k20hcmus/FishEye8K

1import os2import numpy as np3import copy4import motmetrics as mm5mm.lap.default_solver = 'lap'6from utils.io import read_results, unzip_objs7 8 9class Evaluator(object):10 11    def __init__(self, data_root, seq_name, data_type):12        self.data_root = data_root13        self.seq_name = seq_name14        self.data_type = data_type15 16        self.load_annotations()17        self.reset_accumulator()18 19    def load_annotations(self):20        assert self.data_type == 'mot'21 22        gt_filename = os.path.join(self.data_root, self.seq_name, 'gt', 'gt.txt')23        self.gt_frame_dict = read_results(gt_filename, self.data_type, is_gt=True)24        self.gt_ignore_frame_dict = read_results(gt_filename, self.data_type, is_ignore=True)25 26    def reset_accumulator(self):27        self.acc = mm.MOTAccumulator(auto_id=True)28 29    def eval_frame(self, frame_id, trk_tlwhs, trk_ids, rtn_events=False):30        # results31        trk_tlwhs = np.copy(trk_tlwhs)32        trk_ids = np.copy(trk_ids)33 34        # gts35        gt_objs = self.gt_frame_dict.get(frame_id, [])36        gt_tlwhs, gt_ids = unzip_objs(gt_objs)[:2]37 38        # ignore boxes39        ignore_objs = self.gt_ignore_frame_dict.get(frame_id, [])40        ignore_tlwhs = unzip_objs(ignore_objs)[0]41 42 43        # remove ignored results44        keep = np.ones(len(trk_tlwhs), dtype=bool)45        iou_distance = mm.distances.iou_matrix(ignore_tlwhs, trk_tlwhs, max_iou=0.5)46        if len(iou_distance) > 0:47            match_is, match_js = mm.lap.linear_sum_assignment(iou_distance)48            match_is, match_js = map(lambda a: np.asarray(a, dtype=int), [match_is, match_js])49            match_ious = iou_distance[match_is, match_js]50 51            match_js = np.asarray(match_js, dtype=int)52            match_js = match_js[np.logical_not(np.isnan(match_ious))]53            keep[match_js] = False54            trk_tlwhs = trk_tlwhs[keep]55            trk_ids = trk_ids[keep]56 57        # get distance matrix58        iou_distance = mm.distances.iou_matrix(gt_tlwhs, trk_tlwhs, max_iou=0.5)59 60        # acc61        self.acc.update(gt_ids, trk_ids, iou_distance)62 63        if rtn_events and iou_distance.size > 0 and hasattr(self.acc, 'last_mot_events'):64            events = self.acc.last_mot_events  # only supported by https://github.com/longcw/py-motmetrics65        else:66            events = None67        return events68 69    def eval_file(self, filename):70        self.reset_accumulator()71 72        result_frame_dict = read_results(filename, self.data_type, is_gt=False)73        frames = sorted(list(set(self.gt_frame_dict.keys()) | set(result_frame_dict.keys())))74        for frame_id in frames:75            trk_objs = result_frame_dict.get(frame_id, [])76            trk_tlwhs, trk_ids = unzip_objs(trk_objs)[:2]77            self.eval_frame(frame_id, trk_tlwhs, trk_ids, rtn_events=False)78 79        return self.acc80 81    @staticmethod82    def get_summary(accs, names, metrics=('mota', 'num_switches', 'idp', 'idr', 'idf1', 'precision', 'recall')):83        names = copy.deepcopy(names)84        if metrics is None:85            metrics = mm.metrics.motchallenge_metrics86        metrics = copy.deepcopy(metrics)87 88        mh = mm.metrics.create()89        summary = mh.compute_many(90            accs,91            metrics=metrics,92            names=names,93            generate_overall=True94        )95 96        return summary97 98    @staticmethod99    def save_summary(summary, filename):100        import pandas as pd101        writer = pd.ExcelWriter(filename)102        summary.to_excel(writer)103        writer.save()104