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