Adaptive Threshold implementation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = cv2.imread('Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_014.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f6b9c67d810>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xlrd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "wb = xlrd.open_workbook(\"Drishti-GS1_files/Drishti-GS1_diagnosis.xlsx\") \n",
    "sheet = wb.sheet_by_index(0) \n",
    "val = [sheet.col_values(1)[5:],sheet.col_values(8)[5:]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[\"drishtiGS_001'\",\n",
       "  \"drishtiGS_002'\",\n",
       "  \"drishtiGS_003'\",\n",
       "  \"drishtiGS_004'\",\n",
       "  \"drishtiGS_005'\",\n",
       "  \"drishtiGS_006'\",\n",
       "  \"drishtiGS_007'\",\n",
       "  \"drishtiGS_008'\",\n",
       "  \"drishtiGS_009'\",\n",
       "  \"drishtiGS_010'\",\n",
       "  \"drishtiGS_011'\",\n",
       "  \"drishtiGS_012'\",\n",
       "  \"drishtiGS_013'\",\n",
       "  \"drishtiGS_014'\",\n",
       "  \"drishtiGS_015'\",\n",
       "  \"drishtiGS_016'\",\n",
       "  \"drishtiGS_017'\",\n",
       "  \"drishtiGS_018'\",\n",
       "  \"drishtiGS_019'\",\n",
       "  \"drishtiGS_020'\",\n",
       "  \"drishtiGS_021'\",\n",
       "  \"drishtiGS_022'\",\n",
       "  \"drishtiGS_023'\",\n",
       "  \"drishtiGS_024'\",\n",
       "  \"drishtiGS_025'\",\n",
       "  \"drishtiGS_026'\",\n",
       "  \"drishtiGS_027'\",\n",
       "  \"drishtiGS_028'\",\n",
       "  \"drishtiGS_029'\",\n",
       "  \"drishtiGS_030'\",\n",
       "  \"drishtiGS_031'\",\n",
       "  \"drishtiGS_032'\",\n",
       "  \"drishtiGS_033'\",\n",
       "  \"drishtiGS_034'\",\n",
       "  \"drishtiGS_035'\",\n",
       "  \"drishtiGS_036'\",\n",
       "  \"drishtiGS_037'\",\n",
       "  \"drishtiGS_038'\",\n",
       "  \"drishtiGS_039'\",\n",
       "  \"drishtiGS_040'\",\n",
       "  \"drishtiGS_041'\",\n",
       "  \"drishtiGS_042'\",\n",
       "  \"drishtiGS_043'\",\n",
       "  \"drishtiGS_044'\",\n",
       "  \"drishtiGS_045'\",\n",
       "  \"drishtiGS_046'\",\n",
       "  \"drishtiGS_047'\",\n",
       "  \"drishtiGS_048'\",\n",
       "  \"drishtiGS_049'\",\n",
       "  \"drishtiGS_050'\",\n",
       "  \"drishtiGS_051'\",\n",
       "  \"drishtiGS_052'\",\n",
       "  \"drishtiGS_053'\",\n",
       "  \"drishtiGS_054'\",\n",
       "  \"drishtiGS_055'\",\n",
       "  \"drishtiGS_056'\",\n",
       "  \"drishtiGS_057'\",\n",
       "  \"drishtiGS_058'\",\n",
       "  \"drishtiGS_059'\",\n",
       "  \"drishtiGS_060'\",\n",
       "  \"drishtiGS_061'\",\n",
       "  \"drishtiGS_062'\",\n",
       "  \"drishtiGS_063'\",\n",
       "  \"drishtiGS_064'\",\n",
       "  \"drishtiGS_065'\",\n",
       "  \"drishtiGS_066'\",\n",
       "  \"drishtiGS_067'\",\n",
       "  \"drishtiGS_068'\",\n",
       "  \"drishtiGS_069'\",\n",
       "  \"drishtiGS_070'\",\n",
       "  \"drishtiGS_071'\",\n",
       "  \"drishtiGS_072'\",\n",
       "  \"drishtiGS_073'\",\n",
       "  \"drishtiGS_074'\",\n",
       "  \"drishtiGS_075'\",\n",
       "  \"drishtiGS_076'\",\n",
       "  \"drishtiGS_077'\",\n",
       "  \"drishtiGS_078'\",\n",
       "  \"drishtiGS_079'\",\n",
       "  \"drishtiGS_080'\",\n",
       "  \"drishtiGS_081'\",\n",
       "  \"drishtiGS_082'\",\n",
       "  \"drishtiGS_083'\",\n",
       "  \"drishtiGS_084'\",\n",
       "  \"drishtiGS_085'\",\n",
       "  \"drishtiGS_086'\",\n",
       "  \"drishtiGS_087'\",\n",
       "  \"drishtiGS_088'\",\n",
       "  \"drishtiGS_089'\",\n",
       "  \"drishtiGS_090'\",\n",
       "  \"drishtiGS_091'\",\n",
       "  \"drishtiGS_092'\",\n",
       "  \"drishtiGS_093'\",\n",
       "  \"drishtiGS_094'\",\n",
       "  \"drishtiGS_095'\",\n",
       "  \"drishtiGS_096'\",\n",
       "  \"drishtiGS_097'\",\n",
       "  \"drishtiGS_098'\",\n",
       "  \"drishtiGS_099'\",\n",
       "  \"drishtiGS_100'\",\n",
       "  \"drishtiGS_101'\"],\n",
       " ['Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Normal',\n",
       "  'Normal',\n",
       "  'Normal',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Normal',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Normal',\n",
       "  'Normal',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Normal',\n",
       "  'Glaucomatous',\n",
       "  'Normal',\n",
       "  'Normal',\n",
       "  'Normal',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Normal',\n",
       "  'Normal',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Normal',\n",
