| import os
|
| from typing import Dict
|
| import numpy as np
|
|
|
| from tracking_utils.log import logger
|
|
|
|
|
| def write_results(filename, results_dict: Dict, data_type: str):
|
| if not filename:
|
| return
|
| path = os.path.dirname(filename)
|
| if not os.path.exists(path):
|
| os.makedirs(path)
|
|
|
| if data_type in ('mot', 'mcmot', 'lab'):
|
| save_format = '{frame},{id},{x1},{y1},{w},{h},1,-1,-1,-1\n'
|
| elif data_type == 'kitti':
|
| save_format = '{frame} {id} pedestrian -1 -1 -10 {x1} {y1} {x2} {y2} -1 -1 -1 -1000 -1000 -1000 -10 {score}\n'
|
| else:
|
| raise ValueError(data_type)
|
|
|
| with open(filename, 'w') as f:
|
| for frame_id, frame_data in results_dict.items():
|
| if data_type == 'kitti':
|
| frame_id -= 1
|
| for tlwh, track_id in frame_data:
|
| if track_id < 0:
|
| continue
|
| x1, y1, w, h = tlwh
|
| x2, y2 = x1 + w, y1 + h
|
| line = save_format.format(frame=frame_id, id=track_id, x1=x1, y1=y1, x2=x2, y2=y2, w=w, h=h, score=1.0)
|
| f.write(line)
|
| logger.info('Save results to {}'.format(filename))
|
|
|
|
|
| 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])
|
|
|
|
|
|
|
|
|
|
|
|
|
| tlwh = tuple(map(float, linelist[2:6]))
|
|
|
| target_id = 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 |
