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Task02_Heart/.idea/.gitignore

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Task02_Heart/.idea/.name

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+show_image_and_label.py

Task02_Heart/.idea/Task02_Heart.iml

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+</module>

Task02_Heart/.idea/deployment.xml

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Task02_Heart/.idea/inspectionProfiles/profiles_settings.xml

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Task02_Heart/.idea/misc.xml

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+<project version="4">
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Task02_Heart/.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
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+</project>

Task02_Heart/.idea/workspace.xml

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Task02_Heart/data-augment.py

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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()

Task02_Heart/data-show.py

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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
+# patient009_frame13_03.png
+# patient030_frame01_07.png
+# patient049_frame11_02.png
+# patient069_frame01_00.png
+# patient085_frame01_09.png
+
+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()
+# image_path=r'C:\Users\zhang\PycharmProjects\mmsegmentation\data\mr-cardiac\train\image'
+# label_path=r'C:\Users\zhang\PycharmProjects\mmsegmentation\data\mr-cardiac\train\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))
+# for path in tqdm.tqdm(image_paths):
+
+# filename=path.split('\\')[-1]
+# print(path,filename)
+
+image = cv2.imread('./mri_train_2d/image/mr_train_1011_image_100.png',0)
+mask = cv2.imread('./mri_train_2d/label/mr_train_1011_image_100.png',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]
+
+f, ax = plt.subplots(2, 7, figsize=(32,8),squeeze=True)
+plt.axis('off')
+
+image=A.resize(image,256,256)
+mask=A.resize(mask,256,256)
+ax[0,0].imshow(image,cmap='gray',)
+ax[0,0].axis('off')
+
+ax[1,0].imshow(mask,cmap='CMRmap')
+ax[1,0].axis('off')
+plt.gcf().tight_layout()
+aug=A.VerticalFlip()
+augmented = aug(image=image, mask=mask)
+image_heavy = augmented['image']
+mask_heavy = augmented['mask']
+ax[0,1].imshow(image_heavy,cmap='gray')
+ax[0,1].axis('off')
+ax[1,1].imshow(mask_heavy,cmap='CMRmap')
+ax[1,1].axis('off')
+
+aug=A.RandomRotate90()
+augmented = aug(image=image, mask=mask)
+image_heavy = augmented['image']
+mask_heavy = augmented['mask']
+ax[0,2].imshow(image_heavy,cmap='gray')
+ax[0,2].axis('off')
+ax[1,2].imshow(mask_heavy,cmap='CMRmap')
+ax[1,2].axis('off')
+
+
+aug=A.RandomSizedCrop(min_max_height=(100,200), height=256,
+                      width=256)
+augmented = aug(image=image, mask=mask)
+image_heavy = augmented['image']
+mask_heavy = augmented['mask']
+ax[0,3].imshow(image_heavy,cmap='gray')
+ax[0,3].axis('off')
+ax[1,3].imshow(mask_heavy,cmap='CMRmap')
+ax[1,3].axis('off')
+
+aug=A.ElasticTransform(alpha=120, sigma=120 * 0.05,
+                               alpha_affine=120 * 0.03)
+augmented = aug(image=image, mask=mask)
+image_heavy = augmented['image']
+mask_heavy = augmented['mask']
+ax[0,4].imshow(image_heavy,cmap='gray')
+ax[0,4].axis('off')
+ax[1,4].imshow(mask_heavy,cmap='CMRmap')
+ax[1,4].axis('off')
+
+
+aug=A.GridDistortion()
+augmented = aug(image=image, mask=mask)
+image_heavy = augmented['image']
+mask_heavy = augmented['mask']
+ax[0,5].imshow(image_heavy,cmap='gray')
+ax[0,5].axis('off')
+ax[1,5].imshow(mask_heavy,cmap='CMRmap')
+ax[1,5].axis('off')
+
+aug=A.OpticalDistortion(distort_limit=2, shift_limit=0.5)
+augmented = aug(image=image, mask=mask)
+image_heavy = augmented['image']
+mask_heavy = augmented['mask']
+ax[0,6].imshow(image_heavy,cmap='gray')
+ax[0,6].axis('off')
+
+
+ax[1,6].imshow(mask_heavy,cmap='CMRmap')
+ax[1,6].axis('off')
+plt.tight_layout()
+plt.subplots_adjust(wspace=0,hspace=0)
+plt.show()
+# f.savefig('a.png',bbox_inches='tight')
+
+
+# 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('train','aug_train'),'aug_v2'+filename),image_heavy)
+#     # cv2.imwrite(os.path.join(label_path.replace('train', 'aug_train'),
+#     #                          'aug_v2' + filename), mask_heavy)
+# visualize(image_heavy, mask_heavy, original_image=image,
+#           original_mask=mask)
+    # visualize(image, mask)
+# print(type(image),image_heavy.shape)
+
+# break

