20), font,2,(255,255,255),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv2.imshow('Image', img)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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