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Zero
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import celldetection as cd
import numpy as np
from skimage import img_as_ubyte, exposure
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
__all__ = ['normalize_img', 'normalize_channel', 'multi_norm']
def normalize_img(img, gamma_spread=17, lower_gamma_bound=.6, percentile=99.88):
log = []
if img.dtype.kind == 'f': # floats
if img.max() < 256:
img = img_as_ubyte(img / 255)
log.append('img_as_ubyte')
else:
v = 99.95
img = cd.data.normalize_percentile(img, v)
log.append(f'cd.data.normalize_percentile(img, {v})')
elif img.itemsize > 1:
img = cd.data.normalize_percentile(img, percentile)
log.append(f'cd.data.normalize_percentile(img, {percentile})')
mean_thresh = np.pi * gamma_spread
if img.mean() < mean_thresh:
gamma = (1 - ((np.cos(1 / gamma_spread * img.mean()) + 1) / 2)) * (1 - lower_gamma_bound) + lower_gamma_bound
log.append(f'(img / 255) ** {gamma}')
img = (img / 255) ** gamma
img = img_as_ubyte(img)
return img, log
def normalize_channel(img, lower=1, upper=99):
non_zero_vals = img[np.nonzero(img)]
percentiles = np.percentile(non_zero_vals, [lower, upper])
if percentiles[1] - percentiles[0] > 0.001:
img_norm = exposure.rescale_intensity(img, in_range=(percentiles[0], percentiles[1]), out_range='uint8')
else:
img_norm = img
return img_norm.astype(np.uint8)
def multi_norm(img, method):
if method == 'prov':
img = normalize_channel(img)
elif method == 'rand-mix' or method == 'cstm-mix':
img0 = normalize_channel(img)
img1, log = normalize_img(img)
if method == 'rand-mix':
alpha = np.random.uniform(0., 1.)
else:
is_grayscale = img.ndim == 2 or (img.ndim == 3 and img.shape[2] == 1)
alpha = 0.
if not is_grayscale:
if img[..., 2].mean() > 200 and img[..., 2].std() < 20:
alpha = 1.
else:
if img1.mean() < 45 and img1.std() < 33:
alpha = .5
img = np.clip(alpha * img0 + (1 - alpha) * img1, 0, 255).astype(img0.dtype)
else:
img, log = normalize_img(img)
return img
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