<reponame>raktim-mondol/DeepLearningCamelyon
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from PIL import Image
import os.path as osp
import openslide
from pathlib import Path
from scipy.misc import imsave as saveim
from skimage.filters import threshold_otsu
import glob
#before importing HDFStore, make sure 'tables' is installed by pip3 install tables
from pandas import HDFStore
from openslide.deepzoom import DeepZoomGenerator
import cv2 as cv2
from skimage import io
import xml.etree.ElementTree as et
import pandas as pd
import math

#setup for path 
#BASE_TRUTH_DIR = Path('/home/ubuntu/data/Ground_Truth_Extracted/Mask')

#slide_path = '/home/ubuntu/data/slides/Tumor_009.tif'
#truth_path = str(BASE_TRUTH_DIR / 'Tumor_009_Mask.tif')
#BASE_TRUTH_DIR = Path('/Users/liw17/Downloads/camelyontest/Mask')

#slide_path = '/Users/liw17/Downloads/camelyontest/slides/tumor_026.tif'
#truth_path = osp.join(BASE_TRUTH_DIR, 'tumor_026_mask.tif')


#slide = openslide.open_slide(slide_path)
#truth = openslide.open_slide(truth_path)
print('Hi, patch extraction can take a while, please be patient...')
slide_path = '/home/wli/Downloads/CAMELYON16/training/tumor'
slide_path_normal = '/home/wli/Downloads/CAMELYON16/training/normal'

anno_path = '/home/wli/Downloads/CAMELYON16/training/Lesion_annotations'
BASE_TRUTH_DIR = '/home/wli/Downloads/CAMELYON16/masking'
slide_paths = glob.glob(osp.join(slide_path, '*.tif'))
slide_paths.sort()
slide_paths_normal = glob.glob(osp.join(slide_path_normal, '*.tif'))
slide_paths_normal.sort()
slide_paths_total = slide_paths
#slide_paths_total = slide_paths + slide_paths_normal
BASE_TRUTH_DIRS = glob.glob(osp.join(BASE_TRUTH_DIR, '*.tif'))
Anno_paths = glob.glob(osp.join(anno_path, '*.xml'))
BASE_TRUTH_DIRS.sort()

#image_pair = zip(tumor_paths, anno_tumor_paths)  
#image_pair = list(image_mask_pair)

#for color normalization
#lut = np.asarray(Image.open(filename)).squeeze()
#def apply_lut(tile, lut):
#  """ Apply look-up-table to tile to normalize H&E staining. """
#  ps = tile.shape # tile size is (rows, cols, channels)
#  reshaped_tile = tile.reshape((ps[0]*ps[1], 3))
#  normalized_tile = np.zeros((ps[0]*ps[1], 3))
#  idxs = range(ps[0]*ps[1])  
#  Index = 256 * 256 * reshaped_tile[idxs,0] + 256 * reshaped_tile[idxs,1] + reshaped_tile[idxs,2]
#  normalized_tile[idxs] = lut[Index.astype(int)]
#  return normalized_tile.reshape(ps[0], ps[1], 3).astype(np.uint8)


# go through all the file


def convert_xml_df (file):
    parseXML = et.parse(file)
    root = parseXML.getroot()
    dfcols = ['Name', 'Order', 'X', 'Y']
    df_xml = pd.DataFrame(columns=dfcols)
    for child in root.iter('Annotation'):
        for coordinate in child.iter('Coordinate'):
            Name = child.attrib.get('Name')
            Order = coordinate.attrib.get('Order')
            X_coord = float(coordinate.attrib.get('X'))
           # X_coord = X_coord - 30000
            #X_coord = ((X_coord)*dims[0])/Ximageorg
            Y_coord = float(coordinate.attrib.get('Y'))
           # Y_coord = Y_coord - 155000
            #Y_coord = ((Y_coord)*dims[1])/Yimageorg
            df_xml = df_xml.append(pd.Series([Name, Order, X_coord, Y_coord], index = dfcols), ignore_index=True)
            df_xml = pd.DataFrame(df_xml)
    return (df_xml)

def random_crop(slide, truth, thresh, crop_size, bbox):
    
