Complex Matlab struct mat file read by python

问题

I know the version issues of mat files which correspond to different loading modules in python, namely scipy.io and h5py. I also searched a lot of similar problems like scipy.io.loadmat nested structures (i.e. dictionaries) and How to preserve Matlab struct when accessing in python?. But they both fail when it comes to more complex mat files. My anno_bbox.mat file structure is shown as follows:

The first two level:

In the size:

In the hoi:

In the hoi bboxhuman:

When I use spio.loadmat('anno_bbox.mat', struct_as_record=False, squeeze_me=True), it could only get the first level information as a dictionary.

>>> anno_bbox.keys()
dict_keys(['__header__', '__version__', '__globals__', 'bbox_test', 
'bbox_train', 'list_action'])
>>> bbox_test = anno_bbox['bbox_test']
>>> bbox_test.keys()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'numpy.ndarray' object has no attribute 'keys'
>>> bbox_test
array([<scipy.io.matlab.mio5_params.mat_struct object at 0x7fa8660ab128>,
   <scipy.io.matlab.mio5_params.mat_struct object at 0x7fa8660ab2b0>,
   <scipy.io.matlab.mio5_params.mat_struct object at 0x7fa8660ab710>,
   ...,
   <scipy.io.matlab.mio5_params.mat_struct object at 0x7fa8622ec4a8>,
   <scipy.io.matlab.mio5_params.mat_struct object at 0x7fa8622ecb00>,
   <scipy.io.matlab.mio5_params.mat_struct object at 0x7fa8622f1198>], dtype=object)

I don't know what to do next. It is too complicated for me. The file is available at anno_bbox.mat (8.7MB)

回答1:

I get (working from the shared file is a good idea on this case):

Loading with:

data = io.loadmat('../Downloads/anno_bbox.mat')

I get:

In [96]: data['bbox_test'].dtype
Out[96]: dtype([('filename', 'O'), ('size', 'O'), ('hoi', 'O')])
In [97]: data['bbox_test'].shape
Out[97]: (1, 9658)

I could have assigned bbox_test=data['bbox_test']. This variable has 9658 records, with three fields, each with object dtype.

So there's a filename (a string embedded in a 1 element array)

In [101]: data['bbox_test'][0,0]['filename']
Out[101]: array(['HICO_test2015_00000001.jpg'], dtype='<U26')

size has 3 fields, with 3 numbers embedded in arrays (2d matlab matrices):

In [102]: data['bbox_test'][0,0]['size']
Out[102]: 
array([[(array([[640]], dtype=uint16), array([[427]], dtype=uint16), array([[3]], dtype=uint8))]],
      dtype=[('width', 'O'), ('height', 'O'), ('depth', 'O')])
In [112]: data['bbox_test'][0,0]['size'][0,0].item()
Out[112]: 
(array([[640]], dtype=uint16),
 array([[427]], dtype=uint16),
 array([[3]], dtype=uint8))

hoi is more complicated:

In [103]: data['bbox_test'][0,0]['hoi']
Out[103]: 
array([[(array([[246]], dtype=uint8), array([[(array([[320]], dtype=uint16), array([[359]], dtype=uint16), array([[306]], dtype=uint16), array([[349]], dtype=uint16)),...
      dtype=[('id', 'O'), ('bboxhuman', 'O'), ('bboxobject', 'O'), ('connection', 'O'), ('invis', 'O')])


In [126]: data['bbox_test'][0,1]['hoi']['id']
Out[126]: 
array([[array([[132]], dtype=uint8), array([[140]], dtype=uint8),
        array([[144]], dtype=uint8)]], dtype=object)
In [130]: data['bbox_test'][0,1]['hoi']['bboxhuman'][0,0]
Out[130]: 
array([[(array([[226]], dtype=uint8), array([[340]], dtype=uint16), array([[18]], dtype=uint8), array([[210]], dtype=uint8))]],
      dtype=[('x1', 'O'), ('x2', 'O'), ('y1', 'O'), ('y2', 'O')])

So the data that you show in the MATLAB structures is all there, in a nested structure of arrays (often 2d (1,1) shape), object dtype or multiple fields.

Going back and loading with squeeze_me I get a simpler:

In [133]: data['bbox_test'][1]['hoi']['bboxhuman']
Out[133]: 
array([array((226, 340, 18, 210),
      dtype=[('x1', 'O'), ('x2', 'O'), ('y1', 'O'), ('y2', 'O')]),
       array((230, 356, 19, 212),
      dtype=[('x1', 'O'), ('x2', 'O'), ('y1', 'O'), ('y2', 'O')]),
       array((234, 342, 13, 202),
      dtype=[('x1', 'O'), ('x2', 'O'), ('y1', 'O'), ('y2', 'O')])],
      dtype=object)

With struct_as_record='False', I get

In [136]: data['bbox_test'][1]
Out[136]: <scipy.io.matlab.mio5_params.mat_struct at 0x7f90841e9748>

Looking at the attributes of this rec I see I can access 'fields' by attribute name:

In [137]: rec = data['bbox_test'][1]
In [138]: rec.filename
Out[138]: 'HICO_test2015_00000002.jpg'
In [139]: rec.size
Out[139]: <scipy.io.matlab.mio5_params.mat_struct at 0x7f90841e9b38>

In [141]: rec.size.width
Out[141]: 640
In [142]: rec.hoi
Out[142]: 
array([<scipy.io.matlab.mio5_params.mat_struct object at 0x7f90841e9be0>,
       <scipy.io.matlab.mio5_params.mat_struct object at 0x7f90841e9e10>,
       <scipy.io.matlab.mio5_params.mat_struct object at 0x7f90841ee0b8>],
      dtype=object)

In [145]: rec.hoi[1].bboxhuman
Out[145]: <scipy.io.matlab.mio5_params.mat_struct at 0x7f90841e9f98>
In [146]: rec.hoi[1].bboxhuman.x1
Out[146]: 230

In [147]: vars(rec.hoi[1].bboxhuman)
Out[147]: 
{'_fieldnames': ['x1', 'x2', 'y1', 'y2'],
 'x1': 230,
 'x2': 356,
 'y1': 19,
 'y2': 212}

and so on.

来源：https://stackoverflow.com/questions/48970785/complex-matlab-struct-mat-file-read-by-python

标签

python

matlab

scipy

mat