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fix outpainting image padding

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Qing 2023-11-15 17:18:32 +08:00
parent 1571018cfd
commit b8a030f83a
1 changed files with 22 additions and 37 deletions
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59
lama_cleaner/model/g_diffuser_bot.py
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@ -85,59 +85,44 @@ def image_blur(data, std=3.14, mode="linear"):
return np.real(convolve(data, kernel / np.sqrt(np.sum(kernel * kernel))))
def soften_mask(np_rgba_image, softness, space):
def soften_mask(mask_img, softness, space):
if softness == 0:
return np_rgba_image
return mask_img
softness = min(softness, 1.0)
space = np.clip(space, 0.0, 1.0)
original_max_opacity = np.max(np_rgba_image[:, :, 3])
out_mask = np_rgba_image[:, :, 3] <= 0.0
blurred_mask = image_blur(np_rgba_image[:, :, 3], 3.5 / softness, mode="linear")
original_max_opacity = np.max(mask_img)
out_mask = mask_img <= 0.0
blurred_mask = image_blur(mask_img, 3.5 / softness, mode="linear")
blurred_mask = np.maximum(blurred_mask - np.max(blurred_mask[out_mask]), 0.0)
np_rgba_image[
:, :, 3
] *= blurred_mask # preserve partial opacity in original input mask
np_rgba_image[:, :, 3] /= np.max(np_rgba_image[:, :, 3]) # renormalize
np_rgba_image[:, :, 3] = np.clip(
np_rgba_image[:, :, 3] - space, 0.0, 1.0
) # make space
np_rgba_image[:, :, 3] /= np.max(np_rgba_image[:, :, 3]) # and renormalize again
np_rgba_image[:, :, 3] *= original_max_opacity # restore original max opacity
return np_rgba_image
mask_img *= blurred_mask # preserve partial opacity in original input mask
mask_img /= np.max(mask_img) # renormalize
mask_img = np.clip(mask_img - space, 0.0, 1.0) # make space
mask_img /= np.max(mask_img) # and renormalize again
mask_img *= original_max_opacity # restore original max opacity
return mask_img
def expand_image(
cv2_img, top: int, right: int, bottom: int, left: int, softness: float, space: float
):
assert cv2_img.shape[2] == 3
origin_h, origin_w = cv2_img.shape[:2]
new_width = cv2_img.shape[1] + left + right
new_height = cv2_img.shape[0] + top + bottom
new_img = np.zeros((new_height, new_width, 4), np.uint8) # expanded image is rgba
print(
"Expanding input image from {0}x{1} to {2}x{3}".format(
cv2_img.shape[1], cv2_img.shape[0], new_width, new_height
)
)
if cv2_img.shape[2] == 3: # rgb input image
new_img[
top : top + cv2_img.shape[0], left : left + cv2_img.shape[1], 0:3
] = cv2_img
new_img[
top : top + cv2_img.shape[0], left : left + cv2_img.shape[1], 3
] = 255 # fully opaque
elif cv2_img.shape[2] == 4: # rgba input image
new_img[top : top + cv2_img.shape[0], left : left + cv2_img.shape[1]] = cv2_img
else:
raise Exception(
"Unsupported image format: {0} channels".format(cv2_img.shape[2])
# TODO: which is better?
# new_img = np.random.randint(0, 255, (new_height, new_width, 3), np.uint8)
new_img = cv2.copyMakeBorder(
cv2_img, top, bottom, left, right, cv2.BORDER_REPLICATE
)
mask_img = np.zeros((new_height, new_width), np.uint8)
mask_img[top : top + cv2_img.shape[0], left : left + cv2_img.shape[1]] = 255
if softness > 0.0:
new_img = soften_mask(new_img / 255.0, softness / 100.0, space / 100.0)
new_img = (np.clip(new_img, 0.0, 1.0) * 255.0).astype(np.uint8)
mask_img = soften_mask(mask_img / 255.0, softness / 100.0, space / 100.0)
mask_img = (np.clip(mask_img, 0.0, 1.0) * 255.0).astype(np.uint8)
mask_image = 255.0 - new_img[:, :, 3] # extract mask from alpha channel and invert
mask_image = 255.0 - mask_img # extract mask from alpha channel and invert
rgb_init_image = (
0.0 + new_img[:, :, 0:3]
) # strip mask from init_img leaving only rgb channels
@ -153,7 +138,7 @@ def expand_image(
hard_mask[:, origin_w // 2 :] = 255
hard_mask = cv2.copyMakeBorder(
hard_mask, top, bottom, left, right, cv2.BORDER_CONSTANT, value=255
hard_mask, top, bottom, left, right, cv2.BORDER_DEFAULT, value=255
)
mask_image = np.where(hard_mask > 0, mask_image, 0)
return rgb_init_image.astype(np.uint8), mask_image.astype(np.uint8)