       "  'Normal',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Normal',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Normal',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Normal',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Normal',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Glaucomatous',\n",
       "  'Normal',\n",
       "  'Normal',\n",
       "  'Normal',\n",
       "  'Normal',\n",
       "  'Normal',\n",
       "  'Normal',\n",
       "  'Normal',\n",
       "  'Normal',\n",
       "  'Normal',\n",
       "  'Normal',\n",
       "  'Normal',\n",
       "  'Normal',\n",
       "  'Normal']]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_image(path):\n",
    "    return cv2.imread(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to load image and their name\n",
    "def load_set(folder, shuffle=False):\n",
    "    img_list = sorted(glob.glob(os.path.join(folder, '*.png')) + \\\n",
    "                      glob.glob(os.path.join(folder, '*.jpg')) + \\\n",
    "                      glob.glob(os.path.join(folder, '*.jpeg')))\n",
    "    if shuffle:\n",
    "        np.random.shuffle(img_list)\n",
    "    data = []\n",
    "    filenames = []\n",
    "    for img_fn in img_list:\n",
    "        img = load_image(img_fn)\n",
    "        data.append(img)\n",
    "        filenames.append(img_fn)\n",
    "    return data, filenames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#DATA EXTRACTION FUNCTION\n",
    "def extract_DRISHTI_GS_train(db_folder,cdr,train_data):\n",
    "\n",
    "    file_codes_all,exp1,exp2,exp3,exp4 = [], [], [], [], []\n",
    "    if train_data:\n",
    "        set_path = os.path.join(db_folder, 'Drishti-GS1_files','Drishti-GS1_files', 'Training')\n",
    "    else:\n",
    "        set_path = os.path.join(db_folder, 'Drishti-GS1_files','Drishti-GS1_files', 'Test')\n",
    "    images_path = os.path.join(set_path, 'Images')\n",
    "    X_all, file_names = load_set(images_path)\n",
    "    rel_file_names = [os.path.split(fn)[-1] for fn in file_names]\n",
    "    rel_file_names_wo_ext = [fn[:fn.rfind('.')] for fn in rel_file_names]\n",
    "    if train_data:\n",
    "        file_codes = [fn[fn.find('_'):] for fn in rel_file_names_wo_ext]\n",
    "    else:\n",
    "        file_codes = [fn[fn.find('_'):] for fn in rel_file_names_wo_ext]\n",
    "    file_codes_all.extend(file_codes)\n",
    "    \n",
    "    for fn in rel_file_names_wo_ext:\n",
    "        if cdr:\n",
    "            if train_data:\n",
    "                CDR = open(os.path.join(set_path, 'GT', fn,fn + '_cdrValues.txt'),'r')\n",
    "            else:\n",
    "                CDR = open(os.path.join(set_path, 'Test_GT', fn,fn + '_cdrValues.txt'),'r')\n",
    "            CDR = list(CDR)\n",
    "            CDR = CDR[0].split()\n",
    "            exp1.append(float(CDR[0]))\n",
    "            exp2.append(float(CDR[1]))\n",
    "            exp3.append(float(CDR[2]))\n",
    "            exp4.append(float(CDR[3]))\n",
    "            \n",
    "    return X_all, file_codes_all,exp1,exp2,exp3,exp4,file_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GET DATA\n",
    "X_all,file_codes_all,exp1,exp2,exp3,exp4,file_names = extract_DRISHTI_GS_train('',True,False) #put the folder where dataset is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(51,)\n",
      "(1751, 2049, 3)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/chella/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "print(np.array(X_all).shape)\n",
    "print(np.array(X_all[0]).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_001.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_003.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_005.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_006.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_007.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_009.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_011.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_013.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_014.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_019.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_020.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_021.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_023.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_025.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_027.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_028.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_029.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_030.