    #width, height = slide.level_dimensions[0]
    dy, dx = crop_size
    x = np.random.randint(bbox[0], bbox[1] - dx + 1)
    y = np.random.randint(bbox[2], bbox[3] - dy + 1)
    #x = np.random.choice(range(width - dx + 1), replace = False)
    #y = np.random.choice(range(height - dy +1), replace = False)
    index=[x, y]
    #print(index)
    #cropped_img = (image[x:(x+dx), y:(y+dy),:], rgb_binary[x:(x+dx), y:(y+dy)], mask[x:(x+dx), y:(y+dy)])
    rgb_image = slide.read_region((x, y), 0, crop_size)
    rgb_mask = truth.read_region((x, y), 0, crop_size)
    rgb_mask = (cv2.cvtColor(np.array(rgb_mask), cv2.COLOR_RGB2GRAY) > 0).astype(int)
    rgb_array = np.array(rgb_image)
    hsv_rgbimage = cv2.cvtColor(rgb_array, cv2.COLOR_BGR2HSV)
    rgb_binary = cv2.inRange(hsv_rgbimage, thresh[0], thresh[1])
    #cropped_img = image[x:(x+dx), y:(y+dy),:]
    #cropped_binary = rgb_binary[x:(x+dx), y:(y+dy)]
    #cropped_mask = mask[x:(x+dx), y:(y+dy)]
    print(index)
    return (rgb_image, rgb_binary, rgb_mask, index)

def random_crop2(slide, truth, thresh, crop_size, bboxt):
    
    #width, height = slide.level_dimensions[0]
    dy, dx = crop_size
    x = np.random.randint(bboxt[0], bboxt[1] - dx + 1)
    y = np.random.randint(bboxt[2], bboxt[3] - dy + 1)
    #x = np.random.choice(range(width - dx + 1), replace = False)
    #y = np.random.choice(range(height - dy +1), replace = False)
    index=[x, y]
    #print(index)
    #cropped_img = (image[x:(x+dx), y:(y+dy),:], rgb_binary[x:(x+dx), y:(y+dy)], mask[x:(x+dx), y:(y+dy)])
    rgb_image = slide.read_region((x, y), 0, crop_size)
    rgb_mask = truth.read_region((x, y), 0, crop_size)
    rgb_mask = (cv2.cvtColor(np.array(rgb_mask), cv2.COLOR_RGB2GRAY) > 0).astype(int)
    rgb_array = np.array(rgb_image)
    hsv_rgbimage = cv2.cvtColor(rgb_array, cv2.COLOR_BGR2HSV)
    rgb_binary = cv2.inRange(hsv_rgbimage, thresh[0], thresh[1])
    #cropped_img = image[x:(x+dx), y:(y+dy),:]
    #cropped_binary = rgb_binary[x:(x+dx), y:(y+dy)]
    #cropped_mask = mask[x:(x+dx), y:(y+dy)]
    print(index)
    return (rgb_image, rgb_binary, rgb_mask, index)

def random_crop_normal(slide, thresh, crop_size, bboxt):
    
    #width, height = slide.level_dimensions[0]
    dy, dx = crop_size
    x = np.random.randint(bboxt[0], bboxt[1] - dx + 1)
    y = np.random.randint(bboxt[2], bboxt[3] - dy + 1)
    index=[x, y]
    #cropped_img = (image[x:(x+dx), y:(y+dy),:], rgb_binary[x:(x+dx), y:(y+dy)], mask[x:(x+dx), y:(y+dy)])
    rgb_image = slide.read_region((x, y), 0, crop_size)
    #rgb_mask = truth.read_region((x, y), 0, crop_size)
    #rgb_mask = (cv2.cvtColor(np.array(mask), cv2.COLOR_RGB2GRAY) > 0).astype(int)
    #rgb_grey = np.array(rgb_image.convert('L'))
    #rgb_binary = (rgb_grey < thresh).astype(int)
    rgb_array = np.array(rgb_image)
    hsv_rgbimage = cv2.cvtColor(rgb_array, cv2.COLOR_BGR2HSV)
    rgb_binary = cv2.inRange(hsv_rgbimage, thresh[0], thresh[1])
    #cropped_img = image[x:(x+dx), y:(y+dy),:]
    #cropped_binary = rgb_binary[x:(x+dx), y:(y+dy)]
    #cropped_mask = mask[x:(x+dx), y:(y+dy)]
    return (rgb_image, rgb_binary, index)


