model_simple_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import os.path as osp\n",
    "import openslide\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "# 이부분 python 에서는 뺴주기\n",
    "\n",
    "from skimage.filters import threshold_otsu\n",
    "from openslide.deepzoom import DeepZoomGenerator\n",
    "import cv2\n",
    "from keras.utils.np_utils import to_categorical\n",
    "\n",
    "# network\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Lambda, Dropout\n",
    "from keras.layers.convolutional import Convolution2D, Conv2DTranspose\n",
    "from keras.layers.pooling import MaxPooling2D\n",
    "from keras.models import load_model\n",
    "\n",
    "# Unet\n",
    "import numpy as np \n",
    "import os\n",
    "\n",
    "import skimage.transform as trans\n",
    "#import numpy as np\n",
    "from keras.models import *\n",
    "from keras.layers import *\n",
    "from keras.optimizers import *\n",
    "from keras.callbacks import ModelCheckpoint, LearningRateScheduler\n",
    "from keras import backend as keras\n",
    "\n",
    "\n",
    "# train\n",
    "\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "from datetime import datetime\n",
    "\n",
    "# evaluate\n",
    "import matplotlib.gridspec as gridspec\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from tqdm import tqdm\n",
    "\n",
    "import math\n",
    "from PIL import Image\n",
    "from xml.etree.ElementTree import ElementTree, Element, SubElement\n",
    "from io import BytesIO\n",
    "import skimage.io as io\n",
    "\n",
    "from tensorflow.python.client import device_lib\n",
    "#print(device_lib.list_local_devices())\n",
    "import keras.backend.tensorflow_backend as K\n",
    "from sklearn import metrics\n",
    "\n",
    "from keras.preprocessing.image import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "PATCH_SIZE = 256\n",
    "IS_TRAIN = True\n",
    "def find_patches_from_slide(slide_path, truth_path, patch_size=PATCH_SIZE,filter_non_tissue=True,filter_only_all_tumor=True):\n",
    "    \n",
    "    slide_contains_tumor = 'pos' in slide_path\n",
    "    \n",
    "    ############### read_region을 위한 start, level, size를 구함 #######################\n",
    "    BOUNDS_OFFSET_PROPS = (openslide.PROPERTY_NAME_BOUNDS_X, openslide.PROPERTY_NAME_BOUNDS_Y)\n",
    "    BOUNDS_SIZE_PROPS = (openslide.PROPERTY_NAME_BOUNDS_WIDTH, openslide.PROPERTY_NAME_BOUNDS_HEIGHT)\n",
    "\n",
    "    if slide_contains_tumor:\n",
    "        with openslide.open_slide(slide_path) as slide:\n",
    "            start = (int(slide.properties.get('openslide.bounds-x',0)),int(slide.properties.get('openslide.bounds-y',0)))\n",
    "            level = np.log2(patch_size) \n",
    "            level = int(level)\n",
    "            \n",
    "            size_scale = tuple(int(slide.properties.get(prop, l0_lim)) / l0_lim\n",
    "                            for prop, l0_lim in zip(BOUNDS_SIZE_PROPS,\n",
    "                            slide.dimensions))\n",
    "            _l_dimensions = tuple(tuple(int(math.ceil(l_lim * scale))\n",
    "                            for l_lim, scale in zip(l_size, size_scale))\n",
    "                            for l_size in slide.level_dimensions)\n",
    "            size = _l_dimensions[level]\n",
    "            \n",
    "            slide4 = slide.read_region(start,level,size) \n",
    "    else :\n",
    "        with openslide.open_slide(slide_path) as slide:\n",
    "            start = (0,0)\n",
    "            level = np.log2(patch_size) \n",
    "            level = int(level)\n",
    "            \n",
    "            size_scale = (1,1)\n",
    "            _l_dimensions = tuple(tuple(int(math.ceil(l_lim * scale))\n",
    "                            for l_lim, scale in zip(l_size, size_scale))\n",
    "                            for l_size in slide.level_dimensions)\n",
    "            size = _l_dimensions[level]\n",
    "            \n",
    "            slide4 = slide.read_region(start,level,size) \n",
    "    ####################################################################################\n",
    "    \n",
    "    \n",
    "    # is_tissue 부분 \n",
    "    slide4_grey = np.array(slide4.convert('L'))\n",
    "    binary = slide4_grey > 0  # black이면 0임\n",
    "    \n",
    "    # 검은색 제외하고 흰색영역(배경이라고 여겨지는)에 대해서도 작업해주어야함.\n",
    "    slide4_not_black = slide4_grey[slide4_grey>0]\n",
    "    thresh = threshold_otsu(slide4_not_black)\n",
    "    \n",
    "    I, J = slide4_grey.shape\n",
    "    for i in range(I):\n",
    "        for j in range(J):\n",
    "            if slide4_grey[i,j] > thresh :\n",
    "                binary[i,j] = False\n",
    "    patches = pd.DataFrame(pd.DataFrame(binary).stack())\n",
    "    patches['is_tissue'] = patches[0]\n",
    "    patches.drop(0, axis=1,inplace =True)\n",
    "    patches.loc[:,'slide_path'] = slide_path\n",
    "    \n",
    "#     # Test 이면 \n",
    "#     if IS_TEST:\n",
    "#         return patches\n",
    "\n",
    "    # is_tumor 부분\n",
    "    if slide_contains_tumor:\n",
    "        with openslide.open_slide(truth_path) as truth:\n",
    "            thumbnail_truth = truth.get_thumbnail(size) \n",
    "        \n",
    "        patches_y = pd.DataFrame(pd.DataFrame(np.array(thumbnail_truth.convert(\"L\"))).stack())\n",
    "        patches_y['is_tumor'] = patches_y[0] > 0\n",
    "        \n",
    "        # mask된 영역이 애매할 수도 있으므로\n",
    "        patches_y['is_all_tumor'] = patches_y[0] == 255\n",
    "        patches_y.drop(0, axis=1, inplace=True)\n",
    "        samples = pd.concat([patches, patches_y], axis=1) #len(samples)\n",
    "    else:\n",
    "        samples = patches\n",
    "        #dfmi.loc[:,('one','second')] = value\n",
    "        samples.loc[:,'is_tumor'] = False\n",
    "        samples.loc[:,'is_all_tumor'] = False\n",
    "    \n",
    "    if filter_non_tissue:\n",
