def run_eval(exp_name: str) -> Mapping[str, Any]:
""" """
pred_log_dir = f"{_ROOT}/test_data/eval_tracking_dummy_logs_pred"
gt_log_dir = f"{_ROOT}/test_data/eval_tracking_dummy_logs_gt"
out_fpath = f"{_ROOT}/test_data/{exp_name}.txt"
out_file = open(out_fpath, "w")
eval_tracks(
path_tracker_output_root=pred_log_dir,
path_dataset_root=gt_log_dir,
d_min=0,
d_max=100,
out_file=out_file,
centroid_method="average",
diffatt=None,
category="VEHICLE",
)
out_file.close()
with open(out_fpath, "r") as f:
result_lines = f.readlines()
result_vals = result_lines[0].strip().split(" ")
fn, num_frames, mota, motp_c, motp_o, motp_i, idf1 = result_vals[:7]
most_track, most_lost, num_fp, num_miss, num_sw, num_frag = result_vals[
7:]
result_dict = {
"filename": fn,
"num_frames": int(num_frames),
"mota": float(mota),
"motp_c": float(motp_c),
"motp_o": float(motp_o),
"motp_i": float(motp_i),
"idf1": float(idf1),
"most_track": float(most_track),
"most_lost": float(most_lost),
"num_fp": int(num_fp),
"num_miss": int(num_miss),
"num_sw": int(num_sw),
"num_frag": int(num_frag),
}
shutil.rmtree(pred_log_dir)
shutil.rmtree(gt_log_dir)
return result_dict
def test_evaluation_track():
"""
test eval_tracks function
"""
# for testing, consider all point
eval_tracking.min_point_num = 0
centroid_methods = ["label_center", "average"]
temp_output = TEST_DATA_LOC / "tmp"
out_file = open(temp_output, "w+")
log = "1"
log_location = TEST_DATA_LOC / log
# sanity check, gt and results exactly the same
track_results_location = log_location / "per_sweep_annotations_amodal"
cm = centroid_methods[1]
eval_tracking.eval_tracks(track_results_location, os.fspath(log_location), D_MIN, D_MAX, out_file, cm)
# _,n_frame,mota,motp, idf1, mostly_tracked,mostly_lost,num_false_positives, num_misses,num_switches,num_fragmentations = out_file.read().strip().split(' ')
out_file.close()
def test_evaluation_track():
"""
test eval_tracks function
"""
# for testing, consider all point
eval_tracking.min_point_num = 0
centroid_methods = ["label_center", "average"]
temp_output = TEST_DATA_LOC / "tmp"
out_file = open(temp_output, "w+")
log = "1"
log_location = TEST_DATA_LOC / log
# sanity check, gt and results exactly the same
track_results_location = log_location / "per_sweep_annotations_amodal"
cm = centroid_methods[1]
eval_tracking.eval_tracks([track_results_location],
[os.fspath(log_location)], D_MIN, D_MAX,
out_file, cm)
out_file.close()
thr_estimate=thr_estimate,
thr_prune=thr_prune,
ps=ps)
print(f"Elapsed tracking vehicle {time.time() - time_start}")
time_start = time.time()
with open(filename_v, "a+") as out_file:
out_file.write(
f"{p_vel_mult} min_conf {min_conf} match_thr {match_threshold} {thr_prune} {thr_estimate} {ps} "
)
eval_tracks(path_results,
path_data,
0,
100,
out_file,
'average',
diffatt=None,
category='VEHICLE')
print(f"Elapsed evaluating vehicle {time.time() - time_start}")
if os.path.exists(path_results):
shutil.rmtree(path_results)
match_threshold = 0.001
min_conf = 0.26
time_start = time.time()
# Pedestrian
def dump_scenario_json(centers, yaw_angles, log_id, is_gt, run_eval=True):
"""
Egovehicle stationary (represented by `o`).
Seqeuence of 4-nanosecond timestamps.
"""
t_objs = TrackedObjects(log_id=log_id, is_gt=is_gt)
l = 2
w = 2
h = 1
track_id = 'obj_a'
label_class = 'VEHICLE'
cx, cy, cz = centers[0]
qx, qy, qz, qw = yaw_to_quaternion3d(yaw=yaw_angles[0])
tor = TrackedObjRec(l, w, h, qx, qy, qz, qw, cx, cy, cz, track_id,
label_class)
t_objs.add_obj(tor, ts_ns=0)
cx, cy, cz = centers[1]
qx, qy, qz, qw = yaw_to_quaternion3d(yaw=yaw_angles[1])
tor = TrackedObjRec(l, w, h, qx, qy, qz, qw, cx, cy, cz, track_id,
label_class)
t_objs.add_obj(tor, ts_ns=1)
cx, cy, cz = centers[2]
qx, qy, qz, qw = yaw_to_quaternion3d(yaw=yaw_angles[2])
tor = TrackedObjRec(l, w, h, qx, qy, qz, qw, cx, cy, cz, track_id,
label_class)
t_objs.add_obj(tor, ts_ns=2)
cx, cy, cz = centers[3]
qx, qy, qz, qw = yaw_to_quaternion3d(yaw=yaw_angles[3])
tor = TrackedObjRec(l, w, h, qx, qy, qz, qw, cx, cy, cz, track_id,
label_class)
t_objs.add_obj(tor, ts_ns=3)
t_objs.save_to_disk()
if not run_eval:
return None
pred_log_dir = f'{_ROOT}/test_data/eval_tracking_dummy_logs_pred'
gt_log_dir = f'{_ROOT}/test_data/eval_tracking_dummy_logs_gt'
out_fpath = f'{_ROOT}/test_data/{log_id}.txt'
out_file = open(out_fpath, 'w')
eval_tracks(path_tracker_output_root=pred_log_dir,
path_dataset_root=gt_log_dir,
d_min=0,
d_max=100,
out_file=out_file,
centroid_method="average",
diffatt=None,
category='VEHICLE')
out_file.close()
with open(out_fpath, 'r') as f:
result_lines = f.readlines()
result_vals = result_lines[0].strip().split(' ')
fn, num_frames, mota, motp_c, motp_o, motp_i, idf1 = result_vals[:7]
most_track, most_lost, num_fp, num_miss, num_sw, num_frag = result_vals[
7:]
# Todo: change `num_flag` to `num_frag`
result_dict = {
'filename': fn,
'num_frames': int(num_frames),
'mota': float(mota),
'motp_c': float(motp_c),
'motp_o': float(motp_o),
'motp_i': float(motp_i),
'idf1': float(idf1),
'most_track': float(most_track),
'most_lost': float(most_lost),
'num_fp': int(num_fp),
'num_miss': int(num_miss),
'num_sw': int(num_sw),
'num_frag': int(num_frag),
}
shutil.rmtree(pred_log_dir)
shutil.rmtree(gt_log_dir)
return result_dict