File size: 4,265 Bytes
1c40643 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 | import sys
import cv2
import matplotlib.pyplot as plt
import nibabel as nib
import os
import glob
# from scipy.ndimage import zoom
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')
print(filename)
# 获取image，转换成合适的维度
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, :, :])
# 获取归一化的坐标
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)
# label_nii=image[indices[0]:indices[1],indices[2]:indices[3],indices[4]:indices[5]]
# print(np.unique(label_nii))
# data_nii=data[indices[0]:indices[1],indices[2]:indices[3],indices[4]:indices[5]]
label_nii=image[indices[0]:indices[1],:]
# print(np.unique(label_nii))
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,:, :])
# print(np.unique(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)
# np.save('./imagesTr/pos_labels.npy', pos_label)
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() |