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_034.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_039.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_043.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_048.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_050.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_052.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_053.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_054.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_055.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_056.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_059.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_065.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_067.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_070.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_071.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_072.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_073.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_074.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_077.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_078.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_079.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_082.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_083.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_085.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_086.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_087.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_091.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_093.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_095.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_096.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_097.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_099.png',\n",
       " 'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_100.png']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# FUNCTION TO SEGMENT CUP AND DISK\n",
    "def segment(image,plot_seg,plot_hist,name):\n",
    "\n",
    "    image = image[400:1500,400:1500,:] \n",
    "\n",
    "    Abo,Ago,Aro = cv2.split(image) \n",
    "    M = 100\n",
    "    filter = signal.gaussian(M, std=6)\n",
    "    STDf = filter.std()\n",
    "    \n",
    "\n",
    "    Ar = Aro - Aro.mean() - Aro.std() \n",
    "    Mr = Ar.mean()          \n",
    "    SDr = Ar.std()                          \n",
    "    Thr = 0.5*M - STDf - SDr            \n",
    "\n",
    "    \n",
    "    M = 30\n",
    "    filter_cup = signal.gaussian(M, std=6) \n",
    "    STDf = filter_cup.std()  \n",
    "\n",
    "    Ag = Ago - Ago.mean() - Ago.std()\n",
    "    Mg = Ag.mean()                        \n",
    "    SDg = Ag.std()                        \n",
    "    Thg = 0.5*M +2*STDf + 2*SDg + Mg     \n",
    "    #print(Thg)\n",
    "    \n",
    "    \n",
    "    hist,bins = np.histogram(Ag.ravel(),256,[0,256])  \n",
    "    histr,binsr = np.histogram(Ar.ravel(),256,[0,256])\n",
    "\n",
    "\n",
    "    smooth_hist_g=np.convolve(filter,hist)  \n",
    "    smooth_hist_r=np.convolve(filter_cup,histr) \n",
    "    \n",
    "    if plot_hist:\n",
    "        plt.figure(figsize = (8,8))\n",
    "        plt.subplot(2, 2, 1)\n",
    "        plt.plot(hist)\n",
    "        plt.title(\"Preprocessed Green Channel\")\n",
    "\n",
    "        plt.subplot(2, 2, 2)\n",
    "        plt.plot(smooth_hist_g)\n",
    "        plt.title(\"Smoothed Histogram Green Channel\")\n",
    "\n",
    "        plt.subplot(2, 2, 3)\n",
    "        plt.plot(histr)\n",
    "        plt.title(\"Preprocessed Red Channel\")\n",
    "\n",
    "        plt.subplot(2, 2, 4)\n",
    "        plt.plot(smooth_hist_r)\n",
    "        plt.title(\"Smoothed Histogram Red Channel\")\n",
    "\n",
    "        plt.show()\n",
    "    \n",
    "    r,c = Ag.shape\n",
    "    Dd = np.zeros(shape=(r,c)) \n",
    "    Dc = np.zeros(shape=(r,c)) \n",
    "\n",
    "    for i in range(1,r):\n",
    "        for j in range(1,c):\n",
    "            if Ar[i,j]>Thr:\n",
    "                Dd[i,j]=255\n",
    "            else:\n",
    "                Dd[i,j]=0\n",
    "\n",
    "    for i in range(1,r):\n",
    "        for j in range(1,c):\n",
    "        \n",
    "            if Ag[i,j]>Thg:\n",
    "                Dc[i,j]=255\n",
    "            else:\n",
    "                Dc[i,j]=0\n",
    "    \n",
    "    name = name[47:]\n",
    "     \n",
    "    cv2.imwrite('paper 1 results/'+name+' disk.png',Dd)\n",
    "    plt.imsave('paper 1 results/'+name+' cup.png',Dc)\n",
