use sum of histogram, remove unused imports
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@ -1,5 +1,5 @@
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from numpy import random
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from PIL import Image, ImageStat
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from PIL import Image
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from typing import Tuple
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import numpy as np
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@ -15,7 +15,7 @@ def blend_mask_inverse_source(source: Tuple[int, int, int], mask: Tuple[int, int
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)
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def blend_source_histogram(source_image: Image, dims: Tuple[int, int], sigma = 200) -> Tuple[float, float, float]:
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def blend_source_histogram(source_image: Image, dims: Tuple[int, int]) -> Tuple[float, float, float]:
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r, g, b = source_image.split()
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width, height = dims
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@ -23,12 +23,9 @@ def blend_source_histogram(source_image: Image, dims: Tuple[int, int], sigma = 2
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hist_g = g.histogram()
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hist_b = b.histogram()
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stats = ImageStat.Stat(source_image)
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sum_r, sum_g, sum_b = stats.sum
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noise_r = random.choice(256, p=np.divide(np.copy(hist_r), sum_r), size=width*height)
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noise_g = random.choice(256, p=np.divide(np.copy(hist_g), sum_g), size=width*height)
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noise_b = random.choice(256, p=np.divide(np.copy(hist_b), sum_b), size=width*height)
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noise_r = random.choice(256, p=np.divide(np.copy(hist_r), np.sum(hist_r)), size=width*height)
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noise_g = random.choice(256, p=np.divide(np.copy(hist_g), np.sum(hist_g)), size=width*height)
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noise_b = random.choice(256, p=np.divide(np.copy(hist_b), np.sum(hist_b)), size=width*height)
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noise = Image.fromarray(zip(noise_r, noise_g, noise_b))
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