TSMCF/interpolation.py at master · lihuining/TSMCF · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
import numpy as np
import os
import glob
import motmetrics as mm

from yolox.evaluators.evaluation import Evaluator


def mkdir_if_missing(d):
    if not os.path.exists(d):
        os.makedirs(d)


def eval_mota(data_root, txt_path):
    accs = []
    # seqs = sorted([s for s in os.listdir(data_root) if s.endswith('FRCNN')])
    seqs = sorted([s for s in os.listdir(data_root)])
    for seq in seqs:
        video_out_path = os.path.join(txt_path, seq + '.txt')
        evaluator = Evaluator(data_root, seq, 'mot')
        accs.append(evaluator.eval_file(video_out_path))
    metrics = mm.metrics.motchallenge_metrics
    mh = mm.metrics.create()
    summary = Evaluator.get_summary(accs, seqs, metrics)
    strsummary = mm.io.render_summary(
        summary,
        formatters=mh.formatters,
        namemap=mm.io.motchallenge_metric_names
    )
    print(strsummary)


def get_mota(data_root, txt_path):
    accs = []
    # seqs = sorted([s for s in os.listdir(data_root) if s.endswith('FRCNN')]) #
    seqs = sorted([s for s in os.listdir(data_root)])
    for seq in seqs:
        video_out_path = os.path.join(txt_path, seq + '.txt')
        evaluator = Evaluator(data_root, seq, 'mot')
        accs.append(evaluator.eval_file(video_out_path))
    metrics = mm.metrics.motchallenge_metrics
    mh = mm.metrics.create()
    summary = Evaluator.get_summary(accs, seqs, metrics)
    strsummary = mm.io.render_summary(
        summary,
        formatters=mh.formatters,
        namemap=mm.io.motchallenge_metric_names
    )
    mota = float(strsummary.split(' ')[-6][:-1])
    return mota


def write_results_score(filename, results):
    save_format = '{frame},{id},{x1},{y1},{w},{h},{s},-1,-1,-1\n'
    with open(filename, 'w') as f:
        for i in range(results.shape[0]):
            frame_data = results[i]
            frame_id = int(frame_data[0])
            track_id = int(frame_data[1])
            x1, y1, w, h = frame_data[2:6]
            score = frame_data[6]
            line = save_format.format(frame=frame_id, id=track_id, x1=x1, y1=y1, w=w, h=h, s=-1)
            f.write(line)


def dti(txt_path, save_path, n_min=25, n_dti=20):
    seq_txts = sorted(glob.glob(os.path.join(txt_path, '*.txt')))
    for seq_txt in seq_txts:
        seq_name = seq_txt.split('/')[-1] # MOT20-01.txt
        seq_data = np.loadtxt(seq_txt, dtype=np.float64, delimiter=',')
        min_id = int(np.min(seq_data[:, 1])) # 最小id
        max_id = int(np.max(seq_data[:, 1])) # 最大id
        seq_results = np.zeros((1, 10), dtype=np.float64)
        for track_id in range(min_id, max_id + 1):
            index = (seq_data[:, 1] == track_id)
            tracklet = seq_data[index]
            tracklet_dti = tracklet
            if tracklet.shape[0] == 0:
                continue
            n_frame = tracklet.shape[0] # tracklet跨越的帧数
            n_conf = np.sum(tracklet[:, 6] > 0.5)
            if n_frame > n_min:
                frames = tracklet[:, 0]
                frames_dti = {}
                for i in range(0, n_frame):
                    right_frame = frames[i]
                    if i > 0:
                        left_frame = frames[i - 1]
                    else:
                        left_frame = frames[i]
                    # disconnected track interpolation
                    if 1 < right_frame - left_frame < n_dti:
                        num_bi = int(right_frame - left_frame - 1)
                        right_bbox = tracklet[i, 2:6]
                        left_bbox = tracklet[i - 1, 2:6]
                        for j in range(1, num_bi + 1):
                            curr_frame = j + left_frame
                            curr_bbox = (curr_frame - left_frame) * (right_bbox - left_bbox) / \
                                        (right_frame - left_frame) + left_bbox
                            frames_dti[curr_frame] = curr_bbox
                num_dti = len(frames_dti.keys())
                if num_dti > 0:
                    data_dti = np.zeros((num_dti, 10), dtype=np.float64)
                    for n in range(num_dti):
                        data_dti[n, 0] = list(frames_dti.keys())[n]
                        data_dti[n, 1] = track_id
                        data_dti[n, 2:6] = frames_dti[list(frames_dti.keys())[n]]
                        data_dti[n, 6:] = [1, -1, -1, -1]
                    tracklet_dti = np.vstack((tracklet, data_dti))
            seq_results = np.vstack((seq_results, tracklet_dti))
        save_seq_txt = os.path.join(save_path, seq_name)
        seq_results = seq_results[1:]
        seq_results = seq_results[seq_results[:, 0].argsort()] # 对第1列元素按照从小到大排序，argsort()取出的是索引
        write_results_score(save_seq_txt, seq_results)


if __name__ == '__main__':
    # data_root = '/home/allenyljiang/Documents/Dataset/MOT20/train'
    # txt_path = 'YOLOX_outputs/yolox_x_mix_mot20_ch/track_results'
    # save_path = 'YOLOX_outputs/yolox_x_mix_mot20_ch/track_results_dti'
    data_root = '/home/allenyljiang/Documents/Dataset/MOT20/train'
    txt_path = '/home/allenyljiang/Documents/SSP_EM/YOLOX_outputs/MOT20'
    save_path = '/home/allenyljiang/Documents/SSP_EM/YOLOX_outputs/MOT20/interpolate'

    mkdir_if_missing(save_path)
    #dti(txt_path, save_path, n_min=10, n_dti=20)
    dti(txt_path, save_path, n_min=5, n_dti=20)
    print('Before DTI: ')
    eval_mota(data_root, txt_path)
    print('After DTI:')
    eval_mota(data_root, save_path)

    '''
    mota_best = 0.0
    best_n_min = 0
    best_n_dti = 0
    for n_min in range(5, 50, 5):
        for n_dti in range(5, 30, 5):
            dti(txt_path, save_path, n_min, n_dti)
            mota = get_mota(data_root, save_path)
            if mota > mota_best:
                mota_best = mota
                best_n_min = n_min
                best_n_dti = n_dti
                print(mota_best, best_n_min, best_n_dti)
    print(mota_best, best_n_min, best_n_dti)
    '''