Lecture 9.2 - Background Subtraction.ipynb

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  {
   "cell_type": "markdown",
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   "source": [
    "## Background Subtraction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Gaussian Mixture-based Background/Foreground Segmentation Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "source": [
    "# OpenCV 2.4.13 only\n",
    "import numpy as np\n",
    "import cv2\n",
    "\n",
    "cap = cv2.VideoCapture('walking.avi')\n",
    "\n",
    "# Initlaize background subtractor\n",
    "foreground_background = cv2.BackgroundSubtractorMOG()\n",
    "\n",
    "while True:\n",
    "    \n",
    "    ret, frame = cap.read()\n",
    "\n",
    "    # Apply background subtractor to get our foreground mask\n",
    "    foreground_mask = foreground_background.apply(frame)\n",
    "\n",
    "    cv2.imshow('Output', foreground_mask)\n",
    "    if cv2.waitKey(1) == 13: \n",
    "        break\n",
    "\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What about using this on our webcam input?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "# OpenCV 2.4.13 only\n",
    "import numpy as np\n",
    "import cv2\n",
    "\n",
    "# Intialize Webcam\n",
    "cap = cv2.VideoCapture(0)\n",
    "\n",
    "# Initlaize background subtractor\n",
    "foreground_background = cv2.BackgroundSubtractorMOG()\n",
    "\n",
    "while True:\n",
    "    \n",
    "    ret, frame = cap.read()\n",
    "\n",
    "    # Apply background subtractor to get our foreground mask\n",
    "    foreground_mask = foreground_background.apply(frame)\n",
    "\n",
    "    cv2.imshow('Output', foreground_mask)\n",
    "    if cv2.waitKey(1) == 13: \n",
    "        break\n",
    "\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's try the Improved adaptive Gausian mixture model for background subtraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# OpenCV 2.4.13\n",
    "import numpy as np\n",
    "import cv2\n",
    "\n",
    "cap = cv2.VideoCapture('walking.avi')\n",
    "\n",
    "# Initlaize background subtractor\n",
    "foreground_background = cv2.BackgroundSubtractorMOG2()\n",
    "\n",
    "while True:\n",
    "    ret, frame = cap.read()\n",
    "\n",
    "    # Apply background subtractor to get our foreground mask\n",
    "    foreground_mask = foreground_background.apply(frame)\n",
    "\n",
    "    cv2.imshow('Output', foreground_mask)\n",
    "    if cv2.waitKey(1) == 13: \n",
    "        break\n",
    "\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying it to our webcam stream"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# OpenCV 2.4.13\n",
    "import numpy as np\n",
    "import cv2\n",
    "\n",
    "# Intialize Webcam\n",
    "cap = cv2.VideoCapture(0)\n",
    "\n",
    "# Initlaize background subtractor\n",
    "foreground_background = cv2.BackgroundSubtractorMOG2()\n",
    "\n",
    "while True:\n",
    "    ret, frame = cap.read()\n",
    "    \n",
    "    # Apply background subtractor to get our foreground mask\n",
    "    foreground_mask = foreground_background.apply(frame)\n",
    "\n",
    "    cv2.imshow('Output', foreground_mask)\n",
    "    if cv2.waitKey(1) == 13: \n",
    "        break\n",
    "\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What about foreground substraction?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "# Initalize webacam and store first frame\n",
    "cap = cv2.VideoCapture(0)\n",
    "ret, frame = cap.read()\n",
    "\n",
    "# Create a flaot numpy array with frame values\n",
    "average = np.float32(frame)\n",
    "\n",
    "while True:\n",
    "    # Get webcam frmae\n",
    "    ret, frame = cap.read()\n",
    "    \n",
    "    # 0.01 is the weight of image, play around to see how it changes\n",
    "    cv2.accumulateWeighted(frame, average, 0.01)\n",
    "    \n",
    "    # Scales, calculates absolute values, and converts the result to 8-bit\n",
    "    background = cv2.convertScaleAbs(average)\n",
    "\n",
    "    cv2.imshow('Input', frame)\n",
    "    cv2.imshow('Disapearing Background', background)\n",
    "    \n",
    "    if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
    "        break\n",
    "\n",
    "cv2.destroyAllWindows()\n",
    "cap.release()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Background Substraction KKN\n",
    "#### OpenCV 3.X only!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# OpenCV 3.1.0\n",
    "import numpy as np\n",
    "import cv2\n",
    "\n",
    "cap = cv2.VideoCapture(0)\n",
    "\n",
    "kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))\n",
    "fgbg = cv2.createBackgroundSubtractorKNN()\n",
    "\n",
    "while(1):\n",
    "    ret, frame = cap.read()\n",
    "\n",
    "    fgmask = fgbg.apply(frame)\n",
    "    fgmask = cv2.morphologyEx(fgmask, cv2.MORPH_OPEN, kernel)\n",
    "\n",
    "    cv2.imshow('frame',fgmask)\n",
    "    \n",
    "    if cv2.waitKey(1) == 13: \n",
    "        break\n",
    "\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  }
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