{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "ccc128d6-b369-cfbc-8a83-d49a138cc912"
   },
   "source": [
    "This kernels aims at segmenting the cervix using the technique presented in this paper: https://www.researchgate.net/publication/24041301_Automatic_Detection_of_Anatomical_Landmarks_in_Uterine_Cervix_Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "fb67719e-6367-0dac-9c81-85acc797b305"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "additional\n",
      "sample_submission.csv\n",
      "test\n",
      "train\n",
      "\n",
      "['126' '344' '36' '307' '71' '9' '337' '202' '279' '91' '46' '475' '30'\n",
      " '402' '183' '15' '101' '405' '349' '12' '74' '466' '232' '165' '251' '424'\n",
      " '216' '508' '100' '97']\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import cv2\n",
    "import math\n",
    "from sklearn import mixture\n",
    "from sklearn.utils import shuffle\n",
    "from skimage import measure\n",
    "from glob import glob\n",
    "import os\n",
    "\n",
    "from subprocess import check_output\n",
    "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
    "#print(check_output([\"ls\", \"../input/test\"]).decode(\"utf8\"))\n",
    "\n",
    "TRAIN_DATA = \"../input/train\"\n",
    "type_1_files = glob(os.path.join(TRAIN_DATA, \"Type_1\", \"*.jpg\"))\n",
    "type_1_ids = np.array([s[len(os.path.join(TRAIN_DATA, \"Type_1\"))+1:-4] for s in type_1_files])\n",
    "type_2_files = glob(os.path.join(TRAIN_DATA, \"Type_2\", \"*.jpg\"))\n",
    "type_2_ids = np.array([s[len(os.path.join(TRAIN_DATA, \"Type_2\"))+1:-4] for s in type_2_files])\n",
    "type_3_files = glob(os.path.join(TRAIN_DATA, \"Type_3\", \"*.jpg\"))\n",
    "type_3_ids = np.array([s[len(os.path.join(TRAIN_DATA, \"Type_3\"))+1:-4] for s in type_3_files])\n",
    "\n",
    "TEST_DATA = \"../input/test\"\n",
    "\n",
    "type_1_files = glob(os.path.join(TEST_DATA,\"*.jpg\"))\n",
    "type_1_ids = np.array([s[len(os.path.join(TEST_DATA))+1:-4] for s in type_1_files])\n",
    "\n",
    "type_1_ids = type_1_ids[:30]\n",
    "\n",
    "print(type_1_ids)\n",
    "\n",
    "\n",
    "def get_filename(image_id, image_type):\n",
    "    \"\"\"\n",
    "    Method to get image file path from its id and type   \n",
    "    \"\"\"\n",
    "    if image_type == \"Type_1\" or \\\n",
    "        image_type == \"Type_2\" or \\\n",
    "        image_type == \"Type_3\":\n",
    "        data_path = os.path.join(TRAIN_DATA, image_type)\n",
    "    elif image_type == \"Test\":\n",
    "        data_path = TEST_DATA\n",
    "    elif image_type == \"AType_1\" or \\\n",
    "          image_type == \"AType_2\" or \\\n",
    "          image_type == \"AType_3\":\n",
    "        data_path = os.path.join(ADDITIONAL_DATA, image_type)\n",
    "    else:\n",
    "        raise Exception(\"Image type '%s' is not recognized\" % image_type)\n",
    "\n",
    "    ext = 'jpg'\n",
    "    return os.path.join(data_path, \"{}.{}\".format(image_id, ext))\n",
    "\n",
    "def get_image_data(image_id, image_type):\n",
    "    \"\"\"\n",
    "    Method to get image data as np.array specifying image id and type\n",
    "    \"\"\"\n",
    "    fname = get_filename(image_id, image_type)\n",
    "    img = cv2.imread(fname)\n",
    "    assert img is not None, \"Failed to read image : %s, %s\" % (image_id, image_type)\n",
    "    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "    return img"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "d905eee8-7e3a-11f4-0ef6-8aa3f5e11395"
   },
   "source": [
    "First, we crop the image in order to remove the circular frames that might be present. This is done by finding the largest inscribed rectangle to the thresholded image. The image is then cropped to this rectangle. (see these videos for an explanation of the algorithm: https://www.youtube.com/watch?v=g8bSdXCG-lA, https://www.youtube.com/watch?v=VNbkzsnllsU)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "b3f0968b-7f53-1646-b624-ad2c137a0e3f"
   },
   "outputs": [],
   "source": [
    "def maxHist(hist):\n",
    "    maxArea = (0, 0, 0)\n",
    "    height = []\n",
    "    position = []\n",
    "    for i in range(len(hist)):\n",
    "        if (len(height) == 0):\n",