def testduplicates(list):   
    for each in list:  
        count = list.count(each)  
        if count > 1:  
            z = 0
        else:
        
            z = 1
    return z  

#sampletotal = pd.DataFrame([])
crop_size = [256, 256]
i=0
while i < len(slide_paths):
    #sampletotal = pd.DataFrame([])
    base_truth_dir = Path(BASE_TRUTH_DIR)
    anno_path = Path(anno_path)
    slide_contains_tumor = osp.basename(slide_paths_total[i]).startswith('tumor_')
    
    with openslide.open_slide(slide_paths_total[i]) as slide:
        thumbnail = slide.get_thumbnail((slide.dimensions[0] / 256, slide.dimensions[1] / 256))
        thum = np.array(thumbnail)
        hsv_image = cv2.cvtColor(thum, cv2.COLOR_BGR2HSV)
        h, s, v = cv2.split(hsv_image)
        hthresh = threshold_otsu(h)
        sthresh = threshold_otsu(s)
        vthresh = threshold_otsu(v)
        # be min value for v can be changed later
        minhsv = np.array([hthresh, sthresh, 70], np.uint8)
        maxhsv = np.array([180, 255, vthresh], np.uint8)
        thresh = [minhsv, maxhsv]
        #extraction the countor for tissue

        rgbbinary = cv2.inRange(hsv_image, thresh[0], thresh[1])
        _, contours, _ = cv2.findContours(rgbbinary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        bboxtcols = ['xmin', 'xmax', 'ymin', 'ymax']
        bboxt = pd.DataFrame(columns=bboxtcols)
        for c in contours:
            (x, y, w, h) = cv2.boundingRect(c)
            bboxt = bboxt.append(pd.Series([x, x+w, y, y+h], index = bboxtcols), ignore_index=True)
            bboxt = pd.DataFrame(bboxt)
            
        xxmin = list(bboxt['xmin'].get_values())
        xxmax = list(bboxt['xmax'].get_values())
        yymin = list(bboxt['ymin'].get_values())
        yymax = list(bboxt['ymax'].get_values())
        bboxt = math.floor(np.min(xxmin)*256), math.floor(np.max(xxmax)*256), math.floor(np.min(yymin)*256), math.floor(np.max(yymax)*256)

       # thumbnail_grey = np.array(thumbnail.convert('L')) # convert to grayscale
        #thresh = threshold_otsu(thumbnail_grey)
        #binary = thumbnail_grey > thresh
    
        #patches = pd.DataFrame(pd.DataFrame(binary).stack())
        #patches['is_tissue'] = ~patches[0]
        #patches.drop(0, axis=1, inplace=True)
        #patches['slide_path'] = slide_paths[i]
        #patches['threshold']=thresh
    
    if slide_contains_tumor:
        truth_slide_path = base_truth_dir / osp.basename(slide_paths_total[i]).replace('.tif', '_mask.tif')
        Anno_pathxml = anno_path / osp.basename(slide_paths_total[i]).replace('.tif', '.xml')

        with openslide.open_slide(str(truth_slide_path)) as truth:

          
          slide = openslide.open_slide(slide_paths_total[i])
          annotations = convert_xml_df(str(Anno_pathxml))
          x_values = list(annotations['X'].get_values())
          y_values = list(annotations['Y'].get_values())
          bbox = math.floor(np.min(x_values)), math.floor(np.max(x_values)), math.floor(np.min(y_values)), math.floor(np.max(y_values))
          m=0
          #a = []
          while m in range(0, 1000):
            r=random_crop(slide, truth, thresh, crop_size, bbox)
            if (cv2.countNonZero(r[2]) > crop_size[0]*crop_size[1]*0.5) and (m <= 1000):
    
              saveim('/home/wli/Downloads/test/tumor/%s_%d_%d.png' % (osp.splitext(osp.basename(slide_paths_total[i]))[0], r[3][0], r[3][1]), r[0])
        
              io.imsave('/home/wli/Downloads/test/mask/%s_%d_%d_mask.png' % (osp.splitext(osp.basename(slide_paths_total[i]))[0], r[3][0], r[3][1]), r[2])

              print(r[2])

