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import os |
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import numpy as np |
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import copy |
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import motmetrics as mm |
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mm.lap.default_solver = 'lap' |
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import os |
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from typing import Dict |
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import numpy as np |
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import logging |
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def read_results(filename, data_type: str, is_gt=False, is_ignore=False): |
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if data_type in ('mot', 'lab'): |
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read_fun = read_mot_results |
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else: |
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raise ValueError('Unknown data type: {}'.format(data_type)) |
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return read_fun(filename, is_gt, is_ignore) |
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def read_mot_results(filename, is_gt, is_ignore): |
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valid_labels = {1} |
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ignore_labels = {0, 2, 7, 8, 12} |
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results_dict = dict() |
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if os.path.isfile(filename): |
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with open(filename, 'r') as f: |
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for line in f.readlines(): |
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linelist = line.split(',') |
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if len(linelist) < 7: |
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continue |
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fid = int(linelist[0]) |
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if fid < 1: |
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continue |
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results_dict.setdefault(fid, list()) |
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if is_gt: |
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if 'MOT16-' in filename or 'MOT17-' in filename: |
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label = int(float(linelist[7])) |
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mark = int(float(linelist[6])) |
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if mark == 0 or label not in valid_labels: |
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continue |
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score = 1 |
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elif is_ignore: |
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if 'MOT16-' in filename or 'MOT17-' in filename: |
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label = int(float(linelist[7])) |
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vis_ratio = float(linelist[8]) |
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if label not in ignore_labels and vis_ratio >= 0: |
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continue |
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elif 'MOT15' in filename: |
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label = int(float(linelist[6])) |
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if label not in ignore_labels: |
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continue |
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else: |
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continue |
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score = 1 |
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else: |
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score = float(linelist[6]) |
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tlwh = tuple(map(float, linelist[2:6])) |
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target_id = int(linelist[1]) |
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results_dict[fid].append((tlwh, target_id, score)) |
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return results_dict |
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def unzip_objs(objs): |
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if len(objs) > 0: |
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tlwhs, ids, scores = zip(*objs) |
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else: |
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tlwhs, ids, scores = [], [], [] |
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tlwhs = np.asarray(tlwhs, dtype=float).reshape(-1, 4) |
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return tlwhs, ids, scores |
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class Evaluator(object): |
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def __init__(self, data_root, seq_name, data_type='mot'): |
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self.data_root = data_root |
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self.seq_name = seq_name |
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self.data_type = data_type |
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self.load_annotations() |
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self.reset_accumulator() |
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def load_annotations(self): |
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assert self.data_type == 'mot' |
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gt_filename = os.path.join(self.data_root, self.seq_name, 'gt', 'gt.txt') |
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self.gt_frame_dict = read_results(gt_filename, self.data_type, is_gt=True) |
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self.gt_ignore_frame_dict = read_results(gt_filename, self.data_type, is_ignore=True) |
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def reset_accumulator(self): |
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self.acc = mm.MOTAccumulator(auto_id=True) |
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def eval_frame(self, frame_id, trk_tlwhs, trk_ids, rtn_events=False): |
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trk_tlwhs = np.copy(trk_tlwhs) |
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trk_ids = np.copy(trk_ids) |
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gt_objs = self.gt_frame_dict.get(frame_id, []) |
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gt_tlwhs, gt_ids = unzip_objs(gt_objs)[:2] |
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ignore_objs = self.gt_ignore_frame_dict.get(frame_id, []) |
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ignore_tlwhs = unzip_objs(ignore_objs)[0] |
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keep = np.ones(len(trk_tlwhs), dtype=bool) |
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iou_distance = mm.distances.iou_matrix(ignore_tlwhs, trk_tlwhs, max_iou=0.5) |
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if len(iou_distance) > 0: |
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match_is, match_js = mm.lap.linear_sum_assignment(iou_distance) |
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match_is, match_js = map(lambda a: np.asarray(a, dtype=int), [match_is, match_js]) |
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match_ious = iou_distance[match_is, match_js] |
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match_js = np.asarray(match_js, dtype=int) |
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match_js = match_js[np.logical_not(np.isnan(match_ious))] |
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keep[match_js] = False |
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trk_tlwhs = trk_tlwhs[keep] |
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trk_ids = trk_ids[keep] |
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iou_distance = mm.distances.iou_matrix(gt_tlwhs, trk_tlwhs, max_iou=0.5) |
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self.acc.update(gt_ids, trk_ids, iou_distance) |
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if rtn_events and iou_distance.size > 0 and hasattr(self.acc, 'last_mot_events'): |
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events = self.acc.last_mot_events |
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else: |
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events = None |
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return events |
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def eval_file(self, filename): |
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self.reset_accumulator() |
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result_frame_dict = read_results(filename, self.data_type, is_gt=False) |
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frames = sorted(list(set(self.gt_frame_dict.keys()) | set(result_frame_dict.keys()))) |
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for frame_id in frames: |
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trk_objs = result_frame_dict.get(frame_id, []) |
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trk_tlwhs, trk_ids = unzip_objs(trk_objs)[:2] |
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self.eval_frame(frame_id, trk_tlwhs, trk_ids, rtn_events=False) |
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return self.acc |
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@staticmethod |
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def get_summary(accs, names, metrics=('mota', 'num_switches', 'idp', 'idr', 'idf1', 'precision', 'recall')): |
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names = copy.deepcopy(names) |
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if metrics is None: |
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metrics = mm.metrics.motchallenge_metrics |
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metrics = copy.deepcopy(metrics) |
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mh = mm.metrics.create() |
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summary = mh.compute_many( |
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accs, |
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metrics=metrics, |
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names=names, |
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generate_overall=True |
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) |
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return summary |
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@staticmethod |
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def save_summary(summary, filename): |
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import pandas as pd |
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writer = pd.ExcelWriter(filename) |
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summary.to_excel(writer) |
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writer.save() |
