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											2020-05-10 15:00:02 +00:00
										 |  |  | # Requirements: numpy, scipy, Pillow, | 
					
						
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											2020-06-11 03:39:31 +00:00
										 |  |  | from __future__ import print_function | 
					
						
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										 |  |  | import sys | 
					
						
							|  |  |  | import numpy as np | 
					
						
							|  |  |  | from scipy import ndimage | 
					
						
							|  |  |  | from PIL import Image, ImageFilter, ImageChops | 
					
						
							|  |  |  | import math | 
					
						
							|  |  |  | from os import listdir | 
					
						
							|  |  |  | from os.path import isdir, isfile | 
					
						
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 | 
					
						
							|  |  |  | roberts_cross_v = np.array([[0, 0, 0], | 
					
						
							|  |  |  |                             [0, 1, 0], | 
					
						
							|  |  |  |                             [0, 0, -1]]) | 
					
						
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 | 
					
						
							|  |  |  | roberts_cross_h = np.array([[0, 0, 0], | 
					
						
							|  |  |  |                             [0, 0, 1], | 
					
						
							|  |  |  |                             [0, -1, 0]]) | 
					
						
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							|  |  |  | def rgb2gray(rgb): | 
					
						
							|  |  |  |     return np.dot(rgb[..., :3], [0.2989, 0.5870, 0.1140]) | 
					
						
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							|  |  |  | def save_image(data, outfilename, src_image): | 
					
						
							|  |  |  |     img = Image.fromarray(np.asarray( | 
					
						
							|  |  |  |         np.clip(data, 0, 255), dtype="uint8"), "L") | 
					
						
							|  |  |  |     dest = Image.new("RGBA", (img.width, img.height)) | 
					
						
							|  |  |  |     src = img.load() | 
					
						
							|  |  |  |     dst = dest.load() | 
					
						
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							|  |  |  |     realSrc = src_image.load() | 
					
						
							|  |  |  |     mask = src_image.filter(ImageFilter.GaussianBlur(10)).load() | 
					
						
							|  |  |  |     orig = src_image.load() | 
					
						
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 | 
					
						
							|  |  |  |     for x in range(img.width): | 
					
						
							|  |  |  |         for y in range(img.height): | 
					
						
							|  |  |  |             realpixl = realSrc[x, y] | 
					
						
							|  |  |  |             greyval = float(src[x, y]) | 
					
						
							|  |  |  |             greyval = min(255.0, greyval) | 
					
						
							|  |  |  |             greyval = math.pow( | 
					
						
							|  |  |  |                 min(1, float(greyval / 255.0 * 1)), 1.5) * 255.0 * 1 | 
					
						
							|  |  |  |             greyval = max(0, greyval) | 
					
						
							|  |  |  |             alpha = mask[x, y][3] / 255.0 * 1 | 
					
						
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 | 
					
						
							|  |  |  |             edgeFactor = src[x, y] / 255.0 | 
					
						
							|  |  |  |             noEdge = 1 - edgeFactor | 
					
						
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 | 
					
						
							|  |  |  |             shadow = min(1, 1 - realpixl[3] / 255.0 - edgeFactor) | 
					
						
							|  |  |  |             noShadow = 1 - shadow | 
					
						
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							|  |  |  |             dst[x, y] = ( | 
					
						
							|  |  |  |                 min(255, int((realpixl[0] / 255.0 * 0.4 + 0.6) * 104 * 1.1)), | 
					
						
							|  |  |  |                 min(255, int((realpixl[1] / 255.0 * 0.4 + 0.6) * 200 * 1.1)), | 
					
						
							|  |  |  |                 min(255, int((realpixl[2] / 255.0 * 0.4 + 0.6) * 255 * 1.1)), | 
					
						
							|  |  |  |                 min(255, int(float(realpixl[3]) * (0.6 + 5 * edgeFactor)))) | 
					
						
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							|  |  |  |     dest.save(outfilename) | 
					
						
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							|  |  |  | def roberts_cross(infilename, outfilename): | 
					
						
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										 |  |  |     print("Processing", infilename) | 
					
						
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										 |  |  |     img = Image.open(infilename) | 
					
						
							|  |  |  |     img.load() | 
					
						
							|  |  |  |     img = img.filter(ImageFilter.GaussianBlur(0.5)) | 
					
						
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							|  |  |  |     image = rgb2gray(np.asarray(img, dtype="int32")) | 
					
						
							|  |  |  |     vertical = ndimage.convolve(image, roberts_cross_v) | 
					
						
							|  |  |  |     horizontal = ndimage.convolve(image, roberts_cross_h) | 
					
						
							|  |  |  |     output_image = np.sqrt(np.square(horizontal) + np.square(vertical)) | 
					
						
							|  |  |  |     save_image(output_image, outfilename, img) | 
					
						
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							|  |  |  | def generateUiPreview(srcPath, buildingId): | 
					
						
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										 |  |  |     print(srcPath, buildingId) | 
					
						
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										 |  |  |     img = Image.open(srcPath) | 
					
						
							|  |  |  |     img.load() | 
					
						
							|  |  |  |     img.thumbnail((110, 110), Image.ANTIALIAS) | 
					
						
							|  |  |  |     img.save("../res/ui/hud/building_previews/" + buildingId + ".png") | 
					
						
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							|  |  |  |     img = img.convert("LA") | 
					
						
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							|  |  |  |     data = img.load() | 
					
						
							|  |  |  |     for x in range(img.width): | 
					
						
							|  |  |  |         for y in range(img.height): | 
					
						
							|  |  |  |             data[x, y] = (data[x, y][0], int(data[x, y][1] * 0.5)) | 
					
						
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							|  |  |  |     img.save("../res/ui/hud/building_previews/" + buildingId + "_disabled.png") | 
					
						
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							|  |  |  | buildings = listdir("buildings") | 
					
						
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							|  |  |  | for buildingId in buildings: | 
					
						
							|  |  |  |     if "hub" in buildingId: | 
					
						
							|  |  |  |         continue | 
					
						
							|  |  |  |     roberts_cross("buildings/" + buildingId + "", "blueprints/" + buildingId + "") |