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)
@@ -43,8 +43,8 @@ def reinhard_color_transfer(target, source, clip=False, preserve_paper=False, so
     target = cv2.cvtColor(target, cv2.COLOR_BGR2LAB).astype(np.float32)
 
     # compute color statistics for the source and target images
-	src_input = source if source_mask is None else source*source_mask
+    src_input = source if source_mask is None else source * source_mask
-	tgt_input = target if target_mask is None else target*target_mask
+    tgt_input = target if target_mask is None else target * target_mask
     (lMeanSrc, lStdSrc, aMeanSrc, aStdSrc, bMeanSrc, bStdSrc) = lab_image_stats(src_input)
     (lMeanTar, lStdTar, aMeanTar, aStdTar, bMeanTar, bStdTar) = lab_image_stats(tgt_input)
 
@@ -85,20 +85,21 @@ 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)
+    t = t.transpose(2, 0, 1).reshape(3, -1)
     Ct = t.dot(t.T) / t.shape[1] + eps * np.eye(t.shape[0])
     mu_s = source_img.mean(0).mean(0)
     s = source_img - mu_s
-    s = s.transpose(2,0,1).reshape(3,-1)
+    s = s.transpose(2, 0, 1).reshape(3, -1)
     Cs = s.dot(s.T) / s.shape[1] + eps * np.eye(s.shape[0])
     if mode == 'chol':
         chol_t = np.linalg.cholesky(Ct)
@@ -117,12 +118,13 @@ def linear_color_transfer(target_img, source_img, mode='pca', eps=1e-5):
         eva_QtCsQt, eve_QtCsQt = np.linalg.eigh(Qt_Cs_Qt)
         QtCsQt = eve_QtCsQt.dot(np.sqrt(np.diag(eva_QtCsQt))).dot(eve_QtCsQt.T)
         ts = np.linalg.inv(Qt).dot(QtCsQt).dot(np.linalg.inv(Qt)).dot(t)
-    matched_img = ts.reshape(*target_img.transpose(2,0,1).shape).transpose(1,2,0)
+    matched_img = ts.reshape(*target_img.transpose(2, 0, 1).shape).transpose(1, 2, 0)
     matched_img += mu_s
-    matched_img[matched_img>1] = 1
+    matched_img[matched_img > 1] = 1
-    matched_img[matched_img<0] = 0
+    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
@@ -176,16 +180,16 @@ 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
+    h, w, c = src_im.shape
-    matched_R = channel_hist_match(src_im[:,:,0], tar_im[:,:,0], hist_match_threshold, None)
+    matched_R = channel_hist_match(src_im[:, :, 0], tar_im[:, :, 0], hist_match_threshold, None)
-    matched_G = channel_hist_match(src_im[:,:,1], tar_im[:,:,1], hist_match_threshold, None)
+    matched_G = channel_hist_match(src_im[:, :, 1], tar_im[:, :, 1], hist_match_threshold, None)
-    matched_B = channel_hist_match(src_im[:,:,2], tar_im[:,:,2], hist_match_threshold, None)
+    matched_B = channel_hist_match(src_im[:, :, 2], tar_im[:, :, 2], hist_match_threshold, None)
 
     to_stack = (matched_R, matched_G, matched_B)
     for i in range(3, c):
-        to_stack += ( src_im[:,:,i],)
+        to_stack += (src_im[:, :, i],)
-
 
     matched = np.stack(to_stack, axis=-1).astype(src_im.dtype)
     return matched