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 knn_plot(pc_path=''):
f_list = [
base_path + '/' + i for i in os.listdir(base_path)
if os.path.splitext(i)[1] == '.ply'
]
for j, i in enumerate(f_list):
if j < 4:
pc = PointCloud(i)
pc.down_sample(number_of_downsample=4096)
pc.add_noise(factor=0.04)
pc.add_outlier(factor=0.04)
fig = pc.compute_key_points(
percentage=0.02,
resolution_control=1 / 15,
rate=0.05,
use_deficiency=False,
show_result=True) # get the key points id
f = mlab.gcf() # this two line for mlab.screenshot to work
f.scene._lift()
mlab.savefig(filename=str(j) + '_0.png')
mlab.close()
colorset = np.random.random((100, 3))
fig = pc.generate_k_neighbor(k=32,
show_result=True,
colorset=colorset)
f = mlab.gcf() # this two line for mlab.screenshot to work
f.scene._lift()
mlab.savefig(filename=str(j) + '_1.png')
mlab.close()
fig = pc.generate_k_neighbor(k=64,
show_result=True,
colorset=colorset)
f = mlab.gcf() # this two line for mlab.screenshot to work
f.scene._lift()
mlab.savefig(filename=str(j) + '_2.png')
mlab.close()
fig = pc.generate_k_neighbor(k=128,
show_result=True,
colorset=colorset)
f = mlab.gcf() # this two line for mlab.screenshot to work
f.scene._lift()
mlab.savefig(filename=str(j) + '_3.png')
mlab.close()
def noise_outliers(pointclous, noise=0.05, outliers=0.05):
fig = plt.figure(figsize=(38, 20), dpi=600, facecolor='w')
for j, i in enumerate(pointclous):
pc = PointCloud(i)
pc.down_sample(number_of_downsample=1024)
for k in range(4):
if k == 3:
k = 4
pc.add_noise(factor=k * noise)
pc.add_outlier(factor=k * outliers)
m_fig = mlab.figure(bgcolor=(1, 1, 1))
mlab.points3d(pc.position[:, 0],
pc.position[:, 1],
pc.position[:, 2],
pc.position[:, 2] * 10**-2 + 1,
colormap='Spectral',
scale_factor=2,
figure=m_fig)
# mlab.gcf().scene.parallel_projection = True # parallel projection
f = mlab.gcf() # this two line for mlab.screenshot to work
f.scene._lift()
# mlab.show() # for testing
img = mlab.screenshot(figure=m_fig)
mlab.close()
if k == 4:
k = 3
ax = fig.add_subplot(4, 8, (j + 1) + k * 8)
ax.imshow(img)
ax.set_axis_off()
plt.subplots_adjust(wspace=0, hspace=0)
plt.show()
def augment_data(base_path='',
pc_path='',
add_noise=0.04,
add_outlier=0.04,
n=5000,
not_project=False,
show_result=False):
pc = PointCloud(pc_path)
pc.down_sample()
if add_noise is not None:
pc.add_noise(factor=add_noise)
if add_outlier is not None:
pc.add_outlier(factor=add_outlier)
if not_project:
for i in range(n):
if i % 10 == 0:
print('saving number', i + 1,
'th lab random sample point clouds')
temp = deepcopy(pc)
temp.down_sample(number_of_downsample=1024)
np.savetxt(base_path + '/random_sample' + str(i) + '.txt',
temp.position,
delimiter=' ')
else:
for i in range(n):
if i % 10 == 0:
print('saving number', i + 1, 'th lab_project point clouds')
#pc.cut_by_plane() # todo manually
#pc2 = PointCloud(pc.visible)
pc2 = pc
try:
pc2.half_by_plane(n=1024, grid_resolution=(200, 200))
except:
try:
pc2.half_by_plane(n=1024, grid_resolution=(250, 250))
except:
try:
pc2.half_by_plane(n=1024, grid_resolution=(300, 300))
except:
pc2.half_by_plane(n=1024, grid_resolution=(650, 650))
np.savetxt(base_path + '/lab_project' + str(i) + '.txt',
pc2.visible,
delimiter=' ') # pc.visible will variant
if show_result:
dir_list = [
base_path + '/' + i for i in os.listdir(base_path)
if os.path.isdir(i)
]
fig = mlab.figure(size=(1000, 1000), bgcolor=(1, 1, 1))
for i in dir_list:
color = np.random.random((1, 3))
pc = np.loadtxt(i + '/lab_project1')
mlab.points3d(pc[:, 0],
pc[:, 1],
pc[:, 2],
pc[:, 2] * 10**-9,
color=color,
figure=fig)
def test_data(h5_path='', rand_trans=False, showinone=False):
"""
test and show if the h5 point cloud data is generate correctly
:param h5_path:
:param rand_trans:
:param showinone: show the whole batch of point clouds in one picture
:return:
"""
h5file = read_data(h5_path)
trainset = h5file['train_set'][...]
train_local = h5file['train_set_local'][...]
print('train_local:', train_local, 'train_local shape:', train_local.shape)
if rand_trans:
trainset += -300 + 600 * np.random.random(
size=(20000, 1, 3)) # 20000 * 1024 * 3
if showinone:
ind = np.random.choice(20000, 20)
points = trainset[ind, :, :]
points = np.reshape(points, [-1, 3])
points = PointCloud(points)
points.show()
for i in range(1):
fig = plt.figure()
for k in range(4):
a = np.squeeze(trainset[1 + k * 5000, :, :])
a = PointCloud(a)
origin = a.show(not_show=True)
mlab.show(origin)
mlab.gcf().scene.parallel_projection = True # parallel projection
f = mlab.gcf() # this two line for mlab.screenshot to work
f.scene._lift()
img = mlab.screenshot(antialiased=False)
mlab.close()
ax = fig.add_subplot(4, 4, 1 + k * 4)
ax.imshow(img)
ax.set_axis_off()
a.add_noise(factor=5 / 100)
noise = a.show(not_show=True)
mlab.show(noise)
mlab.gcf().scene.parallel_projection = True # parallel projection
f = mlab.gcf() # this two line for mlab.screenshot to work
f.scene._lift()
img = mlab.screenshot()
mlab.close()
ax = fig.add_subplot(4, 4, 2 + k * 4)
ax.imshow(img)
ax.set_axis_off()
a.add_outlier(factor=5 / 100)
outlier = a.show(not_show=True)
mlab.show(outlier)
mlab.gcf().scene.parallel_projection = True # parallel projection
f = mlab.gcf() # this two line for mlab.screenshot to work
f.scene._lift()
img = mlab.screenshot()
mlab.close()
ax = fig.add_subplot(4, 4, 4 + k * 4)
ax.imshow(img)
ax.set_axis_off()
a = np.squeeze(trainset[1 + k * 5000, :, :])
a = PointCloud(a)
a.add_outlier(factor=5 / 100)
outlier = a.show(not_show=True)
mlab.show(outlier)
mlab.gcf().scene.parallel_projection = True # parallel projection
f = mlab.gcf() # this two line for mlab.screenshot to work
f.scene._lift()
img = mlab.screenshot()
mlab.close()
ax = fig.add_subplot(4, 4, 3 + k * 4)
ax.imshow(img)
ax.set_axis_off()
plt.subplots_adjust(wspace=0, hspace=0)
plt.show()