    "    \n",
    "    if plot_seg:\n",
    "        plt.imshow(Dd, cmap = 'gray', interpolation = 'bicubic')\n",
    "        plt.axis(\"off\")\n",
    "        plt.title(\"Optic Disk\")\n",
    "        plt.show()\n",
    "        \n",
    "        plt.imshow(Dc, cmap = 'gray', interpolation = 'bicubic')\n",
    "        plt.axis(\"off\")\n",
    "        plt.title(\"Optic Cup\")\n",
    "        plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# FUNCTION TO CALCULATE CDR\n",
    "def cdr(cup,disc,plot):\n",
    "    \n",
    "    #morphological closing and opening operations\n",
    "    R1 = cv2.morphologyEx(cup, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(2,2)), iterations = 1)\n",
    "    r1 = cv2.morphologyEx(R1, cv2.MORPH_OPEN, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(7,7)), iterations = 1)\n",
    "    R2 = cv2.morphologyEx(r1, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(1,21)), iterations = 1)\n",
    "    r2 = cv2.morphologyEx(R2, cv2.MORPH_OPEN, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(21,1)), iterations = 1)\n",
    "    R3 = cv2.morphologyEx(r2, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(33,33)), iterations = 2)\n",
    "    img = R3\n",
    "    ret,thresh = cv2.threshold(img,127,255,0)\n",
    "    contours,hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) #Getting all possible contours in the segmented image\n",
    "    cup_diameter = 0\n",
    "    largest_area = 0\n",
    "    el_cup = contours[0]\n",
    "    if len(contours) != 0:\n",
    "        for i in range(len(contours)):\n",
    "            if len(contours[i]) >= 5:\n",
    "                area = cv2.contourArea(contours[i]) \n",
    "                if (area>largest_area):\n",
    "                    largest_area=area\n",
    "                    index = i\n",
    "                    el_cup = cv2.fitEllipse(contours[i])\n",
    "                \n",
    "    cv2.ellipse(img,el_cup,(140,60,150),3)  \n",
    "    x,y,w,h = cv2.boundingRect(contours[index]) \n",
    "    cup_diameter = max(w,h) \n",
    "\n",
    "    #morphological closing and opening operations\n",
    "    R1 = cv2.morphologyEx(disc, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(2,2)), iterations = 1)\n",
    "    r1 = cv2.morphologyEx(R1, cv2.MORPH_OPEN, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(7,7)), iterations = 1)\n",
    "    R2 = cv2.morphologyEx(r1, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(1,21)), iterations = 1)\n",
    "    r2 = cv2.morphologyEx(R2, cv2.MORPH_OPEN, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(21,1)), iterations = 1)\n",
    "    R3 = cv2.morphologyEx(r2, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(33,33)), iterations = 1)\n",
    "    r3 = cv2.morphologyEx(R3, cv2.MORPH_OPEN, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(43,43)), iterations = 1)\n",
    "    img2 = r3\n",
    "    \n",
    "    ret,thresh = cv2.threshold(img2,127,255,0)\n",
    "    contours,hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)\n",
    "    disk_diameter = 0\n",
    "    largest_area = 0\n",
    "    el_disc = el_cup\n",
    "    if len(contours) != 0:\n",
    "          for i in range(len(contours)):\n",
    "            if len(contours[i]) >= 5:\n",
    "                area = cv2.contourArea(contours[i]) \n",
    "                if (area>largest_area):\n",
    "                    largest_area=area\n",
    "                    index = i\n",
    "                    el_disc = cv2.fitEllipse(contours[i])\n",
    "                    \n",
    "            cv2.ellipse(img2,el_disc,(140,60,150),10) \n",
    "            x,y,w,h = cv2.boundingRect(contours[index]) \n",
    "    disk_diameter = max(w,h)\n",
    "                \n",
    "    if plot:\n",
    "        plt.imshow(img2, 'gray',interpolation = 'bicubic')\n",
    "        plt.axis(\"off\")\n",
    "        plt.title(\"Optic Disk\")\n",
    "        plt.show()\n",
    "        plt.imshow(img, 'gray')\n",
    "        plt.axis(\"off\")\n",
    "        plt.title(\"Optic Cup\")\n",
    "        plt.show()\n",
    "        \n",
    "    if(disk_diameter == 0): return 1 \n",
    "    cdr = cup_diameter/disk_diameter \n",
    "    return cdr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_001 Exp1_cdr: 0.85 Exp2_cdr: 0.82 Exp3_cdr: 0.8 Exp4_cdr: 0.82 Pred_cdr: 0.8323863636363636\n"
     ]
    }
   ],
   "source": [
    "# MAIN FUNCTION\n",
    "CDR = [] \n",
    "VAL = [] \n",
    "count = 0\n",
    "i=1\n",
    "while(i):\n",
    "    set_path = os.path.join('',file_names[i]) \n",
