63 lines
2.2 KiB
Python
63 lines
2.2 KiB
Python
from numpy import random
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from PIL import Image
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from typing import Tuple
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import numpy as np
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def blend_mask_inverse_source(source: Tuple[int, int, int], mask: Tuple[int, int, int], noise: int) -> Tuple[int, int, int]:
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m = float(noise) / 256
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n = 1.0 - m
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return (
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int((source[0] * n) + (mask[0] * m)),
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int((source[1] * n) + (mask[1] * m)),
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int((source[2] * n) + (mask[2] * m)),
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)
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def blend_source_histogram(source_image: Image, dims: Tuple[int, int]) -> Tuple[float, float, float]:
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r, g, b = source_image.split()
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width, height = dims
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size = width * height
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hist_r = r.histogram()
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hist_g = g.histogram()
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hist_b = b.histogram()
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noise_r = random.choice(256, p=np.divide(np.copy(hist_r), np.sum(hist_r)), size=size)
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noise_g = random.choice(256, p=np.divide(np.copy(hist_g), np.sum(hist_g)), size=size)
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noise_b = random.choice(256, p=np.divide(np.copy(hist_b), np.sum(hist_b)), size=size)
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noise_pixels = np.dstack((noise_r, noise_g, noise_b))
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noise = Image.fromarray(np.reshape(noise_pixels), (width * 3, height)))
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return noise
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# based on https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/master/scripts/outpainting_mk_2.py#L175-L232
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def expand_image(source_image: Image, mask_image: Image, dims: Tuple[int, int, int, int], fill = 'white', blend_source=blend_source_histogram, blend_op=blend_mask_inverse_source):
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left, right, top, bottom = dims
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full_width = left + source_image.width + right
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full_height = top + source_image.height + bottom
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full_source = Image.new('RGB', (full_width, full_height), fill)
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full_source.paste(source_image, (left, top))
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full_mask = Image.new('RGB', (full_width, full_height), fill)
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full_mask.paste(mask_image, (left, top))
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full_noise = blend_source(source_image, (full_width, full_height))
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for x in range(full_source.width):
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for y in range(full_source.height):
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mask_color = full_mask.getpixel((x, y))
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noise_color = full_noise.getpixel((x, y))
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source_color = full_source.getpixel((x, y))
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if mask_color[0] > 0:
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full_source.putpixel((x, y), blend_op(source_color, mask_color, noise_color))
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return (full_source, full_mask, (full_width, full_height))
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