    "        samples = samples[samples.is_tissue == True] # remove patches with no tissue #samples = samples[samples.is_tissue == True]\n",
    "    \n",
    "    if filter_only_all_tumor :\n",
    "        samples['tile_loc'] = list(samples.index)\n",
    "        all_tissue_samples1 = samples[samples.is_tumor==False]\n",
    "        all_tissue_samples1 = all_tissue_samples1.append(samples[samples.is_all_tumor==True])\n",
    "        \n",
    "        all_tissue_samples1.reset_index(inplace=True, drop=True)\n",
    "    else :\n",
    "        return samples\n",
    "    \n",
    "    return all_tissue_samples1\n",
    "NUM_CLASSES = 2 # not_tumor, tumor\n",
    "\n",
    "file_handles=[]\n",
    "def gen_imgs(all_image_path, all_mask_path, samples, batch_size, patch_size = PATCH_SIZE, shuffle=True):\n",
    "   \n",
    "    num_samples = len(samples)\n",
    "    # 특정 몇개의 slide만 open 해서 쓰기\n",
    "    # 4개씩 묶었으니까 \n",
    "  \n",
    "    slide_path0 = all_image_path[0]\n",
    "    slide_path1 = all_image_path[1]\n",
    "    slide_path2 = all_image_path[2]\n",
    "    slide_path3 = all_image_path[3]\n",
    "    \n",
    "    \n",
    "    # slide 0~3 까지 미리 열어두기\n",
    "    slide0 = openslide.open_slide(slide_path0)\n",
    "    slide1 = openslide.open_slide(slide_path1)\n",
    "    slide2 = openslide.open_slide(slide_path2)\n",
    "    slide3 = openslide.open_slide(slide_path3)\n",
    "    file_handles.append(slide0)\n",
    "    file_handles.append(slide1)\n",
    "    file_handles.append(slide2)\n",
    "    file_handles.append(slide3)\n",
    "    \n",
    "    # with openslide.open_slide(slide_path) as slide\n",
    "    tiles0 = DeepZoomGenerator(slide0,tile_size=patch_size, overlap=0, limit_bounds=False) \n",
    "    tiles1 = DeepZoomGenerator(slide1,tile_size=patch_size, overlap=0, limit_bounds=False)\n",
    "    tiles2 = DeepZoomGenerator(slide2,tile_size=patch_size, overlap=0, limit_bounds=False)\n",
    "    tiles3 = DeepZoomGenerator(slide3,tile_size=patch_size, overlap=0, limit_bounds=False)\n",
    "    \n",
    "    \n",
    "    if 'pos' in slide_path0:\n",
    "        start_x0 = int(slide0.properties.get('openslide.bounds-x',0))\n",
    "        start_y0 = int(slide0.properties.get('openslide.bounds-y',0))\n",
    "        start_x0 = start_x0 / patch_size\n",
    "        start_y0 = start_y0 / patch_size\n",
    "        \n",
    "        truth0 = openslide.open_slide(all_mask_path[0])\n",
    "        truth_tiles0 = DeepZoomGenerator(truth0, tile_size=16, overlap=0, limit_bounds=False) \n",
    "        \n",
    "    else : \n",
    "        start_x0 = 0\n",
    "        start_y0 = 0\n",
    "    \n",
    "    if 'pos' in slide_path1:\n",
    "        start_x1 = int(slide1.properties.get('openslide.bounds-x',0))\n",
    "        start_y1 = int(slide1.properties.get('openslide.bounds-y',0))\n",
    "        start_x1 = start_x1 / patch_size\n",
    "        start_y1 = start_y1 / patch_size\n",
    "        \n",
    "        truth1 = openslide.open_slide(all_mask_path[1])\n",
    "        truth_tiles1 = DeepZoomGenerator(truth1, tile_size=16, overlap=0, limit_bounds=False) \n",
    "        \n",
    "    else : \n",
    "        start_x1 = 0\n",
    "        start_y1 = 0\n",
    "    \n",
    "    if 'pos' in slide_path2:\n",
    "        start_x2 = int(slide2.properties.get('openslide.bounds-x',0))\n",
    "        start_y2 = int(slide2.properties.get('openslide.bounds-y',0))\n",
    "        start_x2 = start_x2 / patch_size\n",
    "        start_y2 = start_y2 / patch_size\n",
    "        \n",
    "        truth2 = openslide.open_slide(all_mask_path[2])\n",
    "        truth_tiles2 = DeepZoomGenerator(truth2, tile_size=16, overlap=0, limit_bounds=False) \n",
    "        \n",
    "    else : \n",
    "        start_x2 = 0\n",
    "        start_y2 = 0\n",
    "        \n",
    "    if 'pos' in slide_path3:\n",
    "        start_x3 = int(slide3.properties.get('openslide.bounds-x',0))\n",
    "        start_y3 = int(slide3.properties.get('openslide.bounds-y',0))\n",
    "        start_x3 = start_x3 / patch_size\n",
    "        start_y3 = start_y3 / patch_size\n",
    "        \n",
    "        truth3 = openslide.open_slide(all_mask_path[3])\n",
    "        truth_tiles3 = DeepZoomGenerator(truth3, tile_size=16, overlap=0, limit_bounds=False) \n",
    "        \n",
    "    else : \n",
    "        start_x3 = 0\n",
    "        start_y3 = 0\n",
    "    \n",
    "\n",
    "    \n",
    "    for epo in range(10): # Loop forever so the generator never terminates\n",
    "        if shuffle:\n",
    "            samples = samples.sample(frac=1) # shuffle samples\n",
    "\n",
    "        for offset in range(0, num_samples, batch_size):\n",
    "            batch_samples = samples.iloc[offset:offset+batch_size]\n",
    "            images = []\n",
    "            masks = []\n",
    "            for _, batch_sample in batch_samples.iterrows(): # 배치마다 deep zoom 하네 약간 비효율적\n",
    "                \n",
    "                # 여기서 하나씩 4개 체크해서 해당되는 부분으로 가야지. for 4번 돌리면서 가야한다.\n",
    "                mask_size_up = np.zeros((patch_size,patch_size))\n",
    "                a,b=mask_size_up.shape\n",
    "                \n",
    "                if batch_sample.slide_path == slide_path0:\n",
    "                    x, y = batch_sample.tile_loc[::-1]\n",
    "                    x += start_x0\n",
    "                    y += start_y0\n",
    "                    img = tiles0.get_tile(tiles0.level_count-1, (x,y))\n",
    "                    if 'pos' in slide_path0:\n",
    "                        mask = truth_tiles0.get_tile(truth_tiles0.level_count-1, batch_sample.tile_loc[::-1])\n",
    "                        mask = (cv2.cvtColor(np.array(mask), cv2.COLOR_RGB2GRAY) > 0).astype(int)\n",
    "                            # mask_size_up , 16 to 256\n",
    "                        for i in range(a):\n",
    "                            for j in range(b) :\n",