              
              #a.append(r[3])
              #z = testduplicates(a)
              m=m+1
            
            else:
              m=m

          n=0
          #b=[]
          while n in range(0, 1000):
            #slide = openslide.open_slide(slide_paths[i])
            r=random_crop2(slide, truth, thresh, crop_size, bboxt)
            if (cv2.countNonZero(r[1]) > crop_size[0]*crop_size[1]*0.2) and (cv2.countNonZero(r[2]) == 0) and (n <= 1000):

              saveim('/home/wli/Downloads/test/normal/%s_%d_%d.png' % (osp.splitext(osp.basename(slide_paths_total[i]))[0], r[3][0], r[3][1]),r[0])
              io.imsave('/home/wli/Downloads/test/nmask/%s_%d_%d_mask.png' % (osp.splitext(osp.basename(slide_paths_total[i]))[0], r[3][0], r[3][1]),r[2])

            
              #b.append(r[3])
              #zz = testduplicates(b)
              
              n=n+1

            else:
             n=n 

    else:
        o=0    
        slide = openslide.open_slide(slide_paths_total[i])
        #c=[]
        while o in range(0, 1000):
            nr=random_crop_normal(slide, thresh, crop_size, bboxt)
            if (cv2.countNonZero(r[1]) > crop_size[0]*crop_size[1]*0.2) and (o <= 1000):
               nmask = np.zeros((256, 256))

               saveim('/home/wli/Downloads/test/normal/%s_%d_%d.png' % (osp.splitext(osp.basename(slide_paths_total[i]))[0], nr[2][0], nr[2][1]),nr[0])
               io.imsave('/home/wli/Downloads/test/nmask/%s_%d_%d_mask.png' % (osp.splitext(osp.basename(slide_paths_total[i]))[0], nr[2][0], nr[2][1]), nmask)
               
               #c.append(r[3])
               
               #zzz = testduplicates(c)
               o=o+1

            else:
             o=o 

    i=i+1

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from PIL import Image
import os.path as osp
import openslide
from pathlib import Path
from scipy.misc import imsave as saveim
from skimage.filters import threshold_otsu
import glob
#before importing HDFStore, make sure 'tables' is installed by pip3 install tables
from pandas import HDFStore
from openslide.deepzoom import DeepZoomGenerator
import cv2 as cv2
from skimage import io
import xml.etree.ElementTree as et
import pandas as pd
import math

#setup for path 
#BASE_TRUTH_DIR = Path('/home/ubuntu/data/Ground_Truth_Extracted/Mask')

#slide_path = '/home/ubuntu/data/slides/Tumor_009.tif'
#truth_path = str(BASE_TRUTH_DIR / 'Tumor_009_Mask.tif')
#BASE_TRUTH_DIR = Path('/Users/liw17/Downloads/camelyontest/Mask')

#slide_path = '/Users/liw17/Downloads/camelyontest/slides/tumor_026.tif'
#truth_path = osp.join(BASE_TRUTH_DIR, 'tumor_026_mask.tif')


#slide = openslide.open_slide(slide_path)
#truth = openslide.open_slide(truth_path)
print('Hi, patch extraction can take a while, please be patient...')
slide_path = '/home/wli/Downloads/CAMELYON16/training/tumor'
slide_path_normal = '/home/wli/Downloads/CAMELYON16/training/normal'

anno_path = '/home/wli/Downloads/CAMELYON16/training/Lesion_annotations'
BASE_TRUTH_DIR = '/home/wli/Downloads/CAMELYON16/masking'
slide_paths = glob.glob(osp.join(slide_path, '*.tif'))
slide_paths.sort()
slide_paths_normal = glob.glob(osp.join(slide_path_normal, '*.tif'))
slide_paths_normal.sort()
slide_paths_total = slide_paths
#slide_paths_total = slide_paths + slide_paths_normal
BASE_TRUTH_DIRS = glob.glob(osp.join(BASE_TRUTH_DIR, '*.tif'))
Anno_paths = glob.glob(osp.join(anno_path, '*.xml'))
BASE_TRUTH_DIRS.sort()

#image_pair = zip(tumor_paths, anno_tumor_paths)  
#image_pair = list(image_mask_pair)

