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flatten noise data

This commit is contained in:
Sean Sube 2023-01-14 15:44:19 -06:00
parent 1220155d39
commit e05ed35f6b
1 changed files with 5 additions and 4 deletions
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9
api/onnx_web/image.py
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@ -18,16 +18,17 @@ def blend_mask_inverse_source(source: Tuple[int, int, int], mask: Tuple[int, int
def blend_source_histogram(source_image: Image, dims: Tuple[int, int]) -> Tuple[float, float, float]:
r, g, b = source_image.split()
width, height = dims
size = width * height
hist_r = r.histogram()
hist_g = g.histogram()
hist_b = b.histogram()
noise_r = random.choice(256, p=np.divide(np.copy(hist_r), np.sum(hist_r)), size=width*height)
noise_g = random.choice(256, p=np.divide(np.copy(hist_g), np.sum(hist_g)), size=width*height)
noise_b = random.choice(256, p=np.divide(np.copy(hist_b), np.sum(hist_b)), size=width*height)
noise_r = random.choice(256, p=np.divide(np.copy(hist_r), np.sum(hist_r)), size=size)
noise_g = random.choice(256, p=np.divide(np.copy(hist_g), np.sum(hist_g)), size=size)
noise_b = random.choice(256, p=np.divide(np.copy(hist_b), np.sum(hist_b)), size=size)
noise = Image.fromarray(zip(noise_r, noise_g, noise_b))
noise = Image.fromarray(np.reshape(np.dstack(noise_r, noise_g, noise_b), size*3))
return noise