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perf: The real mean between images and the acceleration of denoise processing
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1 changed files with 3 additions and 13 deletions
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from PIL import Image, ImageChops
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from tqdm import tqdm
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import numpy as np
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def denoise(files):
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bias = 1
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image = Image.open(files[0])
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for file in tqdm(files):
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alpha = 1 / bias
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im2 = Image.open(file)
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im3 = Image.blend(image, im2, alpha)
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image = im3
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bias += 1
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return image
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images = [np.asarray(Image.open(file)) for file in tqdm(files)]
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return Image.fromarray(np.uint8(np.mean(images, axis=0)))
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def startracks(files):
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