def compute_normal_and_curvature(points, k = 30):
"""
Args:
points: pointcloud-Nx3
k: number of pointset
Returns:
data = [x, y, z, nx, ny, nz, curvature]
"""
assert points.shape[1] == 3
points = pd.DataFrame(points)
points.columns = ['x', 'y', 'z']
cloud = PyntCloud(points)
k_neighbors = cloud.get_neighbors(k=k)
cloud_normal = copy.copy(cloud)
cloud_normal = cloud_normal.add_scalar_field("normals", k_neighbors=k_neighbors)
ev = copy.copy(cloud)
ev = cloud.add_scalar_field("eigen_values", k_neighbors=k_neighbors)
cloud.add_scalar_field("curvature", ev=ev)
points = cloud.points
nx = "nx(" + (k + 1).__str__() + ")"
ny = "ny(" + (k + 1).__str__() + ")"
nz = "nz(" + (k + 1).__str__() + ")"
curvature = "curvature(" + (k + 1).__str__() + ")"
filter_columns = ['x', 'y', 'z', nx, ny, nz, curvature]
data = points.reindex(columns=filter_columns)
return data
def findPlane(points):
indices = array([range(0, len(points.data.coordinate[0]))])
pointsWithIndices = concatenate((indices.T, points.data.coordinate[0]),
axis=1)
cloud = PyntCloud(
DataFrame(pointsWithIndices, columns=['index', 'x', 'y', 'z']))
cloud.add_scalar_field('plane_fit', max_dist=1)
plane = cloud.points.query('is_plane == 1').to_numpy()
return plane.T[0].astype(int)
def find_plane(points):
pointcloud = PyntCloud(points)
pointcloud.add_scalar_field("plane_fit",
max_dist=0.2,
max_iterations=1000,
n_inliers_to_stop=None)
inliers = pointcloud.points[pointcloud.points.is_plane == 1]
outliers = pointcloud.points[pointcloud.points.is_plane == 0]
return pointcloud
return inliers, outliers
def computeNormals(model):
cloudable = pd.DataFrame(data=np.transpose(model), columns=['x', 'y', 'z'])
# calculates a pointcloud of the input model using a pandas DataFrame
cloud = PyntCloud(cloudable)
# use neighbors to get normals from pointcloud
neighbors = cloud.get_neighbors(k=10)
cloud.add_scalar_field('normals', k_neighbors=neighbors)
# extract normals from the altered DataFrame
normals = np.transpose(np.asarray(cloudable.loc[:, 'nx(10)':'nz(10)']))
master = np.repeat(masterOrig, len(normals[0]), axis=1)
# taking the dot product column-wise; the 'ij,ij->' notation is saying:
# take dot product of ith row, jth column of normals and ith row, jth column of master
# then, create boolean mask array for normal comparison
I = np.einsum('ij,ij->j', normals, master) < 0
# flip all values in column if I is true at that column (dot prod < 0)
normals[:, I] = -normals[:, I]
return (normals)
def pyntcloud_and_eigenvalues():
cloud = PyntCloud(pd.DataFrame(
data={
"x": np.array([0, 0, 1, -1, 0, 0], dtype=np.float32),
"y": np.array([0, 1, 0, 0, -1, 0], dtype=np.float32),
"z": np.array([0, 0, 0, 0, 0, 2], dtype=np.float32)
}))
k_neighbors = cloud.get_neighbors(k=4)
ev = cloud.add_scalar_field("eigen_values", k_neighbors=k_neighbors)
return cloud, ev
#Loading the point cloud as a Pandas DataFrame. Give Path in line 101 accordingly.
data = pd.read_csv('cloud(rgb).txt',sep = " ")
data.columns = ['x','y','z','red','green','blue','1','2','3','4','5']
#data = data.drop(['1','2','3','4','5'],axis = 1)
cloud = PyntCloud(data)
print(cloud)
#Cloud.points is a Pandas Dataframe.
#print(cloud.points.columns)
# Calculating the best fit plane on the point cloud data and getting the equation of the plane in return
_,eq = cloud.add_scalar_field(name = 'plane_fit',max_dist = 5*1e-2)
print(eq)
a = eq[0]
b = eq[1]
c = eq[2]
d = eq[3]
#dis = abs((a * x1 + b * y1 + c * z1 + d))
e = (math.sqrt(a * a + b * b + c * c))
# Making a mask over the points where the inliers of the best fit plane are selected
mask = cloud.points['is_plane'] == 1
np.savetxt(r'Plane_inliers.txt',cloud.points[mask], fmt = '%f')
#cloud.points[mask]