#for color normalization
#lut = np.asarray(Image.open(filename)).squeeze()
#def apply_lut(tile, lut):
#  """ Apply look-up-table to tile to normalize H&E staining. """
#  ps = tile.shape # tile size is (rows, cols, channels)
#  reshaped_tile = tile.reshape((ps[0]*ps[1], 3))
#  normalized_tile = np.zeros((ps[0]*ps[1], 3))
#  idxs = range(ps[0]*ps[1])  
#  Index = 256 * 256 * reshaped_tile[idxs,0] + 256 * reshaped_tile[idxs,1] + reshaped_tile[idxs,2]
#  normalized_tile[idxs] = lut[Index.astype(int)]
#  return normalized_tile.reshape(ps[0], ps[1], 3).astype(np.uint8)


# go through all the file


def convert_xml_df (file):
    parseXML = et.parse(file)
    root = parseXML.getroot()
    dfcols = ['Name', 'Order', 'X', 'Y']
    df_xml = pd.DataFrame(columns=dfcols)
    for child in root.iter('Annotation'):
        for coordinate in child.iter('Coordinate'):
            Name = child.attrib.get('Name')
            Order = coordinate.attrib.get('Order')
            X_coord = float(coordinate.attrib.get('X'))
           # X_coord = X_coord - 30000
            #X_coord = ((X_coord)*dims[0])/Ximageorg
            Y_coord = float(coordinate.attrib.get('Y'))
           # Y_coord = Y_coord - 155000
            #Y_coord = ((Y_coord)*dims[1])/Yimageorg
            df_xml = df_xml.append(pd.Series([Name, Order, X_coord, Y_coord], index = dfcols), ignore_index=True)
            df_xml = pd.DataFrame(df_xml)
    return (df_xml)

def random_crop(slide, truth, thresh, crop_size, bbox):
    
    #width, height = slide.level_dimensions[0]
    dy, dx = crop_size
    x = np.random.randint(bbox[0], bbox[1] - dx + 1)
    y = np.random.randint(bbox[2], bbox[3] - dy + 1)
    #x = np.random.choice(range(width - dx + 1), replace = False)
    #y = np.random.choice(range(height - dy +1), replace = False)
    index=[x, y]
    #print(index)
    #cropped_img = (image[x:(x+dx), y:(y+dy),:], rgb_binary[x:(x+dx), y:(y+dy)], mask[x:(x+dx), y:(y+dy)])
    rgb_image = slide.read_region((x, y), 0, crop_size)
    rgb_mask = truth.read_region((x, y), 0, crop_size)
    rgb_mask = (cv2.cvtColor(np.array(rgb_mask), cv2.COLOR_RGB2GRAY) > 0).astype(int)
    rgb_array = np.array(rgb_image)
    hsv_rgbimage = cv2.cvtColor(rgb_array, cv2.COLOR_BGR2HSV)
    rgb_binary = cv2.inRange(hsv_rgbimage, thresh[0], thresh[1])
    #cropped_img = image[x:(x+dx), y:(y+dy),:]
    #cropped_binary = rgb_binary[x:(x+dx), y:(y+dy)]
    #cropped_mask = mask[x:(x+dx), y:(y+dy)]
    print(index)
    return (rgb_image, rgb_binary, rgb_mask, index)

def random_crop2(slide, truth, thresh, crop_size, bboxt):
    
    #width, height = slide.level_dimensions[0]
    dy, dx = crop_size
    x = np.random.randint(bboxt[0], bboxt[1] - dx + 1)
    y = np.random.randint(bboxt[2], bboxt[3] - dy + 1)
    #x = np.random.choice(range(width - dx + 1), replace = False)
    #y = np.random.choice(range(height - dy +1), replace = False)
    index=[x, y]
    #print(index)
    #cropped_img = (image[x:(x+dx), y:(y+dy),:], rgb_binary[x:(x+dx), y:(y+dy)], mask[x:(x+dx), y:(y+dy)])
    rgb_image = slide.read_region((x, y), 0, crop_size)
    rgb_mask = truth.read_region((x, y), 0, crop_size)
    rgb_mask = (cv2.cvtColor(np.array(rgb_mask), cv2.COLOR_RGB2GRAY) > 0).astype(int)
    rgb_array = np.array(rgb_image)
    hsv_rgbimage = cv2.cvtColor(rgb_array, cv2.COLOR_BGR2HSV)
    rgb_binary = cv2.inRange(hsv_rgbimage, thresh[0], thresh[1])
    #cropped_img = image[x:(x+dx), y:(y+dy),:]
    #cropped_binary = rgb_binary[x:(x+dx), y:(y+dy)]
    #cropped_mask = mask[x:(x+dx), y:(y+dy)]
    print(index)
    return (rgb_image, rgb_binary, rgb_mask, index)

def random_crop_normal(slide, thresh, crop_size, bboxt):
    
