def test_pairwise_temporal_iou():
target_segments = np.array([])
candidate_segments = np.array([])
with pytest.raises(ValueError):
pairwise_temporal_iou(target_segments, candidate_segments)
# test temporal iou
target_segments = np.array([[1, 2], [2, 3]])
candidate_segments = np.array([[2, 3], [2.5, 3]])
temporal_iou = pairwise_temporal_iou(candidate_segments, target_segments)
assert_array_equal(temporal_iou, [[0, 0], [1, 0.5]])
# test temporal overlap_self
target_segments = np.array([[1, 2], [2, 3]])
candidate_segments = np.array([[2, 3], [2.5, 3]])
temporal_iou, temporal_overlap_self = pairwise_temporal_iou(
candidate_segments, target_segments, calculate_overlap_self=True)
assert_array_equal(temporal_overlap_self, [[0, 0], [1, 1]])
# test temporal overlap_self when candidate_segments is 1d
target_segments = np.array([[1, 2], [2, 3]])
candidate_segments = np.array([2.5, 3])
temporal_iou, temporal_overlap_self = pairwise_temporal_iou(
candidate_segments, target_segments, calculate_overlap_self=True)
assert_array_equal(temporal_overlap_self, [0, 1])
def test_pairwise_temporal_iou():
target_segments = np.array([])
candidate_segments = np.array([])
with pytest.raises(ValueError):
pairwise_temporal_iou(target_segments, candidate_segments)
target_segments = np.array([[1, 2], [2, 3]])
candidate_segments = np.array([[2, 3], [2.5, 3]])
temporal_iou = pairwise_temporal_iou(target_segments, candidate_segments)
assert_array_equal(temporal_iou, [[0, 1], [0, 0.5]])
args = parse_args()
formatted_proposal_file = open(args.formatted_proposal_file, 'w')
# The activity index file is constructed according to
# 'https://github.com/activitynet/ActivityNet/blob/master/Evaluation/eval_classification.py'
activity_index, class_idx = {}, 0
for line in open(args.activity_index_file).readlines():
activity_index[line.strip()] = class_idx
class_idx += 1
video_infos = load_annotations(args.ann_file)
ground_truth = import_ground_truth(video_infos, activity_index)
proposal, num_proposals = import_proposals(
mmcv.load(args.proposal_file)['results'])
video_idx = 0
for video_info in video_infos:
video_id = video_info['video_name'][2:]
num_frames = video_info['duration_frame']
fps = video_info['fps']
tiou, t_overlap = pairwise_temporal_iou(
proposal[video_id][:, :2].astype(float),
ground_truth[video_id][:, :2].astype(float),
calculate_overlap_self=True)
dump_formatted_proposal(video_idx, video_id, num_frames, fps,
ground_truth[video_id], proposal[video_id],
tiou, t_overlap, formatted_proposal_file)
video_idx += 1
formatted_proposal_file.close()