    "                                k = i//16\n",
    "                                l = j//16\n",
    "                                mask_size_up[i,j] = mask[k,l]\n",
    "                    \n",
    "                elif batch_sample.slide_path == slide_path1:\n",
    "                    x, y = batch_sample.tile_loc[::-1]\n",
    "                    x += start_x1\n",
    "                    y += start_y1\n",
    "                    img = tiles1.get_tile(tiles1.level_count-1, (x,y))\n",
    "                    if 'pos' in slide_path1:\n",
    "                        mask = truth_tiles1.get_tile(truth_tiles1.level_count-1, batch_sample.tile_loc[::-1])\n",
    "                        mask = (cv2.cvtColor(np.array(mask), cv2.COLOR_RGB2GRAY) > 0).astype(int)\n",
    "                            # mask_size_up , 16 to 256\n",
    "                        for i in range(a):\n",
    "                            for j in range(b) :\n",
    "                                k = i//16\n",
    "                                l = j//16\n",
    "                                mask_size_up[i,j] = mask[k,l]\n",
    "                \n",
    "                elif batch_sample.slide_path == slide_path2:\n",
    "                    x, y = batch_sample.tile_loc[::-1]\n",
    "                    x += start_x2\n",
    "                    y += start_y2\n",
    "                    img = tiles2.get_tile(tiles2.level_count-1, (x,y))\n",
    "                    if 'pos' in slide_path2:\n",
    "                        mask = truth_tiles2.get_tile(truth_tiles2.level_count-1, batch_sample.tile_loc[::-1])\n",
    "                        mask = (cv2.cvtColor(np.array(mask), cv2.COLOR_RGB2GRAY) > 0).astype(int)\n",
    "                            # mask_size_up , 16 to 256\n",
    "                        for i in range(a):\n",
    "                            for j in range(b) :\n",
    "                                k = i//16\n",
    "                                l = j//16\n",
    "                                mask_size_up[i,j] = mask[k,l]\n",
    "                \n",
    "                else:\n",
    "                    x, y = batch_sample.tile_loc[::-1]\n",
    "                    x += start_x3\n",
    "                    y += start_y3\n",
    "                    img = tiles3.get_tile(tiles3.level_count-1, (x,y))\n",
    "                    if 'pos' in slide_path3:\n",
    "                        mask = truth_tiles3.get_tile(truth_tiles3.level_count-1, batch_sample.tile_loc[::-1])\n",
    "                        mask = (cv2.cvtColor(np.array(mask), cv2.COLOR_RGB2GRAY) > 0).astype(int)\n",
    "                            # mask_size_up , 16 to 256\n",
    "                        for i in range(a):\n",
    "                            for j in range(b) :\n",
    "                                k = i//16\n",
    "                                l = j//16\n",
    "                                mask_size_up[i,j] = mask[k,l]\n",
    "                \n",
    "                    \n",
    "\n",
    "                images.append(np.array(img))\n",
    "                masks.append(mask_size_up)\n",
    "\n",
    "            X_train = np.array(images)\n",
    "            y_train = np.array(masks)\n",
    "            #print('x_train_shape :', X_train.shape)\n",
    "            \n",
    "            y_train = to_categorical(y_train, num_classes=2).reshape(y_train.shape[0], patch_size, patch_size, 2) \n",
    "            #print('y_train_shape : ',y_train.shape)\n",
    "            \n",
    "            #X_train, y_train = datagen().flow(X_train,y = y_train,batch_size = batch_size)\n",
    "            X_train, y_train = next(ImageDataGenerator(\n",
    "                rotation_range=90,\n",
    "                horizontal_flip=True,\n",
    "                vertical_flip=True,\n",
    "                brightness_range =(0.25,1.)).flow(X_train,y=y_train,batch_size=batch_size))\n",
    "            #print(X_train.shape)\n",
    "            #print(y_train.shape)\n",
    "            yield X_train, y_train\n",
    "            \n",
    "def predict_batch_from_model(patches, model):\n",
    "    \"\"\"Predict which pixels are tumor.\n",
    "    \n",
    "    input: patch: `batch_size`x256x256x3, rgb image\n",
    "    input: model: keras model\n",
    "    output: prediction: 256x256x1, per-pixel tumor probability\n",
    "    \"\"\"\n",
    "    predictions = model.predict(patches)\n",
    "    predictions = predictions[:, :, :, 1]\n",
    "    return predictions\n",
    "def predict_from_model(patch, model):\n",
    "    \"\"\"Predict which pixels are tumor.\n",
    "    \n",
    "    input: patch: 256x256x3, rgb image\n",
    "    input: model: keras model\n",
    "    output: prediction: 256x256x1, per-pixel tumor probability\n",
    "    \"\"\"\n",
    "    \n",
    "    prediction = model.predict(patch.reshape(1, 256, 256, 3))\n",
    "    prediction = prediction[:, :, :, 1].reshape(256, 256)\n",
    "    return prediction\n",
    "\n",
    "def predict_from_model_n(patch, model):\n",
    "    \"\"\"Predict which pixels are tumor.\n",
    "    \n",
    "    input: patch: 256x256x3, rgb image\n",
    "    input: model: keras model\n",
    "    output: prediction: 256x256x1, per-pixel tumor probability\n",
    "    \"\"\"\n",
    "    \n",
    "    prediction = model.predict(patch.reshape(1, 256, 256, 3))\n",
    "    prediction = prediction[:, :, :, 0].reshape(256, 256)\n",
    "    return prediction\n",
    "\n",
    "def simple_model(pretrained_weights = None):\n",
    "    model = Sequential()\n",
    "    model.add(Lambda(lambda x: x / 255.0 - 0.5, input_shape=(256, 256, 3)))\n",
    "    model.add(Convolution2D(100, (3, 3), strides=(2, 2), activation='elu', padding='same'))\n",
    "    model.add(MaxPooling2D())\n",
    "    model.add(Convolution2D(200, (3, 3), strides=(2, 2), activation='elu', padding='same'))\n",
    "    model.add(MaxPooling2D())\n",
