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()

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()

"""

:param X: B x n features

:param Y: (B, ) labels

:param title:

:return:

"""

Y = Y.astype(np.int)

tsne = manifold.TSNE(n_components=3, perplexity=30, early_exaggeration=4.0, learning_rate=1000, init='pca',

random_state=0, n_iter=10000, verbose=0, method='barnes_hut', angle=0.5)

X_tsne = tsne.fit_transform(X)

n = np.shape(X_tsne)[0]

#坐标缩放到[0,1]区间

x_min, x_max = np.min(X_tsne, axis=0), np.max(X_tsne, axis=0)

X_tsne = (X_tsne - x_min) / (x_max - x_min) # n x 3

#降维后的坐标为（X[i, 0], X[i, 1],X[i,2]），在该位置画出对应的digits

fig = plt.figure()

ax = fig.add_subplot(1, 1, 1, projection='3d')

mfig = mlab.figure(size=(1920, 1080), bgcolor=(1, 1, 1))

point_clouds = point_clouds / 1000 # n x 1024 x 3 you have to shrink point cloud for clearity

nb_points = np.shape(point_clouds)[1]

point_clouds += np.tile(X_tsne[:, np.newaxis, :], (1, nb_points, 1)) # n x 1024 x 3

for i in range(n):

ax.text(X_tsne[i, 0], X_tsne[i, 1], X_tsne[i, 2], str(Y[i]),

color=plt.cm.Set1((Y[i]+1) / 10.), # plt.cm.Set1(y[i] / 10.)

fontdict={'weight': 'bold', 'size': 24})

if point_clouds is not None:

mlab.points3d(point_clouds[i, :, 0], point_clouds[i, :, 1], point_clouds[i, :, 2],

point_clouds[i, :, 2] * 10 ** -9 + 0.5,

color=plt.cm.Set1((Y[i] + 1) / 10.)[:3],

scale_factor=0.003, figure=mfig)

else:

mlab.points3d(X_tsne[i, 0], X_tsne[i, 1], X_tsne[i, 2], X_tsne[i, 2]*10**-9+0.5,

color=plt.cm.Set1((Y[i] + 1) / 10.)[:3],

scale_factor=0.01, figure=mfig)

ax.grid(False)

if title is not None:

plt.title(title)

plt.grid(b=None)

plt.show()

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)

"""

input to the network should be cubic grid point cloud

:param net_input_point_cloud: 1024 x 3

:param first_layrer_output: 1024 x 64 number of points x features

:return:

"""

assert np.shape(first_layrer_output)[1] > square_plot_nb

nb_points = np.shape(net_input_point_cloud)[0]

fig = plt.figure(figsize=(19, 10), dpi=300, facecolor='w')

small_value = np.ones([square_plot_nb, 2])/.0 # create array of infs

for i in range(np.shape(first_layrer_output)[1]):

values = np.reshape(first_layrer_output[:, i], [-1])

idx = np.argpartition(values, int(nb_points*vis_rate))

idx = idx[:int(nb_points*vis_rate)]

mean_value = np.mean(idx)

if mean_value < np.max(small_value[:, 0]):

small_value[np.argmax(small_value, axis=0), :] = np.array([mean_value, i])

for j in range(np.shape(small_value)[0]):

i = int(small_value[j, 1])

values = np.reshape(first_layrer_output[:, i], [-1])

idx = np.argpartition(values, int(nb_points * vis_rate))

idx = idx[:int(nb_points * vis_rate)]

m_fig = mlab.figure(size=(500, 500), bgcolor=(1, 1, 1))

points = mlab.points3d(net_input_point_cloud[idx, 0], net_input_point_cloud[idx, 1], net_input_point_cloud[idx, 2],

net_input_point_cloud[idx, 2] * 10 ** -2 + 1, colormap='Spectral', scale_factor=0.1, figure=m_fig,

resolution=64)

points.glyph.glyph_source.glyph_source.phi_resolution = 64

points.glyph.glyph_source.glyph_source.theta_resolution = 64

# 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)

ax = fig.add_subplot(int(math.sqrt(square_plot_nb)), int(math.sqrt(square_plot_nb)), (j + 1))

ax.imshow(img)

ax.set_axis_off()

mlab.close()

plt.subplots_adjust(wspace=0, hspace=0)

"""

:param pc_path:

:param samples:

:param chamfer:

:param method: ball-default 0.05*range knn-default 64 points octree-default 64 points kdtree-default 3 layer

:return:

"""

f_list = [pc_path + '/' + i for j, i in enumerate(os.listdir(pc_path)) if

os.path.splitext(i)[1] == '.txt' and j < samples]

for i in f_list:

pc = PointCloud(i)

if method =='ball':

features = pc.generate_r_neighbor()

elif method =='knn':

pass

elif method == 'octree':

pass

elif method == 'kdtree':

source_o3dpc = o3d.geometry.PointCloud()

source_o3dpc.points = o3d.utility.Vector3dVector(sourcepc.position)

target_o3dpc = o3d.geometry.PointCloud()

target_o3dpc.points = o3d.utility.Vector3dVector(targetpc.position)

threshold = 0.02

draw_registration_result(source, target, trans_init)

print("Initial alignment")

evaluation = o3d.evaluate_registration(source, target,

threshold, trans_init)

print('evaluation:', evaluation)

print("Apply point-to-point ICP")

reg_p2p = o3d.registration_icp(source, target, threshold, init=np.eye(4),

estimation_method=o3d.TransformationEstimationPointToPoint())

print(reg_p2p)

print("reg_p2p Transformation is:")

print(reg_p2p.transformation)

print("transformation type is :", type(reg_p2p.transformation))

target.transform(trans_init)

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')

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')

if colorset is None:

colorset = [(226,50,226),(202,44,66),(111,41,66),(43,173,80),(51,200,200),(255,1,128),(23,48,217),(24,121,73)]

f_list = [pcs_path + '/' + i for i in os.listdir(pcs_path) if os.path.splitext(i)[1] == '.ply']

mfig = mlab.figure(bgcolor=(1, 1, 1))

for j,i in enumerate(f_list):

if j <=7:

pc = PointCloud(i)

mlab.points3d(pc.position[:, 0], pc.position[:, 1], pc.position[:, 2],

pc.position[:, 2] * 10 ** -9 + 1,

color=tuple((np.asarray(colorset[j],dtype=np.float)/255).tolist()),

scale_factor=3, figure=mfig)

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()

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')