import glob
import random
import os
import cv2
import numpy as np
import tqdm
from matplotlib import pyplot as plt

import albumentations as A


def visualize(image, mask, original_image=None, original_mask=None):
    fontsize = 18

    if original_image is None and original_mask is None:
        f, ax = plt.subplots(2, 1, figsize=(8, 8))

        ax[0].imshow(image)
        ax[1].imshow(mask)
    else:
        f, ax = plt.subplots(2, 2, figsize=(8, 8))

        ax[0, 0].imshow(original_image)
        ax[0, 0].set_title('Original image', fontsize=fontsize)

        ax[1, 0].imshow(original_mask)
        ax[1, 0].set_title('Original mask', fontsize=fontsize)

        ax[0, 1].imshow(image)
        ax[0, 1].set_title('Transformed image', fontsize=fontsize)

        ax[1, 1].imshow(mask)
        ax[1, 1].set_title('Transformed mask', fontsize=fontsize)
    plt.show()
def augment_by_times():
    image_path=r'C:\Users\zhang\PycharmProjects\mmsegmentation\data\mr-cardiac\mri_train_2d\image'
    label_path=r'C:\Users\zhang\PycharmProjects\mmsegmentation\data\mr-cardiac\mri_train_2d\label'

    image_paths=glob.glob(os.path.join(image_path,'*.png'))
    # test=cv2.imread(r'C:\Users\zhang\PycharmProjects\mmsegmentation\data\mr-cardiac\train\label\mr_train_1001_image_100.png')
    # print(np.unique(test))
    times=2
    for i in range(times):
        for path in tqdm.tqdm(image_paths):

            filename=path.split('\\')[-1]
            # print(path,filename)

            image = cv2.imread(path,0)
            mask = cv2.imread(os.path.join(label_path,filename),0)
            # print(image.dtype,mask.dtype,np.unique(mask))
            # print(np.unique(mask))
            # print(image.shape, mask.shape)
            original_height, original_width = image.shape[:2]
            aug = A.Compose([
                A.PadIfNeeded(min_height=128,min_width=128,value=0,p=1),
                # A.RandomSizedCrop(min_max_height=(128,256), height=original_height,
                #                   width=original_width, p=0.5),
                A.VerticalFlip(p=0.5),
                A.RandomRotate90(p=0.5),
                A.OneOf([
                    A.ElasticTransform(alpha=120, sigma=120 * 0.05,
                                       alpha_affine=120 * 0.03, p=0.5),
                    A.GridDistortion(p=0.5),
                    A.OpticalDistortion(distort_limit=2, shift_limit=0.5, p=1)
                ], p=0.8),
                A.CLAHE(p=0.8),
                A.RandomBrightnessContrast(p=0.8),
                A.RandomGamma(p=0.8)
                ]
            )



            # random.seed(11)
            augmented = aug(image=image, mask=mask)

            image_heavy = augmented['image']
            mask_heavy = augmented['mask']
            # print(mask_heavy.shape,np.unique(mask_heavy))
            label_num=len(np.unique(mask_heavy))
            # print(image_heavy.dtype,mask_heavy.dtype)
            if label_num>=2:
                # print(filename,np.unique(mask_heavy))
                cv2.imwrite(os.path.join(image_path.replace('mri_train_2d','mri_aug_2d'),f'aug{i+1}_'+filename),image_heavy)
                cv2.imwrite(os.path.join(label_path.replace('mri_train_2d', 'mri_aug_2d'),
                                         f'aug{i+1}_' + filename), mask_heavy)
                # visualize(image_heavy, mask_heavy, original_image=image,
                #           original_mask=mask)
                # visualize(image, mask)
            # print(type(image),image_heavy.shape)

            # break
if __name__ == '__main__':
    augment_by_times()