    "    model.add(Convolution2D(300, (3, 3), activation='elu', padding='same'))\n",
    "    model.add(Convolution2D(300, (3, 3), activation='elu',  padding='same'))\n",
    "    model.add(Dropout(0.2))\n",
    "    model.add(Convolution2D(2, (1, 1))) # this is called upscore layer for some reason?\n",
    "    model.add(Conv2DTranspose(2, (31, 31), strides=(16, 16), activation='softmax', padding='same'))\n",
    "\n",
    "    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    \n",
    "    if(pretrained_weights):\n",
    "        model.load_weights(pretrained_weights)\n",
    "        \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image_path # :  157\n",
      "mask_patch # :  157\n"
     ]
    }
   ],
   "source": [
    "def read_data_path():\n",
    "    image_paths = []\n",
    "    with open('train.txt','r') as f:\n",
    "        for line in f:\n",
    "            line = line.rstrip('\\n')\n",
    "            image_paths.append(line)\n",
    "    #print('image_path # : ',len(image_paths))\n",
    "\n",
    "    tumor_mask_paths = []\n",
    "\n",
    "    with open('train_mask.txt','r') as f:\n",
    "        for line in f:\n",
    "            line = line.rstrip('\\n')\n",
    "            tumor_mask_paths.append(line)\n",
    "    #print('mask_patch # : ',len(tumor_mask_paths))\n",
    "    \n",
    "    return image_paths, tumor_mask_paths\n",
    "\n",
    "def read_test_data_path():\n",
    "    image_paths = []\n",
    "    with open('test.txt','r') as f:\n",
    "        for line in f:\n",
    "            line = line.rstrip('\\n')\n",
    "            image_paths.append(line)\n",
    "    #print('image_path # : ',len(image_paths))\n",
    "    \n",
    "    return image_paths\n",
    "\n",
    "test_image_paths = read_test_data_path()\n",
    "image_paths, tumor_mask_paths = read_data_path()\n",
    "image_paths = []\n",
    "with open('train.txt','r') as f:\n",
    "    for line in f:\n",
    "        line = line.rstrip('\\n')\n",
    "        image_paths.append(line)\n",
    "print('image_path # : ',len(image_paths))\n",
    "\n",
    "tumor_mask_paths = []\n",
    "with open('train_mask.txt','r') as f:\n",
    "    for line in f:\n",
    "        line = line.rstrip('\\n')\n",
    "        tumor_mask_paths.append(line)\n",
    "print('mask_patch # : ',len(tumor_mask_paths))\n",
    "\n",
    "slide_4_list_1 = [[102,104,29,44],[144,55,30,18],[125,56,35,40],[54,65,21,36],[139,82,1,49],[73,108,7,23],[107,117,24,52],[106,103,27,13]\n",
    "               ,[105,151,15,2],[75,100,41,9],[156,113,32,37],[150,88,39,10],[84,122,5,50],[93,118,53,47],[87,78,45,34],[116,98,48,46],\n",
    "                [72,131,22,42]]\n",
    "slide_4_list_2 = [[109,58,14,28],[101,69,11,43],[94,74,3,20],[64,140,17,16],[92,154,8,26],[99,60,0,33],[86,146,25,19],[68,112,38,51],\n",
    "                 [71,136,31,4],[59,91,12,6]]\n",
    "slide_4_list_3 = [[143,132,124,85],[95,120,81,77],[97,96,110,83],[152,128,149,155],[153,111,57,138],[134,135,114,76],\n",
    "                  [123,90,121,61],[147,148,119,142],[66,137,63,80],[70,79,115,133],[129,141,127,145]]\n",
    "slide_4_test = [[55,55,0,0],[55,55,0,0]]\n",
    "\n",
    "all_image_path = []\n",
    "all_mask_path = []\n",
    "for j in range(4):\n",
    "    image_path = image_paths[slide_4_test[0][j]][1:] # 이 부분은 data 읽을때 고치자 ( [1:] 빼야함)\n",
    "    mask_path = tumor_mask_paths[slide_4_test[0][j]][1:] # 이 부분은 data 읽을때 고치자\n",
    "    all_image_path.append(image_path)\n",
    "    all_mask_path.append(mask_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = simple_model('s_1.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ipath = all_image_path[0]\n",
    "tpath = all_mask_path[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total patches in slide: 105213\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False    74490\n",
       "True     30723\n",
       "Name: is_tumor, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_tissue_samples = find_patches_from_slide(ipath,tpath)\n",
    "print('Total patches in slide: %d' % len(all_tissue_samples)) \n",
    "all_tissue_samples.iloc[:10]\n",
    "all_tissue_samples.is_tumor.value_counts() \n",
    "# test_start_time = datetime.now()\n",
    "# sample_gen = gen_imgs(all_image_path,all_mask_path,all_tissue_samples, 1000, shuffle=True)\n",
    "# %time example_X, example_y  = next(sample_gen)\n",
    "# test_end_time = datetime.now()\n",
    "# print(\"5000 gen time: %.1f minutes\" % ((test_end_time - test_start_time).seconds / 60,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_tissue_samples = all_tissue_samples.sample(frac=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_tissue_samples = all_tissue_samples.sample(5000,random_state=42)\n",
    "all_tissue_samples\n",
    "all_tissue_samples.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
    "for train_index, test_index in split.split(all_tissue_samples, all_tissue_samples[\"is_tumor\"]):\n",
    "        train_samples = all_tissue_samples.loc[train_index]\n",
    "        validation_samples = all_tissue_samples.loc[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>is_tissue</th>\n",
       "      <th>slide_path</th>\n",
       "      <th>is_tumor</th>\n",
       "      <th>is_all_tumor</th>\n",