    #width, height = slide.level_dimensions[0]
    dy, dx = crop_size
    x = np.random.randint(bboxt[0], bboxt[1] - dx + 1)
    y = np.random.randint(bboxt[2], bboxt[3] - dy + 1)
    index=[x, y]
    #cropped_img = (image[x:(x+dx), y:(y+dy),:], rgb_binary[x:(x+dx), y:(y+dy)], mask[x:(x+dx), y:(y+dy)])
    rgb_image = slide.read_region((x, y), 0, crop_size)
    #rgb_mask = truth.read_region((x, y), 0, crop_size)
    #rgb_mask = (cv2.cvtColor(np.array(mask), cv2.COLOR_RGB2GRAY) > 0).astype(int)
    #rgb_grey = np.array(rgb_image.convert('L'))
    #rgb_binary = (rgb_grey < thresh).astype(int)
    rgb_array = np.array(rgb_image)
    hsv_rgbimage = cv2.cvtColor(rgb_array, cv2.COLOR_BGR2HSV)
    rgb_binary = cv2.inRange(hsv_rgbimage, thresh[0], thresh[1])
    #cropped_img = image[x:(x+dx), y:(y+dy),:]
    #cropped_binary = rgb_binary[x:(x+dx), y:(y+dy)]
    #cropped_mask = mask[x:(x+dx), y:(y+dy)]
    return (rgb_image, rgb_binary, index)


def testduplicates(list):   
    for each in list:  
        count = list.count(each)  
        if count > 1:  
            z = 0
        else:
        
            z = 1
    return z  

#sampletotal = pd.DataFrame([])
crop_size = [256, 256]
i=0
while i < len(slide_paths):
    #sampletotal = pd.DataFrame([])
    base_truth_dir = Path(BASE_TRUTH_DIR)
    anno_path = Path(anno_path)
    slide_contains_tumor = osp.basename(slide_paths_total[i]).startswith('tumor_')
    
    with openslide.open_slide(slide_paths_total[i]) as slide:
        thumbnail = slide.get_thumbnail((slide.dimensions[0] / 256, slide.dimensions[1] / 256))
        thum = np.array(thumbnail)
        hsv_image = cv2.cvtColor(thum, cv2.COLOR_BGR2HSV)
        h, s, v = cv2.split(hsv_image)
        hthresh = threshold_otsu(h)
        sthresh = threshold_otsu(s)
        vthresh = threshold_otsu(v)
        # be min value for v can be changed later
        minhsv = np.array([hthresh, sthresh, 70], np.uint8)
        maxhsv = np.array([180, 255, vthresh], np.uint8)
        thresh = [minhsv, maxhsv]
        #extraction the countor for tissue

        rgbbinary = cv2.inRange(hsv_image, thresh[0], thresh[1])
        _, contours, _ = cv2.findContours(rgbbinary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        bboxtcols = ['xmin', 'xmax', 'ymin', 'ymax']
        bboxt = pd.DataFrame(columns=bboxtcols)
        for c in contours:
            (x, y, w, h) = cv2.boundingRect(c)
            bboxt = bboxt.append(pd.Series([x, x+w, y, y+h], index = bboxtcols), ignore_index=True)
            bboxt = pd.DataFrame(bboxt)
            
        xxmin = list(bboxt['xmin'].get_values())
        xxmax = list(bboxt['xmax'].get_values())
        yymin = list(bboxt['ymin'].get_values())
        yymax = list(bboxt['ymax'].get_values())
        bboxt = math.floor(np.min(xxmin)*256), math.floor(np.max(xxmax)*256), math.floor(np.min(yymin)*256), math.floor(np.max(yymax)*256)

