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preprocess_data.py
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preprocess_data.py
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from openslide import OpenSlide, OpenSlideUnsupportedFormatError
from PIL import Image
import glob
import os
import numpy as np
import cv2
# modify below directory entries as per your local file system
TRAIN_TUMOR_WSI_PATH = '/home/millpc/Documents/Arjun/Study/Thesis/CAMELYON16/data/CAMELYON16/' \
'TrainingData/Train_Tumor'
TRAIN_NORMAL_WSI_PATH = '/home/millpc/Documents/Arjun/Study/Thesis/CAMELYON16/data/CAMELYON16/' \
'TrainingData/Train_Normal'
TRAIN_TUMOR_MASK_PATH = '/home/millpc/Documents/Arjun/Study/Thesis/CAMELYON16/data/CAMELYON16/TrainingData/' \
'Ground_Truth/Mask'
PROCESSED_PATCHES_NORMAL_NEGATIVE_PATH = '/home/millpc/Documents/Arjun/Study/Thesis/CAMELYON16/data/CAMELYON16/' \
'Processed/patch-based-classification/normal-label-0/'
PROCESSED_PATCHES_TUMOR_NEGATIVE_PATH = '/home/millpc/Documents/Arjun/Study/Thesis/CAMELYON16/data/CAMELYON16/' \
'Processed/patch-based-classification/tumor-label-0/'
PROCESSED_PATCHES_POSITIVE_PATH = '/home/millpc/Documents/Arjun/Study/Thesis/CAMELYON16/data/CAMELYON16/' \
'Processed/patch-based-classification/label-1/'
PROCESSED_PATCHES_FROM_USE_MASK_POSITIVE_PATH = '/home/millpc/Documents/Arjun/Study/Thesis/CAMELYON16/data/CAMELYON16/' \
'Processed/patch-based-classification/use-mask-label-1/'
PATCH_SIZE = 256
PATCH_NORMAL_PREFIX = 'normal_'
PATCH_TUMOR_PREFIX = 'tumor_'
class WSI(object):
"""
# ================================
# Class to annotate WSIs with ROIs
# ================================
"""
index = 0
negative_patch_index = 118456
positive_patch_index = 2230
wsi_paths = []
mask_paths = []
def_level = 7
key = 0
def extract_patches_mask(self, bounding_boxes):
"""
Extract positive patches targeting annotated tumor region
Save extracted patches to desk as .png image files
:param bounding_boxes: list of bounding boxes corresponds to tumor regions
:return:
"""
mag_factor = pow(2, self.level_used)
print('No. of ROIs to extract patches from: %d' % len(bounding_boxes))
for i, bounding_box in enumerate(bounding_boxes):
b_x_start = int(bounding_box[0]) * mag_factor
b_y_start = int(bounding_box[1]) * mag_factor
b_x_end = (int(bounding_box[0]) + int(bounding_box[2])) * mag_factor
b_y_end = (int(bounding_box[1]) + int(bounding_box[3])) * mag_factor
X = np.random.random_integers(b_x_start, high=b_x_end, size=500)
Y = np.random.random_integers(b_y_start, high=b_y_end, size=500)
# X = np.arange(b_x_start, b_x_end-256, 5)
# Y = np.arange(b_y_start, b_y_end-256, 5)
for x, y in zip(X, Y):
mask = self.mask_image.read_region((x, y), 0, (PATCH_SIZE, PATCH_SIZE))
mask_gt = np.array(mask)
mask_gt = cv2.cvtColor(mask_gt, cv2.COLOR_BGR2GRAY)
white_pixel_cnt_gt = cv2.countNonZero(mask_gt)
if white_pixel_cnt_gt > ((PATCH_SIZE * PATCH_SIZE) * 0.90):
# mask = Image.fromarray(mask)
patch = self.wsi_image.read_region((x, y), 0, (PATCH_SIZE, PATCH_SIZE))
patch.save(PROCESSED_PATCHES_FROM_USE_MASK_POSITIVE_PATH + PATCH_TUMOR_PREFIX +