       "      <th>tile_loc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1358</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(174, 142)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3111</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(525, 74)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4314</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(308, 114)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2090</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(558, 135)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4392</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>(229, 235)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>(463, 210)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4011</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(299, 172)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4169</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(84, 156)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2058</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(171, 86)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4885</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(321, 94)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3086</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>(446, 55)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3100</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(44, 241)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(600, 203)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1899</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>(469, 102)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2791</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(336, 181)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4472</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>(471, 175)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3311</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(159, 232)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4739</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(568, 130)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>(525, 116)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4242</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(275, 172)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>924</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>(401, 128)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1196</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(254, 105)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>(446, 124)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4375</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(41, 118)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(279, 130)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4437</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>(395, 185)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1629</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>(489, 257)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4017</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(39, 282)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3746</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(89, 173)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3401</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>(245, 233)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>768</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>(498, 155)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(89, 148)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2298</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(288, 125)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4759</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(523, 132)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2564</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(65, 280)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(537, 196)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3514</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(303, 206)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2374</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(258, 68)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2473</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>(472, 92)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2391</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(247, 131)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2570</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(301, 217)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1543</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(271, 136)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2583</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>(211, 196)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1326</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(156, 203)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3954</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(339, 190)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3755</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>(459, 90)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4107</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(304, 159)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1716</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(573, 98)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1479</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(595, 129)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3313</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(508, 59)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1829</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>(425, 82)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4501</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(154, 245)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3414</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>(532, 264)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2393</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(531, 49)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1490</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(72, 152)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4640</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(132, 303)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4755</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>(477, 266)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3157</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(595, 122)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3000</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(52, 271)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3231</th>\n",