    "            if (hist[i] > 0):\n",
    "                height.append(hist[i])\n",
    "                position.append(i)\n",
    "        else: \n",
    "            if (hist[i] > height[-1]):\n",
    "                height.append(hist[i])\n",
    "                position.append(i)\n",
    "            elif (hist[i] < height[-1]):\n",
    "                while (height[-1] > hist[i]):\n",
    "                    maxHeight = height.pop()\n",
    "                    area = maxHeight * (i-position[-1])\n",
    "                    if (area > maxArea[0]):\n",
    "                        maxArea = (area, position[-1], i)\n",
    "                    last_position = position.pop()\n",
    "                    if (len(height) == 0):\n",
    "                        break\n",
    "                position.append(last_position)\n",
    "                if (len(height) == 0):\n",
    "                    height.append(hist[i])\n",
    "                elif(height[-1] < hist[i]):\n",
    "                    height.append(hist[i])\n",
    "                else:\n",
    "                    position.pop()    \n",
    "    while (len(height) > 0):\n",
    "        maxHeight = height.pop()\n",
    "        last_position = position.pop()\n",
    "        area =  maxHeight * (len(hist) - last_position)\n",
    "        if (area > maxArea[0]):\n",
    "            maxArea = (area, len(hist), last_position)\n",
    "    return maxArea\n",
    "            \n",
    "\n",
    "def maxRect(img):\n",
    "    maxArea = (0, 0, 0)\n",
    "    addMat = np.zeros(img.shape)\n",
    "    for r in range(img.shape[0]):\n",
    "        if r == 0:\n",
    "            addMat[r] = img[r]\n",
    "            area = maxHist(addMat[r])\n",
    "            if area[0] > maxArea[0]:\n",
    "                maxArea = area + (r,)\n",
    "        else:\n",
    "            addMat[r] = img[r] + addMat[r-1]\n",
    "            addMat[r][img[r] == 0] *= 0\n",
    "            area = maxHist(addMat[r])\n",
    "            if area[0] > maxArea[0]:\n",
    "                maxArea = area + (r,)\n",
    "    return (int(maxArea[3]+1-maxArea[0]/abs(maxArea[1]-maxArea[2])), maxArea[2], maxArea[3], maxArea[1], maxArea[0])\n",
    "\n",
    "def cropCircle(img):\n",
    "    if(img.shape[0] > img.shape[1]):\n",
    "        tile_size = (int(img.shape[1]*256/img.shape[0]),256)\n",
    "    else:\n",
    "        tile_size = (256, int(img.shape[0]*256/img.shape[1]))\n",
    "\n",
    "    img = cv2.resize(img, dsize=tile_size)\n",
    "            \n",
    "    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY);\n",
    "    _, thresh = cv2.threshold(gray, 10, 255, cv2.THRESH_BINARY)\n",
    "\n",
    "    _, contours, _ = cv2.findContours(thresh.copy(),cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)\n",
    "\n",
    "    main_contour = sorted(contours, key = cv2.contourArea, reverse = True)[0]\n",
    "            \n",
    "    ff = np.zeros((gray.shape[0],gray.shape[1]), 'uint8') \n",
    "    cv2.drawContours(ff, main_contour, -1, 1, 15)\n",
    "    ff_mask = np.zeros((gray.shape[0]+2,gray.shape[1]+2), 'uint8')\n",
    "    cv2.floodFill(ff, ff_mask, (int(gray.shape[1]/2), int(gray.shape[0]/2)), 1)\n",
    "    #cv2.circle(ff, (int(gray.shape[1]/2), int(gray.shape[0]/2)), 3, 3, -1)\n",
    "    \n",
    "    rect = maxRect(ff)\n",
    "    img_crop = img[min(rect[0],rect[2]):max(rect[0],rect[2]), min(rect[1],rect[3]):max(rect[1],rect[3])]\n",
    "    cv2.rectangle(ff,(min(rect[1],rect[3]),min(rect[0],rect[2])),(max(rect[1],rect[3]),max(rect[0],rect[2])),3,2)\n",
    "\n",
    "    #plt.subplot(121)\n",
    "    #plt.imshow(img)\n",
    "    #plt.subplot(122)\n",
    "    #plt.imshow(ff)\n",
    "    #plt.show()\n",
    "    \n",
    "    return img_crop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "2b329aab-9144-3163-3033-fde55b737cc5"
   },
   "source": [
    "“For an initial delineation of the cervix, we use two features: \n",
    "\n",
    " - the *a* color channel of the source image in Lab color space (the higher the value of *a* , the “redder” the pixel color)\n",
