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 load_velo(self):
"""Load velodyne [x,y,z,reflectance] scan data from binary files."""
# Find all the Velodyne files
velo_path = os.path.join(self.data_path, 'velodyne_points', 'data',
'*.bin')
velo_files = sorted(glob.glob(velo_path))
# Subselect the chosen range of frames, if any
if self.frame_range:
velo_files = [velo_files[i] for i in self.frame_range]
print('Found ' + str(len(velo_files)) + ' Velodyne scans...')
# Read the Velodyne scans. Each point is [x,y,z,reflectance]
self.velo = utils.load_velo_scans(velo_files)
print('done.')
def load_velo(self):
"""Load velodyne [x,y,z,reflectance] scan data from binary files."""
# Find all the Velodyne files
velo_path = os.path.join(
self.data_path, 'velodyne_points', 'data', '*.bin')
velo_files = sorted(glob.glob(velo_path))
# Subselect the chosen range of frames, if any
if self.frame_range:
velo_files = [velo_files[i] for i in self.frame_range]
print('Found ' + str(len(velo_files)) + ' Velodyne scans in ' + velo_path)
# Read the Velodyne scans. Each point is [x,y,z,reflectance]
self.velo = utils.load_velo_scans(velo_files)
print('done.')
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)
top_image = (top_image / np.max(top_image) * 255)
top_image = np.dstack((top_image, top_image, top_image)).astype(np.uint8)
cv2.imwrite('top_image_1.png', top_image)
for i in range(8):
top_image = top[:,:,i]
top_image = top_image - np.min(top_image)
top_image = (top_image / np.max(top_image) * 255)
top_image = np.dstack((top_image, top_image, top_image)).astype(np.uint8)
cv2.imwrite('top_image_{}.png'.format(i), top_image)
if 1:
import mayavi.mlab as mlab
from show_lidar import *
lidars= utils.load_velo_scans(['kitti_005_0000000000.bin'])
lidar=lidars[0]
intensity_max=np.max(lidars[0][:,3])
fig = mlab.figure(figure=None, bgcolor=(0, 0, 0), fgcolor=None, engine=None, size=(500, 500))
mlab.clf(fig)
draw_didi_lidar(fig, lidar, is_grid=1, is_axis=1)
mlab.show()
# 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)
cv2.imwrite('top_image_1.png', top_image)
for i in range(8):
top_image = top[:, :, i]
top_image = top_image - np.min(top_image)
top_image = (top_image / np.max(top_image) * 255)
top_image = np.dstack(
(top_image, top_image, top_image)).astype(np.uint8)
cv2.imwrite('top_image_{}.png'.format(i), top_image)
if 1:
import mayavi.mlab as mlab
from show_lidar import *
lidars = utils.load_velo_scans(['kitti_005_0000000000.bin'])
lidar = lidars[0]
intensity_max = np.max(lidars[0][:, 3])
fig = mlab.figure(figure=None,
bgcolor=(0, 0, 0),
fgcolor=None,
engine=None,
size=(500, 500))
mlab.clf(fig)
draw_didi_lidar(fig, lidar, is_grid=1, is_axis=1)
mlab.show()
# 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)