str(self.positive_patch_index), 'PNG')
self.positive_patch_index += 1
patch.close()
mask.close()
def extract_patches_normal(self, bounding_boxes):
"""
Extract negative patches from Normal WSIs
Save extracted patches to desk as .png image files
:param bounding_boxes: list of bounding boxes corresponds to detected ROIs
:return:
"""
mag_factor = pow(2, self.level_used)
print('No. of ROIs to extract patches from: %d' % len(bounding_boxes))
for i, bounding_box in enumerate(bounding_boxes):
b_x_start = int(bounding_box[0]) * mag_factor
b_y_start = int(bounding_box[1]) * mag_factor
b_x_end = (int(bounding_box[0]) + int(bounding_box[2])) * mag_factor
b_y_end = (int(bounding_box[1]) + int(bounding_box[3])) * mag_factor
X = np.random.random_integers(b_x_start, high=b_x_end, size=500)
Y = np.random.random_integers(b_y_start, high=b_y_end, size=500)
# X = np.arange(b_x_start, b_x_end-256, 5)
# Y = np.arange(b_y_start, b_y_end-256, 5)
for x, y in zip(X, Y):
patch = self.wsi_image.read_region((x, y), 0, (PATCH_SIZE, PATCH_SIZE))
patch_array = np.array(patch)
patch_hsv = cv2.cvtColor(patch_array, cv2.COLOR_BGR2HSV)
# [20, 20, 20]
lower_red = np.array([20, 20, 20])
# [255, 255, 255]
upper_red = np.array([200, 200, 200])
mask = cv2.inRange(patch_hsv, lower_red, upper_red)
white_pixel_cnt = cv2.countNonZero(mask)
if white_pixel_cnt > ((PATCH_SIZE * PATCH_SIZE) * 0.50):
# mask = Image.fromarray(mask)
patch.save(PROCESSED_PATCHES_NORMAL_NEGATIVE_PATH + PATCH_NORMAL_PREFIX +
str(self.negative_patch_index), 'PNG')
# mask.save(PROCESSED_PATCHES_NORMAL_PATH + PATCH_NORMAL_PREFIX + str(self.patch_index),
# 'PNG')
self.negative_patch_index += 1
patch.close()
def extract_patches_tumor(self, bounding_boxes):
"""
Extract both, negative patches from Normal area and positive patches from Tumor area
Save extracted patches to desk as .png image files
:param bounding_boxes: list of bounding boxes corresponds to detected ROIs
:return:
"""
mag_factor = pow(2, self.level_used)
print('No. of ROIs to extract patches from: %d' % len(bounding_boxes))
for i, bounding_box in enumerate(bounding_boxes):
b_x_start = int(bounding_box[0]) * mag_factor
b_y_start = int(bounding_box[1]) * mag_factor
b_x_end = (int(bounding_box[0]) + int(bounding_box[2])) * mag_factor
b_y_end = (int(bounding_box[1]) + int(bounding_box[3])) * mag_factor
X = np.random.random_integers(b_x_start, high=b_x_end, size=500)
Y = np.random.random_integers(b_y_start, high=b_y_end, size=500)
# X = np.arange(b_x_start, b_x_end-256, 5)
# Y = np.arange(b_y_start, b_y_end-256, 5)
for x, y in zip(X, Y):
patch = self.wsi_image.read_region((x, y), 0, (PATCH_SIZE, PATCH_SIZE))
mask = self.mask_image.read_region((x, y), 0, (PATCH_SIZE, PATCH_SIZE))
mask_gt = np.array(mask)
# mask_gt = cv2.cvtColor(mask_gt, cv2.COLOR_BGR2GRAY)
mask_gt = cv2.cvtColor(mask_gt, cv2.COLOR_BGR2GRAY)
patch_array = np.array(patch)
white_pixel_cnt_gt = cv2.countNonZero(mask_gt)
if white_pixel_cnt_gt == 0: # mask_gt does not contain tumor area
patch_hsv = cv2.cvtColor(patch_array, cv2.COLOR_BGR2HSV)
