read_feed.ipynb

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   "cell_type": "code",
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   "metadata": {
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   },
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   "source": [
    "import cv2 \n",
    "import numpy as np \n",
    "import math\n",
    "import scipy.misc\n",
    "import os\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def show_image(im):\n",
    "    cv2.imshow('Image',im)\n",
    "    cv2.waitKey(0)\n",
    "    cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#This function extracts the opposite points of a rectangle.\n",
    "def getp(rect):\n",
    "    x = []; y=[];\n",
    "    for i in range(0,4):\n",
    "        x.append(rect[i][0][0])\n",
    "        y.append(rect[i][0][1])\n",
    "    x.sort()\n",
    "    y.sort()\n",
    "    return[[x[1],x[2]],[y[1],y[2]]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
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   "outputs": [],
   "source": [
    "def show_contours(rects):\n",
    "    for i in range(0,len(rects)) :\n",
    "        area = cv2.contourArea(rects[i])\n",
    "        cv2.drawContours(im,rects,i,(((i%2)+1)*255,(i%2)*255,0),2)\n",
    "        cv2.imshow('before',im)\n",
    "        cv2.waitKey(0)\n",
    "        cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
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   "source": [
    "#This function arranges the images in order from left to right and also gets the diagonal points \n",
    "def arrange(rects):\n",
    "\tar = [[[1001000]]]\n",
    "\tfor r in rects:\n",
    "\t\ttr = getp(r)\n",
    "\t\tprint tr\n",
    "\t\tfor i in range(0, len(ar)):\n",
    "\t\t\tif(tr[0][0] < ar[i][0][0]):\n",
    "\t\t\t\tar.insert(i,tr)\n",
    "\t\t\t\tbreak\n",
    "\treturn ar[:-1]"
   ]
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   "execution_count": 223,
   "metadata": {
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   "source": [
    "def saveimages(crops,n):\n",
    "    os.chdir('/home/yash/Desktop/Neural Networks/Handwriting Recognition/')\n",
    "    for i in range(1,len(crops)+1) :\n",
    "        try:\n",
    "            scipy.misc.imsave('outputs/images/outfile'+str(i+n)+'.jpg', crops[i-1])\n",
    "        except:\n",
    "            print \"ok!\"\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "collapsed": true,
    "deletable": true,
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   "source": [
    "def savedata(crops):\n",
    "    os.chdir( cwd + '/outputs/data')\n",
    "    for i in range(0,len(crops)):\n",
    "        crops[i].dump(\"data\" + str(i+1) +\".data\")\n",
    "    os.chdir(cwd)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "collapsed": true,
    "deletable": true,
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   "source": [
    "#This function provides us with 100 nearly equal rectangles \n",
    "def filter(rects):\n",
    "\ttps = [[0]]\n",
    "\tfor r in rects:\n",
    "\t\tarea = cv2.contourArea(r)\n",
    "\t\tprint area\n",
    "\t\tx = True\n",
    "\t\tfor tp in tps:\n",
    "\t\t\tif(area > 0.8*tp[0] and area < 1.2*tp[0]):\n",
    "\t\t\t\ttp.append(r)\n",
    "\t\t\t\tx = False\n",
    "\t\tif(x):\n",
    "\t\t\ttps.append([area,r])\n",
    "\tfor tp in tps:\n",
    "\t\tif (tp[0] > 28*28):\n",
    "\t\t\treturn tp[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "collapsed": true,
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    "editable": true
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   "outputs": [],
   "source": [
    "def intialize_image(im,i):\n",
    "    print 'data' +str(i)+ '.jpg'\n",
    "    im = cv2.imread('data' +str(i)+ '.jpg')\n",
    "    height,width = im.shape[:2]\n",
    "    print height;print width\n",
    "    rwidth = 650\n",
    "    sfactor = float(width)/rwidth\n",
    "    rheight = int(math.ceil(float(height)/sfactor ))\n",
    "    if(sfactor > 1):\n",
    "        return cv2.resize(im, (rwidth,rheight))\n",
    "    else:\n",
    "        return im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "collapsed": false,
