Lecture 6.3 - Mini Project # 6 - Car & Pedestrian Detection.ipynb

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    "## Mini Project # 6 - Car & Pedestrian Detection\n",
    "\n",
    "**NOTE** \n",
    "- If no video loads after running code, you may need to copy your *opencv_ffmpeg.dll* \n",
    "- From: *C:\\opencv2413\\opencv\\sources\\3rdparty\\ffmpeg*\n",
    "- To: Where your python is installed e.g. *C:\\Anaconda2\\* \\\n",
    "- Once it's copied you'll need to rename the file according to the version of OpenCV you're using.\n",
    "- e.g. if you're using OpenCV 2.4.13 then rename the file as:\n",
    "- **opencv_ffmpeg2413_64.dll** or opencv_ffmpeg2413.dll (if you're using an X86 machine)\n",
    "- **opencv_ffmpeg310_64.dll** or opencv_ffmpeg310.dll (if you're using an X86 machine)\n",
    "\n",
    "To find out where you python.exe is installed, just run these two lines of code:"
   ]
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     "text": [
      "C:\\Users\\krish.naik\\AppData\\Local\\Continuum\\anaconda3\\python.exe\n"
     ]
    }
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   "source": [
    "import sys\n",
    "print(sys.executable)"
   ]
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   "source": [
    "### Pedistrian Detection"
   ]
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   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "# Create our body classifier\n",
    "body_classifier = cv2.CascadeClassifier('Haarcascades\\haarcascade_fullbody.xml')\n",
    "\n",
    "# Initiate video capture for video file\n",
    "cap = cv2.VideoCapture('images/walking.avi')\n",
    "\n",
    "# Loop once video is successfully loaded\n",
    "while cap.isOpened():\n",
    "    \n",
    "    # Read first frame\n",
    "    ret, frame = cap.read()\n",
    "    frame = cv2.resize(frame, None,fx=0.5, fy=0.5, interpolation = cv2.INTER_LINEAR)\n",
    "\n",
    "    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
    "    # Pass frame to our body classifier\n",
    "    bodies = body_classifier.detectMultiScale(gray, 1.2, 3)\n",
    "    \n",
    "    # Extract bounding boxes for any bodies identified\n",
    "    for (x,y,w,h) in bodies:\n",
    "        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 255), 2)\n",
    "        cv2.imshow('Pedestrians', frame)\n",
    "\n",
    "    if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
    "        break\n",
    "\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
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   "metadata": {},
   "source": [
    "### Car Detection\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import time\n",
    "import numpy as np\n",
    "\n",
    "# Create our body classifier\n",
    "car_classifier = cv2.CascadeClassifier('Haarcascades\\haarcascade_car.xml')\n",
    "\n",
    "# Initiate video capture for video file\n",
    "cap = cv2.VideoCapture('images/cars.avi')\n",
    "\n",
    "\n",
    "# Loop once video is successfully loaded\n",
    "while cap.isOpened():\n",
    "    \n",
    "    time.sleep(.05)\n",
    "    # Read first frame\n",
    "    ret, frame = cap.read()\n",
    "    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
    "   \n",
    "    # Pass frame to our car classifier\n",
    "    cars = car_classifier.detectMultiScale(gray, 1.4, 2)\n",
    "    \n",
    "    # Extract bounding boxes for any bodies identified\n",
    "    for (x,y,w,h) in cars:\n",
    "        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 255), 2)\n",
    "        cv2.imshow('Cars', frame)\n",
    "\n",
    "    if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
    "        break\n",
    "\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
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   "source": [
    "- **Full Body / Pedestrian Classifier ** - https://github.com/opencv/opencv/blob/master/data/haarcascades/haarcascade_fullbody.xml\n",
    "- **Car Classifier ** - http://www.codeforge.com/read/241845/cars3.xml__html\n"
   ]
  }
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