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| import cv2 | |
| import numpy as np | |
| np.random.seed(0) | |
| class GaussianBlur(object): | |
| # Implements Gaussian blur as described in the SimCLR paper | |
| def __init__(self, kernel_size, min=0.1, max=2.0): | |
| self.min = min | |
| self.max = max | |
| # kernel size is set to be 10% of the image height/width | |
| self.kernel_size = kernel_size | |
| def __call__(self, sample): | |
| sample = np.array(sample) | |
| # blur the image with a 50% chance | |
| prob = np.random.random_sample() | |
| if prob < 0.5: | |
| # print(self.kernel_size) | |
| sigma = (self.max - self.min) * np.random.random_sample() + self.min | |
| sample = cv2.GaussianBlur(sample, (self.kernel_size, self.kernel_size), sigma) | |
| return sample | |