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refine blending in paste_faces_to_input_image #32

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refine blending in paste_faces_to_input_image #32

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51 changes: 19 additions & 32 deletions facexlib/utils/face_restoration_helper.py
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Original file line number Diff line number Diff line change
Expand Up @@ -299,6 +299,7 @@ def paste_faces_to_input_image(self, save_path=None, upsample_img=None):
inverse_affine[:, 2] += extra_offset
inv_restored = cv2.warpAffine(restored_face, inverse_affine, (w_up, h_up))

mask = np.ones(self.face_size, dtype=np.float32)
if self.use_parse:
# inference
face_input = cv2.resize(restored_face, (512, 512), interpolation=cv2.INTER_LINEAR)
Expand All @@ -313,39 +314,25 @@ def paste_faces_to_input_image(self, save_path=None, upsample_img=None):
MASK_COLORMAP = [0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 255, 0, 0, 0]
for idx, color in enumerate(MASK_COLORMAP):
mask[out == idx] = color
# blur the mask
mask = cv2.GaussianBlur(mask, (101, 101), 11)
mask = cv2.GaussianBlur(mask, (101, 101), 11)
# remove the black borders
thres = 10
mask[:thres, :] = 0
mask[-thres:, :] = 0
mask[:, :thres] = 0
mask[:, -thres:] = 0
mask = mask / 255.

mask = cv2.resize(mask, restored_face.shape[:2])
mask = cv2.warpAffine(mask, inverse_affine, (w_up, h_up), flags=3)
inv_soft_mask = mask[:, :, None]
pasted_face = inv_restored

else: # use square parse maps
mask = np.ones(self.face_size, dtype=np.float32)
inv_mask = cv2.warpAffine(mask, inverse_affine, (w_up, h_up))
# remove the black borders
inv_mask_erosion = cv2.erode(
inv_mask, np.ones((int(2 * self.upscale_factor), int(2 * self.upscale_factor)), np.uint8))
pasted_face = inv_mask_erosion[:, :, None] * inv_restored
total_face_area = np.sum(inv_mask_erosion) # // 3
# compute the fusion edge based on the area of face
w_edge = int(total_face_area**0.5) // 20
erosion_radius = w_edge * 2
inv_mask_center = cv2.erode(inv_mask_erosion, np.ones((erosion_radius, erosion_radius), np.uint8))
blur_size = w_edge * 2
inv_soft_mask = cv2.GaussianBlur(inv_mask_center, (blur_size + 1, blur_size + 1), 0)
if len(upsample_img.shape) == 2: # upsample_img is gray image
upsample_img = upsample_img[:, :, None]
inv_soft_mask = inv_soft_mask[:, :, None]
mask = cv2.resize(mask, restored_face.shape[:2], interpolation=cv2.INTER_NEAREST)

inv_mask = cv2.warpAffine(mask, inverse_affine, (w_up, h_up), flags=3)
# remove the black borders
inv_mask_erosion = cv2.erode(
inv_mask, np.ones((int(2 * self.upscale_factor), int(2 * self.upscale_factor)), np.uint8))
pasted_face = inv_mask_erosion[:, :, None] * inv_restored
total_face_area = np.sum(inv_mask_erosion) # // 3
# compute the fusion edge based on the area of face
w_edge = int(total_face_area**0.5) // 20
erosion_radius = w_edge * 2
inv_mask_center = cv2.erode(inv_mask_erosion, np.ones((erosion_radius, erosion_radius), np.uint8))
blur_size = w_edge * 2

inv_soft_mask = cv2.GaussianBlur(inv_mask_center, (blur_size + 1, blur_size + 1), 0)
if len(upsample_img.shape) == 2: # upsample_img is gray image
upsample_img = upsample_img[:, :, None]
inv_soft_mask = inv_soft_mask[:, :, None]

if len(upsample_img.shape) == 3 and upsample_img.shape[2] == 4: # alpha channel
alpha = upsample_img[:, :, 3:]
Expand Down