187 lines
5.3 KiB
Python
187 lines
5.3 KiB
Python
from numpy import random
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from PIL import Image, ImageChops, ImageFilter
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import numpy as np
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from .params import (
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Border,
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Point,
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)
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def get_pixel_index(x: int, y: int, width: int) -> int:
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return (y * width) + x
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def mask_filter_none(mask_image: Image.Image, dims: Point, origin: Point, fill='white', **kw) -> Image.Image:
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width, height = dims
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noise = Image.new('RGB', (width, height), fill)
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noise.paste(mask_image, origin)
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return noise
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def mask_filter_gaussian_multiply(mask_image: Image.Image, dims: Point, origin: Point, rounds=3, **kw) -> Image.Image:
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'''
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Gaussian blur with multiply, source image centered on white canvas.
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'''
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noise = mask_filter_none(mask_image, dims, origin)
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for i in range(rounds):
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blur = noise.filter(ImageFilter.GaussianBlur(5))
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noise = ImageChops.multiply(noise, blur)
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return noise
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def mask_filter_gaussian_screen(mask_image: Image.Image, dims: Point, origin: Point, rounds=3, **kw) -> Image.Image:
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'''
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Gaussian blur, source image centered on white canvas.
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'''
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noise = mask_filter_none(mask_image, dims, origin)
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for i in range(rounds):
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blur = noise.filter(ImageFilter.GaussianBlur(5))
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noise = ImageChops.screen(noise, blur)
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return noise
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def noise_source_fill_edge(source_image: Image.Image, dims: Point, origin: Point, fill='white', **kw) -> Image.Image:
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'''
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Identity transform, source image centered on white canvas.
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'''
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width, height = dims
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noise = Image.new('RGB', (width, height), fill)
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noise.paste(source_image, origin)
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return noise
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def noise_source_fill_mask(source_image: Image.Image, dims: Point, origin: Point, fill='white', **kw) -> Image.Image:
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'''
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Fill the whole canvas, no source or noise.
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'''
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width, height = dims
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noise = Image.new('RGB', (width, height), fill)
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return noise
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def noise_source_gaussian(source_image: Image.Image, dims: Point, origin: Point, rounds=3, **kw) -> Image.Image:
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'''
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Gaussian blur, source image centered on white canvas.
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'''
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noise = noise_source_uniform(source_image, dims, origin)
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noise.paste(source_image, origin)
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for i in range(rounds):
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noise = noise.filter(ImageFilter.GaussianBlur(5))
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return noise
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def noise_source_uniform(source_image: Image.Image, dims: Point, origin: Point, **kw) -> Image.Image:
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width, height = dims
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size = width * height
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noise_r = random.uniform(0, 256, size=size)
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noise_g = random.uniform(0, 256, size=size)
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noise_b = random.uniform(0, 256, size=size)
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noise = Image.new('RGB', (width, height))
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for x in range(width):
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for y in range(height):
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i = get_pixel_index(x, y, width)
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noise.putpixel((x, y), (
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int(noise_r[i]),
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int(noise_g[i]),
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int(noise_b[i])
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))
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return noise
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def noise_source_normal(source_image: Image.Image, dims: Point, origin: Point, **kw) -> Image.Image:
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width, height = dims
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size = width * height
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noise_r = random.normal(128, 32, size=size)
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noise_g = random.normal(128, 32, size=size)
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noise_b = random.normal(128, 32, size=size)
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noise = Image.new('RGB', (width, height))
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for x in range(width):
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for y in range(height):
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i = get_pixel_index(x, y, width)
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noise.putpixel((x, y), (
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int(noise_r[i]),
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int(noise_g[i]),
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int(noise_b[i])
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))
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return noise
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def noise_source_histogram(source_image: Image.Image, dims: Point, origin: Point, **kw) -> Image.Image:
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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(
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np.copy(hist_r), np.sum(hist_r)), size=size)
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noise_g = random.choice(256, p=np.divide(
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np.copy(hist_g), np.sum(hist_g)), size=size)
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noise_b = random.choice(256, p=np.divide(
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np.copy(hist_b), np.sum(hist_b)), size=size)
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noise = Image.new('RGB', (width, height))
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for x in range(width):
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for y in range(height):
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i = get_pixel_index(x, y, width)
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noise.putpixel((x, y), (
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noise_r[i],
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noise_g[i],
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noise_b[i]
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))
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return noise
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# very loosely 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(
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source_image: Image.Image,
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mask_image: Image.Image,
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expand: Border,
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fill='white',
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noise_source=noise_source_histogram,
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mask_filter=mask_filter_none,
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):
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full_width = expand.left + source_image.width + expand.right
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full_height = expand.top + source_image.height + expand.bottom
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dims = (full_width, full_height)
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origin = (expand.left, expand.top)
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full_source = Image.new('RGB', dims, fill)
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full_source.paste(source_image, origin)
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full_mask = mask_filter(mask_image, dims, origin, fill=fill)
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full_noise = noise_source(source_image, dims, origin, fill=fill)
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full_noise = ImageChops.multiply(full_noise, full_mask)
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full_source = Image.composite(
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full_noise, full_source, full_mask.convert('L'))
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return (full_source, full_mask, full_noise, (full_width, full_height))
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