将列表转换为 Numpy 数组仅使用一个数据集丢失其三个轴中的两个
Convert a list into a Numpy array lose two of its three axis only with one dataset
我有一个 python 代码可以使用 SimpleITK 库读取 NIFTI 图像。然后它将这些图像转换为 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 个文件。他们每个人都有48个切片。
当我拥有所有 20 名患者的所有 48 个切片时，我将列表转换为 Numpy 数组。
我这样做是因为我是 Python 的新手，我不知道任何其他方法。
密码是：
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 个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
代码的最终输出是：
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：
我不明白为什么 flair_array 有这个形状：(960,)。
flair_array dtype 是 object.
我已经尝试过相同的代码，没有做任何更改，并且运行良好。它也有 20 个患者，每个 FLAIR.nii.gz 文件也有 48 个切片。
其输出：
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_dataset 列表。
所以在 flair_dataset 中有一个数组，所有 960 张图像都堆叠在一起。每张图片大小为240, 240.
你是批量加载图片，还是在图片上包含48层的深度？
如果是这样，请尝试附加数组而不是扩展它。然后你把加载的数组放在它的列表条目中。
一种情况
flair_array.shape: (960,)
flair_array.dtype: object
在另一个
flair_array.shape: (960, 240, 240)
flair_array.dtype: float32
你制作这些：
flair_array = np.array(flair_dataset)
如果flair_dataset的所有元素具有相同的形状，它可以从它们创建一个多维数组。
但是如果列表中的一个或多个数组的形状不同，它就不得不放弃多维目标，而只是制作一个对象 dtype 数组，它非常像一个列表——包含引用到原始数组。
原始列表中的大部分元素是
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
但我也看到了一些
brain_image size: (256, 232, 48)
brain_array Shape: (48, 232, 256)
第二组都是
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
当人们询问 (n,) 形状时，当他们期望 (n,m,p) 时，我怀疑第一个具有由元素形状混合引起的 object dtype。这就是我问 dtype.
的原因
我有一个 python 代码可以使用 SimpleITK 库读取 NIFTI 图像。然后它将这些图像转换为 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 个文件。他们每个人都有48个切片。
当我拥有所有 20 名患者的所有 48 个切片时，我将列表转换为 Numpy 数组。
我这样做是因为我是 Python 的新手，我不知道任何其他方法。
密码是：
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 个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
代码的最终输出是：
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：
我不明白为什么 flair_array 有这个形状：(960,)。
flair_array dtype 是 object.
我已经尝试过相同的代码，没有做任何更改，并且运行良好。它也有 20 个患者，每个 FLAIR.nii.gz 文件也有 48 个切片。
其输出：
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_dataset 列表。
所以在 flair_dataset 中有一个数组，所有 960 张图像都堆叠在一起。每张图片大小为240, 240.
你是批量加载图片，还是在图片上包含48层的深度？ 如果是这样，请尝试附加数组而不是扩展它。然后你把加载的数组放在它的列表条目中。
一种情况
flair_array.shape: (960,)
flair_array.dtype: object
在另一个
flair_array.shape: (960, 240, 240)
flair_array.dtype: float32
你制作这些：
flair_array = np.array(flair_dataset)
如果flair_dataset的所有元素具有相同的形状，它可以从它们创建一个多维数组。
但是如果列表中的一个或多个数组的形状不同，它就不得不放弃多维目标，而只是制作一个对象 dtype 数组，它非常像一个列表——包含引用到原始数组。
原始列表中的大部分元素是
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
但我也看到了一些
brain_image size: (256, 232, 48)
brain_array Shape: (48, 232, 256)
第二组都是
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
当人们询问 (n,) 形状时，当他们期望 (n,m,p) 时，我怀疑第一个具有由元素形状混合引起的 object dtype。这就是我问 dtype.