def test_different_iou_threshold(self): boxes = np.array([[0, 0, 20, 100], [0, 0, 20, 80], [200, 200, 210, 300], [200, 200, 210, 250]], dtype=float) boxlist = np_box_list.BoxList(boxes) boxlist.add_field('scores', np.array([0.9, 0.8, 0.7, 0.6])) max_output_size = 4 iou_threshold = .4 expected_boxes = np.array([ [0, 0, 20, 100], [200, 200, 210, 300], ], dtype=float) nms_boxlist = np_box_list_ops.non_max_suppression( boxlist, max_output_size, iou_threshold) self.assertAllClose(nms_boxlist.get(), expected_boxes) iou_threshold = .5 expected_boxes = np.array( [[0, 0, 20, 100], [200, 200, 210, 300], [200, 200, 210, 250]], dtype=float) nms_boxlist = np_box_list_ops.non_max_suppression( boxlist, max_output_size, iou_threshold) self.assertAllClose(nms_boxlist.get(), expected_boxes) iou_threshold = .8 expected_boxes = np.array([[0, 0, 20, 100], [0, 0, 20, 80], [200, 200, 210, 300], [200, 200, 210, 250]], dtype=float) nms_boxlist = np_box_list_ops.non_max_suppression( boxlist, max_output_size, iou_threshold) self.assertAllClose(nms_boxlist.get(), expected_boxes)
def test_with_no_scores_field(self): boxlist = np_box_list.BoxList(self._boxes) max_output_size = 3 iou_threshold = 0.5 with self.assertRaises(ValueError): np_box_list_ops.non_max_suppression(boxlist, max_output_size, iou_threshold)
def _compute_tp_fp_for_single_class(self, detected_boxes, detected_scores, groundtruth_boxes, groundtruth_is_difficult_list): """Labels boxes detected with the same class from the same image as tp/fp. Args: detected_boxes: A numpy array of shape [N, 4] representing detected box coordinates detected_scores: A 1-d numpy array of length N representing classification score groundtruth_boxes: A numpy array of shape [M, 4] representing ground truth box coordinates groundtruth_is_difficult_list: A boolean numpy array of length M denoting whether a ground truth box is a difficult instance or not Returns: scores: A numpy array representing the detection scores tp_fp_labels: a boolean numpy array indicating whether a detection is a true positive. """ if groundtruth_boxes.size == 0: is_gt_box_detected = np.array([], dtype=bool) else: is_gt_box_detected = np.zeros(groundtruth_boxes.shape[0], dtype=bool) if detected_boxes.size == 0: return np.array([], dtype=float), np.array( [], dtype=bool), is_gt_box_detected detected_boxlist = np_box_list.BoxList(detected_boxes) detected_boxlist.add_field('scores', detected_scores) detected_boxlist = np_box_list_ops.non_max_suppression( detected_boxlist, self.nms_max_output_boxes, self.nms_iou_threshold) scores = detected_boxlist.get_field('scores') if groundtruth_boxes.size == 0: return scores, np.zeros(detected_boxlist.num_boxes(), dtype=bool), is_gt_box_detected gt_boxlist = np_box_list.BoxList(groundtruth_boxes) iou = np_box_list_ops.iou(detected_boxlist, gt_boxlist) max_overlap_gt_ids = np.argmax(iou, axis=1) tp_fp_labels = np.zeros(detected_boxlist.num_boxes(), dtype=bool) is_matched_to_difficult_box = np.zeros(detected_boxlist.num_boxes(), dtype=bool) for i in range(detected_boxlist.num_boxes()): gt_id = max_overlap_gt_ids[i] if iou[i, gt_id] >= self.matching_iou_threshold: if not groundtruth_is_difficult_list[gt_id]: if not is_gt_box_detected[gt_id]: tp_fp_labels[i] = True is_gt_box_detected[gt_id] = True else: is_matched_to_difficult_box[i] = True return scores[~is_matched_to_difficult_box], tp_fp_labels[ ~is_matched_to_difficult_box], is_gt_box_detected
def test_select_from_ten_indentical_boxes(self): boxes = np.array(10 * [[0, 0, 1, 1]], dtype=float) boxlist = np_box_list.BoxList(boxes) boxlist.add_field('scores', np.array(10 * [0.8])) iou_threshold = .5 max_output_size = 3 expected_boxes = np.array([[0, 0, 1, 1]], dtype=float) nms_boxlist = np_box_list_ops.non_max_suppression( boxlist, max_output_size, iou_threshold) self.assertAllClose(nms_boxlist.get(), expected_boxes)
def test_select_at_most_two_from_three_clusters(self): boxlist = np_box_list.BoxList(self._boxes) boxlist.add_field('scores', np.array([.9, .75, .6, .95, .5, .3], dtype=float)) max_output_size = 2 iou_threshold = 0.5 expected_boxes = np.array([[0, 10, 1, 11], [0, 0, 1, 1]], dtype=float) nms_boxlist = np_box_list_ops.non_max_suppression( boxlist, max_output_size, iou_threshold) self.assertAllClose(nms_boxlist.get(), expected_boxes)
def test_nms_disabled_max_output_size_equals_three(self): boxlist = np_box_list.BoxList(self._boxes) boxlist.add_field('scores', np.array([.9, .75, .6, .95, .2, .3], dtype=float)) max_output_size = 3 iou_threshold = 1. # No NMS expected_boxes = np.array( [[0, 10, 1, 11], [0, 0, 1, 1], [0, 0.1, 1, 1.1]], dtype=float) nms_boxlist = np_box_list_ops.non_max_suppression( boxlist, max_output_size, iou_threshold) self.assertAllClose(nms_boxlist.get(), expected_boxes)
def _get_overlaps_and_scores_box_mode(self, detected_boxes, detected_scores, groundtruth_boxes, groundtruth_is_group_of_list): """Computes overlaps and scores between detected and groudntruth boxes. Args: detected_boxes: A numpy array of shape [N, 4] representing detected box coordinates detected_scores: A 1-d numpy array of length N representing classification score groundtruth_boxes: A numpy array of shape [M, 4] representing ground truth box coordinates groundtruth_is_group_of_list: A boolean numpy array of length M denoting whether a ground truth box has group-of tag. If a groundtruth box is group-of box, every detection matching this box is ignored. Returns: iou: A float numpy array of size [num_detected_boxes, num_gt_boxes]. If gt_non_group_of_boxlist.num_boxes() == 0 it will be None. ioa: A float numpy array of size [num_detected_boxes, num_gt_boxes]. If gt_group_of_boxlist.num_boxes() == 0 it will be None. scores: The score of the detected boxlist. num_boxes: Number of non-maximum suppressed detected boxes. """ detected_boxlist = np_box_list.BoxList(detected_boxes) detected_boxlist.add_field('scores', detected_scores) detected_boxlist = np_box_list_ops.non_max_suppression( detected_boxlist, self.nms_max_output_boxes, self.nms_iou_threshold) gt_non_group_of_boxlist = np_box_list.BoxList( groundtruth_boxes[~groundtruth_is_group_of_list]) gt_group_of_boxlist = np_box_list.BoxList( groundtruth_boxes[groundtruth_is_group_of_list]) iou = np_box_list_ops.iou(detected_boxlist, gt_non_group_of_boxlist) ioa = np.transpose( np_box_list_ops.ioa(gt_group_of_boxlist, detected_boxlist)) scores = detected_boxlist.get_field('scores') num_boxes = detected_boxlist.num_boxes() return iou, ioa, scores, num_boxes