Source code for pyntcloud.core_class

import os

import numpy as np
import pandas as pd

from .structures.base import StructuresDict
from .filters import ALL_FILTERS
from .io import FROM_FILE, TO_FILE, FROM_INSTANCE, TO_INSTANCE
from .neighbors import k_neighbors, r_neighbors
from .plot import DESCRIPTION, AVAILABLE_BACKENDS
from .plot.matplotlib_backend import plot_with_matplotlib
from .plot.threejs_backend import plot_with_threejs
from .plot.pythreejs_backend import plot_with_pythreejs
from .plot.pyvista_backend import plot_with_pyvista
from .samplers import ALL_SAMPLERS
from .scalar_fields import ALL_SF
from .structures import ALL_STRUCTURES
from .utils.dataframe import convert_columns_dtype


class PyntCloud(object):
    """A Pythonic Point Cloud."""

    def __init__(self, points, mesh=None, structures=None, **kwargs):
        """Create PyntCloud.

        Parameters
        ----------
        points: pd.DataFrame
            DataFrame of N rows by M columns.
            Each row represents one point of the point cloud.
            Each column represents one scalar field associated to its corresponding point.

        mesh: pd.DataFrame or None, optional
            Default: None
            Triangular mesh associated with points.

        structures: dict, optional
            Map key(base.Structure.id) to val(base.Structure)

        kwargs: custom attributes
        """
        self.points = points
        self.mesh = mesh
        self.structures = StructuresDict()
        structures = structures or {}
        for key, val in structures.items():
            self.structures[key] = val
        for key, val in kwargs.items():
            setattr(self, key, val)
        # store raw xyz values to share memory along structures
        self.xyz = self.points[["x", "y", "z"]].values
        self.centroid = self.xyz.mean(0)

    def __repr__(self):
        default = [
            "_PyntCloud__points",
            "_PyntCloud__mesh",
            "structures",
            "xyz",
            "centroid"
        ]
        others = ["\n\t {}: {}".format(x, str(type(getattr(self, x))))
                  for x in self.__dict__ if x not in default]

        if self.mesh is None:
            n_faces = 0
        else:
            n_faces = len(self.mesh)

        return DESCRIPTION.format(
            len(self.points), len(self.points.columns) - 3,
            n_faces,
            self.structures.n_kdtrees,
            self.structures.n_voxelgrids,
            self.centroid[0], self.centroid[1], self.centroid[2],
            "".join(others))

    @property
    def points(self):
        return self.__points

    @points.setter
    def points(self, df):
        if not isinstance(df, pd.DataFrame):
            raise TypeError("Points argument must be a DataFrame")
        elif not set(['x', 'y', 'z']).issubset(df.columns):
            raise ValueError("Points must have x, y and z coordinates")
        self._update_points(df)

    @property
    def mesh(self):
        return self.__mesh

    @mesh.setter
    def mesh(self, df):
        # allow PyntCloud to don't have mesh assigned
        if df is not None:
            if not isinstance(df, pd.DataFrame):
                raise TypeError("Mesh argument must be a DataFrame")
            elif not set(['v1', 'v2', 'v3']).issubset(df.columns):
                print(df.columns)
                raise ValueError(
                    "Mesh must have v1, v2 and v3 columns, at least")
            self.__mesh = df
        else:
            self.__mesh = None