lower_red = np.array([20, 20, 20])
upper_red = np.array([200, 200, 200])
mask_patch = cv2.inRange(patch_hsv, lower_red, upper_red)
white_pixel_cnt = cv2.countNonZero(mask_patch)
if white_pixel_cnt > ((PATCH_SIZE * PATCH_SIZE) * 0.50):
# mask = Image.fromarray(mask)
patch.save(PROCESSED_PATCHES_TUMOR_NEGATIVE_PATH + PATCH_NORMAL_PREFIX +
str(self.negative_patch_index), 'PNG')
# mask.save(PROCESSED_PATCHES_NORMAL_PATH + PATCH_NORMAL_PREFIX + str(self.patch_index),
# 'PNG')
self.negative_patch_index += 1
else: # mask_gt contains tumor area
if white_pixel_cnt_gt >= ((PATCH_SIZE * PATCH_SIZE) * 0.85):
patch.save(PROCESSED_PATCHES_POSITIVE_PATH + PATCH_TUMOR_PREFIX +
str(self.positive_patch_index), 'PNG')
self.positive_patch_index += 1
patch.close()
mask.close()
def read_wsi_mask(self, wsi_path, mask_path):
try:
self.cur_wsi_path = wsi_path
self.wsi_image = OpenSlide(wsi_path)
self.mask_image = OpenSlide(mask_path)
self.level_used = min(self.def_level, self.wsi_image.level_count - 1, self.mask_image.level_count - 1)
self.mask_pil = self.mask_image.read_region((0, 0), self.level_used,
self.mask_image.level_dimensions[self.level_used])
self.mask = np.array(self.mask_pil)
except OpenSlideUnsupportedFormatError:
print('Exception: OpenSlideUnsupportedFormatError')
return False
return True
def read_wsi_normal(self, wsi_path):
"""
# =====================================================================================
# read WSI image and resize
# Due to memory constraint, we use down sampled (4th level, 1/32 resolution) image
# ======================================================================================
"""
try:
self.cur_wsi_path = wsi_path
self.wsi_image = OpenSlide(wsi_path)
self.level_used = min(self.def_level, self.wsi_image.level_count - 1)
self.rgb_image_pil = self.wsi_image.read_region((0, 0), self.level_used,
self.wsi_image.level_dimensions[self.level_used])
self.rgb_image = np.array(self.rgb_image_pil)
except OpenSlideUnsupportedFormatError:
print('Exception: OpenSlideUnsupportedFormatError')
return False
return True
def read_wsi_tumor(self, wsi_path, mask_path):
"""
# =====================================================================================
# read WSI image and resize
# Due to memory constraint, we use down sampled (4th level, 1/32 resolution) image
# ======================================================================================
"""
try:
self.cur_wsi_path = wsi_path
self.wsi_image = OpenSlide(wsi_path)
self.mask_image = OpenSlide(mask_path)
self.level_used = min(self.def_level, self.wsi_image.level_count - 1, self.mask_image.level_count - 1)
self.rgb_image_pil = self.wsi_image.read_region((0, 0), self.level_used,
self.wsi_image.level_dimensions[self.level_used])
self.rgb_image = np.array(self.rgb_image_pil)
except OpenSlideUnsupportedFormatError:
print('Exception: OpenSlideUnsupportedFormatError')
return False
return True
def find_roi_n_extract_patches_mask(self):
mask = cv2.cvtColor(self.mask, cv2.COLOR_BGR2GRAY)
contour_mask, bounding_boxes = self.get_image_contours_mask(np.array(mask), np.array(self.mask))
