def setUp(self):
matching_iou_threshold = 0.5
self.eval = per_image_vrd_evaluation.PerImageVRDEvaluation(
matching_iou_threshold)
box_data_type = np.dtype([('subject', 'f4', (4, )),
('object', 'f4', (4, ))])
label_data_type = np.dtype([('subject', 'i4'), ('object', 'i4'),
('relation', 'i4')])
self.detected_box_tuples = np.array([([0, 0, 1, 1], [1, 1, 2, 2]),
([0, 0, 1.1, 1], [1, 1, 2, 2]),
([1, 1, 2, 2], [0, 0, 1.1, 1]),
([0, 0, 1, 1], [3, 4, 5, 6])],
dtype=box_data_type)
self.detected_class_tuples = np.array([(1, 2, 3), (1, 2, 3), (1, 2, 3),
(1, 4, 5)],
dtype=label_data_type)
self.detected_scores = np.array([0.2, 0.8, 0.1, 0.5], dtype=float)
self.groundtruth_box_tuples = np.array(
[([0, 0, 1, 1], [1, 1, 2, 2]), ([1, 1, 2, 2], [0, 0, 1.1, 1]),
([0, 0, 1, 1], [3, 4, 5, 5.5])],
dtype=box_data_type)
self.groundtruth_class_tuples = np.array([(1, 2, 3), (1, 7, 3),
(1, 4, 5)],
dtype=label_data_type)
def setUp(self):
matching_iou_threshold = 0.5
self.eval = per_image_vrd_evaluation.PerImageVRDEvaluation(
matching_iou_threshold)
box_data_type = np.dtype([('subject', 'f4', (4,)), ('object', 'f4', (4,))])
self.detected_box_tuples = np.array(
[([0, 0, 1, 1], [1, 1, 2, 2]), ([0, 0, 1.1, 1], [1, 1, 2, 2]),
([1, 1, 2, 2], [0, 0, 1.1, 1])],
dtype=box_data_type)
self.detected_scores = np.array([0.2, 0.8, 0.1], dtype=float)
self.groundtruth_box_tuples = np.array(
[([0, 0, 1, 1], [1, 1, 2, 2])], dtype=box_data_type)
def __init__(self, matching_iou_threshold=0.5):
"""Constructor.
Args:
matching_iou_threshold: IOU threshold to use for matching groundtruth
boxes to detection boxes.
"""
self._per_image_eval = per_image_vrd_evaluation.PerImageVRDEvaluation(
matching_iou_threshold=matching_iou_threshold)
self._groundtruth_box_tuples = {}
self._groundtruth_class_tuples = {}
self._num_gt_instances = 0
self._num_gt_imgs = 0
self._num_gt_instances_per_relationship = {}
self.clear_detections()
