def evaluate_tracking(sequences, track_dir, gt_dir):
all_info = []
for seqname in sequences:
track_res = os.path.join(track_dir, seqname, 'res.txt')
gt_file = os.path.join(gt_dir, seqname, 'gt.txt')
assert os.path.exists(track_res) and os.path.exists(gt_file), 'Either tracking result or groundtruth directory does not exist'
trackDB = read_txt_to_struct(track_res)
gtDB = read_txt_to_struct(gt_file)
gtDB, distractor_ids = extract_valid_gt_data(gtDB)
metrics, extra_info = evaluate_sequence(trackDB, gtDB, distractor_ids)
print_metrics(seqname + ' Evaluation', metrics)
all_info.append(extra_info)
all_metrics = evaluate_bm(all_info)
print_metrics('Summary Evaluation', all_metrics)
def evaluate_new(results, gt_file):
res = []
for i, track in results.items():
for frame, bb in track.items():
x1 = bb[0]
y1 = bb[1]
x2 = bb[2]
y2 = bb[3]
res.append([
float(frame + 1),
float(i + 1),
float(x1 + 1),
float(y1 + 1),
float(x2 + 1),
float(y2 + 1),
float(-1),
float(-1),
float(-1),
float(-1)
])
trackDB = np.array(res)
gtDB = read_txt_to_struct(gt_file)
# manipulate mot15 to fit mot16
#gtDB[:,7] = gtDB[:,6]
#gtDB[:,6] = 1
#gtDB[:,8] = 1
#gtDB = gtDB[:,:9]
gtDB, distractor_ids = extract_valid_gt_data(gtDB)
metrics, extra_info, clear_mot_info, ML_PT_MT = evaluate_sequence(
trackDB, gtDB, distractor_ids)
print_metrics(' Evaluation', metrics)
# evaluate sizes of ML_PT_MT
ML_PT_MT_sizes = [[]]
ML_PT_MT_vis = [[]]
for i, p in enumerate(ML_PT_MT, 1):
ML_PT_MT_sizes.append([])
ML_PT_MT_vis.append([])
for tr_id in p:
gt_in_person = np.where(gtDB[:, 1] == tr_id)[0]
#w = gtDB[gt_in_person, 4]
h = gtDB[gt_in_person, 5] - gtDB[gt_in_person, 3]
#sizes = (w*h).mean()
ML_PT_MT_sizes[i].append(h.mean())
ML_PT_MT_sizes[0].append(h.mean())
vis = gtDB[gt_in_person, 8]
ML_PT_MT_vis[i].append(vis.mean())
ML_PT_MT_vis[0].append(vis.mean())
print("\t\theight\tvis")
print("MT: \t\t{:.2f}\t{:.2f}".format(np.mean(ML_PT_MT_sizes[3]),
np.mean(ML_PT_MT_vis[3])))
print("PT: \t\t{:.2f}\t{:.2f}".format(np.mean(ML_PT_MT_sizes[2]),
np.mean(ML_PT_MT_vis[2])))
print("ML: \t\t{:.2f}\t{:.2f}".format(np.mean(ML_PT_MT_sizes[1]),
np.mean(ML_PT_MT_vis[1])))
print("total (mean of\t{:.2f}\t{:.2f}\ntrack means):".format(
np.mean(ML_PT_MT_sizes[0]), np.mean(ML_PT_MT_vis[0])))
return clear_mot_info