def create_training_data():
training_data = []
print("Start loading training_data")
rotater = create_rotator()
counter = 0
for k in range(0, 441):
cloud = PyntCloud.from_file("B%d.ply" % (k))
cloud.add_scalar_field("hsv")
voxelgrid_id = cloud.add_structure("voxelgrid", x_y_z=[128, 128, 128])
points = cloud.get_sample("voxelgrid_centroids",
voxelgrid_id=voxelgrid_id)
new_cloud = PyntCloud(points)
cloud1_test = new_cloud.get_sample(name="points_random", n=1024)
new_cloud = PyntCloud(cloud1_test)
xyz_load = np.asarray(new_cloud.points)
#print(xyz_load)
training_data.append([xyz_load])
#new_cloud.to_file("out_file_%d.ply" % (k))
#for r in range(0,len(rotater)):
#training_data.append([])
#for i in range(0,len(xyz_load)):
#c = np.dot(xyz_load[i],rotater[r])
#training_data[counter].append([c[0],c[1],c[2]])
#counter = counter + 1
print(len(training_data))
print("data loaded")
print("shuffle training data")
training_data = np.asarray(training_data)
print("getting Trainingdata into the right format")
training_data = training_data.reshape(441, 3072)
print(" trainingdata formated")
return training_data
def uniform_mesh_sampling(V,
F,
num_samples=2048,
grid_res=64,
mesh_samples=10**4):
X_ = sample_faces(V, F, n_samples=mesh_samples)
cloud = np_mat_to_pandas(X_)
cloud = PyntCloud(cloud)
voxelgrid_id = cloud.add_structure("voxelgrid",
n_x=grid_res,
n_y=grid_res,
n_z=grid_res)
# new_cloud = cloud.get_sample("voxelgrid_nearest", voxelgrid_id=voxelgrid_id, as_PyntCloud=True)
new_cloud = cloud.get_sample("voxelgrid_centroids",
voxelgrid_id=voxelgrid_id,
as_PyntCloud=True)
X = new_cloud.points.as_matrix()
X = sample_points(X, num_samples)
return X
pcd_list.append(jittered)
points = np.concatenate((pcd_list), axis=0)
# Normalize coordinates
centroid = np.mean(points, axis=0)
points -= centroid
furthest_distance = np.max(np.sqrt(np.sum(abs(points)**2, axis=-1)))
points /= furthest_distance
# Recreate pandas points from normalized points
dataset = pd.DataFrame({
'x': points[:, 0],
'y': points[:, 1],
'z': points[:, 2]
})
norm_point_cloud = PyntCloud(dataset)
sampled_cloud = norm_point_cloud.get_sample("points_random",
n=NUM_POINT,
as_PyntCloud=True)
points = sampled_cloud.points[["x", "y", "z"]].values
sampled_cloud.to_file(
os.path.join(BASE_DIR, "data", "assets", "test", "norm_" + ply_file))
#point_cloud = ply_data["vertex"].data
point_cloud_list = [[row[0], row[1], row[2]] for row in points]
#result_array[idx] = point_cloud_list
data_list.append(point_cloud_list)
# top = max([len(sublist) for sublist in data_list])
# Indexes from 'shape_names.txt'
labels_array = np.array(label_list, dtype="uint8")
# for L in data_list:
# for coords in L:
x = np.swapaxes(x, 0, 1)
#x = x[np.where(([0, -40] <= x[:, :2]) & (x[:, :2] <= [70, 40]))]
x = x[(x[:, 0] >= 0) & (x[:, 0] <= 70) & (x[:, 1] >= -40) & (x[:, 1] <= 40)]
print(x.shape)
df = pd.DataFrame(x, columns=['x', 'y', 'z'])
print(df.describe())
cloud = PyntCloud(df)
voxelgrid_id = cloud.add_structure("voxelgrid", n_x=512, n_y=448, n_z=32)
voxelgrid = cloud.structures[voxelgrid_id]
density_feature_vector = voxelgrid.get_feature_vector(mode="density")
print(density_feature_vector.shape)
new_cloud = cloud.get_sample("voxelgrid_nearest",
n=8,
voxelgrid_id=voxelgrid_id,
as_PyntCloud=True)
#new_cloud.plot()
print(new_cloud)
e_time = time.time()
print(e_time - s_time)
"""
x_points = x[:, 0]
y_points = x[:, 1]
_, ax = plt.subplots(1, 1, figsize=(7, 8))
ax.scatter(x_points, y_points, s=0.2)
"""
#plt.show()
