<jupyter_start><jupyter_text>![lol](img/seg_od_clustering.png)<jupyter_code>np.savetxt('examples/000066.bbox', clusters)<jupyter_output><empty_output><jupyter_text>![lol](img/seg_od_clustering_bbox_000066.png)  Sequence: bboxes and stats getting<jupyter_code>import glob
seq = '00'
scan_lst = sorted(glob.glob("../dataset/sequences/" + seq + "/velodyne/*.bin"))
labels_lst = sorted(glob.glob("../dataset/sequences/" + seq + "/labels/*.label"))
assert len(scan_lst) == len(labels_lst)
print('READ OK, number of scans ', len(scan_lst))
from scripts import test
test = imp.reload(test)
%%time
# n_scan = -1
write_path = '../dataset/sequences/' + seq + '/clusters/'
# write_path = None
bbox_dct, exec_time_dct, stats = test.get_bbox_and_stat(scan_lst, labels_lst, mapped_obstacles_lst,
                                                        pipeline.pipeline, write_path=write_path, seg_model=user,
                                                        OBB=False, detailed=True, **params)
steps = list(stats[0].keys())
steps_exec_time = []
for step in steps:
    steps_exec_time.append([stat[step] for stat in stats])
plt.figure(figsize=(14,5))
for i, step in enumerate(steps):
    plt.plot(steps_exec_time[i], label=step)
plt.legend(loc='best')
plt.xlabel('Scan')
plt.ylabel('Time, seconds')
plt.grid()
plt.show()
time_stat = list(exec_time_dct.values())
print('Min process time {} s'.format(min(time_stat)))
print('Mean process time {} s'.format(np.mean(time_stat)))
print('Max process time {} s'.format(max(time_stat)))
ox = [int(x) for x in list(exec_time_dct.keys())]
oy = time_stat
plt.figure(figsize=(16, 5))
plt.plot(ox, oy)
plt.xlabel('Scan')
plt.ylabel('Time, seconds')
plt.grid()
plt.xticks(rotation=45)
plt.show()<jupyter_output><empty_output>
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