def eval_frame(self, frame_id, trk_tlwhs, trk_ids, rtn_events=False):
# results
trk_tlwhs = np.copy(trk_tlwhs)
trk_ids = np.copy(trk_ids)
# gts
gt_objs = self.gt_frame_dict.get(frame_id, [])
gt_tlwhs, gt_ids = unzip_objs(gt_objs)[:2]
# ignore boxes
ignore_objs = self.gt_ignore_frame_dict.get(frame_id, [])
ignore_tlwhs = unzip_objs(ignore_objs)[0]
# remove ignored results
keep = np.ones(len(trk_tlwhs), dtype=bool)
iou_distance = mm.distances.iou_matrix(ignore_tlwhs,
trk_tlwhs,
max_iou=0.5)
if len(iou_distance) > 0:
match_is, match_js = mm.lap.linear_sum_assignment(iou_distance)
match_is, match_js = map(lambda a: np.asarray(a, dtype=int),
[match_is, match_js])
match_ious = iou_distance[match_is, match_js]
match_js = np.asarray(match_js, dtype=int)
match_js = match_js[np.logical_not(np.isnan(match_ious))]
keep[match_js] = False
trk_tlwhs = trk_tlwhs[keep]
trk_ids = trk_ids[keep]
#match_is, match_js = mm.lap.linear_sum_assignment(iou_distance)
#match_is, match_js = map(lambda a: np.asarray(a, dtype=int), [match_is, match_js])
#match_ious = iou_distance[match_is, match_js]
#match_js = np.asarray(match_js, dtype=int)
#match_js = match_js[np.logical_not(np.isnan(match_ious))]
#keep[match_js] = False
#trk_tlwhs = trk_tlwhs[keep]
#trk_ids = trk_ids[keep]
# get distance matrix
iou_distance = mm.distances.iou_matrix(gt_tlwhs,
trk_tlwhs,
max_iou=0.5)
# acc
if self.return_fn and self.return_fp:
_, oids_masked, hids_masked = self.acc.update(
gt_ids, trk_ids, iou_distance)
else:
self.acc.update(gt_ids, trk_ids, iou_distance)
if rtn_events and iou_distance.size > 0 and hasattr(
self.acc, 'last_mot_events'):
events = self.acc.last_mot_events # only supported by https://github.com/longcw/py-motmetrics
else:
events = None
if self.return_fn and self.return_fp:
return events, oids_masked, hids_masked
return events
def eval_file(self, filename):
self.reset_accumulator()
result_frame_dict = read_results(filename, self.data_type, is_gt=False)
#frames = sorted(list(set(self.gt_frame_dict.keys()) | set(result_frame_dict.keys())))
frames = sorted(list(set(result_frame_dict.keys())))
for frame_id in frames:
trk_objs = result_frame_dict.get(frame_id, [])
trk_tlwhs, trk_ids = unzip_objs(trk_objs)[:2]
self.eval_frame(frame_id, trk_tlwhs, trk_ids, rtn_events=False)
return self.acc
def eval_file(self, filename):
self.reset_accumulator()
if self.return_fn and self.return_fp:
fns = {}
fps = {}
matched = {}
fp_confs = {}
result_frame_dict = read_results(filename, self.data_type, is_gt=False)
frames = sorted(
list(
set(self.gt_frame_dict.keys())
| set(result_frame_dict.keys())))
for frame_id in frames:
trk_objs = result_frame_dict.get(frame_id, [])
trk_tlwhs, trk_ids, trk_confs = unzip_objs(trk_objs)
trk_confs = np.array(trk_confs)
if self.return_fn and self.return_fp:
_, oids_masked, hids_masked = self.eval_frame(frame_id,
trk_tlwhs,
trk_ids,
rtn_events=False)
gt_objs = self.gt_frame_dict.get(frame_id, [])
gt_tlwhs, gt_ids = unzip_objs(gt_objs)[:2]
# These are the matched boxes
matched[frame_id] = {}
matched[frame_id]['gt'] = gt_tlwhs[oids_masked]
matched[frame_id]['trk'] = trk_tlwhs[hids_masked]
matched[frame_id]['trk_confs'] = trk_confs[hids_masked]
# FN FP
fns[frame_id] = gt_tlwhs[~oids_masked]
fps[frame_id] = trk_tlwhs[~hids_masked]
fp_confs[frame_id] = trk_confs[~hids_masked]
else:
self.eval_frame(frame_id, trk_tlwhs, trk_ids, rtn_events=False)
if self.return_fn and self.return_fp:
return self.acc, fns, fps, matched, fp_confs
return self.acc
def eval_file(self, filename):
self.reset_accumulator()
result_frame_dict = read_results(filename, self.data_type,
is_gt=False) # 加载检测结果
frames = sorted(
list(
set(self.gt_frame_dict.keys())
| set(result_frame_dict.keys()))) # 帧的并集
for frame_id in frames:
trk_objs = result_frame_dict.get(frame_id, [])
trk_tlwhs, trk_ids = unzip_objs(trk_objs)[:2] # 预测track中的边框、id
self.eval_frame(frame_id, trk_tlwhs, trk_ids,
rtn_events=False) # 用于和ground truth进行计算
return self.acc