# contour_mask = cv2.resize(contour_mask, (0, 0), fx=0.40, fy=0.40)
# cv2.imshow('contour_mask', np.array(contour_mask))
self.mask_pil.close()
self.extract_patches_mask(bounding_boxes)
self.wsi_image.close()
self.mask_image.close()
def find_roi_n_extract_patches_normal(self):
hsv = cv2.cvtColor(self.rgb_image, cv2.COLOR_BGR2HSV)
# [20, 20, 20]
lower_red = np.array([20, 50, 20])
# [255, 255, 255]
upper_red = np.array([200, 150, 200])
mask = cv2.inRange(hsv, lower_red, upper_red)
# (50, 50)
close_kernel = np.ones((25, 25), dtype=np.uint8)
image_close = Image.fromarray(cv2.morphologyEx(np.array(mask), cv2.MORPH_CLOSE, close_kernel))
# (30, 30)
open_kernel = np.ones((30, 30), dtype=np.uint8)
image_open = Image.fromarray(cv2.morphologyEx(np.array(image_close), cv2.MORPH_OPEN, open_kernel))
contour_rgb, bounding_boxes = self.get_image_contours_normal(np.array(image_open), self.rgb_image)
# contour_rgb = cv2.resize(contour_rgb, (0, 0), fx=0.40, fy=0.40)
# cv2.imshow('contour_rgb', np.array(contour_rgb))
self.rgb_image_pil.close()
self.extract_patches_normal(bounding_boxes)
self.wsi_image.close()
def find_roi_n_extract_patches_tumor(self):
hsv = cv2.cvtColor(self.rgb_image, cv2.COLOR_BGR2HSV)
lower_red = np.array([20, 20, 20])
upper_red = np.array([255, 255, 255])
mask = cv2.inRange(hsv, lower_red, upper_red)
# (50, 50)
close_kernel = np.ones((50, 50), dtype=np.uint8)
image_close = Image.fromarray(cv2.morphologyEx(np.array(mask), cv2.MORPH_CLOSE, close_kernel))
# (30, 30)
open_kernel = np.ones((30, 30), dtype=np.uint8)
image_open = Image.fromarray(cv2.morphologyEx(np.array(image_close), cv2.MORPH_OPEN, open_kernel))
contour_rgb, bounding_boxes = self.get_image_contours_tumor(np.array(image_open), self.rgb_image)
# contour_rgb = cv2.resize(contour_rgb, (0, 0), fx=0.40, fy=0.40)
# cv2.imshow('contour_rgb', np.array(contour_rgb))
self.rgb_image_pil.close()
self.extract_patches_tumor(bounding_boxes)
self.wsi_image.close()
self.mask_image.close()
@staticmethod
def get_image_contours_mask(cont_img, mask_img):
_, contours, _ = cv2.findContours(cont_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
bounding_boxes = [cv2.boundingRect(c) for c in contours]
contours_mask_image_array = np.array(mask_img)
line_color = (255, 0, 0) # blue color code
cv2.drawContours(contours_mask_image_array, contours, -1, line_color, 1)
return contours_mask_image_array, bounding_boxes
@staticmethod
def get_image_contours_normal(cont_img, rgb_image):
_, contours, _ = cv2.findContours(cont_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
bounding_boxes = [cv2.boundingRect(c) for c in contours]
contours_rgb_image_array = np.array(rgb_image)
line_color = (255, 0, 0) # blue color code
cv2.drawContours(contours_rgb_image_array, contours, -1, line_color, 3)
return contours_rgb_image_array, bounding_boxes
@staticmethod
def get_image_contours_tumor(cont_img, rgb_image):
_, contours, _ = cv2.findContours(cont_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
bounding_boxes = [cv2.boundingRect(c) for c in contours]