    " - *R*, the distance of a pixel from the image center. The *R* feature provides spatial information and supports the extraction of continuous regions within the image plane.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "bd01c6dc-c40c-a9c4-5c0c-f93d44568553"
   },
   "outputs": [],
   "source": [
    "def Ra_space(img, Ra_ratio, a_threshold):\n",
    "    imgLab = cv2.cvtColor(img, cv2.COLOR_RGB2LAB);\n",
    "    w = img.shape[0]\n",
    "    h = img.shape[1]\n",
    "    Ra = np.zeros((w*h, 2))\n",
    "    for i in range(w):\n",
    "        for j in range(h):\n",
    "            R = math.sqrt((w/2-i)*(w/2-i) + (h/2-j)*(h/2-j))\n",
    "            Ra[i*h+j, 0] = R\n",
    "            Ra[i*h+j, 1] = min(imgLab[i][j][1], a_threshold)\n",
    "            \n",
    "    Ra[:,0] /= max(Ra[:,0])\n",
    "    Ra[:,0] *= Ra_ratio\n",
    "    Ra[:,1] /= max(Ra[:,1])\n",
    "\n",
    "    return Ra"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "08069a8d-3546-64f4-74a8-514f5ecaff0d"
   },
   "source": [
    "\"The image is separated next into two clusters in the 2-D (*a-R*) feature space; we use Gaussian mixture modeling, initialized by a K-means procedure.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "f427b590-cfbc-a307-09ef-4ca41df3a8b1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['100', '101', '12', '126', '15', '165', '183', '202', '216', '232', '251', '279', '30', '307', '337', '344', '349', '36', '402', '405', '424', '46', '466', '475', '508', '71', '74', '9', '91', '97']\n",
      "100\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "num must be 1 <= num <= 2, not 3",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-edc51f26a549>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     68\u001b[0m         \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m122\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m         \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m         \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m123\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     71\u001b[0m         \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36msubplot\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1042\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1043\u001b[0m     \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgcf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1044\u001b[0;31m     \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1045\u001b[0m     \u001b[0mbbox\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1046\u001b[0m     \u001b[0mbyebye\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36madd_subplot\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1018\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_axstack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1019\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1020\u001b[0;31m             \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msubplot_class_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprojection_class\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1021\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1022\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_axstack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/axes/_subplots.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, fig, *args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m                     raise ValueError(\n\u001b[1;32m     63\u001b[0m                         \"num must be 1 <= num <= {maxn}, not {num}\".format(\n\u001b[0;32m---> 64\u001b[0;31m                             maxn=rows*cols, num=num))\n\u001b[0m\u001b[1;32m     65\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_subplotspec\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGridSpec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcols\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m                 \u001b[0;31m# num - 1 for converting from MATLAB to python indexing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: num must be 1 <= num <= 2, not 3"
     ]
    },
    {
     "data": {
