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https://github.com/storytold/storyteller-ml.git
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salt
51 lines
1.7 KiB
Python
51 lines
1.7 KiB
Python
import cv2
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import numpy as np
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def histogram_transform(img: np.ndarray, means: np.ndarray, stds: np.ndarray,
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target_means: np.ndarray, target_stds: np.ndarray):
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means = means.reshape((1, 1, 3))
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stds = stds.reshape((1, 1, 3))
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target_means = target_means.reshape((1, 1, 3))
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target_stds = target_stds.reshape((1, 1, 3))
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x = img.astype(np.float32)
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x = (x - means) * target_stds / stds + target_means
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# x = np.round(x)
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# x = np.clip(x, 0, 255)
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# x = x.astype(np.uint8)
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return x
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def blend(a: np.ndarray,
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b: np.ndarray,
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min_error: np.ndarray,
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weight1=0.5,
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weight2=0.5):
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a = cv2.cvtColor(a, cv2.COLOR_BGR2Lab)
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b = cv2.cvtColor(b, cv2.COLOR_BGR2Lab)
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min_error = cv2.cvtColor(min_error, cv2.COLOR_BGR2Lab)
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a_mean = np.mean(a, axis=(0, 1))
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a_std = np.std(a, axis=(0, 1))
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b_mean = np.mean(b, axis=(0, 1))
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b_std = np.std(b, axis=(0, 1))
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min_error_mean = np.mean(min_error, axis=(0, 1))
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min_error_std = np.std(min_error, axis=(0, 1))
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t_mean_val = 0.5 * 256
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t_std_val = (1 / 36) * 256
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t_mean = np.ones([3], dtype=np.float32) * t_mean_val
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t_std = np.ones([3], dtype=np.float32) * t_std_val
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a = histogram_transform(a, a_mean, a_std, t_mean, t_std)
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b = histogram_transform(b, b_mean, b_std, t_mean, t_std)
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ab = (a * weight1 + b * weight2 - t_mean_val) / 0.5 + t_mean_val
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ab_mean = np.mean(ab, axis=(0, 1))
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ab_std = np.std(ab, axis=(0, 1))
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ab = histogram_transform(ab, ab_mean, ab_std, min_error_mean,
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min_error_std)
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ab = np.round(ab)
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ab = np.clip(ab, 0, 255)
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ab = ab.astype(np.uint8)
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ab = cv2.cvtColor(ab, cv2.COLOR_Lab2BGR)
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return ab
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