       "      <td>True</td>\n",
       "      <td>data/train/image/positive/Slide003.mrxs</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>(251, 59)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      is_tissue                               slide_path  is_tumor  \\\n",
       "1358       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "3111       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "4314       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "2090       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "4392       True  data/train/image/positive/Slide003.mrxs      True   \n",
       "210        True  data/train/image/positive/Slide003.mrxs      True   \n",
       "4011       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "4169       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "2058       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "4885       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "3086       True  data/train/image/positive/Slide003.mrxs      True   \n",
       "3100       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "1103       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "1899       True  data/train/image/positive/Slide003.mrxs      True   \n",
       "2791       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "4472       True  data/train/image/positive/Slide003.mrxs      True   \n",
       "3311       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "4739       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "225        True  data/train/image/positive/Slide003.mrxs      True   \n",
       "4242       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "924        True  data/train/image/positive/Slide003.mrxs      True   \n",
       "1196       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "1389       True  data/train/image/positive/Slide003.mrxs      True   \n",
       "4375       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "564        True  data/train/image/positive/Slide003.mrxs     False   \n",
       "4437       True  data/train/image/positive/Slide003.mrxs      True   \n",
       "1629       True  data/train/image/positive/Slide003.mrxs      True   \n",
       "4017       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "3746       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "3401       True  data/train/image/positive/Slide003.mrxs      True   \n",
       "...         ...                                      ...       ...   \n",
       "768        True  data/train/image/positive/Slide003.mrxs      True   \n",
       "315        True  data/train/image/positive/Slide003.mrxs     False   \n",
       "2298       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "4759       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "2564       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "1984       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "3514       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "2374       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "2473       True  data/train/image/positive/Slide003.mrxs      True   \n",
       "2391       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "2570       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "1543       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "2583       True  data/train/image/positive/Slide003.mrxs      True   \n",
       "1326       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "3954       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "3755       True  data/train/image/positive/Slide003.mrxs      True   \n",
       "4107       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "1716       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "1479       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "3313       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "1829       True  data/train/image/positive/Slide003.mrxs      True   \n",
       "4501       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "3414       True  data/train/image/positive/Slide003.mrxs      True   \n",
       "2393       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "1490       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "4640       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "4755       True  data/train/image/positive/Slide003.mrxs      True   \n",
       "3157       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "3000       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "3231       True  data/train/image/positive/Slide003.mrxs     False   \n",
       "\n",
       "      is_all_tumor    tile_loc  \n",
       "1358         False  (174, 142)  \n",
       "3111         False   (525, 74)  \n",
       "4314         False  (308, 114)  \n",
       "2090         False  (558, 135)  \n",
       "4392          True  (229, 235)  \n",
       "210           True  (463, 210)  \n",
       "4011         False  (299, 172)  \n",
       "4169         False   (84, 156)  \n",
       "2058         False   (171, 86)  \n",
       "4885         False   (321, 94)  \n",
       "3086          True   (446, 55)  \n",
       "3100         False   (44, 241)  \n",
       "1103         False  (600, 203)  \n",
       "1899          True  (469, 102)  \n",
       "2791         False  (336, 181)  \n",
       "4472          True  (471, 175)  \n",
       "3311         False  (159, 232)  \n",
       "4739         False  (568, 130)  \n",
       "225           True  (525, 116)  \n",
       "4242         False  (275, 172)  \n",
       "924           True  (401, 128)  \n",
       "1196         False  (254, 105)  \n",
       "1389          True  (446, 124)  \n",