Task02_Heart/data_proc_for_3d.py

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+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')
+    for path in image_paths:
+        folder = 'mri_train_2d1'
+        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
+        image = np.array(image, dtype=np.int8)
+        image = np.swapaxes(image, 1, 2)
+        image = np.swapaxes(image, 0, 1)
+        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()
+    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
+
+
+class NIIDataset(Dataset):
+    def __init__(self, path,resize_shape):
+        super().__init__()
+        self.image_paths = glob.glob(path)
+        label_path=path[:-8]+'pos_labels.npy'
+        self.labels=np.load(label_path)
+        self.resize_shape=resize_shape
+    def __len__(self):
+        return len(self.image_paths)
+    def __getitem__(self, index):
+        image=np.array(nib.load(self.image_paths[index]).dataobj)
+        image=transform.resize(image,output_shape=self.resize_shape)[np.newaxis,:]
+        label=self.labels[index]
+        return image,label
+
+if __name__ == '__main__':
+    # handle_image_and_label()
+    dataset = NIIDataset(path='./imagesTr/*.nii.gz',resize_shape=(128,320,320))
+    dataloader=DataLoader(dataset,batch_size=2,shuffle=True)
+    test_dataloader=DataLoader(dataset,batch_size=1)
+    device="cuda" if torch.cuda.is_available() else "cpu"
+    print(device)
+    model=HalfVNet().to(device)
+    criterion=torch.nn.L1Loss(reduction='sum').to(device)
+    optimizer=torch.optim.AdamW(model.parameters(),lr=0.001)
+    scaler=GradScaler()
+    EPOCHS=100
+
+    # weights=torch.load('./weights/vnet_100.pth')
+    # model.load_state_dict(weights)
+
+    TRAIN=True
+    TEST=True
+    if TRAIN:
+        for epoch in range(1,EPOCHS+1):
+            model.train()
+            losses=[]
+            train_bar=tqdm(dataloader,file=sys.stdout)
+            for step,(images,labels) in enumerate(train_bar):
+                with autocast():
+                    images=images.to(device)
+                    labels=labels.to(torch.float32).to(device)
+                    output=model(images)
+
+                    optimizer.zero_grad()
+                    loss=criterion(output,labels)
+                    # loss.backward()
+                    # optimizer.step()
+                    scaler.scale(loss).backward()
+                    scaler.step(optimizer)
+                    scaler.update()
+                losses.append(loss.item())
+            # print(f"epoch:{epoch},mean loss:{sum(losses)/len(losses)}")
+                train_bar.set_postfix(epoch=epoch,step=step,step_loss=loss.item(),mean_loss=sum(losses)/len(losses))
+            if epoch%10==0:
+                torch.save(model.state_dict(),f'./weights/vnet_{epoch}.pth')
+    if TEST:
+        weights=torch.load('./weights/vnet_100.pth')
+        model.load_state_dict(weights)
+        model.eval()
+        for step,(images,labels) in enumerate(dataloader):
+            with autocast():
+                images = images.to(device)
+                labels = labels.to(torch.float32).to(device)
+                output = model(images)
+                print("labels:",labels)
+                print("predicts:",output)

