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fixed rct color transfer function, incorrectly clip colors before
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iperov
parent
1aa9463edf
commit
7326771c02
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@ -1,7 +1,9 @@
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import cv2
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import numexpr as ne
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import numpy as np
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from numpy import linalg as npla
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import scipy as sp
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from numpy import linalg as npla
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def color_transfer_sot(src,trg, steps=10, batch_size=5, reg_sigmaXY=16.0, reg_sigmaV=5.0):
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"""
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@ -133,89 +135,57 @@ def color_transfer_idt(i0, i1, bins=256, n_rot=20):
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return np.clip ( d0.T.reshape ( (h,w,c) ).astype(i0.dtype) , 0, 1)
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def reinhard_color_transfer(target, source, clip=False, preserve_paper=False, source_mask=None, target_mask=None):
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"""
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Transfers the color distribution from the source to the target
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image using the mean and standard deviations of the L*a*b*
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color space.
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def reinhard_color_transfer(target : np.ndarray, source : np.ndarray, target_mask : np.ndarray = None, source_mask : np.ndarray = None, mask_cutoff=0.5) -> np.ndarray:
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"""
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Transfer color using rct method.
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This implementation is (loosely) based on to the "Color Transfer
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between Images" paper by Reinhard et al., 2001.
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target np.ndarray H W 3C (BGR) np.float32
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source np.ndarray H W 3C (BGR) np.float32
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Parameters:
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-------
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source: NumPy array
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OpenCV image in BGR color space (the source image)
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target: NumPy array
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OpenCV image in BGR color space (the target image)
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clip: Should components of L*a*b* image be scaled by np.clip before
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converting back to BGR color space?
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If False then components will be min-max scaled appropriately.
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Clipping will keep target image brightness truer to the input.
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Scaling will adjust image brightness to avoid washed out portions
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in the resulting color transfer that can be caused by clipping.
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preserve_paper: Should color transfer strictly follow methodology
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layed out in original paper? The method does not always produce
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aesthetically pleasing results.
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If False then L*a*b* components will scaled using the reciprocal of
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the scaling factor proposed in the paper. This method seems to produce
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more consistently aesthetically pleasing results
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target_mask(None) np.ndarray H W 1C np.float32
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source_mask(None) np.ndarray H W 1C np.float32
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mask_cutoff(0.5) float
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Returns:
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-------
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transfer: NumPy array
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OpenCV image (w, h, 3) NumPy array (uint8)
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"""
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masks are used to limit the space where color statistics will be computed to adjust the target
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reference: Color Transfer between Images https://www.cs.tau.ac.il/~turkel/imagepapers/ColorTransfer.pdf
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"""
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source = cv2.cvtColor(source, cv2.COLOR_BGR2LAB)
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target = cv2.cvtColor(target, cv2.COLOR_BGR2LAB)
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# convert the images from the RGB to L*ab* color space, being
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# sure to utilizing the floating point data type (note: OpenCV
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# expects floats to be 32-bit, so use that instead of 64-bit)
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source = cv2.cvtColor(source, cv2.COLOR_BGR2LAB).astype(np.float32)
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target = cv2.cvtColor(target, cv2.COLOR_BGR2LAB).astype(np.float32)
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source_input = source
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if source_mask is not None:
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source_input = source_input.copy()
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source_input[source_mask[...,0] < mask_cutoff] = [0,0,0]
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target_input = target
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if target_mask is not None:
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target_input = target_input.copy()
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target_input[target_mask[...,0] < mask_cutoff] = [0,0,0]
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# compute color statistics for the source and target images
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src_input = source if source_mask is None else source*source_mask
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tgt_input = target if target_mask is None else target*target_mask
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(lMeanSrc, lStdSrc, aMeanSrc, aStdSrc, bMeanSrc, bStdSrc) = lab_image_stats(src_input)
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(lMeanTar, lStdTar, aMeanTar, aStdTar, bMeanTar, bStdTar) = lab_image_stats(tgt_input)
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target_l_mean, target_l_std, target_a_mean, target_a_std, target_b_mean, target_b_std, \
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= target_input[...,0].mean(), target_input[...,0].std(), target_input[...,1].mean(), target_input[...,1].std(), target_input[...,2].mean(), target_input[...,2].std()
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source_l_mean, source_l_std, source_a_mean, source_a_std, source_b_mean, source_b_std, \
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= source_input[...,0].mean(), source_input[...,0].std(), source_input[...,1].mean(), source_input[...,1].std(), source_input[...,2].mean(), source_input[...,2].std()
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# not as in the paper: scale by the standard deviations using reciprocal of paper proposed factor
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target_l = target[...,0]
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target_l = ne.evaluate('(target_l - target_l_mean) * source_l_std / target_l_std + source_l_mean')
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# subtract the means from the target image