       "4375         False   (41, 118)  \n",
       "564          False  (279, 130)  \n",
       "4437          True  (395, 185)  \n",
       "1629          True  (489, 257)  \n",
       "4017         False   (39, 282)  \n",
       "3746         False   (89, 173)  \n",
       "3401          True  (245, 233)  \n",
       "...            ...         ...  \n",
       "768           True  (498, 155)  \n",
       "315          False   (89, 148)  \n",
       "2298         False  (288, 125)  \n",
       "4759         False  (523, 132)  \n",
       "2564         False   (65, 280)  \n",
       "1984         False  (537, 196)  \n",
       "3514         False  (303, 206)  \n",
       "2374         False   (258, 68)  \n",
       "2473          True   (472, 92)  \n",
       "2391         False  (247, 131)  \n",
       "2570         False  (301, 217)  \n",
       "1543         False  (271, 136)  \n",
       "2583          True  (211, 196)  \n",
       "1326         False  (156, 203)  \n",
       "3954         False  (339, 190)  \n",
       "3755          True   (459, 90)  \n",
       "4107         False  (304, 159)  \n",
       "1716         False   (573, 98)  \n",
       "1479         False  (595, 129)  \n",
       "3313         False   (508, 59)  \n",
       "1829          True   (425, 82)  \n",
       "4501         False  (154, 245)  \n",
       "3414          True  (532, 264)  \n",
       "2393         False   (531, 49)  \n",
       "1490         False   (72, 152)  \n",
       "4640         False  (132, 303)  \n",
       "4755          True  (477, 266)  \n",
       "3157         False  (595, 122)  \n",
       "3000         False   (52, 271)  \n",
       "3231         False   (251, 59)  \n",
       "\n",
       "[1000 rows x 5 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'samples' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-45192904abbb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msample_gen\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgen_imgs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msamples\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m13\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'time'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'example_X, example_y  = next(sample_gen)'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'samples' is not defined"
     ]
    }
   ],
   "source": [
    "sample_gen = gen_imgs(samples.sample(10, random_state=13), 10, shuffle=False)\n",
    "%time example_X, example_y  = next(sample_gen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = ['is_tissue','slide_path','is_tumor','is_all_tumor','tile_loc']\n",
    "\n",
    "BATCH_SIZE = 128\n",
    "N_EPOCHS = 2\n",
    "\n",
    "for i in range(len(slide_4_test)):\n",
    "    \n",
    "    # [1] dataset , 2 pos, 2 neg, mean ratio = 3:1\n",
    "    four_samples = pd.DataFrame(columns = columns)\n",
    "    four_image_path = list()\n",
    "    four_mask_path = list()    \n",
    "    for j in range(4):\n",
    "        image_path = image_paths[slide_4_test[i][j]][1:] # 이 부분은 data 읽을때 고치자 ( [1:] 빼야함)\n",
    "        mask_path = tumor_mask_paths[slide_4_test[i][j]][1:] # 이 부분은 data 읽을때 고치자\n",
    "        samples = find_patches_from_slide(image_path, mask_path)\n",
    "        \n",
    "        four_samples = four_samples.append(samples)   \n",
    "        four_image_path.append(image_path)\n",
    "        four_mask_path.append(mask_path)\n",
    "    NUM_SAMPLES = len(four_samples)\n",
    "    if NUM_SAMPLES > 50000:\n",
    "        NUM_SAMPLES = 50000\n",
    "    \n",
    "    samples = four_samples.sample(NUM_SAMPLES, random_state=42)\n",
    "    samples.reset_index(drop=True, inplace=True)\n",
    "    \n",
    "    tumor_samples = four_samples[four_samples.is_tumor == True]\n",
    "    print(len(tumor_samples))\n",
    "    non_tumor_samples = four_samples[four_samples.is_tumor == False]\n",
    "    print(len(non_tumor_samples))\n",
    "    non_tumor_samples_3_ratio = non_tumor_samples.sample(len(tumor_samples) * 3, random_state = 42,replace=True)\n",
    "    \n",
    "    all_sample = tumor_samples.append(non_tumor_samples_3_ratio)\n",
    "\n",
    "    split = StratifiedShuffleSplit(n_splits=1, test_size=0.1, random_state=42)\n",
    "    for train_index, test_index in split.split(samples, samples[\"is_tumor\"]):\n",
    "            train_samples = samples.loc[train_index]\n",
    "            validation_samples = samples.loc[test_index]\n",
    "    \n",
    "    train_generator = gen_imgs(four_image_path,four_mask_path,train_samples, BATCH_SIZE)\n",
    "    validation_generator = gen_imgs(four_image_path,four_mask_path,validation_samples, BATCH_SIZE)\n",
    "    \n",
    "    train_start_time = datetime.now()\n",
    "    history = model.fit_generator(train_generator, np.ceil(len(train_samples) / BATCH_SIZE),\n",
    "        validation_data=validation_generator,\n",
    "        validation_steps=np.ceil(len(validation_samples) / BATCH_SIZE),\n",
    "        epochs=N_EPOCHS)\n",
    "    if file_handles != []:\n",
    "        for fh in file_handles:\n",
    "            fh.close()\n",
    "    file_handles=[]\n",
    "    #del train_generator\n",
    "    #del validation_generator\n",
    "    train_end_time = datetime.now()\n",
    "    print(\"Model training time: %.1f minutes\" % ((train_end_time - train_start_time).seconds / 60,))\n",
    "    model.save('s_1.h5')\n",
    "    # split\n",
    "    # data gen : all_image_path, all_mask_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('s_1.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total patches in slide: 105213\n",
      "Wall time: 4min 39s\n",
      "0.9944371525566231\n"
     ]
    }
   ],
   "source": [
    "ipath = all_image_path[0]\n",
    "tpath = all_mask_path[0]\n",
    "\n",
    "all_tissue_samples = find_patches_from_slide(ipath,tpath)\n",
    "print('Total patches in slide: %d' % len(all_tissue_samples)) \n",