Task02_Heart/dataprocess.py

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+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
+#todo:先裁剪出bounding box，在resize成统一大小
+# from imblearn.over_sampling import SMOTE
+
+# image_paths=glob.glob('./train/label/*.png')
+# cnt_mp={0:0,1:0,2:0,3:0,4:0,5:0,6:0,7:0}
+# for path in image_paths:
+#     image=cv2.imread(path)[:,:,0]
+#     for i in cnt_mp:
+#         cnt_mp[i]+=np.sum(image==i)
+#
+#     # break
+# print(cnt_mp)
+# cnt_mp.pop(0)
+# cnt=0
+# for i in cnt_mp:
+#     cnt+=cnt_mp[i]
+# for i in cnt_mp:
+#     print(i,(cnt/len(cnt_mp)/cnt_mp[i]))
+
+
+# image_paths=glob.glob('./mr_train/*_image.nii.gz')
+# for path in image_paths:
+#     # print(path)
+#
+#     filename=path.split('\\')[-1].split('.')[0]
+#
+#     print(filename)
+#     image=nib.load(path).dataobj
+#     image=np.floor((image-np.min(image))/(np.max(image)-np.min(image))*255)
+#     # image=zoom(image,[256/image.shape[0],256/image.shape[1],128/image.shape[2]],order=0)
+#     # print(image.dtype)
+#
+#     for i in range(image.shape[-1]):
+#
+#         cv2.imwrite(os.path.join('train/image',f'{filename}_{i}.png'),image[:,:,i])
+#
+#     # break
+# #
+
+# os.path.join('')
+# image_paths=glob.glob('./ct_train/*_image.nii.gz')
+# for path in image_paths:
+#     data_info=nib.load(path)
+#     h_,w_,d_=data_info.header['pixdim'][1:4]
+#     h,w,d=data_info.shape
+#     data=data_info.get_fdata()
+#
+#     print(data.shape)
+#     print('实际大小',int(h*h_),int(w*w_),int(d*d_))
+#     # part_image=data[data!=-1024]
+#     # data=(data-np.mean(data))/np.std(data)
+#     # data=cv2.imread('../xm12/train_image/01001.png')
+#     # data=(data-np.mean(part_image))/np.std(part_image)
+#     print('取值范围', data.min(), data.max())
+#     # plt.imshow(data[:, :, 0],cmap='gray')
+#     # plt.show()
+#
+#     # nonzero_mask = np.zeros(data.shape[1:], dtype=bool)
+#     # for c in range(data.shape[0]):
+#     #     this_mask = data[c] != 0
+#     #     nonzero_mask = nonzero_mask | this_mask
+#     # nonzero_mask = binary_fill_holes(nonzero_mask)
+#     break
+def handle_image_and_label():
+    cnt=0
+    image_paths=glob.glob(r'C:\Users\zhang\PycharmProjects\mmsegmentation\data\Task02_Heart\labelsTr\*.nii.gz')
+    for path in image_paths:
+        # print(path)
+        # if cnt<16:
+        #     folder='mri_train_2d'
+        # else:
+        #     folder='mri_test_2d'
+        folder='mri_train_2d'
+        filename=path.split('\\')[-1].split('.')[0].replace('label','image')
+
+        print(filename)
+
+        image=nib.load(path).dataobj
+        image=np.array(image,dtype=np.int8)
+        # print(image.shape)
+        # print(np.unique(image))
+        '''
+        label_map = [0, 1]
+
+        for i, v in enumerate(label_map):
+            image = np.where(image == v, i, image)
+        image = np.where(image == 421, 2, image)
+
+        # 能用的label-resize
+        rows, cols, dim = 256,256,image.shape[-1]
+        orig_rows, orig_cols, orig_dim = image.shape
+
+        row_scale = float(orig_rows) / rows
+        col_scale = float(orig_cols) / cols
+        dim_scale = float(orig_dim) / dim
+
+        map_rows, map_cols, map_dims = np.mgrid[:rows, :cols, :dim]
+        map_rows = row_scale * (map_rows + 0.5) - 0.5
+        map_cols = col_scale * (map_cols + 0.5) - 0.5
+        map_dims = dim_scale * (map_dims + 0.5) - 0.5
+
+        coord_map = np.array([map_rows, map_cols, map_dims])
+
+
+        image=map_coordinates(image, coord_map, order=1)
+        '''
+        # 自己写的label-resize
+        # print(image.shape,type(image),image.dtype,np.unique(image))
+        # break
+        # print(np.unique(image))
+
+        # final_index=[]
+        # # print(np.unique(image,axis=0).shape)
+        # # print(np.unique(image, axis=1).shape)
+        # # print(np.unique(image, axis=2).shape)
+        # temp_index=[]
+        # for i in range(0,image.shape[0]):
+        #     if len(np.unique(image[i,:,:]))!=1:
+        #         temp_index.append(i)
+        #         break
+        # for i in range(image.shape[0]-1,0,-1):
+        #     if len(np.unique(image[i,:,:])) != 1:
+        #         temp_index.append(i)
+        #         break
