182 lines
6.6 KiB
Python
182 lines
6.6 KiB
Python
# code copy from: https://github.com/parlance-zz/g-diffuser-bot
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import cv2
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import numpy as np
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def np_img_grey_to_rgb(data):
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if data.ndim == 3:
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return data
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return np.expand_dims(data, 2) * np.ones((1, 1, 3))
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def convolve(data1, data2): # fast convolution with fft
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if data1.ndim != data2.ndim: # promote to rgb if mismatch
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if data1.ndim < 3:
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data1 = np_img_grey_to_rgb(data1)
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if data2.ndim < 3:
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data2 = np_img_grey_to_rgb(data2)
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return ifft2(fft2(data1) * fft2(data2))
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def fft2(data):
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if data.ndim > 2: # multiple channels
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out_fft = np.zeros(
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(data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128
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)
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for c in range(data.shape[2]):
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c_data = data[:, :, c]
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out_fft[:, :, c] = np.fft.fft2(np.fft.fftshift(c_data), norm="ortho")
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out_fft[:, :, c] = np.fft.ifftshift(out_fft[:, :, c])
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else: # single channel
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out_fft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128)
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out_fft[:, :] = np.fft.fft2(np.fft.fftshift(data), norm="ortho")
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out_fft[:, :] = np.fft.ifftshift(out_fft[:, :])
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return out_fft
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def ifft2(data):
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if data.ndim > 2: # multiple channels
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out_ifft = np.zeros(
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(data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128
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)
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for c in range(data.shape[2]):
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c_data = data[:, :, c]
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out_ifft[:, :, c] = np.fft.ifft2(np.fft.fftshift(c_data), norm="ortho")
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out_ifft[:, :, c] = np.fft.ifftshift(out_ifft[:, :, c])
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else: # single channel
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out_ifft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128)
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out_ifft[:, :] = np.fft.ifft2(np.fft.fftshift(data), norm="ortho")
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out_ifft[:, :] = np.fft.ifftshift(out_ifft[:, :])
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return out_ifft
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def get_gradient_kernel(width, height, std=3.14, mode="linear"):
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window_scale_x = float(
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width / min(width, height)
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) # for non-square aspect ratios we still want a circular kernel
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window_scale_y = float(height / min(width, height))
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if mode == "gaussian":
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x = (np.arange(width) / width * 2.0 - 1.0) * window_scale_x
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kx = np.exp(-x * x * std)
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if window_scale_x != window_scale_y:
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y = (np.arange(height) / height * 2.0 - 1.0) * window_scale_y
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ky = np.exp(-y * y * std)
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else:
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y = x
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ky = kx
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return np.outer(kx, ky)
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elif mode == "linear":
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x = (np.arange(width) / width * 2.0 - 1.0) * window_scale_x
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if window_scale_x != window_scale_y:
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y = (np.arange(height) / height * 2.0 - 1.0) * window_scale_y
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else:
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y = x
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return np.clip(1.0 - np.sqrt(np.add.outer(x * x, y * y)) * std / 3.14, 0.0, 1.0)
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else:
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raise Exception("Error: Unknown mode in get_gradient_kernel: {0}".format(mode))
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def image_blur(data, std=3.14, mode="linear"):
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width = data.shape[0]
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height = data.shape[1]
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kernel = get_gradient_kernel(width, height, std, mode=mode)
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return np.real(convolve(data, kernel / np.sqrt(np.sum(kernel * kernel))))
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def soften_mask(np_rgba_image, softness, space):
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if softness == 0:
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return np_rgba_image
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softness = min(softness, 1.0)
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space = np.clip(space, 0.0, 1.0)
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original_max_opacity = np.max(np_rgba_image[:, :, 3])
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out_mask = np_rgba_image[:, :, 3] <= 0.0
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blurred_mask = image_blur(np_rgba_image[:, :, 3], 3.5 / softness, mode="linear")
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blurred_mask = np.maximum(blurred_mask - np.max(blurred_mask[out_mask]), 0.0)
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np_rgba_image[
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:, :, 3
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] *= blurred_mask # preserve partial opacity in original input mask
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np_rgba_image[:, :, 3] /= np.max(np_rgba_image[:, :, 3]) # renormalize
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np_rgba_image[:, :, 3] = np.clip(
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np_rgba_image[:, :, 3] - space, 0.0, 1.0
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) # make space
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np_rgba_image[:, :, 3] /= np.max(np_rgba_image[:, :, 3]) # and renormalize again
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np_rgba_image[:, :, 3] *= original_max_opacity # restore original max opacity
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return np_rgba_image
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def expand_image(
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cv2_img, top: int, right: int, bottom: int, left: int, softness: float, space: float
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):
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origin_h, origin_w = cv2_img.shape[:2]
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new_width = cv2_img.shape[1] + left + right
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new_height = cv2_img.shape[0] + top + bottom
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new_img = np.zeros((new_height, new_width, 4), np.uint8) # expanded image is rgba
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print(
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"Expanding input image from {0}x{1} to {2}x{3}".format(
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cv2_img.shape[1], cv2_img.shape[0], new_width, new_height
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)
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)
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if cv2_img.shape[2] == 3: # rgb input image
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new_img[
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top : top + cv2_img.shape[0], left : left + cv2_img.shape[1], 0:3
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] = cv2_img
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new_img[
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top : top + cv2_img.shape[0], left : left + cv2_img.shape[1], 3
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] = 255 # fully opaque
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elif cv2_img.shape[2] == 4: # rgba input image
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new_img[top : top + cv2_img.shape[0], left : left + cv2_img.shape[1]] = cv2_img
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else:
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raise Exception(
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"Unsupported image format: {0} channels".format(cv2_img.shape[2])
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)
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if softness > 0.0:
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new_img = soften_mask(new_img / 255.0, softness / 100.0, space / 100.0)
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new_img = (np.clip(new_img, 0.0, 1.0) * 255.0).astype(np.uint8)
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mask_image = 255.0 - new_img[:, :, 3] # extract mask from alpha channel and invert
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rgb_init_image = (
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0.0 + new_img[:, :, 0:3]
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) # strip mask from init_img leaving only rgb channels
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hard_mask = np.zeros_like(cv2_img[:, :, 0])
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if top != 0:
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hard_mask[0 : origin_h // 2, :] = 255
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if bottom != 0:
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hard_mask[origin_h // 2 :, :] = 255
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if left != 0:
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hard_mask[:, 0 : origin_w // 2] = 255
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if right != 0:
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hard_mask[:, origin_w // 2 :] = 255
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hard_mask = cv2.copyMakeBorder(
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hard_mask, top, bottom, left, right, cv2.BORDER_CONSTANT, value=255
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)
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mask_image = np.where(hard_mask > 0, mask_image, 0)
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return rgb_init_image.astype(np.uint8), mask_image.astype(np.uint8)
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if __name__ == "__main__":
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from pathlib import Path
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current_dir = Path(__file__).parent.absolute().resolve()
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image_path = current_dir.parent / "tests" / "bunny.jpeg"
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init_image = cv2.imread(str(image_path))
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init_image, mask_image = expand_image(
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init_image,
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200,
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200,
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0,
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0,
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60,
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50,
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)
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print(mask_image.dtype, mask_image.min(), mask_image.max())
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print(init_image.dtype, init_image.min(), init_image.max())
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mask_image = mask_image.astype(np.uint8)
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init_image = init_image.astype(np.uint8)
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cv2.imwrite("expanded_image.png", init_image)
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cv2.imwrite("expanded_mask.png", mask_image)
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