def batch_process_image(images_in, save_dir, depth):
normalizer = VahadaneNormalizer()
i1 = read_image("./lbp_pic/reference_pic/zs_abnormal.png")
normalizer.fit(i1)
for image_in in images_in:
process_image(normalizer, image_in, save_dir, depth)
def process_image(normalizer, image_in, save_dir, depth):
tokens = image_in.rsplit(os.sep, depth + 1)
image_out = os.path.join(save_dir, *tokens[1:])
parent_dir = os.path.dirname(image_out)
os.makedirs(parent_dir, exist_ok=True)
r_img = read_image(image_in)
target = normalizer.transform(r_img)
target = cv2.cvtColor(target, cv2.COLOR_RGB2BGR)
cv2.imwrite(image_out, target)
def process_image(normalizer, image_in, save_dir, depth):
tokens = image_in.rsplit(os.sep, depth + 1)
image_out = os.path.join(save_dir, *tokens[1:])
if os.path.isfile(
image_out): # pass if result image has already been generated
return
os.makedirs(os.path.dirname(image_out), exist_ok=True)
r_img = read_image(image_in)
target = normalizer.transform(r_img)
target = cv2.cvtColor(target, cv2.COLOR_RGB2BGR)
cv2.imwrite(image_out, target)
def PrepNormalizer(normalization, targetPath):
if "Reinhard" == normalization:
n = ReinhardNormalizer()
elif "Macenko" == normalization:
n = MacenkoNormalizer()
elif "Vahadane" == normalization:
n = VahadaneNormalizer()
else:
print("No knowned normalization given..")
targetImg = read_image(targetPath)
n.fit(targetImg)
return n
I_prime = np.clip((255 * np.exp(-1 * np.dot(
source_concentrations, self.stain_matrix).reshape(self.Ishape))),
0, 255).astype(np.uint8)
if new_stain_mat:
stain_matrix = self.fetcher.compute(I_prime, just_stain=True)
return I_prime, stain_matrix
else:
return I_prime
if __name__ == '__main__':
import matplotlib.pyplot as plt
from staintools.utils import visual as vu, misc as mu
I = vu.read_image('data/i1.png')
# Test 0
augmentor = TellezAugmentor('Vahadane')
augmentor.fit(I)
print('Fitting done')
h, w, c = I.shape
stack = np.zeros([10, h, w, c])
for i in range(10):
stack[i] = augmentor.augment(new_stain_mat=False)
vu.patch_grid(stack, width=5)
# Test 1
for method in ['Macenko', 'Vahadane']:
augmentor = TellezAugmentor(method=method)
augmentor.fit(I)
import openslide
from staintools import MacenkoNormalizer
from staintools.utils.visual import read_image, show
import scipy.misc as mc
import os
import numpy as np
from matplotlib import pyplot as plt
from skimage import measure
from skimage.morphology import dilation, erosion, square
dir = "/home/yunzhe/Downloads/segmentation_training_set/"
source_file = "/home/yunzhe/Downloads/segmentation_training_set/image02.png"
save_dir = "/home/yunzhe/norm_data_new/"
source = read_image(source_file)
normalizer = MacenkoNormalizer()
normalizer.fit(source)
filter = [[0, 1, 0], [1, 1, 1], [0, 1, 0]]
for filename in os.listdir(dir):
if filename.endswith(".png") and not filename.endswith("_poly.png"):
target = read_image(dir + filename)
if dir + filename == source_file:
mc.imsave(save_dir + filename, target)
else:
norm_target = normalizer.transform(target)
show(norm_target, fig_size=(4, 4))
mc.imsave(save_dir + filename, norm_target)
filename_suffix = filename.split(".")[0]
mask_file = dir + filename_suffix + "_mask.txt"
if os.path.exists(mask_file):
width = 0
height = 0