python 读取的复杂 Matlab struct mat 文件

Question

我知道python中不同加载模块对应的mat文件的版本问题，即scipy.io和h5py。我也搜了很多类似scipy.io.loadmat nested structures (i.e. dictionaries) and How to preserve Matlab struct when accessing in python?这样的问题。但是当涉及到更复杂的 mat 文件时，它们都失败了。我的anno_bbox.mat文件结构如下：

前二级：

中尺码：

在海中：

在 bboxhuman 中：

当我使用spio.loadmat('anno_bbox.mat', struct_as_record=False, squeeze_me=True)时，它只能获取第一级信息作为字典。

>>> 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)

我不知道接下来要做什么。这对我来说太复杂了。该文件位于 anno_bbox.mat (8.7MB)

Answer 1

我明白了（在这种情况下使用共享文件是个好主意）：

正在加载：

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

我得到：

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)

我本可以分配 bbox_test=data['bbox_test']。这个变量有9658条记录，三个字段，每个字段都有object dtype。

所以有一个文件名（嵌入在 1 元素数组中的字符串）

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

size 有 3 个字段，3 个数字嵌入数组（二维 matlab 矩阵）：

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比较复杂：

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')])

所以您在 MATLAB 结构中显示的数据都在那里，在数组的嵌套结构（通常是 2d (1,1) 形状）、对象 dtype 或多个字段中。

返回并加载 squeeze_me 我得到一个更简单的：

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)

有了struct_as_record='False'，我得到

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

查看此 rec 的属性，我发现我可以通过属性名称访问 'fields'：

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}

等等。

Answer 2

我已更改答案：

from scipy.io import loadmat, matlab
def load_mat(filename):
    """
    This function should be called instead of direct scipy.io.loadmat
    as it cures the problem of not properly recovering python dictionaries
    from mat files. It calls the function check keys to cure all entries
    which are still mat-objects
    """

    def _check_vars(d):
        """
        Checks if entries in dictionary are mat-objects. If yes
        todict is called to change them to nested dictionaries
        """
        for key in d:
            if isinstance(d[key], matlab.mio5_params.mat_struct):
                d[key] = _todict(d[key])
            elif isinstance(d[key], np.ndarray):
                d[key] = _toarray(d[key])
        return d

    def _todict(matobj):
        """
        A recursive function which constructs from matobjects nested dictionaries
        """
        d = {}
        for strg in matobj._fieldnames:
            elem = matobj.__dict__[strg]
            if isinstance(elem, matlab.mio5_params.mat_struct):
                d[strg] = _todict(elem)
            elif isinstance(elem, np.ndarray):
                d[strg] = _toarray(elem)
            else:
                d[strg] = elem
        return d

    def _toarray(ndarray):
        """
        A recursive function which constructs ndarray from cellarrays
        (which are loaded as numpy ndarrays), recursing into the elements
        if they contain matobjects.
        """
        if ndarray.dtype != 'float64':
            elem_list = []
            for sub_elem in ndarray:
                if isinstance(sub_elem, matlab.mio5_params.mat_struct):
                    elem_list.append(_todict(sub_elem))
                elif isinstance(sub_elem, np.ndarray):
                    elem_list.append(_toarray(sub_elem))
                else:
                    elem_list.append(sub_elem)
            return np.array(elem_list)
        else:
            return ndarray

    data = loadmat(filename, struct_as_record=False, squeeze_me=True)
    return _check_vars(data)

如果它是带有结构的matrix/cells，它可以通过变量，并且通过不通过没有结构的矩阵来使其更快。

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