    "    image = cv2.imread(set_path,1)\n",
    "    segment(image,True,True,set_path)\n",
    "    name = file_names[i][47:]\n",
    "    cup = cv2.imread('paper 1 results/'+name+' cup.png',0) \n",
    "    disc = cv2.imread('paper 1 results/'+name+' disk.png',0) \n",
    "    cdr_cal = cdr(cup,disc,True)\n",
    "    if(val[1][int(file_codes_all[count][1:])-1] == 'Glaucomatous'):\n",
    "        VAL.append(1)\n",
    "    else:\n",
    "        VAL.append(0)\n",
    "    CDR.append(cdr_cal)\n",
    "    print(file_codes_all[count],'Exp1_cdr:',exp1[count],'Exp2_cdr:',exp2[count],'Exp3_cdr:',exp3[count],'Exp4_cdr:',exp4[count],'Pred_cdr:',cdr_cal)\n",
    "    count+=1\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Drishti-GS1_files/Drishti-GS1_files/Test/Images/drishtiGS_003.png'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "table = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MAIN FUNCTION\n",
    "CDR = [] \n",
    "VAL = [] \n",
    "count = 0\n",
    "for i in range(len(X_all)):\n",
    "    set_path = os.path.join('',file_names[i]) \n",
    "    image = cv2.imread(set_path,1)\n",
    "    segment(image,False,False,set_path)\n",
    "    name = file_names[i][47:]\n",
    "    cup = cv2.imread('paper 1 results/'+name+' cup.png',0) \n",
    "    disc = cv2.imread('paper 1 results/'+name+' disk.png',0) \n",
    "    cdr_cal = cdr(cup,disc,False)\n",
    "    if(val[1][int(file_codes_all[count][1:])-1] == 'Glaucomatous'):\n",
    "        VAL.append(1)\n",
    "    else:\n",
    "        VAL.append(0)\n",
    "    CDR.append(cdr_cal)\n",
    "    table.append([file_codes_all[count], exp1[count], exp2[count], exp3[count], exp4[count], cdr_cal])\n",
    "    count+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>File Code</th>\n",
       "      <th>Expert1</th>\n",
       "      <th>Expert2</th>\n",
       "      <th>Expert3</th>\n",
       "      <th>Expert4</th>\n",
       "      <th>predicted cdr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>_001</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.674107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>_003</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.832386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>_005</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.757225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>_006</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.342391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>_007</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.624079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>_009</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.967164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>_011</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.981884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>_013</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>_014</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.800570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>_019</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.787368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>_020</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.756691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>_021</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.808989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>_023</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.584775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>_025</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.836634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>_027</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.953704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>_028</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.87</td>\n",
       "      <td>1.015038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>_029</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.401575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>_030</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.557545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>_034</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.595238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>_039</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.686842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>_043</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.467692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>_048</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.665127