       # thumbnail_grey = np.array(thumbnail.convert('L')) # convert to grayscale
        #thresh = threshold_otsu(thumbnail_grey)
        #binary = thumbnail_grey > thresh
    
        #patches = pd.DataFrame(pd.DataFrame(binary).stack())
        #patches['is_tissue'] = ~patches[0]
        #patches.drop(0, axis=1, inplace=True)
        #patches['slide_path'] = slide_paths[i]
        #patches['threshold']=thresh
    
    if slide_contains_tumor:
        truth_slide_path = base_truth_dir / osp.basename(slide_paths_total[i]).replace('.tif', '_mask.tif')
        Anno_pathxml = anno_path / osp.basename(slide_paths_total[i]).replace('.tif', '.xml')

        with openslide.open_slide(str(truth_slide_path)) as truth:

          
          slide = openslide.open_slide(slide_paths_total[i])
          annotations = convert_xml_df(str(Anno_pathxml))
          x_values = list(annotations['X'].get_values())
          y_values = list(annotations['Y'].get_values())
          bbox = math.floor(np.min(x_values)), math.floor(np.max(x_values)), math.floor(np.min(y_values)), math.floor(np.max(y_values))
          m=0
          #a = []
          while m in range(0, 1000):
            r=random_crop(slide, truth, thresh, crop_size, bbox)
            if (cv2.countNonZero(r[2]) > crop_size[0]*crop_size[1]*0.5) and (m <= 1000):
    
              saveim('/home/wli/Downloads/test/tumor/%s_%d_%d.png' % (osp.splitext(osp.basename(slide_paths_total[i]))[0], r[3][0], r[3][1]), r[0])
        
              io.imsave('/home/wli/Downloads/test/mask/%s_%d_%d_mask.png' % (osp.splitext(osp.basename(slide_paths_total[i]))[0], r[3][0], r[3][1]), r[2])

              print(r[2])

              
              #a.append(r[3])
              #z = testduplicates(a)
              m=m+1
            
            else:
              m=m

          n=0
          #b=[]
          while n in range(0, 1000):
            #slide = openslide.open_slide(slide_paths[i])
            r=random_crop2(slide, truth, thresh, crop_size, bboxt)
            if (cv2.countNonZero(r[1]) > crop_size[0]*crop_size[1]*0.2) and (cv2.countNonZero(r[2]) == 0) and (n <= 1000):

              saveim('/home/wli/Downloads/test/normal/%s_%d_%d.png' % (osp.splitext(osp.basename(slide_paths_total[i]))[0], r[3][0], r[3][1]),r[0])
              io.imsave('/home/wli/Downloads/test/nmask/%s_%d_%d_mask.png' % (osp.splitext(osp.basename(slide_paths_total[i]))[0], r[3][0], r[3][1]),r[2])

            
              #b.append(r[3])
              #zz = testduplicates(b)
              
              n=n+1

            else:
             n=n 

    else:
        o=0    
        slide = openslide.open_slide(slide_paths_total[i])
        #c=[]
        while o in range(0, 1000):
            nr=random_crop_normal(slide, thresh, crop_size, bboxt)
            if (cv2.countNonZero(r[1]) > crop_size[0]*crop_size[1]*0.2) and (o <= 1000):
               nmask = np.zeros((256, 256))

               saveim('/home/wli/Downloads/test/normal/%s_%d_%d.png' % (osp.splitext(osp.basename(slide_paths_total[i]))[0], nr[2][0], nr[2][1]),nr[0])
               io.imsave('/home/wli/Downloads/test/nmask/%s_%d_%d_mask.png' % (osp.splitext(osp.basename(slide_paths_total[i]))[0], nr[2][0], nr[2][1]), nmask)
               
               #c.append(r[3])
               
