mirror of
https://github.com/invoke-ai/InvokeAI
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61 lines
2.4 KiB
Python
61 lines
2.4 KiB
Python
from typing import Tuple
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import numpy as np
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from PIL import Image
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def infill_mosaic(
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image: Image.Image,
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tile_shape: Tuple[int, int] = (64, 64),
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min_color: Tuple[int, int, int, int] = (0, 0, 0, 0),
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max_color: Tuple[int, int, int, int] = (255, 255, 255, 0),
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) -> Image.Image:
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"""
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image:PIL - A PIL Image
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tile_shape: Tuple[int,int] - Tile width & Tile Height
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min_color: Tuple[int,int,int] - RGB values for the lowest color to clip to (0-255)
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max_color: Tuple[int,int,int] - RGB values for the highest color to clip to (0-255)
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"""
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np_image = np.array(image) # Convert image to np array
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alpha = np_image[:, :, 3] # Get the mask from the alpha channel of the image
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non_transparent_pixels = np_image[alpha != 0, :3] # List of non-transparent pixels
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# Create color tiles to paste in the empty areas of the image
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tile_width, tile_height = tile_shape
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# Clip the range of colors in the image to a particular spectrum only
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r_min, g_min, b_min, _ = min_color
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r_max, g_max, b_max, _ = max_color
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non_transparent_pixels[:, 0] = np.clip(non_transparent_pixels[:, 0], r_min, r_max)
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non_transparent_pixels[:, 1] = np.clip(non_transparent_pixels[:, 1], g_min, g_max)
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non_transparent_pixels[:, 2] = np.clip(non_transparent_pixels[:, 2], b_min, b_max)
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tiles = []
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for _ in range(256):
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color = non_transparent_pixels[np.random.randint(len(non_transparent_pixels))]
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tile = np.zeros((tile_height, tile_width, 3), dtype=np.uint8)
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tile[:, :] = color
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tiles.append(tile)
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# Fill the transparent area with tiles
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filled_image = np.zeros((image.height, image.width, 3), dtype=np.uint8)
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for x in range(image.width):
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for y in range(image.height):
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tile = tiles[np.random.randint(len(tiles))]
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try:
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filled_image[
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y - (y % tile_height) : y - (y % tile_height) + tile_height,
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x - (x % tile_width) : x - (x % tile_width) + tile_width,
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] = tile
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except ValueError:
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# Need to handle edge cases - literally
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pass
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filled_image = Image.fromarray(filled_image) # Convert the filled tiles image to PIL
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image = Image.composite(
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image, filled_image, image.split()[-1]
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) # Composite the original image on top of the filled tiles
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return image
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