def eval_seq()

in container-dp/resources/FairMOT/track.py [0:0]

• 37 lines of code
• 12 McCabe index (conditional complexity)

def eval_seq(opt, dataloader, data_type, result_filename, save_dir=None, show_image=True, frame_rate=30, use_cuda=True):
    if save_dir:
        mkdir_if_missing(save_dir)
    tracker = JDETracker(opt, frame_rate=frame_rate)
    timer = Timer()
    results = []
    frame_id = 0
    #for path, img, img0 in dataloader:
    for i, (path, img, img0) in enumerate(dataloader):
        #if i % 8 != 0:
            #continue
        if frame_id % 20 == 0:
            logger.info('Processing frame {} ({:.2f} fps)'.format(frame_id, 1. / max(1e-5, timer.average_time)))

        # run tracking
        timer.tic()
        if use_cuda:
            blob = torch.from_numpy(img).cuda().unsqueeze(0)
        else:
            blob = torch.from_numpy(img).unsqueeze(0)
        online_targets = tracker.update(blob, img0)
        online_tlwhs = []
        online_ids = []
        #online_scores = []
        for t in online_targets:
            tlwh = t.tlwh
            tid = t.track_id
            vertical = tlwh[2] / tlwh[3] > 1.6
            if tlwh[2] * tlwh[3] > opt.min_box_area and not vertical:
                online_tlwhs.append(tlwh)
                online_ids.append(tid)
                #online_scores.append(t.score)
        timer.toc()
        # save results
        results.append((frame_id + 1, online_tlwhs, online_ids))
        #results.append((frame_id + 1, online_tlwhs, online_ids, online_scores))
        if show_image or save_dir is not None:
            online_im = vis.plot_tracking(img0, online_tlwhs, online_ids, frame_id=frame_id,
                                          fps=1. / timer.average_time)
        if show_image:
            cv2.imshow('online_im', online_im)
        if save_dir is not None:
            cv2.imwrite(os.path.join(save_dir, '{:05d}.jpg'.format(frame_id)), online_im)
        frame_id += 1
    # save results
    write_results(result_filename, results, data_type)
    #write_results_score(result_filename, results, data_type)
    return frame_id, timer.average_time, timer.calls