def test_points():
"""PyntCloud.points.
- Points must be a pandas DataFrame
- DataFrame must have at least "x", "y" and "z" named columns
- When PyntCloud.points is re-assigned all structures must be removed
"""
points = np.random.rand(10, 3)
# not dataframe
with pytest.raises(TypeError):
PyntCloud(points)
points = pd.DataFrame(points)
# not x, y, z
with pytest.raises(ValueError):
PyntCloud(points)
points = pd.DataFrame(points.values, columns=["x", "y", "z"])
assert PyntCloud(points)
cloud = PyntCloud(points)
cloud.add_structure("voxelgrid")
assert len(cloud.structures) == 1
# dummy filter
x_above_05 = cloud.points["x"] > 0.5
cloud.points = cloud.points[x_above_05]
assert len(cloud.structures) == 0
#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]
# Removing the inliers from the Point Cloud
cloud.points = cloud.points.drop(cloud.points[mask].index)
# Calculating the distance of all the points from the Best Fit Plane
def distance(x1,y1,z1):
return (a * x1 + b * y1 + c * z1 + d) / e
cloud.points['distance'] = list(map(distance,cloud.points['x'],cloud.points['y'],cloud.points['z']))
np.savetxt(r'Plane_outliers_behind.txt',cloud.points[cloud.points['distance'] < 0],fmt = '%f')
feature_map_3d = haralick_features_3d(mat, win, dis, theta, levels, props)
# Results in different columns of the feature_map. Uncomment to use other features.
result = feature_map_3d[:, :, :, 0] #Contrast
# result = feature_map_3d[:,:,:,1] #Homogeneity
# result = feature_map_3d[:,:,:,2] #ASM
# result = feature_map_3d[:,:,:,3] #Energy
# result = feature_map_3d[:,:,:,4] #Correlation
# Normalizing the result
result = ((result - np.min(result)) / (np.max(result) - np.min(result))) * 255
# Mapping the results to the Point Cloud
for i in range(len(cloud.points.index)):
cloud.points.at[i, 'red'] = result[voxel.voxel_x[i], voxel.voxel_y[i],
voxel.voxel_z[i]]
cloud.points.at[i, 'green'] = result[voxel.voxel_x[i], voxel.voxel_y[i],
voxel.voxel_z[i]]
cloud.points.at[i, 'blue'] = result[voxel.voxel_x[i], voxel.voxel_y[i],
voxel.voxel_z[i]]
cloud.points = cloud.points.drop('grayscale', 1)
#cloud.to_file("Contrast_05.txt")
np.savetxt(r'Contrast_02.txt', cloud.points.values, fmt='%f')
end = time.time()
print(end - start)