+        # final_index.append(temp_index)
+        #
+        # temp_index = []
+        # for i in range(0,image.shape[1]):
+        #     if len(np.unique(image[:, i, :])) != 1:
+        #         temp_index.append(i)
+        #         break
+        # for i in range(image.shape[1] - 1, 0, -1):
+        #     if len(np.unique(image[:, i, :])) != 1:
+        #         temp_index.append(i)
+        #         break
+        # final_index.append(temp_index)
+        #
+        # temp_index = []
+        # for i in range(0,image.shape[2]):
+        #     if len(np.unique(image[:, :, i])) != 1:
+        #         temp_index.append(i)
+        #         break
+        # for i in range(image.shape[2] - 1, 0, -1):
+        #     if len(np.unique(image[:, :, i])) != 1:
+        #         temp_index.append(i)
+        #         break
+        # final_index.append(temp_index)
+        #
+        # print(final_index)
+
+        # image=image[final_index[0][0]:final_index[0][1],
+        #       final_index[1][0]:final_index[1][1],
+        #       final_index[2][0]:final_index[2][1]]
+
+        # 1 2 3
+
+        # 注意用保存为图片时，数值类型不要unsigned
+        image=image.astype(np.int8)
+        print(np.unique(image),type(image),image.shape,image.dtype)
+        # print(np.unique(image[:,:,60]))
+        for i in range(image.shape[-1]):
+            cv2.imwrite(os.path.join(f'{folder}/label',f'{filename}_{i}.png'),image[:,:,i])
+
+        # np.save(os.path.join('./train-3d/label',filename),image)
+
+        # if len(np.unique(image>=3)):
+            # print(image.shape)
+            # for i in range(image.shape[-1]):
+            #     cv2.imwrite(os.path.join('train/label',f'{filename}_{i}.png'),image[:,:,i])
+        # filename=filename.replace('label','image')
+
+        image = np.array(nib.load(os.path.join('imagesTr', filename + '.nii.gz')).dataobj)
+        print(np.unique(image))
+
+        # f,ax=plt.subplots(2,1)
+        # ax[0].imshow(image[:,:,0])
+
+
+        # ct处理方式
+        # image=((image+1024)/4095)*255
+        # mri处理方式
+        image=((image-np.min(image))/(np.max(image)-np.min(image)))*255
+
+
+        # print(np.unique(image))
+        # ax[1].imshow(image[:,:,0])
+        # plt.show()
+        # print(image[:,:,0][128])
+            # image = np.floor(
+            #     (image - np.min(image)) / (np.max(image) - np.min(image)) * 255)
+        # image = image[final_index[0][0]:final_index[0][1],
+        #         final_index[1][0]:final_index[1][1],
+        #         final_index[2][0]:final_index[2][1]]
+        # image=(image-np.min(image))/(np.max(image)-np.min(image))*255
+
+        # 能用的image-resize
+        '''
+        image=skimage.transform.resize(image,(256,256,image.shape[-1]),order=3)
+'''
+        # image=image.astype(np.float32)
+        # np.save(os.path.join('./train-3d/image',filename),image)
+        print(image.dtype)
+        # print(np.unique(image[:,:,60]))
+        for i in range(image.shape[-1]):
+            cv2.imwrite(os.path.join(f'{folder}/image',f'{filename}_{i}.png'),image[:,:,i])
+        cnt+=1
+        # break
+if __name__ == '__main__':
+
+    # paths=glob.glob(r'C:\Users\zhang\PycharmProjects\mmsegmentation\data\Task02_Heart\imagesTr\*.nii.gz')
+    # for p in paths:
+    #
+    #     img=nib.load(p).dataobj
+    #     print(np.min(img),np.max(img))
+
+
+    handle_image_and_label()
+
+    img=cv2.imread('./mri_train_2d/image/la_003_60.png',0)
+    label=cv2.imread('./mri_train_2d/label/la_003_60.png',0)
+    print(np.unique(label))
+    plt.subplot(1,2,1)
+    plt.imshow(img)
+    plt.subplot(1,2,2)
+    plt.imshow(label,cmap='gray',interpolation='none')
+    plt.show()
+
+    # img_3d=nib.load('./mr_train/mr_train_1011_image.nii.gz').dataobj
+    # label_3d=nib.load('./mr_train/mr_train_1011_label.nii.gz').dataobj
+    #
+    # # print(img_3d.shape)
+    # img_slice=img_3d[:,:,90]
+    #
+    # label_slice=label_3d[:,:,90]
+    # # label_slice=np.where(label_slice==420,2,label_slice)
+    # # label_slice = np.where(label_slice == 850, 7, label_slice)
+    # print(np.unique(label_slice))
+    # fig,ax=plt.subplots(1,2)
+    # ax[0].imshow(img_slice,cmap='gray')
+    # ax[1].imshow(label_slice,cmap='CMRmap')
+    # plt.show()