    "deletable": true,
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   "outputs": [],
   "source": [
    "def save(i):\n",
    "    os.chdir('/home/yash/Desktop/Neural Networks/Handwriting Recognition/dataset/')\n",
    "    #Saves the dimens of image, I wish to resize the image proportional to its original dimensions.\n",
    "    #The scaling factor is such that width will be 1000 else lesser for low pixel image.\n",
    "    im = cv2.imread('data' +str(i)+ '.jpg')\n",
    "    im = intialize_image(im,i)\n",
    "    height,width = im.shape[:2]\n",
    "    \n",
    "    \n",
    "    imgray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)\n",
    "    imgray = cv2.fastNlMeansDenoising(imgray,None,10,7,21)\n",
    "    #Thresholding requires a grayscale image 2nd param : threshvalue and 3rd param : maxValue of a pixel\n",
    "    ret,thresh = cv2.threshold(imgray,125,255,0)\n",
    "    show_image(thresh)\n",
    "    ret,thresh = cv2.threshold(imgray,125,255,0)\n",
    "    thresh = (255-thresh)\n",
    "\n",
    "    #Closing is dialation followed by erosion helps to fill out the gaps left out by creases in paper or disconnected components.\n",
    "    #Size of kernel is area of sliding window, I think it should be proportional to the size of image/boxes we will be using.\n",
    "    ki = int(math.ceil(float(width)/100))\n",
    "    # kernel = np.ones((ki,ki), np.uint8)\n",
    "    kernel = np.ones((4,4), np.uint8)\n",
    "    thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)\n",
    "    #Countours are curves joining all the continuous points having same colour or intensity.\n",
    "    #http://opencvpython.blogspot.in/2012/06/hi-this-article-is-tutorial-which-try.html\n",
    "    #The result \"contours\" is a Python list, where it contains all objects boundary points as separate lists.\n",
    "    #Whichever element of contoeur is to be drawn set the 2nd param accordingly on an index of zero, -1 to show all the contours \n",
    "    #Last arguement draws the boundary in pixels pass -1 for a filled image.\n",
    "    thresh2=thresh.copy()\n",
    "    im2, contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)\n",
    "\n",
    "\n",
    "    #Contour Approximation to detect shapes.\n",
    "    approx = []\n",
    "    for k in range(0,len(contours)) :\n",
    "        epsilon = 0.025*cv2.arcLength (contours[k],True)\n",
    "        approx.append(cv2.approxPolyDP(contours[k],epsilon,True))\n",
    "    #Separate the ones which are rectangles. :)\n",
    "    im = intialize_image(im,i)\n",
    "    rects = []\n",
    "    for j in range(0,len(approx)):\n",
    "        if(len(approx[j]) == 4):\n",
    "            rects.append(approx[j])\n",
    "\n",
    "    #Filter unnecessary rectangles if detected.\n",
    "    #Change this 6 by the no of integers required to be detected from the image which are placed in boxes.\n",
    "    print len(rects)\n",
    "    # rects = filter(rects)\n",
    "    # #Display detected rectangles not arranged on x axis till now.\n",
    "    # intialize_image(im)\n",
    "    # show_contours(rects)\n",
    "    print len(rects)\n",
    "\n",
    "    #Arrange in ascending order of x and put in opposite points \n",
    "    frects = arrange(rects)\n",
    "\n",
    "    #Cropping required from the inner sides of the edges.\n",
    "    im = intialize_image(im,i)\n",
    "    crops = []\n",
    "    for r in frects:\n",
    "        crops.append( im[ r[1][0]:r[1][1], r[0][0]:r[0][1] ] )\n",
    "    # for crop in crops:\n",
    "    #     cv2.imshow('crops',crop)\n",
    "    #     cv2.waitKey(0)\n",
    "    #     cv2.destroyAllWindows()\n",
    "    print i\n",
    "    saveimages(crops,(i+1)*1000)\n"
   ]
  },
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     "output_type": "stream",
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      "data2.jpg\n",
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      "data3.jpg\n",
      "3195\n",
      "2235\n",
      "3\n",
      "ok!\n",
      "data4.jpg\n",
      "3187\n",
      "2182\n",
      "data4.jpg\n",
      "3187\n",
      "2182\n",
      "121\n",