    "all_tissue_samples.iloc[:10]\n",
    "all_tissue_samples.is_tumor.value_counts() \n",
    "test_start_time = datetime.now()\n",
    "sample_gen = gen_imgs(all_image_path,all_mask_path,all_tissue_samples, 5000, shuffle=True)\n",
    "%time example_X, example_y  = next(sample_gen)\n",
    "test_end_time = datetime.now()\n",
    "print(\"5000 gen time: %.1f minutes\" % ((test_end_time - test_start_time).seconds / 60,))\n",
    "\n",
    "start_x = PATCH_SIZE//4\n",
    "start_y = PATCH_SIZE//4\n",
    "pred_size = PATCH_SIZE//2\n",
    "\n",
    "test_start_time = datetime.now()\n",
    "preds = []\n",
    "labels = []\n",
    "for i in range(5000):\n",
    "    prediction = predict_from_model(example_X[i],model)\n",
    "    pred_X = np.zeros((pred_size,pred_size))\n",
    "    y = example_y[i].argmax\n",
    "    for x in range(start_x,start_x+pred_size):\n",
    "        for y in range(start_y, start_y+pred_size):\n",
    "            pred_X[x-start_x][y-start_y] = prediction[x][y]\n",
    "            \n",
    "    pred_s = pd.Series(pred_X.flatten())\n",
    "    max_p = np.max(pred_s)\n",
    "    \n",
    "    y = np.max(example_y[i].argmax(axis=2))\n",
    "    preds.append(max_p)\n",
    "    labels.append(y)\n",
    "test_end_time = datetime.now()\n",
    "print(\"Model test time: %.1f minutes\" % ((test_end_time - test_start_time).seconds / 60,))    \n",
    "fpr, tpr, thresholds = metrics.roc_curve(labels,preds,pos_label=1)\n",
    "print(metrics.auc(fpr,tpr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total patches in slide: 105213\n",
      "Wall time: 4min 41s\n",
      "5000 gen time: 4.7 minutes\n",
      "Model test time: 0.6 minutes\n",
      "0.9947376610561872\n"
     ]
    }
   ],
   "source": [
    "ipath = all_image_path[0]\n",
    "tpath = all_mask_path[0]\n",
    "\n",
    "all_tissue_samples = find_patches_from_slide(ipath,tpath)\n",
    "print('Total patches in slide: %d' % len(all_tissue_samples)) \n",
    "all_tissue_samples.iloc[:10]\n",
    "all_tissue_samples.is_tumor.value_counts() \n",
    "test_start_time = datetime.now()\n",
    "sample_gen = gen_imgs(all_image_path,all_mask_path,all_tissue_samples, 5000, shuffle=True)\n",
    "%time example_X, example_y  = next(sample_gen)\n",
    "test_end_time = datetime.now()\n",
    "print(\"5000 gen time: %.1f minutes\" % ((test_end_time - test_start_time).seconds / 60,))\n",
    "\n",
    "start_x = PATCH_SIZE//4\n",
    "start_y = PATCH_SIZE//4\n",
    "pred_size = PATCH_SIZE//2\n",
    "\n",
    "test_start_time = datetime.now()\n",
    "preds = []\n",
    "labels = []\n",
    "for i in range(5000):\n",
    "    prediction = predict_from_model(example_X[i],model)\n",
    "    pred_X = np.zeros((pred_size,pred_size))\n",
    "    y = example_y[i].argmax\n",
    "    pred_s = pd.Series(prediction.flatten())\n",
    "    max_p = np.max(pred_s)\n",
    "    \n",
    "    y = np.max(example_y[i].argmax(axis=2))\n",
    "    preds.append(max_p)\n",
    "    labels.append(y)\n",
    "test_end_time = datetime.now()\n",
    "print(\"Model test time: %.1f minutes\" % ((test_end_time - test_start_time).seconds / 60,))    \n",
    "fpr, tpr, thresholds = metrics.roc_curve(labels,preds,pos_label=1)\n",
    "print(metrics.auc(fpr,tpr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total patches in slide: 105213\n",
      "Wall time: 4min 35s\n",
      "5000 gen time: 4.6 minutes\n",
      "Model test time: 3.4 minutes\n",
      "0.9952130818359953\n"
     ]
    }
   ],
   "source": [
    "ipath = all_image_path[0]\n",
    "tpath = all_mask_path[0]\n",
    "\n",
    "all_tissue_samples = find_patches_from_slide(ipath,tpath)\n",
    "print('Total patches in slide: %d' % len(all_tissue_samples)) \n",
    "all_tissue_samples.iloc[:10]\n",
    "all_tissue_samples.is_tumor.value_counts() \n",
    "test_start_time = datetime.now()\n",
    "sample_gen = gen_imgs(all_image_path,all_mask_path,all_tissue_samples, 5000, shuffle=True)\n",
    "%time example_X, example_y  = next(sample_gen)\n",
    "test_end_time = datetime.now()\n",
    "print(\"5000 gen time: %.1f minutes\" % ((test_end_time - test_start_time).seconds / 60,))\n",
    "\n",
    "start_x = 32\n",
    "start_y = 32\n",
    "pred_size = 192\n",
    "\n",
    "test_start_time = datetime.now()\n",
    "preds = []\n",
    "labels = []\n",
    "for i in range(5000):\n",
    "    prediction = predict_from_model(example_X[i],model)\n",
    "    pred_X = np.zeros((pred_size,pred_size))\n",
    "    y = example_y[i].argmax\n",
    "    for x in range(start_x,start_x+pred_size):\n",
    "        for y in range(start_y, start_y+pred_size):\n",
    "            pred_X[x-start_x][y-start_y] = prediction[x][y]\n",
    "            \n",
    "    pred_s = pd.Series(pred_X.flatten())\n",
    "    max_p = np.max(pred_s)\n",
    "    \n",
    "    y = np.max(example_y[i].argmax(axis=2))\n",
    "    preds.append(max_p)\n",
    "    labels.append(y)\n",
    "test_end_time = datetime.now()\n",
    "print(\"Model test time: %.1f minutes\" % ((test_end_time - test_start_time).seconds / 60,))    \n",
    "fpr, tpr, thresholds = metrics.roc_curve(labels,preds,pos_label=1)\n",
    "print(metrics.auc(fpr,tpr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0004315432452131063"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_pred_x = np.max(preds)\n",
    "max_pred_x\n",
    "\n",
    "min_pred_x = np.min(preds)\n",
    "min_pred_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model test time: 0.0 minutes\n"
     ]
    }
   ],
   "source": [
    "test_start_time = datetime.now()\n",
    "slide = openslide.open_slide(ipath)\n",
    "tiles = DeepZoomGenerator(slide,tile_size=256,overlap=0, limit_bounds=False) \n",
    "test_end_time = datetime.now()\n",
    "print(\"Model test time: %.1f minutes\" % ((test_end_time - test_start_time).seconds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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