def transform(self, I):
I = ut.standardize_brightness(I)
stain_matrix_source = get_stain_matrix(I)
source_concentrations = ut.get_concentrations(I, stain_matrix_source)
return (255 * np.exp(-1 * np.dot(
source_concentrations, self.stain_matrix_target).reshape(I.shape))
).astype(np.uint8)
def hematoxylin(self, I):
I = ut.standardize_brightness(I)
h, w, c = I.shape
stain_matrix_source = get_stain_matrix(I)
source_concentrations = ut.get_concentrations(I, stain_matrix_source)
H = source_concentrations[:, 0].reshape(h, w)
H = np.exp(-1 * H)
return H
def transform(self, I):
I = ut.standardize_brightness(I)
stain_matrix_source = get_stain_matrix(I)
source_concentrations = ut.get_concentrations(I, stain_matrix_source)
maxC_source = np.percentile(source_concentrations, 99, axis=0).reshape((1, 2))
maxC_target = np.percentile(self.target_concentrations, 99, axis=0).reshape((1, 2))
source_concentrations *= (maxC_target / maxC_source)
return (255 * np.exp(-1 * np.dot(source_concentrations, self.stain_matrix_target).reshape(I.shape))).astype(np.uint8)
def transform(self, I):
I = ut.standardize_brightness(I)
I1, I2, I3 = lab_split(I)
means, stds = get_mean_std(I)
norm1 = ((I1 - means[0]) *
(self.target_stds[0] / stds[0])) + self.target_means[0]
norm2 = ((I2 - means[1]) *
(self.target_stds[1] / stds[1])) + self.target_means[1]
norm3 = ((I3 - means[2]) *
(self.target_stds[2] / stds[2])) + self.target_means[2]
return merge_back(norm1, norm2, norm3)
def fit(self, target):
target = ut.standardize_brightness(target)
self.stain_matrix_target = get_stain_matrix(target)
def fit(self, target):
target = ut.standardize_brightness(target)
self.stain_matrix_target = get_stain_matrix(target)
self.target_concentrations = ut.get_concentrations(
target, self.stain_matrix_target)
def fit(self, target):
target = ut.standardize_brightness(target)
means, stds = get_mean_std(target)
self.target_means = means
self.target_stds = stds