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>_050</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.638978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>_052</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.816121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>_053</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.817500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>_054</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.801948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>_055</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.870550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>_056</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.283489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>_059</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.792332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>_065</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.645946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>_067</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.867209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>_070</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.875691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>_071</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.699732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>_072</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.427711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>_073</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.834286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>_074</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.821530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>_077</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.769430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>_078</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.682796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>_079</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.928934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>_082</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.850877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>_083</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.769976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>_085</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.770992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>_086</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.835165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>_087</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.826211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>_091</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.861272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>_093</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.191549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>_095</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.602978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>_096</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.471875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>_097</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.795506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>_099</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.521327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>_100</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.100295</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   File Code  Expert1  Expert2  Expert3  Expert4  predicted cdr\n",
       "0       _001     0.85     0.82     0.80     0.82       0.674107\n",
       "1       _003     0.83     0.79     0.72     0.79       0.832386\n",
       "2       _005     0.86     0.87     0.81     0.80       0.757225\n",
       "3       _006     0.64     0.77     0.65     0.53       0.342391\n",
       "4       _007     0.86     0.83     0.70     0.62       0.624079\n",
       "5       _009     0.54     0.56     0.39     0.53       0.967164\n",
       "6       _011     0.81     0.81     0.77     0.83       0.981884\n",
       "7       _013     0.80     0.66     0.65     0.62       0.500000\n",
       "8       _014     0.86     0.87     0.84     0.82       0.800570\n",
       "9       _019     0.86     0.91     0.84     0.78       0.787368\n",
       "10      _020     0.88     0.90     0.80     0.78       0.756691\n",
       "11      _021     0.86     0.78     0.82     0.85       0.808989\n",
       "12      _023     0.77     0.82     0.71     0.74       0.584775\n",
