167 lines
4.8 KiB
Python
167 lines
4.8 KiB
Python
from numpy import random
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from PIL import Image, ImageChops, ImageFilter
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from typing import Tuple
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import numpy as np
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def mask_filter_none(mask_image: Image, dims: Tuple[int, int], origin: Tuple[int, int], fill='white') -> 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, dims: Tuple[int, int], origin: Tuple[int, int], rounds=3) -> 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, dims: Tuple[int, int], origin: Tuple[int, int], rounds=3) -> 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(source_image: Image, dims: Tuple[int, int], origin: Tuple[int, int], fill='white') -> 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_gaussian(source_image: Image, dims: Tuple[int, int], origin: Tuple[int, int], rounds=3) -> 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, dims: Tuple[int, int], origin: Tuple[int, int]) -> 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 = x * y
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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, dims: Tuple[int, int], origin: Tuple[int, int]) -> 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 = x * y
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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, dims: Tuple[int, int], origin: Tuple[int, int]) -> 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 = x * y
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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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# 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,
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mask_image: Image,
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expand_by: Tuple[int, int, int, int],
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fill='white',
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noise_source=noise_source_histogram,
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mask_filter=mask_filter_gaussian,
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):
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left, right, top, bottom = expand_by
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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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dims = (full_width, full_height)
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origin = (top, left)
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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)
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full_noise = noise_source(source_image, dims, origin)
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full_source = Image.composite(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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