               #zzz = testduplicates(c)
               o=o+1

            else:
             o=o 

    i=i+1
#before importing HDFStore, make sure 'tables' is installed by pip3 install tables #setup for path #BASE_TRUTH_DIR = Path('/home/ubuntu/data/Ground_Truth_Extracted/Mask') #slide_path = '/home/ubuntu/data/slides/Tumor_009.tif' #truth_path = str(BASE_TRUTH_DIR / 'Tumor_009_Mask.tif') #BASE_TRUTH_DIR = Path('/Users/liw17/Downloads/camelyontest/Mask') #slide_path = '/Users/liw17/Downloads/camelyontest/slides/tumor_026.tif' #truth_path = osp.join(BASE_TRUTH_DIR, 'tumor_026_mask.tif') #slide = openslide.open_slide(slide_path) #truth = openslide.open_slide(truth_path) #slide_paths_total = slide_paths + slide_paths_normal #image_pair = zip(tumor_paths, anno_tumor_paths) #image_pair = list(image_mask_pair) #for color normalization #lut = np.asarray(Image.open(filename)).squeeze() #def apply_lut(tile, lut): #  """ Apply look-up-table to tile to normalize H&E staining. """ #  ps = tile.shape # tile size is (rows, cols, channels) #  reshaped_tile = tile.reshape((ps[0]*ps[1], 3)) #  normalized_tile = np.zeros((ps[0]*ps[1], 3)) #  idxs = range(ps[0]*ps[1]) #  Index = 256 * 256 * reshaped_tile[idxs,0] + 256 * reshaped_tile[idxs,1] + reshaped_tile[idxs,2] #  normalized_tile[idxs] = lut[Index.astype(int)] #  return normalized_tile.reshape(ps[0], ps[1], 3).astype(np.uint8) # go through all the file # X_coord = X_coord - 30000 #X_coord = ((X_coord)*dims[0])/Ximageorg # Y_coord = Y_coord - 155000 #Y_coord = ((Y_coord)*dims[1])/Yimageorg #width, height = slide.level_dimensions[0] #x = np.random.choice(range(width - dx + 1), replace = False) #y = np.random.choice(range(height - dy +1), replace = False) #print(index) #cropped_img = (image[x:(x+dx), y:(y+dy),:], rgb_binary[x:(x+dx), y:(y+dy)], mask[x:(x+dx), y:(y+dy)]) #cropped_img = image[x:(x+dx), y:(y+dy),:] #cropped_binary = rgb_binary[x:(x+dx), y:(y+dy)] #cropped_mask = mask[x:(x+dx), y:(y+dy)] #width, height = slide.level_dimensions[0] #x = np.random.choice(range(width - dx + 1), replace = False) #y = np.random.choice(range(height - dy +1), replace = False) #print(index) #cropped_img = (image[x:(x+dx), y:(y+dy),:], rgb_binary[x:(x+dx), y:(y+dy)], mask[x:(x+dx), y:(y+dy)]) #cropped_img = image[x:(x+dx), y:(y+dy),:] #cropped_binary = rgb_binary[x:(x+dx), y:(y+dy)] #cropped_mask = mask[x:(x+dx), y:(y+dy)] #width, height = slide.level_dimensions[0] #cropped_img = (image[x:(x+dx), y:(y+dy),:], rgb_binary[x:(x+dx), y:(y+dy)], mask[x:(x+dx), y:(y+dy)]) #rgb_mask = truth.read_region((x, y), 0, crop_size) #rgb_mask = (cv2.cvtColor(np.array(mask), cv2.COLOR_RGB2GRAY) > 0).astype(int) #rgb_grey = np.array(rgb_image.convert('L')) #rgb_binary = (rgb_grey < thresh).astype(int) #cropped_img = image[x:(x+dx), y:(y+dy),:] #cropped_binary = rgb_binary[x:(x+dx), y:(y+dy)] #cropped_mask = mask[x:(x+dx), y:(y+dy)] #sampletotal = pd.DataFrame([]) #sampletotal = pd.DataFrame([]) # be min value for v can be changed later #extraction the countor for tissue # thumbnail_grey = np.array(thumbnail.convert('L')) # convert to grayscale #thresh = threshold_otsu(thumbnail_grey) #binary = thumbnail_grey > thresh #patches = pd.DataFrame(pd.DataFrame(binary).stack()) #patches['is_tissue'] = ~patches[0] #patches.drop(0, axis=1, inplace=True) #patches['slide_path'] = slide_paths[i] #patches['threshold']=thresh #a = [] #a.append(r[3]) #z = testduplicates(a) #b=[] #slide = openslide.open_slide(slide_paths[i]) #b.append(r[3]) #zz = testduplicates(b) #c=[] #c.append(r[3]) #zzz = testduplicates(c)