Spaces:
Running
Running
| import numpy as np | |
| def _concat(arr_list, axis=0): | |
| """Avoids a copy if there is only a single element in a list. | |
| """ | |
| if len(arr_list) == 1: | |
| return arr_list[0] | |
| return np.concatenate(arr_list, axis) | |
| def convert_boxes_list_to_boxes_and_indices(boxes_list): | |
| """ | |
| Args: | |
| boxes_list (np.ndarray): list or tuple of ndarray with shape (N_i, 4+K) | |
| Returns: | |
| boxes (ndarray): shape (M, 4+K) where M is sum of N_i. | |
| indices (ndarray): shape (M, 1) where M is sum of N_i. | |
| References: | |
| `mmdet.core.bbox.bbox2roi` in mmdetection | |
| `convert_boxes_to_roi_format` in TorchVision | |
| `modeling.poolers.convert_boxes_to_pooler_format` in detectron2 | |
| """ | |
| assert isinstance(boxes_list, (list, tuple)) | |
| boxes = _concat(boxes_list, axis=0) | |
| indices_list = [np.full((len(b), 1), i, boxes.dtype) | |
| for i, b in enumerate(boxes_list)] | |
| indices = _concat(indices_list, axis=0) | |
| return boxes, indices | |
| def convert_boxes_and_indices_to_boxes_list(boxes, indices, num_indices): | |
| """ | |
| Args: | |
| boxes (np.ndarray): shape (N, 4+K) | |
| indices (np.ndarray): shape (N,) or (N, 1), maybe batch index | |
| in mini-batch or class label index. | |
| num_indices (int): number of index. | |
| Returns: | |
| list (ndarray): boxes list of each index | |
| References: | |
| `mmdet.core.bbox2result` in mmdetection | |
| `mmdet.core.bbox.roi2bbox` in mmdetection | |
| `convert_boxes_to_roi_format` in TorchVision | |
| `modeling.poolers.convert_boxes_to_pooler_format` in detectron2 | |
| """ | |
| boxes = np.asarray(boxes) | |
| indices = np.asarray(indices) | |
| assert boxes.ndim == 2, "boxes ndim must be 2, got {}".format(boxes.ndim) | |
| assert (indices.ndim == 1) or (indices.ndim == 2 and indices.shape[-1] == 1), \ | |
| "indices ndim must be 1 or 2 if last dimension size is 1, got shape {}".format(indices.shape) | |
| assert boxes.shape[0] == indices.shape[0], "the 1st dimension size of boxes and indices "\ | |
| "must be the same, got {} != {}".format(boxes.shape[0], indices.shape[0]) | |
| if boxes.shape[0] == 0: | |
| return [np.zeros((0, boxes.shape[1]), dtype=np.float32) | |
| for i in range(num_indices)] | |
| else: | |
| if indices.ndim == 2: | |
| indices = np.squeeze(indices, axis=-1) | |
| return [boxes[indices == i, :] for i in range(num_indices)] | |