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import cv2 |
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import numpy as np |
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import random |
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def random_brightness_np(image, max_abs_change=50): |
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delta = random.uniform(-max_abs_change, max_abs_change) |
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return np.clip(image + delta, 0, 255) |
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def random_contrast_np(image, strength_range=[0.3, 1.5]): |
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delta = random.uniform(*strength_range) |
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mean = image.mean() |
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return np.clip((image - mean) * delta + mean, 0, 255) |
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def motion_blur_np(img, max_kernel_size=3): |
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mode = np.random.choice(["h", "v", "diag_down", "diag_up"]) |
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ksize = np.random.randint(0, (max_kernel_size + 1) / 2) * 2 + 1 |
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center = int((ksize - 1) / 2) |
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kernel = np.zeros((ksize, ksize)) |
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if mode == "h": |
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kernel[center, :] = 1.0 |
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elif mode == "v": |
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kernel[:, center] = 1.0 |
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elif mode == "diag_down": |
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kernel = np.eye(ksize) |
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elif mode == "diag_up": |
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kernel = np.flip(np.eye(ksize), 0) |
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var = ksize * ksize / 16.0 |
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grid = np.repeat(np.arange(ksize)[:, np.newaxis], ksize, axis=-1) |
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gaussian = np.exp( |
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-(np.square(grid - center) + np.square(grid.T - center)) / (2.0 * var) |
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) |
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kernel *= gaussian |
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kernel /= np.sum(kernel) |
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img = cv2.filter2D(img, -1, kernel) |
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return np.clip(img, 0, 255) |
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def additive_gaussian_noise(image, stddev_range=[5, 95]): |
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stddev = random.uniform(*stddev_range) |
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noise = np.random.normal(size=image.shape, scale=stddev) |
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noisy_image = np.clip(image + noise, 0, 255) |
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return noisy_image |
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def photaug(img): |
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img = random_brightness_np(img) |
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img = random_contrast_np(img) |
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img = motion_blur_np(img) |
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return img |
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