Task02_Heart/dataset.json

@@ -0,0 +1,19 @@
+{ 
+ "name": "LeftAtrium", 
+ "description": "Left atrium segmentation",
+"tensorImageSize": "3D",
+"reference": "King’s College London",
+"licence":"CC-BY-SA 4.0",
+"relase":"1.0 04/05/2018",
+"modality": { 
+	 "0": "MRI"
+ }, 
+ "labels": { 
+	 "0": "background", 
+	 "1": "left atrium"
+ }, 
+ "numTraining": 20, 
+ "numTest": 10,
+ "training":[{"image":"./imagesTr/la_007.nii.gz","label":"./labelsTr/la_007.nii.gz"},{"image":"./imagesTr/la_019.nii.gz","label":"./labelsTr/la_019.nii.gz"},{"image":"./imagesTr/la_023.nii.gz","label":"./labelsTr/la_023.nii.gz"},{"image":"./imagesTr/la_005.nii.gz","label":"./labelsTr/la_005.nii.gz"},{"image":"./imagesTr/la_009.nii.gz","label":"./labelsTr/la_009.nii.gz"},{"image":"./imagesTr/la_017.nii.gz","label":"./labelsTr/la_017.nii.gz"},{"image":"./imagesTr/la_021.nii.gz","label":"./labelsTr/la_021.nii.gz"},{"image":"./imagesTr/la_029.nii.gz","label":"./labelsTr/la_029.nii.gz"},{"image":"./imagesTr/la_003.nii.gz","label":"./labelsTr/la_003.nii.gz"},{"image":"./imagesTr/la_011.nii.gz","label":"./labelsTr/la_011.nii.gz"},{"image":"./imagesTr/la_030.nii.gz","label":"./labelsTr/la_030.nii.gz"},{"image":"./imagesTr/la_022.nii.gz","label":"./labelsTr/la_022.nii.gz"},{"image":"./imagesTr/la_014.nii.gz","label":"./labelsTr/la_014.nii.gz"},{"image":"./imagesTr/la_018.nii.gz","label":"./labelsTr/la_018.nii.gz"},{"image":"./imagesTr/la_020.nii.gz","label":"./labelsTr/la_020.nii.gz"},{"image":"./imagesTr/la_004.nii.gz","label":"./labelsTr/la_004.nii.gz"},{"image":"./imagesTr/la_016.nii.gz","label":"./labelsTr/la_016.nii.gz"},{"image":"./imagesTr/la_024.nii.gz","label":"./labelsTr/la_024.nii.gz"},{"image":"./imagesTr/la_010.nii.gz","label":"./labelsTr/la_010.nii.gz"},{"image":"./imagesTr/la_026.nii.gz","label":"./labelsTr/la_026.nii.gz"}],
+ "test":["./imagesTs/la_015.nii.gz","./imagesTs/la_025.nii.gz","./imagesTs/la_013.nii.gz","./imagesTs/la_001.nii.gz","./imagesTs/la_027.nii.gz","./imagesTs/la_006.nii.gz","./imagesTs/la_008.nii.gz","./imagesTs/la_012.nii.gz","./imagesTs/la_028.nii.gz","./imagesTs/la_002.nii.gz"]
+ }

Task02_Heart/get_precise_seg_part.py

@@ -0,0 +1,111 @@
+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()