[docs] @classmethod def from_file(cls, filename, **kwargs): """Extract data from file and construct a PyntCloud with it. Parameters ---------- filename: str Path to the file from which the data will be read kwargs: only usable in some formats Returns ------- PyntCloud: object PyntCloud instance, containing all valid elements in the file. """ ext = filename.split(".")[-1].upper() if ext not in FROM_FILE: raise ValueError( "Unsupported file format; supported formats are: {}".format(list(FROM_FILE))) else: return cls(**FROM_FILE[ext](filename, **kwargs))
@classmethod def from_instance(cls, library, instance, **kwargs): """Convert library's instance to PyntCloud intstance. Parameters ---------- library: str Name of the library of the instance to be converted from. instance: `library's` instance kwargs: only usable in some formats Returns ------- PyntCloud: object PyntCloud instance, containing all valid elements in the file. """ library = library.upper() if library not in FROM_INSTANCE: raise ValueError( "Unsupported library; supported libraries are: {}".format(list(FROM_INSTANCE))) else: return cls(**FROM_INSTANCE[library](instance, **kwargs))
[docs] def to_file(self, filename, also_save=None, **kwargs): """Save PyntCloud data to file. Parameters ---------- filename: str Path to the file from which the data will be read also_save: list of str, optional Default: None Names of the attributes that will be extracted from the PyntCloud to be saved in addition to points. Usually also_save=["mesh"] kwargs: only usable in some formats """ convert_columns_dtype(self.points, np.float64, np.float32) ext = filename.split(".")[-1].upper() if ext not in TO_FILE: raise ValueError( "Unsupported file format; supported formats are: {}".format(list(TO_FILE))) kwargs["filename"] = filename kwargs["points"] = self.points if also_save is not None: for x in also_save: kwargs[x] = getattr(self, x) TO_FILE[ext](**kwargs)
def to_instance(self, library, **kwargs): """Convert PyntCloud's instance to library's instance. Parameters ---------- library: str Name of the library of the instance to be converted to. kwargs: only usable in some formats """ convert_columns_dtype(self.points, np.float64, np.float32) library = library.upper() if library not in TO_INSTANCE: raise ValueError( "Unsupported library; supported linraries are: {}".format(list(TO_INSTANCE))) return TO_INSTANCE[library](self, **kwargs)
[docs] def add_scalar_field(self, name, **kwargs): """Add one or multiple columns to PyntCloud.points. Parameters ---------- name: str One of the available names. See below. kwargs Vary for each name. See below. Returns ------- sf_added: list of str The name of each of the columns (scalar fields) added. Useful for chaining operations that require string names. Notes ----- Available scalar fields are: **REQUIRE EIGENVALUES** ARGS ev: list of str ev = self.add_scalar_field("eigen_values", ...) sphericity anisotropy linearity omnivariance eigenentropy planarity eigen_sum curvature **REQUIRE K_NEIGHBORS** ARGS k_neighbors: (N, k) ndarray Returned from: self.get_neighbors(k, ...) / manually querying some self.kdtrees[x] / other methods. eigen_decomposition eigen_values **REQUIRE NORMALS** orientation_degrees orientation_radians inclination_radians inclination_degrees **REQUIRE RGB** hsv relative_luminance rgb_intensity **REQUIRE VOXELGRID** ARGS voxelgrid_id: VoxelGrid.id voxelgrid_id = self.add_structure("voxelgrid", ...) voxel_x voxel_y voxel_n voxel_z euclidean_clusters **ONLY REQUIRE XYZ** plane_fit max_dist: float, optional Default: 1e-4 Maximum distance from point to model in order to be considered as inlier. max_iterations: int, optional (Default 100) Maximum number of fitting iterations. sphere_fit max_dist: float, optional Default: 1e-4 Maximum distance from point to model in order to be considered as inlier. max_iterations: int, optional Default: 100 Maximum number of fitting iterations. custom_fit model: subclass of ransac.models.RansacModel Model to be fitted sampler: subclass of ransac.models.RansacSampler Sample method to be used name: str Will be used to name the added column model_kwargs: dict, optional Default: {} Will be passed to single_fit function. sampler_kwargs: dict, optional Default: {} Will be passed to single_fit function. spherical_coords degrees: bool, optional Default: True. Return polar and azimuthal angles in degrees. """ if name in ALL_SF: scalar_field = ALL_SF[name](pyntcloud=self, **kwargs) scalar_field.extract_info() scalar_field.compute() scalar_fields_added = scalar_field.get_and_set() else: raise ValueError("Unsupported scalar field. Check docstring") return scalar_fields_added
