def projection_plot(pcpath='', noise=0.05, outlier=0.05, savefig=False):
f_list = [
pcpath + '/' + i for i in os.listdir(pcpath)
if os.path.splitext(i)[1] == '.ply'
]
fig = plt.figure(figsize=(38, 20), dpi=600, facecolor='w')
colunms = 8
for i, j in enumerate(f_list):
for k in range(colunms):
pc = PointCloud(j)
pc.down_sample(number_of_downsample=10000)
pc.add_noise(noise)
pc.add_outlier(outlier)
pts_size = 2.5
if i == 7:
pts_size = 1
try:
mfig = pc.half_by_plane(n=1024,
grid_resolution=(200, 200),
show_result=pts_size)
except:
try:
mfig = pc.half_by_plane(n=1024,
grid_resolution=(250, 250),
show_result=pts_size)
except:
try:
mfig = pc.half_by_plane(n=1024,
grid_resolution=(300, 300),
show_result=pts_size)
except:
mfig = pc.half_by_plane(n=1024,
grid_resolution=(650, 650),
show_result=pts_size)
f = mlab.gcf() # this two line for mlab.screenshot to work
f.scene._lift()
if savefig:
mlab.savefig(str(i) + str(k) + '.png')
img = mlab.screenshot(figure=mfig)
mlab.close()
ax = fig.add_subplot(len(f_list), colunms, i * colunms + k + 1)
ax.imshow(img)
ax.set_axis_off()
plt.subplots_adjust(wspace=0, hspace=0)
if savefig:
plt.savefig('projection.png')
plt.show()
plt.close()
def scene_seg_dataset(pc_path,
save_path,
samples=1000,
max_nb_pc=5,
show_result=False):
"""
default number of points of each point cloud is 1024
:param pc_path:
:param save_path:
:param max_nb_pc:
:return:
"""
f_list = [
PointCloud(pc_path + '/' + i) for i in os.listdir(pc_path)
if os.path.splitext(i)[1] == '.ply'
]
for i in f_list:
i.down_sample()
nb_classes = len(f_list)
scene_dataset = np.zeros((samples, max_nb_pc * 1024, 3))
scene_label = np.zeros((samples, max_nb_pc * 1024), dtype=np.int32)
rate_180, rate_240, rate_300, rate_360 = [0, 0, 0, 0]
for i in range(samples):
print('generating the {}th scene sample'.format(i))
nb_pc = np.random.choice(max_nb_pc) + 1
nb_pc = max_nb_pc
for j in range(nb_pc):
k = np.random.choice(nb_classes)
pc = f_list[k]
pc.transform()
pc.cut_by_plane()
pc2 = PointCloud(pc.visible)
try:
pc2.half_by_plane(n=1024, grid_resolution=(190, 190))
rate_180 += 1
except:
try:
pc2.half_by_plane(n=1024, grid_resolution=(260, 260))
rate_240 += 1
except:
try:
pc2.half_by_plane(n=1024, grid_resolution=(330, 330))
rate_300 += 1
except:
pc2.half_by_plane(n=1024, grid_resolution=(400, 400))
rate_360 += 1
scene_dataset[i, j * 1024:j * 1024 + 1024, :] = pc2.visible
scene_label[i, j * 1024:j * 1024 + 1024] = k
print('180 240 300 360:', rate_180, rate_240, rate_300, rate_360)
if show_result:
for i in range(1):
scene_pc = scene_dataset[i, :, :]
scene_pc = PointCloud(scene_pc) #
scene_lb = scene_label[i, :]
figure = mlab.figure(size=(1000, 1000), bgcolor=(1, 1, 1))
colors = (np.random.random((nb_classes, 4)) * 255).astype(np.int8)
colors[:, -1] = 255
colors = colors[scene_lb, :]
scalars = np.arange(np.shape(colors)[0])
pts = mlab.quiver3d(scene_pc.position[:, 0],
scene_pc.position[:, 1],
scene_pc.position[:, 2],
scene_pc.position[:, 0] * 10**-9 + 1,
scene_pc.position[:, 0] * 10**-9 + 1,
scene_pc.position[:, 0] * 10**-9 + 1,
scalars=scalars,
scale_factor=1,
mode='sphere',
figure=figure)
pts.glyph.color_mode = 'color_by_scalar'
pts.module_manager.scalar_lut_manager.lut.table = colors
mlab.show()
hdf5_file = h5py.File(save_path, mode='a')
hdf5_file.create_dataset('train_set', (samples, max_nb_pc * 1024, 3),
np.float32) # be careful about the dtype
hdf5_file.create_dataset('train_labels', (samples, max_nb_pc * 1024),
np.uint8)
hdf5_file["train_set"][...] = scene_dataset
hdf5_file["train_labels"][...] = scene_label
hdf5_file.close()