Task02_Heart/half_vnet.py

@@ -0,0 +1,207 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def passthrough(x, **kwargs):
+    return x
+
+def ELUCons(elu, nchan):
+    if elu:
+        return nn.ELU(inplace=True)
+    else:
+        return nn.PReLU(nchan)
+
+# normalization between sub-volumes is necessary
+# for good performance
+class ContBatchNorm3d(nn.modules.batchnorm._BatchNorm):
+    def _check_input_dim(self, input):
+        if input.dim() != 5:
+            raise ValueError('expected 5D input (got {}D input)'
+                             .format(input.dim()))
+        super(ContBatchNorm3d, self)._check_input_dim(input)
+
+    def forward(self, input):
+        self._check_input_dim(input)
+        return F.batch_norm(
+            input, self.running_mean, self.running_var, self.weight, self.bias,
+            True, self.momentum, self.eps)
+
+
+class LUConv(nn.Module):
+    def __init__(self, nchan, elu):
+        super(LUConv, self).__init__()
+        self.relu1 = ELUCons(elu, nchan)
+        self.conv1 = nn.Conv3d(nchan, nchan, kernel_size=5, padding=2)
+        self.bn1 = nn.BatchNorm3d(nchan)
+
+    def forward(self, x):
+        out = self.relu1(self.bn1(self.conv1(x)))
+        return out
+
+
+def _make_nConv(nchan, depth, elu):
+    layers = []
+    for _ in range(depth):
+        layers.append(LUConv(nchan, elu))
+    return nn.Sequential(*layers)
+
+
+class InputTransition(nn.Module):
+    def __init__(self, outChans, elu):
+        super(InputTransition, self).__init__()
+        self.conv1 = nn.Conv3d(1, 16, kernel_size=5, padding=2)
+        self.bn1 = nn.BatchNorm3d(16)
+        self.relu1 = ELUCons(elu, 16)
+
+    def forward(self, x):
+        # do we want a PRELU here as well?
+        out=self.conv1(x)
+        out = self.bn1(out)
+        # split input in to 16 channels
+        x16 = torch.cat((x, x, x, x, x, x, x, x,
+                         x, x, x, x, x, x, x, x), 1)
+        out = self.relu1(torch.add(out, x16))
+        return out
+
+
+class DownTransition(nn.Module):
+    def __init__(self, inChans, nConvs, elu, dropout=False):
+        super(DownTransition, self).__init__()
+        outChans = 2*inChans
+        self.down_conv = nn.Conv3d(inChans, outChans, kernel_size=2, stride=2)
+        self.bn1 = nn.BatchNorm3d(outChans)
+        self.do1 = passthrough
+        self.relu1 = ELUCons(elu, outChans)
+        self.relu2 = ELUCons(elu, outChans)
+        if dropout:
+            self.do1 = nn.Dropout3d()
+        self.ops = _make_nConv(outChans, nConvs, elu)
+
+    def forward(self, x):
+        down = self.relu1(self.bn1(self.down_conv(x)))
+        out = self.do1(down)
+        out = self.ops(out)
+        out = self.relu2(torch.add(out, down))
+        return out
+
+
+class UpTransition(nn.Module):
+    def __init__(self, inChans, outChans, nConvs, elu, dropout=False):
+        super(UpTransition, self).__init__()
+        self.up_conv = nn.ConvTranspose3d(inChans, outChans // 2, kernel_size=2, stride=2)
+        self.bn1 = nn.BatchNorm3d(outChans // 2)
+        self.do1 = passthrough
+        self.do2 = nn.Dropout3d()
+        self.relu1 = ELUCons(elu, outChans // 2)
+        self.relu2 = ELUCons(elu, outChans)
+        if dropout:
+            self.do1 = nn.Dropout3d()
+        self.ops = _make_nConv(outChans, nConvs, elu)
+
+    def forward(self, x, skipx):
+        out = self.do1(x)
+        skipxdo = self.do2(skipx)
+        out = self.relu1(self.bn1(self.up_conv(out)))
+        xcat = torch.cat((out, skipxdo), 1)
+        out = self.ops(xcat)
+        out = self.relu2(torch.add(out, xcat))
+        return out
+
+
+class OutputTransition(nn.Module):
+    def __init__(self, inChans, elu, nll):
+        super(OutputTransition, self).__init__()
