import os import numpy as np import copy import motmetrics as mm mm.lap.default_solver = 'lap' class Evaluator(object): def __init__(self, data_root, seq_name, data_type): self.data_root = data_root self.seq_name = seq_name self.data_type = data_type self.load_annotations() self.reset_accumulator() def load_annotations(self): assert self.data_type == 'mot' gt_filename = os.path.join(self.data_root, self.seq_name, 'gt', 'gt.txt') self.gt_frame_dict = read_results(gt_filename, self.data_type, is_gt=True) self.gt_ignore_frame_dict = read_results(gt_filename, self.data_type, is_ignore=True) def reset_accumulator(self): self.acc = mm.MOTAccumulator(auto_id=True) 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 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 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 @staticmethod def get_summary(accs, names, metrics=('mota', 'num_switches', 'idp', 'idr', 'idf1', 'precision', 'recall')): names = copy.deepcopy(names) if metrics is None: metrics = mm.metrics.motchallenge_metrics metrics = copy.deepcopy(metrics) mh = mm.metrics.create() summary = mh.compute_many( accs, metrics=metrics, names=names, generate_overall=True ) return summary @staticmethod def save_summary(summary, filename): import pandas as pd writer = pd.ExcelWriter(filename) summary.to_excel(writer) writer.save() def read_results(filename, data_type: str, is_gt=False, is_ignore=False): if data_type in ('mot', 'lab'): read_fun = read_mot_results else: raise ValueError('Unknown data type: {}'.format(data_type)) return read_fun(filename, is_gt, is_ignore) """ labels={'ped', ... % 1 'person_on_vhcl', ... % 2 'car', ... % 3 'bicycle', ... % 4 'mbike', ... % 5 'non_mot_vhcl', ... % 6 'static_person', ... % 7 'distractor', ... % 8 'occluder', ... % 9 'occluder_on_grnd', ... %10 'occluder_full', ... % 11 'reflection', ... % 12 'crowd' ... % 13 }; """ def read_mot_results(filename, is_gt, is_ignore): valid_labels = {1} ignore_labels = {2, 7, 8, 12} results_dict = dict() if os.path.isfile(filename): with open(filename, 'r') as f: for line in f.readlines(): linelist = line.split(',') if len(linelist) < 7: continue fid = int(linelist[0]) if fid < 1: continue results_dict.setdefault(fid, list()) box_size = float(linelist[4]) * float(linelist[5]) if is_gt: if 'MOT16-' in filename or 'MOT17-' in filename: label = int(float(linelist[7])) mark = int(float(linelist[6])) if mark == 0 or label not in valid_labels: continue score = 1 elif is_ignore: if 'MOT16-' in filename or 'MOT17-' in filename: label = int(float(linelist[7])) vis_ratio = float(linelist[8]) if label not in ignore_labels and vis_ratio >= 0: continue else: continue score = 1 else: score = float(linelist[6]) #if box_size > 7000: #if box_size <= 7000 or box_size >= 15000: #if box_size < 15000: #continue tlwh = tuple(map(float, linelist[2:6])) target_id = int(linelist[1]) results_dict[fid].append((tlwh, target_id, score)) return results_dict def unzip_objs(objs): if len(objs) > 0: tlwhs, ids, scores = zip(*objs) else: tlwhs, ids, scores = [], [], [] tlwhs = np.asarray(tlwhs, dtype=float).reshape(-1, 4) return tlwhs, ids, scores