[docs] def add_structure(self, name, **kwargs): """Build a structure and add it to the corresponding PyntCloud's attribute. Parameters ---------- name: str One of the available names. See below. kwargs Vary for each name. See below. Returns ------- structure.id: str Identification string associated with the added structure. Useful for chaining operations that require string names. Notes ----- Available structures are: **ONLY REQUIRE XYZ** kdtree leafsize: int, optional Default: 16 The number of points at which the algorithm switches over to brute-force. Has to be positive. voxelgrid x_y_z: list of int, optional Default: [2, 2, 2] The number of segments in which each axis will be divided. x_y_z[0]: x axis x_y_z[1]: y axis x_y_z[2]: z axis If sizes is not None it will be ignored. sizes: list of float, optional Default: None The desired voxel size along each axis. sizes[0]: voxel size along x axis. sizes[1]: voxel size along y axis. sizes[2]: voxel size along z axis. bb_cuboid: bool, optional Default: True If True, the bounding box of the point cloud will be adjusted in order to have all the dimensions of equal length. octree TODO """ if name in ALL_STRUCTURES: info = ALL_STRUCTURES[name].extract_info(pyntcloud=self) structure = ALL_STRUCTURES[name](**info, **kwargs) structure.compute() structure_added = structure.get_and_set(self) else: raise ValueError("Unsupported structure. Check docstring") return structure_added
[docs] def get_filter(self, name, and_apply=False, **kwargs): """Compute filter over PyntCloud's points and return it. Parameters ---------- name: str One of the available names. See below. and_apply: boolean, optional Default: False If True, filter will be applied to self.points kwargs Vary for each name. See below. Returns ------- filter: boolean array Boolean mask indicating wherever a point should be keeped or not. The size of the boolean mask will be the same as the number of points in the pyntcloud. Notes ----- Available filters are: **REQUIRE KDTREE** ARGS kdtree : KDTree.id kdtree = self.add_structure("kdtree", ...) ROR (Radius Outlier Removal) k: int Number of neighbors that will be used to compute the filter. r: float The radius of the sphere with center on each point. The filter will look for the required number of neighboors inside that sphere. SOR (Statistical Outlier Removal) k: int Number of neighbors that will be used to compute the filter. z_max: float The maximum Z score which determines if the point is an outlier. **ONLY REQUIRE XYZ** BBOX (Bounding Box) min_i, max_i: float The bounding box limits for each coordinate. If some limits are missing, the default values are -infinite for the min_i and infinite for the max_i. """ if name in ALL_FILTERS: pointcloud_filter = ALL_FILTERS[name](pyntcloud=self, **kwargs) pointcloud_filter.extract_info() boolean_array = pointcloud_filter.compute() if and_apply: self.apply_filter(boolean_array) return boolean_array else: raise ValueError("Unsupported filter. Check docstring")
[docs] def get_sample(self, name, as_PyntCloud=False, **kwargs): """Return arbitrary number of points sampled by selected method. Parameters ---------- name: str One of the available names. See below. as_PyntCloud: bool, optional Default: False If True, sampled points will be returned as PyntCloud instance. kwargs Vary for each name. See below. Returns ------- sampled_points: (n, 3) ndarray or PyntCloud 'n' vary for each method. PyntCloud if as_PyntCloud Notes ----- Available sampling methods are: **REQUIRE MESH** mesh_random n: int Number of points to be sampled. rgb: bool, optional Default: False Indicates if rgb values will be also sampled normals: bool, optional Default: False Indicates if normals will be also sampled **REQUIRE VOXELGRID** ARGS voxelgrid: VoxelGrid.id voxelgrid = self.add_structure("voxelgrid", ...) voxelgrid_centers voxelgrid_centroids voxelgrid_nearest n: int Number of nearest point per voxel to sample voxelgrid_highest **USE POINTS** points_random n: int Number of points to be sampled. """ if name in ALL_SAMPLERS: sampler = ALL_SAMPLERS[name](pyntcloud=self, **kwargs) sampler.extract_info() sample = sampler.compute() if as_PyntCloud: return PyntCloud(sample) return sample else: raise ValueError("Unsupported sampling method. Check docstring")
[docs] def get_neighbors(self, k=None, r=None, kdtree=None): """For each point finds the indices that compose its neighborhood. Parameters ---------- k: int, optional Default: None For "K-nearest neighbor" search. Number of nearest neighbors that will be used to build the neighborhood. r: float, optional Default: None For "Fixed-radius neighbors" search. Radius of the sphere that will be used to build the neighborhood. kdtree: str, optional Default: None KDTree.id in self.structures. - If **kdtree** is None: The KDTree will be computed and added to PyntCloud as part of the process. - Else: The given KDTree will be used for neighbor search. Returns ------- neighbors: array-like (N, k) ndarray if k is not None. Indices of the 'k' nearest neighbors for the 'N' points. (N,) ndarray of lists if r is not None. Array holding a variable number of indices corresponding to the neighbors with distance < r. """ if kdtree is None: kdtree_id = self.add_structure("kdtree") kdtree = self.structures[kdtree_id] else: kdtree = self.structures[kdtree] if k is not None: return k_neighbors(kdtree, k) elif r is not None: return r_neighbors(kdtree, r) else: raise ValueError("You must supply 'k' or 'r' values.")