       "13      _025     0.84     0.89     0.78     0.89       0.836634\n",
       "14      _027     0.75     0.87     0.64     0.69       0.953704\n",
       "15      _028     0.87     0.88     0.81     0.87       1.015038\n",
       "16      _029     0.77     0.88     0.72     0.78       0.401575\n",
       "17      _030     0.77     0.85     0.61     0.75       0.557545\n",
       "18      _034     0.86     0.80     0.69     0.60       0.595238\n",
       "19      _039     0.86     0.91     0.77     0.79       0.686842\n",
       "20      _043     0.80     0.91     0.79     0.77       0.467692\n",
       "21      _048     0.75     0.78     0.67     0.84       0.665127\n",
       "22      _050     0.77     0.87     0.75     0.74       0.638978\n",
       "23      _052     0.66     0.87     0.70     0.81       0.816121\n",
       "24      _053     0.84     0.91     0.79     0.80       0.817500\n",
       "25      _054     0.90     0.72     0.71     0.82       0.801948\n",
       "26      _055     0.87     0.80     0.84     0.85       0.870550\n",
       "27      _056     0.49     0.48     0.50     0.48       0.283489\n",
       "28      _059     0.81     0.78     0.70     0.81       0.792332\n",
       "29      _065     0.83     0.86     0.82     0.78       0.645946\n",
       "30      _067     0.78     0.69     0.57     0.64       0.867209\n",
       "31      _070     0.87     0.86     0.88     0.84       0.875691\n",
       "32      _071     0.81     0.78     0.68     0.76       0.699732\n",
       "33      _072     0.61     0.59     0.48     0.57       0.427711\n",
       "34      _073     0.87     0.81     0.86     0.75       0.834286\n",
       "35      _074     0.91     0.83     0.84     0.84       0.821530\n",
       "36      _077     0.86     0.76     0.72     0.63       0.769430\n",
       "37      _078     0.61     0.68     0.57     0.64       0.682796\n",
       "38      _079     0.82     0.89     0.75     0.83       0.928934\n",
       "39      _082     0.85     0.82     0.68     0.88       0.850877\n",
       "40      _083     0.81     0.83     0.64     0.64       0.769976\n",
       "41      _085     0.81     0.85     0.70     0.65       0.770992\n",
       "42      _086     0.80     0.88     0.84     0.85       0.835165\n",
       "43      _087     0.77     0.85     0.73     0.81       0.826211\n",
       "44      _091     0.39     0.59     0.55     0.48       0.861272\n",
       "45      _093     0.37     0.48     0.46     0.46       0.191549\n",
       "46      _095     0.37     0.58     0.50     0.45       0.602978\n",
       "47      _096     0.15     0.52     0.47     0.43       0.471875\n",
       "48      _097     0.53     0.53     0.51     0.51       0.795506\n",
       "49      _099     0.62     0.71     0.55     0.50       0.521327\n",
       "50      _100     0.30     0.38     0.31     0.30       0.100295"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table = pd.DataFrame(table, columns = ['File Code', 'Expert1', 'Expert2', 'Expert3', 'Expert4', 'predicted cdr'])\n",
    "display(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "error1,error2,error3,error4 = [],[],[],[]\n",
    "for i in range(len(X_all)):\n",
    "    error1.append(float(exp1[i]) - CDR[i])\n",
    "    error2.append(float(exp2[i]) - CDR[i])\n",
    "    error3.append(float(exp3[i]) - CDR[i])\n",
    "    error4.append(float(exp4[i]) - CDR[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#saving the error and calculated cdr and its label into csv files for training classification model\n",
    "a = pd.DataFrame(error1) \n",
    "b = pd.DataFrame(error2) \n",
    "c = pd.DataFrame(error3) \n",
    "d = pd.DataFrame(error4) \n",
    "x1 = pd.DataFrame(CDR) \n",
    "y1 = pd.DataFrame(VAL) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_all,file_codes_all,exp1,exp2,exp3,exp4,file_names = extract_DRISHTI_GS_train('',True,False) #put the folder where \n",
    "x = (np.array(exp1)+np.array(exp2)+np.array(exp3)+np.array(exp4))/4\n",
    "x = pd.DataFrame(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "x1.to_csv('x1.csv',index=False)\n",
    "y1.to_csv('y1.csv',index=False)\n",
    "x.to_csv('x.csv',index=False)\n",
    "y1.to_csv('y.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/chella/miniconda3/lib/python3.7/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg = LogisticRegression() \n",
    "logreg.fit(x, y1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = logreg.predict(x1)\n",
    "acc = f1_score(y1, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8636363636363636"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}