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| import cv2 | |
| import numpy as np | |
| import random | |
| def random_brightness_np(image, max_abs_change=50): | |
| delta = random.uniform(-max_abs_change, max_abs_change) | |
| return np.clip(image + delta, 0, 255) | |
| def random_contrast_np(image, strength_range=[0.3, 1.5]): | |
| delta = random.uniform(*strength_range) | |
| mean = image.mean() | |
| return np.clip((image - mean) * delta + mean, 0, 255) | |
| def motion_blur_np(img, max_kernel_size=3): | |
| # Either vertial, hozirontal or diagonal blur | |
| mode = np.random.choice(['h', 'v', 'diag_down', 'diag_up']) | |
| ksize = np.random.randint( | |
| 0, (max_kernel_size+1)/2)*2 + 1 # make sure is odd | |
| center = int((ksize-1)/2) | |
| kernel = np.zeros((ksize, ksize)) | |
| if mode == 'h': | |
| kernel[center, :] = 1. | |
| elif mode == 'v': | |
| kernel[:, center] = 1. | |
| elif mode == 'diag_down': | |
| kernel = np.eye(ksize) | |
| elif mode == 'diag_up': | |
| kernel = np.flip(np.eye(ksize), 0) | |
| var = ksize * ksize / 16. | |
| grid = np.repeat(np.arange(ksize)[:, np.newaxis], ksize, axis=-1) | |
| gaussian = np.exp(-(np.square(grid-center) + | |
| np.square(grid.T-center))/(2.*var)) | |
| kernel *= gaussian | |
| kernel /= np.sum(kernel) | |
| img = cv2.filter2D(img, -1, kernel) | |
| return np.clip(img, 0, 255) | |
| def additive_gaussian_noise(image, stddev_range=[5, 95]): | |
| stddev = random.uniform(*stddev_range) | |
| noise = np.random.normal(size=image.shape, scale=stddev) | |
| noisy_image = np.clip(image + noise, 0, 255) | |
| return noisy_image | |
| def photaug(img): | |
| img = random_brightness_np(img) | |
| img = random_contrast_np(img) | |
| # img = additive_gaussian_noise(img) | |
| img = motion_blur_np(img) | |
| return img | |