| | import sys
|
| |
|
| | import cv2
|
| | import matplotlib.pyplot as plt
|
| | import nibabel as nib
|
| | import os
|
| | import glob
|
| |
|
| | import numpy as np
|
| | import skimage.transform
|
| | import torch.optim
|
| | from skimage import transform
|
| | from scipy.ndimage import binary_fill_holes, zoom
|
| | from scipy.ndimage import map_coordinates
|
| | from vnet import VNet
|
| | from half_vnet import HalfVNet
|
| | from torch.utils.data import Dataset, DataLoader
|
| | import torch.nn as nn
|
| | from torch.optim import AdamW
|
| | from torch.cuda.amp import GradScaler
|
| | from torch.cuda.amp import autocast
|
| | from tqdm import tqdm
|
| | def handle_image_and_label():
|
| | cnt = 0
|
| | pos_label = []
|
| | image_paths = glob.glob(r'C:\Users\zhang\PycharmProjects\mmsegmentation\data\Task02_Heart\labelsTr\*.nii.gz')
|
| | data_paths=glob.glob(r'C:\Users\zhang\PycharmProjects\mmsegmentation\data\Task02_Heart\imagesTr\*.nii.gz')
|
| | for i,path in enumerate(image_paths):
|
| | folder = 'mri_z_precise_train_2d'
|
| | filename = path.split('\\')[-1].split('.')[0].replace('label', 'image')
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| |
|
| | print(filename)
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| |
|
| | image = nib.load(path).dataobj
|
| | data=nib.load(data_paths[i]).dataobj
|
| | image = np.array(image, dtype=np.int8)
|
| | image = np.swapaxes(image, 1, 2)
|
| | image = np.swapaxes(image, 0, 1)
|
| | data = np.swapaxes(data, 1, 2)
|
| | data = np.swapaxes(data, 0, 1)
|
| | print(np.min(data),np.max(data))
|
| | data=((data-np.min(data))/(np.max(data)-np.min(data)))*255
|
| | data=np.array(data,dtype=int)
|
| | D, H, W = image.shape
|
| | plt.subplot(1, 3, 1)
|
| | plt.imshow(image[60, :, :])
|
| | image = transform.resize(image, (128, 320, 320))
|
| | plt.subplot(1, 3, 2)
|
| | plt.imshow(image[60, :, :])
|
| |
|
| | z_min, z_max = get_min_and_max_by_axis(image, 0)
|
| | x_min, x_max = get_min_and_max_by_axis(image, 1)
|
| | y_min, y_max = get_min_and_max_by_axis(image, 2)
|
| | label = [z_min, z_max, x_min, x_max, y_min, y_max]
|
| | print(image.shape, label)
|
| | pos_label.append(label)
|
| |
|
| | image = transform.resize(image, (D, 320, 320))
|
| | plt.subplot(1, 3, 3)
|
| | plt.imshow(image[60, :, :])
|
| | plt.show()
|
| |
|
| | indices=[]
|
| | ranges=[128,128,320,320,320,320]
|
| | for i in range(len(label)):
|
| | indices.append(int(label[i]*ranges[i]))
|
| | print(indices)
|
| |
|
| | image = nib.load(path).dataobj
|
| | image = np.array(image, dtype=np.int8)
|
| | image = np.swapaxes(image, 1, 2)
|
| | image = np.swapaxes(image, 0, 1)
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| |
|
| |
|
| |
|
| |
|
| | label_nii=image[indices[0]:indices[1],:]
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| |
|
| | data_nii=data[indices[0]:indices[1],:]
|
| |
|
| | for i in range(label_nii.shape[0]):
|
| | cv2.imwrite(os.path.join(f'{folder}/label', f'{filename}_{i}.png'), label_nii[i,:, :])
|
| |
|
| | for i in range(len(data_nii)):
|
| | cv2.imwrite(os.path.join(f'{folder}/image', f'{filename}_{i}.png'), data_nii[i,:, :])
|
| |
|
| | pos_label = np.array(pos_label)
|
| | print(pos_label.shape)
|
| |
|
| |
|
| |
|
| | def get_min_and_max_by_axis(image, axis, eps=1e-2):
|
| | label_list = []
|
| | length = image.shape[axis]
|
| | if axis == 0:
|
| | for i in range(length):
|
| | if len(np.unique(image[i, :, :])) != 1:
|
| | label_list.append(i)
|
| | elif axis == 1:
|
| | for i in range(length):
|
| | if len(np.unique(image[:, i, :])) != 1:
|
| | label_list.append(i)
|
| | elif axis == 2:
|
| | for i in range(length):
|
| | if len(np.unique(image[:, :, i])) != 1:
|
| | label_list.append(i)
|
| | norm_min, norm_max = min(label_list) / length - eps, max(label_list) / length + eps
|
| | print(min(label_list), int(norm_min * length), max(label_list), int(norm_max * length))
|
| | return norm_min, norm_max
|
| | if __name__ == '__main__':
|
| | handle_image_and_label() |
