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iperov 2021-11-15 19:31:38 +04:00
commit 3d257702b3
1 changed files with 11 additions and 16 deletions
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27
xlib/face/FaceWarper.py
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@ -23,18 +23,16 @@ class FaceWarper:
rnd_state : np.random.RandomState = None,
):
"""
Max quality one-pass face augmentation via geometric transformations with provided values.
Max quality one-pass face augmentation via geometric transformations with provided random range or exact values.
img_to_face_uni_mat Affine2DUniMat
Affine2DUniMat given from FLandmarks2D.calc_cut
it is an uniform affineMat to transform original image to aligned face
align_* rw_grid_*
align_* rw_grid_*
exact augmentation parameters or range for random generation.
"""
self._img_to_face_uni_mat = img_to_face_uni_mat
self._face_to_img_uni_mat = img_to_face_uni_mat.invert()
@ -98,7 +96,9 @@ class FaceWarper:
return new_img
def _gen(self, H, W, random_warp, out_res, rnd_state):
"""generate grid and mask"""
# make identity grid
image_grid = np.stack(np.meshgrid(np.linspace(0., 1.0, H, dtype=np.float32),
np.linspace(0., 1.0, W, dtype=np.float32)), -1)
@ -106,26 +106,21 @@ class FaceWarper:
# make a random face_warp_grid in the space of the face
face_warp_grid = FaceWarper._gen_random_warp_uni_grid_diff(out_res, self._rw_grid_cell_count, 0.12, rnd_state)
# make a randomly transformable mat to transform face_warp_grid from face to image
face_warp_grid_mat = self._face_to_img_uni_mat * self._warp_rnd_mat
# apply random transformation mat of face_warp_grid to mat that transforms face to image
face_warp_grid_uni_mat = self._face_to_img_uni_mat * self._warp_rnd_mat
# warp face_warp_grid to the space of image and merge with image_grid
image_grid += cv2.warpAffine(face_warp_grid, face_warp_grid_mat.to_exact_mat(out_res,out_res, W, H), (W,H), flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT)
# warp face_warp_grid to the space of image using previous mat and merge with image_grid
image_grid += cv2.warpAffine(face_warp_grid, face_warp_grid_uni_mat.to_exact_mat(out_res,out_res, W, H), (W,H), flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT)
# scale uniform grid from to image size
# scale uniform grid to image size
image_grid *= (H-1, W-1)
# apply random transformations for align mat
#img_to_face_rnd_uni_mat = (self._face_to_img_uni_mat * self._align_rnd_mat).invert()
img_to_face_rnd_mat = (self._face_to_img_uni_mat * self._align_rnd_mat).invert().to_exact_mat(W,H,out_res,out_res)
# warp image_grid to face space
image_grid = cv2.warpAffine(image_grid, img_to_face_rnd_mat, (out_res,out_res), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE )
# One-pass remap from original image to aligned face with all transformations
#new_img = cv2.remap(img, image_grid, None, interpolation=cv2.INTER_LANCZOS4)
# make mask to refine image-boundary visible in face space
face_mask = cv2.warpAffine( np.ones( (H,W), dtype=np.uint8), img_to_face_rnd_mat, (out_res,out_res), flags=cv2.INTER_NEAREST)[...,None]