[docs] def get_mesh_vertices(self, rgb=False, normals=False): """Decompose triangles of self.mesh from vertices in self.points. Returns ------- v1, v2, v3: ndarray (N, 3) arrays of vertices so v1[i], v2[i], v3[i] represent the ith triangle """ use_columns = ["x", "y", "z"] if rgb: use_columns.extend(["red", "green", "blue"]) if normals: use_columns.extend(["nx", "ny", "nz"]) points = self.points[use_columns].values v1 = points[self.mesh["v1"].values] v2 = points[self.mesh["v2"].values] v3 = points[self.mesh["v3"].values] return v1, v2, v3
[docs] def apply_filter(self, boolean_array): """Update self.points removing points where filter is False. Parameters ---------- boolean_array: ndarray, dtype bool len(boolean array) must be equal to len(self.points) """ self.points = self.points.loc[boolean_array].reset_index(drop=True)
[docs] def split_on(self, scalar_field, and_return=False, save_format="ply", save_path=os.getcwd()): """Divide the PyntCloud using unique values in given sf. This function will generate PyntClouds by grouping points using the unique values found in the given scalar field. Parameters ---------- scalar_field: str Name of the scalar field to be used for splitting. and_return: boolean, optional Default: False If True, return a list with the splits. save_format: str, optional Default: "ply" Extension used to save the generated PyntClouds. Must be of one of the formats present in pyntcloud.io.TO save_path: str, optional Default: "." Path where the PyntClouds will be saved. """ scalar_field = self.points[scalar_field] splits = {x: PyntCloud(self.points.loc[scalar_field == x]) for x in scalar_field.unique()} if not os.path.exists(save_path): os.makedirs(save_path) for key, val in splits.items(): val.to_file("{}/{}.{}".format(save_path, key, save_format)) if and_return: return splits
def _update_points(self, df): """Utility function. Implicitly called when self.points is assigned.""" self.mesh = None self.structures = StructuresDict() self.__points = df self.xyz = self.__points[["x", "y", "z"]].values self.centroid = self.xyz.mean(0)
[docs] def plot( self, backend=None, scene=None, width=800, height=500, background="black", mesh=False, use_as_color=["red", "green", "blue"], initial_point_size=None, cmap="hsv", polylines=None, linewidth=5, return_scene=False, output_name="pyntcloud_plot", elev=0., azim=90., **kwargs ): """Visualize a PyntCloud using different backends. Parameters ---------- backend: {"pythreejs", "threejs", "pyvista", "matplotlib"}, optional Default: "pythreejs" Used to select one of the available libraries for plotting. width: int, optional Default: 800 height: int, optional Default: 500 background: str, optional Default: "black" Used to select the default color of the background. In some backends, i.e "pythreejs" the background can be dynamically changed. use_as_color: str or ["red", "green", "blue"], optional Default: ["red", "green", "blue"] Indicates which scalar fields will be used to colorize the rendered point cloud. initial_point_size: the initial size of each point in the rendered point cloud. Can be adjusted after rendering using slider. cmap: str, optional Default: "hsv" Color map that will be used to convert a single scalar field into rgb. Check matplotlib cmaps. return_scene: bool, optional Default: False. Used with "pythreejs" backend in order to return the pythreejs.Scene object polylines: list of dict, optional Default None. List of dictionaries defining polylines with the following syntax: polylines=[ { "color": "0xFFFFFF", "vertices": [[0, 0, 0], [0, 0, 1]] }, { { "color": "red", "vertices": [[0, 0, 0], [0, 0, 1], [0, 2, 0] } ] elev: float Elevation angle in the z plane. Used for matplotlib azim: float Azimuth angle in the x,y plane. """ args = locals() args.update(kwargs) backend = args.pop("backend") # Choose fisrt avaialable backend if backend is None and len(AVAILABLE_BACKENDS) > 0: backend = AVAILABLE_BACKENDS[0] elif backend is None: backend = 'pythreejs' # Plot with backend of choice if backend == "matplotlib": return plot_with_matplotlib(self, **args) elif backend == "pythreejs": return plot_with_pythreejs(self, **args) elif backend == "threejs": return plot_with_threejs(self, **args) elif backend == "pyvista": return plot_with_pyvista(self, **args) else: raise NotImplementedError("{} backend is not supported".format(backend))