Formatting

imagelib/color_transfer.py

@@ -1,6 +1,7 @@
 import numpy as np
 import cv2
 
+
 def reinhard_color_transfer(target, source, clip=False, preserve_paper=False, source_mask=None, target_mask=None):
     """
     Transfers the color distribution from the source to the target
@@ -35,7 +36,6 @@ def reinhard_color_transfer(target, source, clip=False, preserve_paper=False, so
         OpenCV image (w, h, 3) NumPy array (uint8)
     """
 
-
     # convert the images from the RGB to L*ab* color space, being
     # sure to utilizing the floating point data type (note: OpenCV
     # expects floats to be 32-bit, so use that instead of 64-bit)
@@ -85,13 +85,14 @@ def reinhard_color_transfer(target, source, clip=False, preserve_paper=False, so
     # return the color transferred image
     return transfer
 
+
 def linear_color_transfer(target_img, source_img, mode='pca', eps=1e-5):
-    '''
+    """
     Matches the colour distribution of the target image to that of the source image
     using a linear transform.
     Images are expected to be of form (w,h,c) and float in [0,1].
     Modes are chol, pca or sym for different choices of basis.
-    '''
+    """
     mu_t = target_img.mean(0).mean(0)
     t = target_img - mu_t
     t = t.transpose(2, 0, 1).reshape(3, -1)
@@ -123,6 +124,7 @@ def linear_color_transfer(target_img, source_img, mode='pca', eps=1e-5):
     matched_img[matched_img < 0] = 0
     return matched_img
 
+
 def lab_image_stats(image):
     # compute the mean and standard deviation of each channel
     (l, a, b) = cv2.split(image)
@@ -133,6 +135,7 @@ def lab_image_stats(image):
     # return the color statistics
     return (lMean, lStd, aMean, aStd, bMean, bStd)
 
+
 def _scale_array(arr, clip=True):
     if clip:
         return np.clip(arr, 0, 255)
@@ -146,6 +149,7 @@ def _scale_array(arr, clip=True):
 
     return arr
 
+
 def channel_hist_match(source, template, hist_match_threshold=255, mask=None):
     # Code borrowed from:
     # https://stackoverflow.com/questions/32655686/histogram-matching-of-two-images-in-python-2-x
@@ -176,6 +180,7 @@ def channel_hist_match(source, template, hist_match_threshold=255, mask=None):
 
     return interp_t_values[bin_idx].reshape(oldshape)
 
+
 def color_hist_match(src_im, tar_im, hist_match_threshold=255):
     h, w, c = src_im.shape
     matched_R = channel_hist_match(src_im[:, :, 0], tar_im[:, :, 0], hist_match_threshold, None)
@@ -186,6 +191,5 @@ def color_hist_match(src_im, tar_im, hist_match_threshold=255):
     for i in range(3, c):
         to_stack += (src_im[:, :, i],)
 
-
     matched = np.stack(to_stack, axis=-1).astype(src_im.dtype)
     return matched