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