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      "data4.jpg\n",
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      "2182\n",
      "4\n",
      "ok!\n",
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      "ok!\n",
      "ok!\n",
      "ok!\n",
      "data5.jpg\n",
      "2971\n",
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      "data5.jpg\n",
      "2971\n",
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      "36\n",
      "36\n",
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      "data5.jpg\n",
      "2971\n",
      "2178\n",
      "5\n",
      "ok!\n",
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      "ok!\n",
      "ok!\n",
      "ok!\n",
      "ok!\n",
      "data6.jpg\n",
      "3171\n",
      "2198\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-231-130029c19d1e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-227-5d83c1cf2ef8>\u001b[0m in \u001b[0;36msave\u001b[0;34m(i)\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;31m#Thresholding requires a grayscale image 2nd param : threshvalue and 3rd param : maxValue of a pixel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mret\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mthresh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthreshold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimgray\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m125\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m255\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0mshow_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthresh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m     \u001b[0mret\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mthresh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthreshold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimgray\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m125\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m255\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0mthresh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m255\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mthresh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-211-bb0e7d35ff75>\u001b[0m in \u001b[0;36mshow_image\u001b[0;34m(im)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mshow_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Image'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwaitKey\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdestroyAllWindows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for i in range (0,8):\n",
    "    save(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data0.jpg\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'shape'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-229-bb108143696b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mintialize_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mim\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetcwd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-226-dd5f392357f6>\u001b[0m in \u001b[0;36mintialize_image\u001b[0;34m(im, i)\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;32mprint\u001b[0m \u001b[0;34m'data'\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m \u001b[0;34m'.jpg'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'data'\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m \u001b[0;34m'.jpg'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mheight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mwidth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0;32mprint\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;32mprint\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mrwidth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m650\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'shape'"
     ]
    }
   ],
   "source": [
    "im = intialize_image(im,0)\n",
    "print os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3301\n",
      "2239\n",
      "116\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "116\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[16, 619], [866, 919]]\n",
      "[[565, 624], [867, 929]]\n",
