result_transforms Namespace Reference

Functions
	bbox2result (bboxes, labels, num_classes)
	outs2results (bboxes=None, labels=None, masks=None, ids=None, num_classes=None, **kwargs)

Function Documentation

bbox2result()

result_transforms.bbox2result	(		bboxes,
			labels,
			num_classes )

Convert detection results to a list of numpy arrays.

Args:
    bboxes (torch.Tensor | np.ndarray): shape (n, 5)
    labels (torch.Tensor | np.ndarray): shape (n, )
    num_classes (int): class number, including background class

Returns:
    list(ndarray): bbox results of each class

Definition at line 4 of file result_transforms.py.

def bbox2result(bboxes, labels, num_classes):
    """Convert detection results to a list of numpy arrays.
 
    Args:
        bboxes (torch.Tensor | np.ndarray): shape (n, 5)
        labels (torch.Tensor | np.ndarray): shape (n, )
        num_classes (int): class number, including background class
 
    Returns:
        list(ndarray): bbox results of each class
    """
    if bboxes.shape[0] == 0:
        return [np.zeros((0, 5), dtype=np.float32) for i in range(num_classes)]
    else:
        if isinstance(bboxes, torch.Tensor):
            bboxes = bboxes.detach().cpu().numpy()
            labels = labels.detach().cpu().numpy()
        return [bboxes[labels == i, :] for i in range(num_classes)]
 
 

outs2results()

result_transforms.outs2results	(		bboxes = None,
			labels = None,
			masks = None,
			ids = None,
			num_classes = None,
		**	kwargs )

Convert tracking/detection results to a list of numpy arrays.

Args:
    bboxes (torch.Tensor | np.ndarray): shape (n, 5)
    labels (torch.Tensor | np.ndarray): shape (n, )
    masks (torch.Tensor | np.ndarray): shape (n, h, w)
    ids (torch.Tensor | np.ndarray): shape (n, )
    num_classes (int): class number, not including background class

Returns:
    dict[str : list(ndarray) | list[list[np.ndarray]]]: tracking/detection
    results of each class. It may contain keys as belows:

    - bbox_results (list[np.ndarray]): Each list denotes bboxes of one
        category.
    - mask_results (list[list[np.ndarray]]): Each outer list denotes masks
        of one category. Each inner list denotes one mask belonging to
        the category. Each mask has shape (h, w).

Definition at line 24 of file result_transforms.py.

                 **kwargs):
    """Convert tracking/detection results to a list of numpy arrays.
 
    Args:
        bboxes (torch.Tensor | np.ndarray): shape (n, 5)
        labels (torch.Tensor | np.ndarray): shape (n, )
        masks (torch.Tensor | np.ndarray): shape (n, h, w)
        ids (torch.Tensor | np.ndarray): shape (n, )
        num_classes (int): class number, not including background class
 
    Returns:
        dict[str : list(ndarray) | list[list[np.ndarray]]]: tracking/detection
        results of each class. It may contain keys as belows:
 
        - bbox_results (list[np.ndarray]): Each list denotes bboxes of one
            category.
        - mask_results (list[list[np.ndarray]]): Each outer list denotes masks
            of one category. Each inner list denotes one mask belonging to
            the category. Each mask has shape (h, w).
    """
    assert labels is not None
    assert num_classes is not None
 
    results = dict()
 
    if ids is not None:
        valid_inds = ids > -1
        ids = ids[valid_inds]
        labels = labels[valid_inds]
 
    if bboxes is not None:
        if ids is not None:
            bboxes = bboxes[valid_inds]
            if bboxes.shape[0] == 0:
                bbox_results = [
                    np.zeros((0, 6), dtype=np.float32)
                    for i in range(num_classes)
                ]
            else:
                if isinstance(bboxes, torch.Tensor):
                    bboxes = bboxes.cpu().numpy()
                    labels = labels.cpu().numpy()
                    ids = ids.cpu().numpy()
                bbox_results = [
                    np.concatenate(
                        (ids[labels == i, None], bboxes[labels == i, :]),
                        axis=1) for i in range(num_classes)
                ]
        else:
            bbox_results = bbox2result(bboxes, labels, num_classes)
        results['bbox_results'] = bbox_results
 
    if masks is not None:
        if ids is not None:
            masks = masks[valid_inds]
        if isinstance(masks, torch.Tensor):
            masks = masks.detach().cpu().numpy()
        masks_results = [[] for _ in range(num_classes)]
        for i in range(bboxes.shape[0]):
            masks_results[labels[i]].append(masks[i])
        results['mask_results'] = masks_results
 
    return results