如何解决Python中的IndexError: index 440 is out ofbounds for axis 0 with size 440？

Question

以下方法正是这样做的，获取多个水平切片并将它们放入二维矩阵中

def slices_matrix_2D(img):
  ''' Transform a 3D MRI image into a 2D image, by obtaining 9 slices 
      and placing them in a 4x4 two-dimensional grid.
      
      All 16 cuts are from a horizontal/axial view. They are selected
      from the 30th to the 60th level of the original 3D image.
      
      Parameters:
        img -- np.ndarray with the 3D image
        
      Returns:
        np.ndarray -- The resulting 2D image
  '''
  
  # create the final 2D image 
  image_2D = np.empty(IMG_2D_SHAPE)
  
  # set the limits and the step
  TOP = 60
  BOTTOM = 30
  STEP = 2
  N_CUTS = 16
  
  # iterator for the cuts
  cut_it = TOP
  # iterator for the rows of the 2D final image
  row_it = 0
  # iterator for the columns of the 2D final image
  col_it = 0
  
  for cutting_time in range(N_CUTS):
    
    # cut
    cut = img[cut_it, :, :]
    cut_it -= STEP
    
    # reset the row iterator and move the
    # col iterator when needed
    if cutting_time in [4, 8, 12]:
      row_it = 0
      col_it += cut.shape[1]
    
    # copy the cut to the 2D image
    for i in range(cut.shape[0]):
      for j in range(cut.shape[1]):
        image_2D[i + row_it, j + col_it] = cut[i, j]
    row_it += cut.shape[0]
  
  # return the final 2D image, with 3 channels
  # this is necessary for working with most pre-trained nets
  return np.repeat(image_2D[None, ...], 3, axis=0).T
  #return image_2D

**以下方法使用之前的 2D 转换从磁盘加载 3D 图像并对其进行转换。还返回图像标签。 **

def load_image_2D(abs_path, labels):
  ''' Load an image (.nii) and its label, from its absolute path.
      Transform it into a 2D image, by obtaining 16 slices and placing them
      in a 4x4 two-dimensional grid.
      
      Parameters:
        abs_path -- Absolute path, filename included
        labels -- Label mapper
        
      Returns:
        img -- The .nii image, converted into a numpy array
        label -- The label of the image (from argument 'labels')
        
  '''
  
  # obtain the label from the path (it is the last directory name)
  label = labels[abs_path.split('/')[-2]]
  
  # load the image with SimpleITK
  sitk_image = sitk.ReadImage(abs_path)
  
  # transform into a numpy array
  img = sitk.GetArrayFromImage(sitk_image)
  
  # apply whitening
  img = preprocessing.whitening(img)
  
  # make the 2D image
  img = slices_matrix_2D(img)
  
  return img, label

定义每个 .tfrecords 的完整文件名。

train_tfrec2D = os.path.join(DB_TF_2D_PATH, TFREC_2D_SS_TRAIN)
test_tfrec2D = os.path.join(DB_TF_2D_PATH, TFREC_2D_SS_TEST)
val_tfrec2D = os.path.join(DB_TF_2D_PATH, TFREC_2D_SS_VAL)

设计创建 .tfrecords 的方法，给定所有图像文件名、文件名和标签映射器（始终为 LABELS 常量）。它使用前面两种方法来加载图像

def create_tf_record_2D(img_filenames, tf_rec_filename, labels):
  ''' Create a TFRecord file, including the information
      of the specified images, after converting them into 
      a 2D grid.
      
      Parameters:
        img_filenames -- Array with the path to every
                         image that is going to be included
                         in the TFRecords file.
        tf_rec_filename -- Name of the TFRecords file.
        labels -- Label mapper
  '''
  
  # open the file
  writer = tf.python_io.TFRecordWriter(tf_rec_filename)
  
  # iterate through all .nii files
  for meta_data in img_filenames:

    # load the image and label
    img, label = load_image_2D(meta_data, labels)

    # create a feature
    feature = {'label': _int64_feature(label),
               'image': _float_feature(img.ravel())}

    # create an example protocol buffer
    example = tf.train.Example(features=tf.train.Features(feature=feature))

    # serialize to string and write on the file
    writer.write(example.SerializeToString())
    
  writer.close()

最后，创建.tfrecords。

create_tf_record_2D(training_set, train_tfrec2D, LABELS)
create_tf_record_2D(test_set, test_tfrec2D, LABELS)
create_tf_record_2D(validation_set, val_tfrec2D, LABELS)

Answer 1

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-119-7ac7d2b5c98e> in <module>
----> 1 create_tf_record_2D(training_set, train_tfrec2D, LABELS)
      2 create_tf_record_2D(test_set, test_tfrec2D, LABELS)
      3 create_tf_record_2D(validation_set, val_tfrec2D, LABELS)

<ipython-input-118-a58a668df074> in create_tf_record_2D(img_filenames, tf_rec_filename, labels)
     19 
     20     # load the image and label
---> 21     img, label = load_image_2D(meta_data, labels)
     22 
     23     # create a feature

<ipython-input-114-8a6e1c4008a3> in load_image_2D(abs_path, labels)
     27 
     28   # make the 2D image
---> 29   img = slices_matrix_2D(img)
     30 
     31   return img, label

<ipython-input-113-9f2419afd597> in slices_matrix_2D(img)
     44     for i in range(cut.shape[0]):
     45       for j in range(cut.shape[1]):
---> 46         image_2D[i + row_it, j + col_it] = cut[i, j]
     47     row_it += cut.shape[0]
     48 
    

IndexError：索引 440 超出尺寸 440 的轴 0 的范围