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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() |