def saliancey2range(resolution_control=0.005):
for j, i in enumerate(f_list):
print(' point cloud is', i)
pc = PointCloud(i)
pc.down_sample(number_of_downsample=2048)
for k in range(4):
if k == 0:
k = -0.5
fig = pc.compute_key_points(percentage=0.1,
show_result=False,
resolution_control=resolution_control,
rate=0.05 * k + 0.05,
use_deficiency=False,
show_saliency=True)
f = mlab.gcf() # this two line for mlab.screenshot to work
f.scene._lift()
img = mlab.screenshot()
mlab.savefig(filename=str(j) + str(k) + '_without.png')
mlab.close()
fig = pc.compute_key_points(percentage=0.1,
show_result=False,
resolution_control=resolution_control,
rate=0.05 * k + 0.05,
use_deficiency=True,
show_saliency=True)
f = mlab.gcf() # this two line for mlab.screenshot to work
f.scene._lift()
img = mlab.screenshot()
mlab.savefig(filename=str(j) + str(k) + '_with.png')
mlab.close()
del pc
def pose_estimation(posefile='', real_single_h5='', model_filepath=''):
scene_idx = 19 # 0-53 for scene objcet 11 3 19 2
model_idx = 2 # 0-7 for 8 class of model objcet
poseset = tables.open_file(posefile, mode='r')
random_pose = poseset.root.random_pose[scene_idx, :]
predict_pose = poseset.root.predict_pose[scene_idx, :]
print('random_pose:',
poseset.root.random_pose[[0, 1, 4, 5, 6, 9, 11, 19], :])
print('predict_pose:',
poseset.root.predict_pose[[0, 1, 4, 5, 6, 9, 11, 19], :])
readh5 = h5py.File(real_single_h5)
for i in [0, 1, 4, 5, 6, 9, 11, 19]:
scene_pc = readh5['train_set'][i, :] # n * 1024 * 3
scene_pc = PointCloud(scene_pc)
scene_pc.save(path='pointcloud/fourkind/' + str(i) + '.ply')
print('scene_pc:', scene_pc)
model_pc = [
model_filepath + '/' + i for i in os.listdir(model_filepath)
if os.path.splitext(i)[1] == '.ply'
][model_idx]
model_pc = PointCloud(model_pc)
model_pc.down_sample()
light = np.array([[1.0, 0.0, 0.0], [0.0, 0.0, 1.0], [1.0, 1.0, 0.0],
[0.0, 1.0, 0.0]])
shade = light * 0.7
light1 = tuple(light[0, :].tolist())
shade1 = tuple(shade[0, :].tolist())
light2 = tuple(light[1, :].tolist())
shade2 = tuple(shade[1, :].tolist())
light3 = tuple(light[2, :].tolist())
shade3 = tuple(shade[2, :].tolist())
light4 = tuple(light[3, :].tolist())
shade4 = tuple(shade[3, :].tolist())
colorset = [[light4, light2], [shade2, light2], [shade3, light3],
[shade4, light4]]
# initial pose :
fig = show_pc.show_trans(scene_pc,
model_pc,
colorset=colorset,
scale=1,
returnfig=True)
filename1 = 'poseestimation/real/before_alignment1.png'
while (True):
if os.path.exists(filename1):
filename1 = filename1.split('.')[0][:-1] + str(
int(filename1.split('.')[0][-1]) + 1) + '.png'
continue
break
f = mlab.gcf() # this two line for mlab.screenshot to work
f.scene._lift()
mlab.savefig(filename=filename1)
print('before image saved')
mlab.close()
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 rneighbor2range():
for j, i in enumerate(f_list):
print(' point cloud is', i)
pc = PointCloud(i)
pc.down_sample(number_of_downsample=2048)
for k in range(4):
fig = pc.generate_r_neighbor(range_rate=0.025 * k + 0.025,
show_result=True)
pc.keypoints = None
f = mlab.gcf() # this two line for mlab.screenshot to work
f.scene._lift()
mlab.savefig(filename=str(j) + str(k) + 'r.png')
mlab.close()
del pc
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 txt2normalply(txt_path, write_path='/ply/'):
"""
:param txt_path:
:param write_path:
:return: nothing, write file into write_path
"""
for i, j, k in os.walk(txt_path):
if i == txt_path:
for m, l in enumerate(k):
a = np.loadtxt(i + '/' + l)
PC = PointCloud(a)
PC.down_sample(number_of_downsample=1024)
pc = o3d.PointCloud()
pc.points = o3d.Vector3dVector(PC.position)
o3d.estimate_normals(
pc, o3d.KDTreeSearchParamHybrid(radius=10, max_nn=10))
o3d.write_point_cloud(i + write_path + str(m) + '.ply', pc)
def key_points_plot(flist):
for i in flist:
Pc = PointCloud(i)
Pc.down_sample(4096)
fig = Pc.compute_key_points(percentage=0.1,
resolution_control=None,
show_result=True)
f = mlab.gcf() # this two line for mlab.screenshot to work
f.scene._lift()
img = mlab.screenshot()
mlab.savefig(filename=str(i) + 'key_points.png')
mlab.close()
fig = Pc.compute_key_points(percentage=0.1,
resolution_control=0.01,
show_result=True)
f = mlab.gcf() # this two line for mlab.screenshot to work
f.scene._lift()
img = mlab.screenshot()
mlab.savefig(filename=str(i) + 'key_points_with_resolution_ctrl.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 save_data(save_path='',
base_path='',
n=5000,
use_key_feature=True,
train_data=True,
nb_types=4,
show_result=False,
normalize=True,
shuffle=True):
"""
transform the txt point clouds into h5py dataset for simplicity. data augmentation of projection is implemented here
:param save_path:
:param n:
:param train_data whether it is training data or it is test data. if its testdata, label is random.
