import torchvision.transforms as transforms
import cv2
import numpy as np


class DataTransform(object):
    def __init__(self, transform):
        self.transform = transform

    def __call__(self, sample):
        xi = self.transform(sample)
        xj = self.transform(sample)
        return xi, xj


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:
            sigma = (self.max - self.min) * np.random.random_sample() + self.min
            sample = cv2.GaussianBlur(sample, (self.kernel_size, self.kernel_size), sigma)

        return sample


def get_data_transform_opes(s, crop_size):
    # get a set of data augmentation transformations as described in the SimCLR paper.
    color_jitter = transforms.ColorJitter(0.8 * s, 0.8 * s, 0.8 * s, 0.2 * s)
    data_transforms = transforms.Compose([transforms.RandomResizedCrop(size=crop_size),
                                          transforms.RandomHorizontalFlip(),
                                          transforms.RandomApply([color_jitter], p=0.8),
                                          transforms.RandomGrayscale(p=0.2),
                                          GaussianBlur(kernel_size=int(0.1 * crop_size)),
                                          transforms.ToTensor()])
    return data_transforms