+        self.conv1 = nn.Conv3d(inChans, 2, kernel_size=5, padding=2)
+        self.bn1 = nn.BatchNorm3d(2)
+        self.conv2 = nn.Conv3d(2, 2, kernel_size=1)
+        self.relu1 = ELUCons(elu, 2)
+        if nll:
+            self.softmax = F.log_softmax
+        else:
+            self.softmax = F.softmax
+
+    def forward(self, x):
+        # convolve 32 down to 2 channels
+        out = self.relu1(self.bn1(self.conv1(x)))
+        out = self.conv2(out)
+        print(out.shape)
+        # make channels the last axis
+        out = out.permute(0, 2, 3, 4, 1).contiguous()
+        # flatten
+        out = out.view(out.numel() // 2, 2)
+        out = self.softmax(out)
+        # treat channel 0 as the predicted output
+        return out
+class AdaptivePoolTransition(nn.Module):
+    def __init__(self,in_channels,out_channels):
+        super().__init__()
+        self.pool=nn.AdaptiveAvgPool3d(1)
+        self.ops=nn.Sequential(
+            nn.Linear(in_channels,in_channels//2),
+            ELUCons(elu=True,nchan=0),
+            nn.Linear(in_channels // 2,out_channels),
+            nn.Sigmoid()
+        )
+    def forward(self,x):
+        B,C,D,H,W=x.shape
+        out=self.pool(x).view(B,C)
+        return self.ops(out)
+
+class HalfVNet(nn.Module):
+    # the number of convolutions in each layer corresponds
+    # to what is in the actual prototxt, not the intent
+    def __init__(self, elu=True, nll=False):
+        super(HalfVNet, self).__init__()
+        self.in_tr = InputTransition(16, elu)
+        self.down_tr32 = DownTransition(16, 1, elu)
+        self.down_tr64 = DownTransition(32, 2, elu)
+        self.down_tr128 = DownTransition(64, 3, elu, dropout=True)
+        self.down_tr256 = DownTransition(128, 2, elu, dropout=True)
+        # self.up_tr256 = UpTransition(256, 256, 2, elu, dropout=True)
+        # self.up_tr128 = UpTransition(256, 128, 2, elu, dropout=True)
+        # self.up_tr64 = UpTransition(128, 64, 1, elu)
+        # self.up_tr32 = UpTransition(64, 32, 1, elu)
+        # self.out_tr = OutputTransition(32, elu, nll)
+        self.pool=AdaptivePoolTransition(256,6)
+    # The network topology as described in the diagram
+    # in the VNet paper
+    # def __init__(self):
+    #     super(VNet, self).__init__()
+    #     self.in_tr =  InputTransition(16)
+    #     # the number of convolutions in each layer corresponds
+    #     # to what is in the actual prototxt, not the intent
+    #     self.down_tr32 = DownTransition(16, 2)
+    #     self.down_tr64 = DownTransition(32, 3)
+    #     self.down_tr128 = DownTransition(64, 3)
+    #     self.down_tr256 = DownTransition(128, 3)
+    #     self.up_tr256 = UpTransition(256, 3)
+    #     self.up_tr128 = UpTransition(128, 3)
+    #     self.up_tr64 = UpTransition(64, 2)
+    #     self.up_tr32 = UpTransition(32, 1)
+    #     self.out_tr = OutputTransition(16)
+    def forward(self, x):
+        out16 = self.in_tr(x)
+        out32 = self.down_tr32(out16)
+        out64 = self.down_tr64(out32)
+        out128 = self.down_tr128(out64)
+        out256 = self.down_tr256(out128)
+        # out = self.up_tr256(out256, out128)
+        # out = self.up_tr128(out, out64)
+        # out = self.up_tr64(out, out32)
+        # out = self.up_tr32(out, out16)
+        # out = self.out_tr(out)
+        out=self.pool(out256)
+        return out
+if __name__ == '__main__':
+    device='cuda' if torch.cuda.is_available() else 'cpu'
+    print(device)
+    net=HalfVNet().to(device)
+    image=torch.randn((1,1,128,320,320)).to(device)
+    with torch.no_grad():
+        out=net(image)
+    print(out.shape)
+    # m = nn.AdaptiveAvgPool3d((1))
+    # input = torch.randn(1, 64, 8, 9, 10)
+    # output = m(input)
+    # print(output.shape)

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