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(l, a, b) = cv2.split(target)
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l -= lMeanTar
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a -= aMeanTar
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b -= bMeanTar
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target_a = target[...,1]
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target_a = ne.evaluate('(target_a - target_a_mean) * source_a_std / target_a_std + source_a_mean')
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target_b = target[...,2]
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target_b = ne.evaluate('(target_b - target_b_mean) * source_b_std / target_b_std + source_b_mean')
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if preserve_paper:
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# scale by the standard deviations using paper proposed factor
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l = (lStdTar / lStdSrc) * l
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a = (aStdTar / aStdSrc) * a
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b = (bStdTar / bStdSrc) * b
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else:
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# scale by the standard deviations using reciprocal of paper proposed factor
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l = (lStdSrc / lStdTar) * l
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a = (aStdSrc / aStdTar) * a
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b = (bStdSrc / bStdTar) * b
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np.clip(target_l, 0, 100, out=target_l)
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np.clip(target_a, -127, 127, out=target_a)
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np.clip(target_b, -127, 127, out=target_b)
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# add in the source mean
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l += lMeanSrc
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a += aMeanSrc
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b += bMeanSrc
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return cv2.cvtColor(np.stack([target_l,target_a,target_b], -1), cv2.COLOR_LAB2BGR)
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# clip/scale the pixel intensities to [0, 255] if they fall
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# outside this range
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l = _scale_array(l, clip=clip)
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a = _scale_array(a, clip=clip)
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b = _scale_array(b, clip=clip)
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# merge the channels together and convert back to the RGB color
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# space, being sure to utilize the 8-bit unsigned integer data
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# type
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transfer = cv2.merge([l, a, b])
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transfer = cv2.cvtColor(transfer.astype(np.uint8), cv2.COLOR_LAB2BGR)
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# return the color transferred image
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return transfer
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def linear_color_transfer(target_img, source_img, mode='pca', eps=1e-5):
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'''
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@ -353,9 +323,7 @@ def color_transfer(ct_mode, img_src, img_trg):
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if ct_mode == 'lct':
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out = linear_color_transfer (img_src, img_trg)
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elif ct_mode == 'rct':
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out = reinhard_color_transfer ( np.clip( img_src*255, 0, 255 ).astype(np.uint8),
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np.clip( img_trg*255, 0, 255 ).astype(np.uint8) )
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out = np.clip( out.astype(np.float32) / 255.0, 0.0, 1.0)
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out = reinhard_color_transfer(img_src, img_trg)
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elif ct_mode == 'mkl':
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out = color_transfer_mkl (img_src, img_trg)
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elif ct_mode == 'idt':
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@ -365,4 +333,4 @@ def color_transfer(ct_mode, img_src, img_trg):
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out = np.clip( out, 0.0, 1.0)
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else:
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raise ValueError(f"unknown ct_mode {ct_mode}")
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return out
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return out
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@ -170,10 +170,7 @@ def MergeMaskedFace (predictor_func, predictor_input_shape,
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if 'seamless' not in cfg.mode and cfg.color_transfer_mode != 0:
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if cfg.color_transfer_mode == 1: #rct
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prd_face_bgr = imagelib.reinhard_color_transfer ( np.clip( prd_face_bgr*wrk_face_mask_area_a*255, 0, 255).astype(np.uint8),
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np.clip( dst_face_bgr*wrk_face_mask_area_a*255, 0, 255).astype(np.uint8), )
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prd_face_bgr = np.clip( prd_face_bgr.astype(np.float32) / 255.0, 0.0, 1.0)
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prd_face_bgr = imagelib.reinhard_color_transfer ( prd_face_bgr*wrk_face_mask_area_a, dst_face_bgr*wrk_face_mask_area_a )
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elif cfg.color_transfer_mode == 2: #lct
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prd_face_bgr = imagelib.linear_color_transfer (prd_face_bgr, dst_face_bgr)
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elif cfg.color_transfer_mode == 3: #mkl
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@ -252,9 +249,7 @@ def MergeMaskedFace (predictor_func, predictor_input_shape,
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if 'seamless' in cfg.mode and cfg.color_transfer_mode != 0:
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if cfg.color_transfer_mode == 1:
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out_face_bgr = imagelib.reinhard_color_transfer ( np.clip(out_face_bgr*wrk_face_mask_area_a*255, 0, 255).astype(np.uint8),
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np.clip(dst_face_bgr*wrk_face_mask_area_a*255, 0, 255).astype(np.uint8) )
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out_face_bgr = np.clip( out_face_bgr.astype(np.float32) / 255.0, 0.0, 1.0)
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out_face_bgr = imagelib.reinhard_color_transfer (out_face_bgr*wrk_face_mask_area_a, dst_face_bgr*wrk_face_mask_area_a)
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elif cfg.color_transfer_mode == 2: #lct
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out_face_bgr = imagelib.linear_color_transfer (out_face_bgr, dst_face_bgr)
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elif cfg.color_transfer_mode == 3: #mkl
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@ -1,5 +1,6 @@
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tqdm
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numpy==1.19.3
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numexpr
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h5py==2.10.0
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opencv-python==4.1.0.25
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ffmpeg-python==0.1.17
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@ -1,5 +1,6 @@
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tqdm
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numpy==1.19.3
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numexpr
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h5py==2.10.0
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opencv-python==4.1.0.25
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ffmpeg-python==0.1.17
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