| import cv2 | |
| import numpy as np | |
| def perform_flash(source, a=5, target=-1, perform_gamma_correction=True): | |
| rows, cols, _ = source.shape | |
| v = np.max(source, axis=2) | |
| vd = np.copy(v) | |
| vd[vd == 0] = 1e-9 | |
| result = source / (a * np.exp(np.mean(np.log(vd))) + np.tile(np.expand_dims(vd, axis=2), (1, 1, 3))) | |
| if perform_gamma_correction: | |
| result **= 1.0 / 2.2 | |
| if target >= 0: | |
| result *= target / np.mean((0.299 * result[:, :, 2] + 0.587 * result[:, :, 1] + 0.114 * result[:, :, 0])) | |
| else: | |
| result *= 255.0 / np.max(result) | |
| return result | |
| def perform_storm(source, a=5, target=-1, kernels=(1, 4, 16, 64, 256), perform_gamma_correction=True): | |
| rows, cols, _ = source.shape | |
| v = np.max(source, axis=2) | |
| vd = np.copy(v) | |
| vd[vd == 0] = 1e-9 | |
| lv = np.log(vd) | |
| result = sum([source / np.tile( | |
| np.expand_dims(a * np.exp(cv2.boxFilter(lv, -1, (int(min(rows // kernel, cols // kernel)),) * 2)) + vd, axis=2), | |
| (1, 1, 3)) for kernel in kernels]) | |
| if perform_gamma_correction: | |
| result **= 1.0 / 2.2 | |
| if target >= 0: | |
| result *= target / np.mean((0.299 * result[:, :, 2] + 0.587 * result[:, :, 1] + 0.114 * result[:, :, 0])) | |
| else: | |
| result *= 255.0 / np.max(result) | |
| return result | |