def fit(self, I):
"""
Fit to an image I.
:param I:
:return:
"""
self.image_shape = I.shape
self.stain_matrix = self.extractor.get_stain_matrix(I)
self.source_concentrations = get_concentrations(I, self.stain_matrix)
self.n_stains = self.source_concentrations.shape[1]
self.tissue_mask = LuminosityThresholdTissueLocator.get_tissue_mask(I).ravel()
def fit_transform(self, target):
"""Fit and transform a target image
:param target: Image RGB uint8.
:return:
"""
self.stain_matrix_target = self.extractor.get_stain_matrix(target)
self.target_concentrations = get_concentrations(
target, self.stain_matrix_target)
self.stain_matrix_target_RGB = convert_OD_to_RGB(
self.stain_matrix_target) # useful to visualize.
tmp = 255 * np.exp(
-1 * np.dot(self.target_concentrations, self.stain_matrix_target))
return tmp.reshape(target.shape).astype(np.uint8)
def transform(self, I):
"""
Transform an image.
:param I: Image RGB uint8.
:return:
"""
stain_matrix_source = self.extractor.get_stain_matrix(I)
source_concentrations = get_concentrations(I, stain_matrix_source)
maxC_source = np.percentile(source_concentrations, 99, axis=0).reshape(
(1, 2))
source_concentrations *= (self.maxC_target / maxC_source)
tmp = 255 * np.exp(
-1 * np.dot(source_concentrations, self.stain_matrix_target))
return tmp.reshape(I.shape).astype(np.uint8)
def fit(self, target):
"""
Fit to a target image.
:param target: Image RGB uint8.
:return:
"""
self.stain_matrix_target = self.extractor.get_stain_matrix(target)
self.target_concentrations = get_concentrations(
target, self.stain_matrix_target)
self.maxC_target = np.percentile(self.target_concentrations,
99,
axis=0).reshape((1, 2))
self.stain_matrix_target_RGB = convert_OD_to_RGB(
self.stain_matrix_target) # useful to visualize.
pict_name = img_num[i]
picture_name1 = img_num[i].split('-')
picture_name2 = ''
for s in picture_name1[:-2]:
picture_name2 = picture_name2 + s + '-'
picture_name2 = picture_name2[:-4]
print(picture_name2)
sum_dir = '/cptjack/totem/barrylee/cut_small_cell/cell_tri-classification/train54-' + img_name + '/'
I_raw = cv2.imread(img_dir + pict_name)
biaozhu_raw = cv2.imread(biaozhu_img)
print(biaozhu_img)
flag_path = pict_name.split('.')[0]
I = cv2.cvtColor(I_raw, cv2.COLOR_BGR2RGB)
I_gray = cv2.cvtColor(I, cv2.COLOR_BGR2GRAY)
stain_matrix_source = extractor.get_stain_matrix(I) # ,regularizer=0.8)
source_concentrations = get_concentrations(
I, stain_matrix_source) # , regularizer=0.1)
b = source_concentrations.reshape(
(I.shape[0], I.shape[1], 2)).astype(np.uint8)
# 打开保存的npy格式的mask文件
with open(mask_dir + pict_name.split('.')[0] + '.npy', 'rb') as fr:
print(mask_dir + pict_name.split('.')[0] + '.npy')
mask = np.load(fr)
mask_sum = []
cnt_list = []
# 将轮廓和面积存入一个变量
for j in range(mask.shape[2]):
outline = mask[:, :, j]
outline = outline.astype(np.uint8)
# print(outline)
image, contours, hierarchy = cv2.findContours(outline, cv2.RETR_TREE,
cv2.CHAIN_APPROX_NONE)