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Jeremy Hummel 2019-08-14 14:03:14 -07:00
commit 4f765d2279
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imagelib/color_transfer.py
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@ -136,20 +136,17 @@ def linear_color_transfer(target_img, source_img, mode='sym', eps=1e-3):
def lab_image_stats(image, mask=None): def lab_image_stats(image, mask=None):
print('np.shape(image):', np.shape(image), )
# compute the mean and standard deviation of each channel # compute the mean and standard deviation of each channel
l, a, b = cv2.split(image) l, a, b = cv2.split(image)
if mask is not None: if mask is not None:
im_mask = np.squeeze(mask) if len(np.shape(mask)) == 3 else mask im_mask = np.squeeze(mask) if len(np.shape(mask)) == 3 else mask
l, a, b = l[im_mask == 1], a[im_mask == 1], b[im_mask == 1] l, a, b = l[im_mask == 1], a[im_mask == 1], b[im_mask == 1]
print('np.shape(im_mask):', np.shape(im_mask))
l_mean, l_std = np.mean(l), np.std(l) l_mean, l_std = np.mean(l), np.std(l)
a_mean, a_std = np.mean(a), np.std(a) a_mean, a_std = np.mean(a), np.std(a)
b_mean, b_std = np.mean(b), np.std(b) b_mean, b_std = np.mean(b), np.std(b)
print('l_mean, l_std, a_mean, a_std, b_mean, b_std:', l_mean, l_std, a_mean, a_std, b_mean, b_std)
# return the color statistics # return the color statistics
return l_mean, l_std, a_mean, a_std, b_mean, b_std return l_mean, l_std, a_mean, a_std, b_mean, b_std