      "[[502, 562], [866, 926]]\n",
      "[[441, 500], [865, 924]]\n",
      "[[380, 438], [863, 923]]\n",
      "[[319, 377], [862, 922]]\n",
      "[[259, 316], [861, 921]]\n",
      "[[199, 256], [860, 920]]\n",
      "[[139, 195], [859, 919]]\n",
      "[[78, 135], [858, 918]]\n",
      "[[18, 74], [858, 917]]\n",
      "[[19, 621], [769, 823]]\n",
      "[[564, 623], [770, 831]]\n",
      "[[502, 561], [770, 829]]\n",
      "[[441, 499], [768, 827]]\n",
      "[[380, 438], [767, 826]]\n",
      "[[320, 377], [767, 825]]\n",
      "[[260, 317], [765, 824]]\n",
      "[[200, 257], [764, 823]]\n",
      "[[140, 197], [764, 823]]\n",
      "[[80, 137], [763, 822]]\n",
      "[[20, 76], [763, 822]]\n",
      "[[21, 619], [674, 728]]\n",
      "[[563, 622], [675, 735]]\n",
      "[[502, 560], [675, 733]]\n",
      "[[441, 499], [674, 732]]\n",
      "[[380, 438], [673, 732]]\n",
      "[[321, 378], [672, 730]]\n",
      "[[261, 318], [672, 729]]\n",
      "[[201, 258], [671, 729]]\n",
      "[[142, 198], [670, 728]]\n",
      "[[82, 138], [670, 727]]\n",
      "[[22, 78], [669, 727]]\n",
      "[[22, 623], [580, 635]]\n",
      "[[563, 621], [581, 640]]\n",
      "[[502, 559], [581, 639]]\n",
      "[[440, 499], [580, 639]]\n",
      "[[381, 438], [580, 637]]\n",
      "[[321, 378], [579, 637]]\n",
      "[[261, 318], [579, 636]]\n",
      "[[202, 259], [578, 636]]\n",
      "[[143, 199], [578, 635]]\n",
      "[[82, 140], [577, 635]]\n",
      "[[24, 80], [577, 634]]\n",
      "[[24, 622], [487, 543]]\n",
      "[[562, 621], [488, 546]]\n",
      "[[501, 559], [488, 545]]\n",
      "[[441, 499], [487, 545]]\n",
      "[[381, 438], [487, 545]]\n",
      "[[322, 378], [487, 544]]\n",
      "[[262, 319], [486, 544]]\n",
      "[[203, 259], [486, 543]]\n",
      "[[144, 200], [486, 543]]\n",
      "[[85, 141], [485, 542]]\n",
      "[[24, 82], [485, 542]]\n",
      "[[25, 622], [394, 452]]\n",
      "[[562, 621], [395, 453]]\n",
      "[[502, 559], [395, 453]]\n",
      "[[441, 499], [395, 453]]\n",
      "[[381, 438], [395, 453]]\n",
      "[[322, 379], [395, 452]]\n",
      "[[262, 320], [395, 452]]\n",
      "[[204, 260], [395, 452]]\n",
      "[[144, 201], [394, 451]]\n",
      "[[86, 142], [394, 451]]\n",
      "[[26, 83], [394, 451]]\n",
      "[[26, 621], [302, 361]]\n",
      "[[441, 499], [303, 361]]\n",
      "[[382, 438], [303, 361]]\n",
      "[[323, 379], [303, 361]]\n",
      "[[263, 320], [303, 361]]\n",
      "[[204, 260], [303, 360]]\n",
      "[[146, 202], [303, 360]]\n",
      "[[86, 143], [303, 360]]\n",
      "[[27, 84], [303, 360]]\n",
      "[[562, 621], [303, 360]]\n",
      "[[502, 559], [302, 361]]\n",
      "[[27, 623], [211, 268]]\n",
      "[[264, 320], [213, 269]]\n",
      "[[204, 261], [212, 269]]\n",
      "[[146, 202], [212, 269]]\n",
      "[[87, 144], [212, 269]]\n",
      "[[28, 84], [212, 269]]\n",
      "[[382, 439], [211, 268]]\n",
      "[[323, 379], [212, 269]]\n",
      "[[502, 560], [210, 267]]\n",
      "[[442, 499], [211, 268]]\n",
      "[[563, 621], [211, 267]]\n",
      "[[28, 622], [120, 176]]\n",
      "[[146, 203], [121, 178]]\n",
      "[[87, 144], [121, 178]]\n",
      "[[29, 85], [121, 178]]\n",
      "[[53, 54], [143, 161]]\n",
      "[[264, 321], [120, 177]]\n",
      "[[287, 288], [143, 160]]\n",
      "[[205, 261], [121, 178]]\n",
      "[[382, 440], [120, 176]]\n",
      "[[406, 407], [146, 158]]\n",
      "[[323, 379], [120, 176]]\n",
      "[[443, 500], [118, 176]]\n",
      "[[468, 469], [143, 160]]\n",
      "[[502, 560], [118, 175]]\n",
      "[[523, 524], [142, 158]]\n",
      "[[563, 621], [117, 174]]\n",
      "[[588, 588], [152, 157]]\n",
      "[[28, 623], [29, 83]]\n",
      "[[29, 85], [30, 87]]\n",
      "[[148, 203], [29, 86]]\n",
      "[[88, 144], [30, 87]]\n",
      "[[265, 321], [28, 85]]\n",
      "[[205, 262], [29, 85]]\n",
      "[[383, 440], [27, 84]]\n",
      "[[324, 380], [28, 85]]\n",
      "[[442, 500], [27, 83]]\n",
      "[[503, 561], [25, 82]]\n",
      "[[563, 622], [25, 80]]\n",
      "3301\n",
      "2239\n",
      "ok!\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
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