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onnx-web
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use sum of histogram, remove unused imports

This commit is contained in:
Sean Sube 2023-01-14 15:36:19 -06:00
parent e9620fd62e
commit 1220155d39
1 changed files with 5 additions and 8 deletions
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13
api/onnx_web/image.py
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@ -1,5 +1,5 @@
from numpy import random
from PIL import Image, ImageStat
from PIL import Image
from typing import Tuple
import numpy as np
@ -15,7 +15,7 @@ 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], sigma = 200) -> Tuple[float, float, float]:
def blend_source_histogram(source_image: Image, dims: Tuple[int, int]) -> Tuple[float, float, float]:
r, g, b = source_image.split()
width, height = dims
@ -23,12 +23,9 @@ def blend_source_histogram(source_image: Image, dims: Tuple[int, int], sigma = 2
hist_g = g.histogram()
hist_b = b.histogram()
stats = ImageStat.Stat(source_image)
sum_r, sum_g, sum_b = stats.sum
noise_r = random.choice(256, p=np.divide(np.copy(hist_r), sum_r), size=width*height)
noise_g = random.choice(256, p=np.divide(np.copy(hist_g), sum_g), size=width*height)
noise_b = random.choice(256, p=np.divide(np.copy(hist_b), sum_b), size=width*height)
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 = Image.fromarray(zip(noise_r, noise_g, noise_b))