      "image/png": 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oDPOEtoAHoTZnq/1eIpAsEEMinUbEK1GVGPu4fizQ/zKom51bk9JVNh5uCpty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cMtIL4u6fS4ke6cq8zGYuEzyhmFRUpeJVaF2JY+TyzczpdOJyfcO5ytyK0U5/xPpZBPpt+c8z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\n/X9ESUSHSBZzWAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff9c8e6ed68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for k, type_ids in enumerate([type_1_ids]):\n",
    "    m = len(type_ids)\n",
    "    train_ids = sorted(type_ids)\n",
    "    counter = 0\n",
    "    \n",
    "    print(train_ids)\n",
    "    for i in range(m):                \n",
    "        image_id = train_ids[counter] \n",
    "        print(image_id)\n",
    "        counter += 1\n",
    "\n",
    "        img = get_image_data(image_id, 'Test')\n",
    "\n",
    "        img = cropCircle(img)\n",
    "        w = img.shape[0]\n",
    "        h = img.shape[1]\n",
    "                        \n",
    "        imgLab = cv2.cvtColor(img, cv2.COLOR_RGB2LAB);\n",
    "        \n",
    "        # Saturating the a-channel at 150 helps avoiding wrong segmentation\n",
    "        # in the case of close-up cervix pictures where the bloody os is falsly segemented as the cervix.\n",
    "        Ra = Ra_space(img, 1.0, 200) \n",
    "        a_channel = np.reshape(Ra[:,1], (w,h))\n",
    "        plt.subplot(121)\n",
    "        plt.imshow(a_channel) \n",
    "\n",
    "        g = mixture.GaussianMixture(n_components = 2, covariance_type = 'diag', random_state = 0, init_params = 'kmeans')\n",
    "        image_array_sample = shuffle(Ra, random_state=0)[:1000]\n",
    "        g.fit(image_array_sample)\n",
    "        labels = g.predict(Ra)\n",
    "        labels += 1 # Add 1 to avoid labeling as 0 since regionprops ignores the 0-label.\n",
    "    \n",
    "        # The cluster that has the highest a-mean is selected.\n",
    "        labels_2D = np.reshape(labels, (w,h))\n",
    "        gg_labels_regions = measure.regionprops(labels_2D, intensity_image = a_channel)\n",
    "        gg_intensity = [prop.mean_intensity for prop in gg_labels_regions]\n",
    "        cervix_cluster = gg_intensity.index(max(gg_intensity)) + 1\n",
    "\n",
    "        mask = np.zeros((w * h,1),'uint8')\n",
    "        mask[labels==cervix_cluster] = 255\n",
    "        mask_2D = np.reshape(mask, (w,h))\n",
    "\n",
    "        cc_labels = measure.label(mask_2D, background=0)\n",
    "        regions = measure.regionprops(cc_labels)\n",
    "        areas = [prop.area for prop in regions]\n",
    "\n",
    "        regions_label = [prop.label for prop in regions]\n",
    "        largestCC_label = regions_label[areas.index(max(areas))]\n",
    "        mask_largestCC = np.zeros((w,h),'uint8')\n",
    "        mask_largestCC[cc_labels==largestCC_label] = 255\n",
    "\n",
    "        img_masked = img.copy()\n",
    "        img_masked[mask_largestCC==0] = (0,0,0)\n",
    "        img_masked_gray = cv2.cvtColor(img_masked, cv2.COLOR_RGB2GRAY);\n",
    "            \n",
    "        _,thresh_mask = cv2.threshold(img_masked_gray,0,255,0)\n",
    "            \n",
    "        kernel = np.ones((11,11), np.uint8)\n",
    "        thresh_mask = cv2.dilate(thresh_mask, kernel, iterations = 1)\n",
    "        thresh_mask = cv2.erode(thresh_mask, kernel, iterations = 2)\n",
    "        _, contours_mask, _ = cv2.findContours(thresh_mask.copy(),cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)\n",
    "\n",
    "        main_contour = sorted(contours_mask, key = cv2.contourArea, reverse = True)[0]\n",
    "                    \n",
    "        x,y,w,h = cv2.boundingRect(main_contour)\n",
    "        cv2.rectangle(img,(x,y),(x+w,y+h),255,2)\n",
    "                        \n",
    "        plt.subplot(122)\n",
    "        plt.imshow(img)\n",
    "        plt.subplot(123)\n",
    "        plt.imshow(img)\n",
    "               \n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "db79632d-8e7d-1289-2761-1f2b742750bb"
   },
   "source": [
    "To do: \n",
    "\n",
    " - Incorrect segmentation in cervix close-up pictures. Identify those...\n",
    " - Feed cropped pictures to a CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_cell_guid": "f9e99c48-7647-2e72-7b6a-648377312b0a"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "_change_revision": 1,
  "_is_fork": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
