mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-07 05:22:06 -07:00
191 lines
7.2 KiB
Python
191 lines
7.2 KiB
Python
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
|
|
image using the mean and standard deviations of the L*a*b*
|
|
color space.
|
|
|
|
This implementation is (loosely) based on to the "Color Transfer
|
|
between Images" paper by Reinhard et al., 2001.
|
|
|
|
Parameters:
|
|
-------
|
|
source: NumPy array
|
|
OpenCV image in BGR color space (the source image)
|
|
target: NumPy array
|
|
OpenCV image in BGR color space (the target image)
|
|
clip: Should components of L*a*b* image be scaled by np.clip before
|
|
converting back to BGR color space?
|
|
If False then components will be min-max scaled appropriately.
|
|
Clipping will keep target image brightness truer to the input.
|
|
Scaling will adjust image brightness to avoid washed out portions
|
|
in the resulting color transfer that can be caused by clipping.
|
|
preserve_paper: Should color transfer strictly follow methodology
|
|
layed out in original paper? The method does not always produce
|
|
aesthetically pleasing results.
|
|
If False then L*a*b* components will scaled using the reciprocal of
|
|
the scaling factor proposed in the paper. This method seems to produce
|
|
more consistently aesthetically pleasing results
|
|
|
|
Returns:
|
|
-------
|
|
transfer: NumPy array
|
|
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)
|
|
source = cv2.cvtColor(source, cv2.COLOR_BGR2LAB).astype(np.float32)
|
|
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
|
|
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)
|
|
|
|
# subtract the means from the target image
|
|
(l, a, b) = cv2.split(target)
|
|
l -= lMeanTar
|
|
a -= aMeanTar
|
|
b -= bMeanTar
|
|
|
|
if preserve_paper:
|
|
# scale by the standard deviations using paper proposed factor
|
|
l = (lStdTar / lStdSrc) * l
|
|
a = (aStdTar / aStdSrc) * a
|
|
b = (bStdTar / bStdSrc) * b
|
|
else:
|
|
# scale by the standard deviations using reciprocal of paper proposed factor
|
|
l = (lStdSrc / lStdTar) * l
|
|
a = (aStdSrc / aStdTar) * a
|
|
b = (bStdSrc / bStdTar) * b
|
|
|
|
# add in the source mean
|
|
l += lMeanSrc
|
|
a += aMeanSrc
|
|
b += bMeanSrc
|
|
|
|
# clip/scale the pixel intensities to [0, 255] if they fall
|
|
# outside this range
|
|
l = _scale_array(l, clip=clip)
|
|
a = _scale_array(a, clip=clip)
|
|
b = _scale_array(b, clip=clip)
|
|
|
|
# merge the channels together and convert back to the RGB color
|
|
# space, being sure to utilize the 8-bit unsigned integer data
|
|
# type
|
|
transfer = cv2.merge([l, a, b])
|
|
transfer = cv2.cvtColor(transfer.astype(np.uint8), cv2.COLOR_LAB2BGR)
|
|
|
|
# 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)
|
|
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)
|
|
Cs = s.dot(s.T) / s.shape[1] + eps * np.eye(s.shape[0])
|
|
if mode == 'chol':
|
|
chol_t = np.linalg.cholesky(Ct)
|
|
chol_s = np.linalg.cholesky(Cs)
|
|
ts = chol_s.dot(np.linalg.inv(chol_t)).dot(t)
|
|
if mode == 'pca':
|
|
eva_t, eve_t = np.linalg.eigh(Ct)
|
|
Qt = eve_t.dot(np.sqrt(np.diag(eva_t))).dot(eve_t.T)
|
|
eva_s, eve_s = np.linalg.eigh(Cs)
|
|
Qs = eve_s.dot(np.sqrt(np.diag(eva_s))).dot(eve_s.T)
|
|
ts = Qs.dot(np.linalg.inv(Qt)).dot(t)
|
|
if mode == 'sym':
|
|
eva_t, eve_t = np.linalg.eigh(Ct)
|
|
Qt = eve_t.dot(np.sqrt(np.diag(eva_t))).dot(eve_t.T)
|
|
Qt_Cs_Qt = Qt.dot(Cs).dot(Qt)
|
|
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 += mu_s
|
|
matched_img[matched_img>1] = 1
|
|
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)
|
|
(lMean, lStd) = (l.mean(), l.std())
|
|
(aMean, aStd) = (a.mean(), a.std())
|
|
(bMean, bStd) = (b.mean(), b.std())
|
|
|
|
# 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)
|
|
|
|
mn = arr.min()
|
|
mx = arr.max()
|
|
scale_range = (max([mn, 0]), min([mx, 255]))
|
|
|
|
if mn < scale_range[0] or mx > scale_range[1]:
|
|
return (scale_range[1] - scale_range[0]) * (arr - mn) / (mx - mn) + scale_range[0]
|
|
|
|
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
|
|
masked_source = source
|
|
masked_template = template
|
|
|
|
if mask is not None:
|
|
masked_source = source * mask
|
|
masked_template = template * mask
|
|
|
|
oldshape = source.shape
|
|
source = source.ravel()
|
|
template = template.ravel()
|
|
masked_source = masked_source.ravel()
|
|
masked_template = masked_template.ravel()
|
|
s_values, bin_idx, s_counts = np.unique(source, return_inverse=True,
|
|
return_counts=True)
|
|
t_values, t_counts = np.unique(template, return_counts=True)
|
|
ms_values, mbin_idx, ms_counts = np.unique(source, return_inverse=True,
|
|
return_counts=True)
|
|
mt_values, mt_counts = np.unique(template, return_counts=True)
|
|
|
|
s_quantiles = np.cumsum(s_counts).astype(np.float64)
|
|
s_quantiles = hist_match_threshold * s_quantiles / s_quantiles[-1]
|
|
t_quantiles = np.cumsum(t_counts).astype(np.float64)
|
|
t_quantiles = 255 * t_quantiles / t_quantiles[-1]
|
|
interp_t_values = np.interp(s_quantiles, t_quantiles, t_values)
|
|
|
|
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)
|
|
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)
|
|
|
|
to_stack = (matched_R, matched_G, matched_B)
|
|
for i in range(3, c):
|
|
to_stack += ( src_im[:,:,i],)
|
|
|
|
|
|
matched = np.stack(to_stack, axis=-1).astype(src_im.dtype)
|
|
return matched
|