:param base_path: path contains txt or ply point cloud data
:param use_key_feature: if you want to use the local key features
:param nb_types: number of classes of used object
:return:
"""
compute_time = []
if train_data:
pc_tile = np.empty(shape=(nb_types * n, 1024, 3))
if use_key_feature:
pc_key_feature = np.empty(shape=(nb_types * n, int(
1024 * 0.1), 9)) # key feature space, 102=1024*0.1,
# 9 for multi-scale eigen-value
#pc_pl = tf.placeholder(tf.float32, shape=(1, 1024, 3))
for k in range(
nb_types
): # number of types of objects model, test data can ignore type label
for i, j in enumerate(range(k * n, (k + 1) * n)):
if i % 10 == 0:
print('reading number', i + 1,
'th lab' + str(k + 1) + ' point clouds')
if use_key_feature:
pc = np.loadtxt(
base_path + '/lab' + str(k + 1) + '/lab_project' +
str(i) + '.txt'
) # pc = tf.convert_to_tensor(pc, dtype=tf.float32)
pc = PointCloud(pc)
if normalize:
pc.normalize(
) # partial point cloud should not normalize
expand = np.expand_dims(pc.position, axis=0)
pc_tile[j, :, :] = expand
# print('*****************************************')
# print('reading point cloud cost time:{}'.format(t1 - t0))
pc_key_eig = get_local_eig_np(
expand, useiss=False) # 1 x nb_keypoints x 9
# print('*****************************************')
# print('get local cost time:{}'.format(t2 - t1))
#pc_key_feature[i, :, :] = np.squeeze(sess.run(pc_key_eig, feed_dict={pc_pl: pc}))
pc_key_feature[j, :, :] = np.squeeze(pc_key_eig)
else:
pc_tile[j, :, :] = np.expand_dims(
np.loadtxt(base_path + '/lab' + str(k + 1) +
'/lab_project' + str(i) + '.txt'),
axis=0)
# print('-----------------------------------------')
# print('one pc cost total:{}second'.format(te-ts))
# print('----------------------------------------')
pc_label = np.tile(np.arange(nb_types), (n, 1)).reshape((-1, ),
order='F')
train_set_shape = (nb_types * n, 1024, 3)
train_set_local_shape = (nb_types * n, 102, 9)
train_label_shape = (nb_types * n, )
else:
ply_list = [
base_path + '/' + i for i in os.listdir(base_path)
if os.path.splitext(i)[1] == '.ply'
or os.path.splitext(i)[1] == '.txt'
]
n = len(ply_list)
less_than_th = []
for i, j in enumerate(ply_list):
pc = PointCloud(j)
if pc.nb_points < 1024:
less_than_th.append(i)
n = n - len(less_than_th)
print("there are: ,", n, ' point clouds with # of points available')
pc_label = np.arange(n)
train_set_shape = (n, 1024, 3)
train_set_local_shape = (n, 102, 9)
train_label_shape = (n, )
pc_tile = np.empty(shape=(n, 1024, 3))
pc_key_feature = np.empty(shape=(n, int(
1024 * 0.1), 9)) # key feature space, 102=1024*0.1,
for i, j in enumerate(ply_list):
if i not in less_than_th:
print(j)
start_time = time.clock()
mypc = PointCloud(j)
if mypc.nb_points > 1024:
mypc.down_sample(number_of_downsample=1024)
if normalize:
mypc.normalize()
expand = np.expand_dims(mypc.position, axis=0)
pc_tile[i, :, :] = expand
pc_key_eig = get_local_eig_np(expand, useiss=False)
end_time = time.clock()
compute_time.append([end_time - start_time])
if use_key_feature:
pc_key_feature[i, :, :] = np.squeeze(pc_key_eig)
hdf5_file = h5py.File(save_path, mode='a')
hdf5_file.create_dataset('train_set', train_set_shape,
np.float32) # be careful about the dtype
hdf5_file.create_dataset('train_labels', train_label_shape, np.uint8)
hdf5_file.create_dataset('train_set_local', train_set_local_shape,
np.float32)
if shuffle:
idx = np.arange(np.shape(pc_tile)[0])
np.random.shuffle(idx)
pc_tile = pc_tile[idx, ...]
pc_label = pc_label[idx, ...]
pc_key_feature = pc_key_feature[idx, ...]
hdf5_file["train_set"][...] = pc_tile
hdf5_file["train_labels"][...] = pc_label
hdf5_file["train_set_local"][...] = pc_key_feature
hdf5_file.close()
return compute_time