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import os
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import shutil
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from time import time
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import numpy as np
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import SimpleITK as sitk
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import nibabel as nib
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import scipy.ndimage as ndimage
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import h5py
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splits = ['train', 'test']
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# 这里根据你的截图路径进行了修改
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# 请确认 data/synapse/Abdomen/RawData/TrainSet 下面是否有 img 和 label 文件夹
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# 如果没有，而是所有文件都在 TrainSet 根目录下，你需要手动把图片放入 img，标签放入 label
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for split in splits:
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if(split == 'train'):
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ct_path = './data/synapse/Abdomen/RawData/TrainSet/img'
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seg_path = './data/synapse/Abdomen/RawData/TrainSet/label'
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save_path = './data/synapse/train_npz/' # 修改为标准输出路径
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else:
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ct_path = './data/synapse/Abdomen/RawData/TestSet/img'
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seg_path = './data/synapse/Abdomen/RawData/TestSet/label'
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save_path = './data/synapse/test_vol_h5/' # 修改为标准输出路径
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if os.path.exists(save_path) is False:
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os.makedirs(save_path) # 使用 makedirs 以防父目录不存在
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upper = 275
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lower = -125
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start_time = time()
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if not os.path.exists(ct_path):
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print(f"Error: 找不到路径 {ct_path}，请检查你的文件夹结构是否包含 img 子文件夹")
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continue
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for ct_file in os.listdir(ct_path):
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# 过滤掉非 nii 文件
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if not ct_file.endswith('.nii.gz'):
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continue
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ct = nib.load(os.path.join(ct_path, ct_file))
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seg = nib.load(os.path.join(seg_path, ct_file.replace('img', 'label')))
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#Convert them to numpy format,
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ct_array = ct.get_fdata()
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seg_array = seg.get_fdata()
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ct_array = np.clip(ct_array, lower, upper)
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#normalize each 3D image to [0, 1]
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ct_array = (ct_array - lower) / (upper - lower)
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ct_array = np.transpose(ct_array, (2, 0, 1))
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seg_array = np.transpose(seg_array, (2, 0, 1))
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print('Processing:', ct_file, 'Shape:', ct_array.shape)
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ct_number = ct_file.split('.')[0]
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if(split == 'test'):
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new_ct_name = ct_number.replace('img', 'case')+'.npy.h5'
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hf = h5py.File(os.path.join(save_path, new_ct_name), 'w')
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hf.create_dataset('image', data=ct_array)
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hf.create_dataset('label', data=seg_array)
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hf.close()
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continue
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for s_idx in range(ct_array.shape[0]):
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ct_array_s = ct_array[s_idx, :, :]
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seg_array_s = seg_array[s_idx, :, :]
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# 过滤掉全黑的切片，节省空间（可选，这里保持原逻辑）
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slice_no = "{:03d}".format(s_idx)
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new_ct_name = ct_number.replace('img', 'case') + '_slice' + slice_no
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np.savez(os.path.join(save_path, new_ct_name), image=ct_array_s, label=seg_array_s)
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print('Already used {:.3f} min'.format((time() - start_time) / 60))
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print('-----------') |
