interpolation.py

import numpy as np
import os
import glob
import motmetrics as mm
import copy
 
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


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]
        seq_data = np.loadtxt(seq_txt, dtype=np.float64, delimiter=',')
        min_id = int(np.min(seq_data[:, 1]))
        max_id = int(np.max(seq_data[:, 1]))
        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]
            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()]
        write_results_score(save_seq_txt, seq_results)


if __name__ == '__main__':
    # data_root = '/data/zelinliu/DanceTrack/dancetrack/test'
    data_root = '/data/zelinliu/MOT17/train'
    txt_path = '/data/zelinliu/TrackSSM/results/val/yolox_m_lt_ddm_1000eps_deeper_800_1rev'
    save_path = '/data/zelinliu/TrackSSM/results/val/yolox_m_lt_ddm_1000eps_deeper_800_1rev_dti'
    
    mkdir_if_missing(save_path)
    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)
    '''