def plot_lidar(lidar):
print(lidar.shape)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
for i in range(len(lidar)):
ax.scatter(lidar[i, 0],
lidar[i, 1],
lidar[i, 2],
s=0.01,
c=[0.5, 0.5, 0.5])
plt.axis('equal')
# ax.set_xlabel('LabelX')
# ax.set_ylabel('LabelY')
# ax.set_zlabel('LabelZ')
# ax.set_xlim(30, 40)
# ax.set_ylim(-10, 10)
# ax.set_zlim(0, 10)
plt.show()
lidars = utils.load_velo_scans(['30.bin'])
intensity_max = np.max(lidars[0][:, 3])
print('shape: ' + str(lidars[0].shape) + ' intensity max: ' +
str(intensity_max))
plot_lidar(lidars[0])
top, top_image = data.lidar_to_top(lidars[0])
cv2.imwrite('30.png', top_image)
def handler_raw(tag):
scan = lidar.load(tag)
top = data.lidar_to_top(scan)
front = p.lidar_to_front_fast(scan)
os.makedirs(os.path.join(cfg.RAW_DATA_SETS_DIR, 'top_view',
*tag.split('/')[:-1]),
exist_ok=True)
os.makedirs(os.path.join(cfg.RAW_DATA_SETS_DIR, 'front_view',
*tag.split('/')[:-1]),
exist_ok=True)
np.save(os.path.join(cfg.RAW_DATA_SETS_DIR, 'top_view', tag + '.npy'), top)
np.save(os.path.join(cfg.RAW_DATA_SETS_DIR, 'front_view', tag + '.npy'),
front)
print(tag)
def handler_test(fname):
scan = np.fromfile(fname, dtype=np.float32).reshape((-1, 4))
top = data.lidar_to_top(scan)
tag = fname.split('/')[-1].split('.')[-2]
front = p.lidar_to_front_fast(scan)
os.makedirs(os.path.join(base_dataset_path, "testing", "front_view"),
exist_ok=True)
os.makedirs(os.path.join(base_dataset_path, "testing", "top_view"),
exist_ok=True)
np.save(
os.path.join(base_dataset_path, "testing", "front_view", tag + '.npy'),
front)
np.save(
os.path.join(base_dataset_path, "testing", "top_view", tag + '.npy'),
top)
print(fname)
def plot_lidar(lidar):
print(lidar.shape)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
for i in range(len(lidar)):
ax.scatter(lidar[i,0], lidar[i,1], lidar[i,2],s=0.01,
c= [0.5,0.5,0.5])
plt.axis('equal')
# ax.set_xlabel('LabelX')
# ax.set_ylabel('LabelY')
# ax.set_zlabel('LabelZ')
# ax.set_xlim(30, 40)
# ax.set_ylim(-10, 10)
# ax.set_zlim(0, 10)
plt.show()
lidars= utils.load_velo_scans(['30.bin'])
intensity_max=np.max(lidars[0][:,3])
print('shape: '+str(lidars[0].shape)+' intensity max: '+str(intensity_max))
plot_lidar(lidars[0])
top, top_image = data.lidar_to_top(lidars[0])
cv2.imwrite('30.png', top_image)
import random
import numpy as np
from data import lidar_to_top, Preprocess
pro = Preprocess()
import pycuda.autoinit
lidar = np.fromfile(
'/home/maxiaojian/data/kitti/object/training/velodyne/007480.bin',
dtype=np.float32)
lidar = lidar.reshape((-1, 4))
t0 = time.time()
top = lidar_to_top_cuda(lidar)
t1 = time.time()
print('done top, {}'.format(t1 - t0))
top_gt = lidar_to_top(lidar)
t2 = time.time()
print('done top cpu, {}'.format(t2 - t1))
assert (top.shape == top_gt.shape)
assert (np.sum(top[..., 0:25] != top_gt[..., 0:25]) == 0)
assert (np.sum(top[..., 26] != top_gt[..., 26]) == 0)
from IPython import embed
embed()
# front = lidar_to_front_cuda(lidar)
# t3 = time.time()
# print('done front, {}'.format(t3-t2))
# front_gt = pro.lidar_to_front_fast(lidar)
# t4 = time.time()
# print('done front cpu, {}'.format(t4-t3))
# assert(front.shape == front_gt.shape)
# assert(np.sum(front != front_gt) == 0)