contours_rgb_image_array = np.array(rgb_image)
line_color = (255, 0, 0) # blue color code
cv2.drawContours(contours_rgb_image_array, contours, -1, line_color, 3)
# cv2.drawContours(mask_image, contours_mask, -1, line_color, 3)
return contours_rgb_image_array, bounding_boxes
def wait(self):
self.key = cv2.waitKey(0) & 0xFF
print('key: %d' % self.key)
if self.key == 27: # escape
return False
elif self.key == 81: # <- (prev)
self.index -= 1
if self.index < 0:
self.index = len(self.wsi_paths) - 1
elif self.key == 83: # -> (next)
self.index += 1
if self.index >= len(self.wsi_paths):
self.index = 0
return True
def run_on_mask_data():
wsi.wsi_paths = glob.glob(os.path.join(TRAIN_TUMOR_WSI_PATH, '*.tif'))
wsi.wsi_paths.sort()
wsi.mask_paths = glob.glob(os.path.join(TRAIN_TUMOR_MASK_PATH, '*.tif'))
wsi.mask_paths.sort()
# ops.wsi_paths = ops.wsi_paths[4:5]
# ops.mask_paths = ops.mask_paths[4:5]
wsi.index = 0
for wsi_path, mask_path in zip(wsi.wsi_paths, wsi.mask_paths):
if wsi.read_wsi_mask(wsi_path, mask_path):
wsi.find_roi_n_extract_patches_mask()
# while True:
# wsi_path = ops.wsi_paths[ops.index]
# mask_path = ops.mask_paths[ops.index]
# if ops.read_wsi_mask(wsi_path, mask_path):
# ops.find_roi_n_extract_patches_mask()
# if not ops.wait():
# break
# else:
# if ops.key == 81:
# ops.index -= 1
# if ops.index < 0:
# ops.index = len(ops.wsi_paths) - 1
# elif ops.key == 83:
# ops.index += 1
# if ops.index >= len(ops.wsi_paths):
# ops.index = 0
def run_on_tumor_data():
wsi.wsi_paths = glob.glob(os.path.join(TRAIN_TUMOR_WSI_PATH, '*.tif'))
wsi.wsi_paths.sort()
wsi.mask_paths = glob.glob(os.path.join(TRAIN_TUMOR_MASK_PATH, '*.tif'))
wsi.mask_paths.sort()
# ops.wsi_paths = ops.wsi_paths[84:85]
# ops.mask_paths = ops.mask_paths[84:85]
wsi.index = 0
for wsi_path, mask_path in zip(wsi.wsi_paths, wsi.mask_paths):
if wsi.read_wsi_tumor(wsi_path, mask_path):
wsi.find_roi_n_extract_patches_tumor()
# while True:
# wsi_path = ops.wsi_paths[ops.index]
# mask_path = ops.mask_paths[ops.index]
# print(wsi_path)
# print(mask_path)
# if ops.read_wsi_tumor(wsi_path, mask_path):
# ops.find_roi_n_extract_patches_tumor()
# if not ops.wait():
# break
# else:
# if ops.key == 81:
# ops.index -= 1
# if ops.index < 0:
# ops.index = len(ops.wsi_paths) - 1
# elif ops.key == 83:
# ops.index += 1
# if ops.index >= len(ops.wsi_paths):
# ops.index = 0
def run_on_normal_data():
wsi.wsi_paths = glob.glob(os.path.join(TRAIN_NORMAL_WSI_PATH, '*.tif'))
wsi.wsi_paths.sort()
# ops.wsi_paths = ops.wsi_paths[:1]
wsi.index = 0
for wsi_path in wsi.wsi_paths:
if wsi.read_wsi_normal(wsi_path):
wsi.find_roi_n_extract_patches_normal()
# while True:
# wsi_path = ops.wsi_paths[ops.index]
# print(wsi_path)
# if ops.read_normal_wsi(wsi_path):
# ops.find_roi_normal()
# if not ops.wait():
# break
# else:
# if ops.key == 81:
# ops.index -= 1
# if ops.index < 0:
# ops.index = len(ops.wsi_paths) - 1
# elif ops.key == 83:
# ops.index += 1
# if ops.index >= len(ops.wsi_paths):
# ops.index = 0
if __name__ == '__main__':
wsi = WSI()
# run_on_tumor_data()
# run_on_normal_data()
run_on_mask_data()