我有一个python代码,它使用SimpleTk库读取NIFTI图像。然后将这些图像转换为Numpy数组。然后,我将Numpy数组扩展到一个列表中
我有20个FLAIR.nii.gz文件。每个都有48片
当我拥有所有20名患者的全部48个切片时,我将列表转换为一个Numpy数组
我这样做是因为我是Python的新手,我不知道其他的方法
代码是:
import os
import SimpleITK as sitk
import numpy as np
flair_dataset = []
# For each patient directory
# data_path is a list with all of the patient's directory.
for i in data_path:
img_path = os.path.join(file_path, i, 'pre')
mask_path = os.path.join(file_path, i)
for name in glob.glob(img_path+'/FLAIR*'):
# Reads images using SimpleITK.
brain_image = sitk.ReadImage(name)
# Get a numpy array from a SimpleITK Image.
brain_array = sitk.GetArrayFromImage(brain_image)
flair_dataset.extend(brain_array)
if debug:
print('brain_image size: ', brain_image.GetSize())
print('brain_array Shape: ', brain_array.shape)
print('flair_dataset length:', len(flair_dataset))
print('flair_dataset length: ', len(flair_dataset))
print('flair_dataset[1] type: ', print(type(flair_dataset[1])))
print('flair_dataset[1] shape: ', print(flair_dataset[1].shape))
flair_array = np.array(flair_dataset)
print('flair_array.shape: ', flair_array.shape)
print('flair_array.dtype: ', flair_array.dtype)
此代码生成此输出(所有FLAIR.nii.gz文件具有相同的形状):
data_path = ['68', '55', '50', '61', '63', '52', '51', '60', '67', '58', '59', '53', '69', '64', '56', '65', '54', '62', '66', '57']
patient_data_path = 68
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
flair_dataset length: 48
Mask list length: 48
patient_data_path = 55
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
flair_dataset length: 96
Mask list length: 96
patient_data_path = 50
brain_image size: (256, 232, 48)
brain_array Shape: (48, 232, 256)
flair_dataset length: 144
WMH image Size: (256, 232, 48)
WMH array Shape: (48, 232, 256)
Mask list length: 144
patient_data_path = 61
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
flair_dataset length: 192
Mask list length: 192
patient_data_path = 63
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
flair_dataset length: 240
Mask list length: 240
patient_data_path = 52
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
flair_dataset length: 288
Mask list length: 288
patient_data_path = 51
brain_image size: (256, 232, 48)
brain_array Shape: (48, 232, 256)
flair_dataset length: 336
WMH image Size: (256, 232, 48)
WMH array Shape: (48, 232, 256)
Mask list length: 336
patient_data_path = 60
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
flair_dataset length: 384
Mask list length: 384
patient_data_path = 67
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
flair_dataset length: 432
Mask list length: 432
patient_data_path = 58
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
flair_dataset length: 480
Mask list length: 480
patient_data_path = 59
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
flair_dataset length: 528
Mask list length: 528
patient_data_path = 53
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
flair_dataset length: 576
Mask list length: 576
patient_data_path = 69
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
flair_dataset length: 624
Mask list length: 624
patient_data_path = 64
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
flair_dataset length: 672
Mask list length: 672
patient_data_path = 56
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
flair_dataset length: 720
Mask list length: 720
patient_data_path = 65
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
flair_dataset length: 768
Mask list length: 768
patient_data_path = 54
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
flair_dataset length: 816
Mask list length: 816
patient_data_path = 62
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
flair_dataset length: 864
Mask list length: 864
patient_data_path = 66
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
flair_dataset length: 912
Mask list length: 912
patient_data_path = 57
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
flair_dataset length: 960
Mask list length: 960
代码的最终输出为:
flair_dataset length: 960
mask_dataset length: 960
flair_dataset[1] type: <class 'numpy.ndarray'>
flair_dataset[1] shape: (256, 232)
flair_array.shape: (960,)
flair_array.dtype: object
我的问题是:
我不明白为什么flair_数组有这种形状:(960,)
。
flair_array dtype
是object
我尝试了相同的代码,没有做任何更改,它工作得非常完美。它也有20名患者,每个FLAIR.nii.gz文件也有48个切片
其产出:
data_path = ['39', '31', '2', '23', '35', '29', '17', '49', '27', '8', '33', '4', '19', '41', '37', '11', '25', '6', '0', '21']
patient_data_path = 39
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 48
Mask list length: 48
patient_data_path = 31
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 96
Mask list length: 96
patient_data_path = 2
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 144
Mask list length: 144
patient_data_path = 23
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 192
Mask list length: 192
patient_data_path = 35
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 240
Mask list length: 240
patient_data_path = 29
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 288
Mask list length: 288
patient_data_path = 17
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 336
Mask list length: 336
patient_data_path = 49
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 384
Mask list length: 384
patient_data_path = 27
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 432
Mask list length: 432
patient_data_path = 8
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 480
Mask list length: 480
patient_data_path = 33
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 528
Mask list length: 528
patient_data_path = 4
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 576
Mask list length: 576
patient_data_path = 19
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 624
Mask list length: 624
patient_data_path = 41
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 672
Mask list length: 672
patient_data_path = 37
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 720
Mask list length: 720
patient_data_path = 11
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 768
Mask list length: 768
patient_data_path = 25
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 816
Mask list length: 816
patient_data_path = 6
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 864
Mask list length: 864
patient_data_path = 0
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 912
Mask list length: 912
patient_data_path = 21
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 960
Mask list length: 960
这是此数据集的最终输出:
flair_dataset length: 960
mask_dataset length: 960
flair_dataset[1] type: <class 'numpy.ndarray'>
flair_dataset[1] shape: (240, 240)
flair_array.shape: (960, 240, 240)
flair_array.dtype: float32
对于第二个数据集,flair_array
是float32
为什么第一个flair_array
形状是(960,)
更新:
在这两个数据集中,brain_array.dtype
总是float32
我认为
flair_dataset.extend(brain_array)
只是用加载的数组扩展了flair_数据集列表。 因此,在flair_dataset
中,有一个包含所有960个图像的on数组。每个图像的大小为240240您是批量加载图像,还是在图像上包含48层的深度? 如果是这样,请尝试追加数组而不是扩展它。然后将加载的数组放入其on列表条目中
有一次
另一方面
您可以通过以下方式制作:
如果
flair_dataset
的所有元素具有相同的形状,则可以从它们创建多维数组但是,如果列表中的一个或多个数组形状不同,它必须放弃多维目标,而只是生成一个对象dtype数组,它非常类似于列表,其中包含对原始数组的引用
在原始列表中,大多数元素是
但我也看到了一些
在第二盘中,所有人都是
当人们询问(n,)形状时,当他们期望(n,m,p)时,我怀疑第一个有一个
object
数据类型,这是由元素形状的混合引起的。这就是为什么我问dtype
相关问题 更多 >
编程相关推荐