Method get_value_in_shape

detrsmpl/data/data_structures/human_data.py:483–536

Get value in a specific shape. For each dim, if the required shape is smaller than current shape, ndarray will be sliced. Otherwise, it will be padded with padding_constant at the end. Args: key (_KT): Key in dict. The value of this key must be

(self,
                           key: _KT,
                           shape: Union[list, tuple],
                           padding_constant: int = 0)

        return value

    def get_value_in_shape(self,
                           key: _KT,
                           shape: Union[list, tuple],
                           padding_constant: int = 0) -> np.ndarray:
        """Get value in a specific shape. For each dim, if the required shape
        is smaller than current shape, ndarray will be sliced. Otherwise, it
        will be padded with padding_constant at the end.

        Args:
            key (_KT):
                Key in dict. The value of this key must be
                an instance of numpy.ndarray.
            shape (Union[list, tuple]):
                Shape of the returned array. Its length
                must be equal to value.ndim. Set -1 for
                a dimension if you do not want to edit it.
            padding_constant (int, optional):
                The value to set the padded values for each axis.
                Defaults to 0.

        Raises:
            ValueError:
                A value in shape is neither positive integer nor -1.

        Returns:
            np.ndarray:
                An array in required shape.
        """
        value = self.get_raw_value(key)
        assert isinstance(value, np.ndarray)
        assert value.ndim == len(shape)
        pad_width_list = []
        slice_list = []
        for dim_index in range(len(shape)):
            if shape[dim_index] == -1:
                # no pad or slice
                pad_width_list.append((0, 0))
                slice_list.append(slice(None))
            elif shape[dim_index] > 0:
                # valid shape value
                wid = shape[dim_index] - value.shape[dim_index]
                if wid > 0:
                    pad_width_list.append((0, wid))
                else:
                    pad_width_list.append((0, 0))
                slice_list.append(slice(0, shape[dim_index]))
            else:
                # invalid
                raise ValueError
        pad_value = np.pad(value,
                           pad_width=pad_width_list,
                           mode='constant',
                           constant_values=padding_constant)
        return pad_value[tuple(slice_list)]

    @overload
    def get_slice(self, stop: int):