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refactoring ImageProcessor

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iperov 2022-05-17 18:30:00 +04:00
parent 7bddc0248c
commit 2d3d9874bf
2 changed files with 117 additions and 6 deletions
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9
apps/DeepFaceLive/backend/FaceMerger.py
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@ -234,13 +234,12 @@ class FaceMergerWorker(BackendWorker):
face_mask = ImageProcessor(face_mask).erode_blur(state.face_mask_erode, state.face_mask_blur, fade_to_border=True).get_image('HWC')
frame_face_mask = ImageProcessor(face_mask).warp_affine(aligned_to_source_uni_mat, frame_width, frame_height).clip2( (1.0/255.0), 0.0, 1.0, 1.0).get_image('HWC')
face_swap_img = ImageProcessor(face_swap_img).to_ufloat32().get_image('HWC')
face_swap_ip = ImageProcessor(face_swap_img).to_ufloat32()
if state.color_transfer == 'rct':
face_align_img = ImageProcessor(face_align_img).to_ufloat32().get_image('HWC')
face_swap_img = lib_ct.rct(face_swap_img, face_align_img, target_mask=face_mask, source_mask=face_mask)
if state.color_transfer == 'rct':
face_swap_img = face_swap_ip.rct(like=face_align_img, mask=face_mask, like_mask=face_mask)
frame_face_swap_img = ImageProcessor(face_swap_img).warp_affine(aligned_to_source_uni_mat, frame_width, frame_height, interpolation=interpolation).get_image('HWC')
frame_face_swap_img = face_swap_ip.warp_affine(aligned_to_source_uni_mat, frame_width, frame_height, interpolation=interpolation).get_image('HWC')
# Combine final frame
opacity = np.float32(state.face_opacity)

114
xlib/image/ImageProcessor.py
View file

@ -499,7 +499,37 @@ class ImageProcessor:
self._img = img
self.to_dtype(dtype)
return self
def to_lab(self) -> 'ImageProcessor':
"""
"""
img = self._img
N,H,W,C = img.shape
if C != 3:
raise Exception('Image channels must be == 3')
img = img.reshape( (N*H,W,C) )
img = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
img = img.reshape( (N,H,W,C) )
self._img = img
return self
def from_lab(self) -> 'ImageProcessor':
"""
"""
img = self._img
N,H,W,C = img.shape
if C != 3:
raise Exception('Image channels must be == 3')
img = img.reshape( (N*H,W,C) )
img = cv2.cvtColor(img, cv2.COLOR_LAB2BGR)
img = img.reshape( (N,H,W,C) )
self._img = img
return self
def jpeg_recompress(self, quality : int, mask = None ) -> 'ImageProcessor':
"""
quality 0-100
@ -532,6 +562,88 @@ class ImageProcessor:
self.to_dtype(dtype)
return self
def patch_to_batch(self, patch_size : int) -> 'ImageProcessor':
img = self._img
N,H,W,C = img.shape
OH, OW = H // patch_size, W // patch_size
img = img.reshape(N,OH,patch_size,OW,patch_size,C)
img = img.transpose(0,2,4,1,3,5)
img = img.reshape(N*patch_size*patch_size,OH,OW,C)
self._img = img
return self
def patch_from_batch(self, patch_size : int) -> 'ImageProcessor':
img = self._img
N,H,W,C = img.shape
ON = N//(patch_size*patch_size)
img = img.reshape(ON,patch_size,patch_size,H,W,C )
img = img.transpose(0,3,1,4,2,5)
img = img.reshape(ON,H*patch_size,W*patch_size,C )
self._img = img
return self
def rct(self, like : np.ndarray, mask : np.ndarray = None, like_mask : np.ndarray = None, mask_cutoff=0.5) -> 'ImageProcessor':
"""
Transfer color using rct method.
like np.ndarray [N][HW][3C] np.uint8/np.float32
mask(None) np.ndarray [N][HW][1C] np.uint8/np.float32
like_mask(None) np.ndarray [N][HW][1C] np.uint8/np.float32
mask_cutoff(0.5) float
masks are used to limit the space where color statistics will be computed to adjust the image
reference: Color Transfer between Images https://www.cs.tau.ac.il/~turkel/imagepapers/ColorTransfer.pdf
"""
dtype = self.get_dtype()
self.to_ufloat32()
self.to_lab()
like_for_stat = ImageProcessor(like).to_ufloat32().to_lab().get_image('NHWC')
if like_mask is not None:
like_mask = ImageProcessor(like_mask).to_ufloat32().ch(1).get_image('NHW')
like_for_stat = like_for_stat.copy()
like_for_stat[like_mask < mask_cutoff] = [0,0,0]
img_for_stat = img = self._img
if mask is not None:
mask = ImageProcessor(mask).to_ufloat32().ch(1).get_image('NHW')
img_for_stat = img_for_stat.copy()
img_for_stat[mask < mask_cutoff] = [0,0,0]
source_l_mean, source_l_std, source_a_mean, source_a_std, source_b_mean, source_b_std, \
= img_for_stat[...,0].mean((1,2), keepdims=True), img_for_stat[...,0].std((1,2), keepdims=True), img_for_stat[...,1].mean((1,2), keepdims=True), img_for_stat[...,1].std((1,2), keepdims=True), img_for_stat[...,2].mean((1,2), keepdims=True), img_for_stat[...,2].std((1,2), keepdims=True)
like_l_mean, like_l_std, like_a_mean, like_a_std, like_b_mean, like_b_std, \
= like_for_stat[...,0].mean((1,2), keepdims=True), like_for_stat[...,0].std((1,2), keepdims=True), like_for_stat[...,1].mean((1,2), keepdims=True), like_for_stat[...,1].std((1,2), keepdims=True), like_for_stat[...,2].mean((1,2), keepdims=True), like_for_stat[...,2].std((1,2), keepdims=True)
# not as in the paper: scale by the standard deviations using reciprocal of paper proposed factor
source_l = img[...,0]
source_l = ne.evaluate('(source_l - source_l_mean) * like_l_std / source_l_std + like_l_mean')
source_a = img[...,1]
source_a = ne.evaluate('(source_a - source_a_mean) * like_a_std / source_a_std + like_a_mean')
source_b = img[...,2]
source_b = ne.evaluate('(source_b - source_b_mean) * like_b_std / source_b_std + like_b_mean')
np.clip(source_l, 0, 100, out=source_l)
np.clip(source_a, -127, 127, out=source_a)
np.clip(source_b, -127, 127, out=source_b)
self._img = np.stack([source_l,source_a,source_b], -1)
self.from_lab()
self.to_dtype(dtype)
return self
def rotate90(self) -> 'ImageProcessor':
self._img = np.rot90(self._img, k=1, axes=(1,2) )