diff --git a/.travis.yml b/.travis.yml index 58fc6b6efaa..b210c218ae2 100644 --- a/.travis.yml +++ b/.travis.yml @@ -11,38 +11,39 @@ env: - PACKAGE='BGL Boolean_set_operations_2 Bounding_volumes ' - PACKAGE='Box_intersection_d CGAL_ImageIO CGAL_ipelets ' - PACKAGE='Circular_kernel_2 Circular_kernel_3 Circulator ' - - PACKAGE='Combinatorial_map Cone_spanners_2 Convex_decomposition_3 ' - - PACKAGE='Convex_hull_2 Convex_hull_3 Convex_hull_d ' - - PACKAGE='Distance_2 Distance_3 Envelope_2 ' - - PACKAGE='Envelope_3 Filtered_kernel Generalized_map ' - - PACKAGE='Generator Geomview GraphicsView ' - - PACKAGE='HalfedgeDS Hash_map Inscribed_areas ' - - PACKAGE='Installation Interpolation Intersections_2 ' - - PACKAGE='Intersections_3 Interval_skip_list Interval_support ' - - PACKAGE='Inventor Jet_fitting_3 Kernel_23 ' - - PACKAGE='Kernel_d Kinetic_data_structures Kinetic_framework ' - - PACKAGE='Linear_cell_complex Matrix_search Mesh_2 ' - - PACKAGE='Mesh_3 Minkowski_sum_2 Minkowski_sum_3 ' - - PACKAGE='Modifier Modular_arithmetic Nef_2 ' - - PACKAGE='Nef_3 Nef_S2 NewKernel_d ' - - PACKAGE='Number_types Optimal_transportation_reconstruction_2 Partition_2 ' - - PACKAGE='Periodic_2_triangulation_2 Periodic_3_triangulation_3 Point_set_2 ' - - PACKAGE='Point_set_3 Point_set_processing_3 Point_set_shape_detection_3 ' - - PACKAGE='Poisson_surface_reconstruction_3 Polygon Polygon_mesh_processing ' - - PACKAGE='Polyhedron Polyhedron_IO Polyline_simplification_2 ' - - PACKAGE='Polynomial Polytope_distance_d Principal_component_analysis ' - - PACKAGE='Profiling_tools QP_solver Random_numbers ' - - PACKAGE='Ridges_3 Scale_space_reconstruction_3 Segment_Delaunay_graph_2 ' - - PACKAGE='Segment_Delaunay_graph_Linf_2 Set_movable_separability_2 Skin_surface_3 ' - - PACKAGE='Snap_rounding_2 Solver_interface Spatial_searching ' - - PACKAGE='Spatial_sorting STL_Extension Straight_skeleton_2 ' - - PACKAGE='Stream_lines_2 Stream_support Subdivision_method_3 ' - - PACKAGE='Surface_mesh Surface_mesh_deformation Surface_mesher ' - - PACKAGE='Surface_mesh_parameterization Surface_mesh_segmentation Surface_mesh_shortest_path ' - - PACKAGE='Surface_mesh_simplification Surface_mesh_skeletonization Sweep_line_2 ' - - PACKAGE='TDS_2 TDS_3 Three ' - - PACKAGE='Triangulation Triangulation_2 Triangulation_3 ' - - PACKAGE='Union_find Visibility_2 Voronoi_diagram_2 ' + - PACKAGE='Classification Combinatorial_map Cone_spanners_2 ' + - PACKAGE='Convex_decomposition_3 Convex_hull_2 Convex_hull_3 ' + - PACKAGE='Convex_hull_d Distance_2 Distance_3 ' + - PACKAGE='Envelope_2 Envelope_3 Filtered_kernel ' + - PACKAGE='Generalized_map Generator Geomview ' + - PACKAGE='GraphicsView HalfedgeDS Hash_map ' + - PACKAGE='Inscribed_areas Installation Interpolation ' + - PACKAGE='Intersections_2 Intersections_3 Interval_skip_list ' + - PACKAGE='Interval_support Inventor Jet_fitting_3 ' + - PACKAGE='Kernel_23 Kernel_d Kinetic_data_structures ' + - PACKAGE='Kinetic_framework Linear_cell_complex Matrix_search ' + - PACKAGE='Mesh_2 Mesh_3 Minkowski_sum_2 ' + - PACKAGE='Minkowski_sum_3 Modifier Modular_arithmetic ' + - PACKAGE='Nef_2 Nef_3 Nef_S2 ' + - PACKAGE='NewKernel_d Number_types Optimal_transportation_reconstruction_2 ' + - PACKAGE='Partition_2 Periodic_2_triangulation_2 Periodic_3_triangulation_3 ' + - PACKAGE='Point_set_2 Point_set_3 Point_set_processing_3 ' + - PACKAGE='Point_set_shape_detection_3 Poisson_surface_reconstruction_3 Polygon ' + - PACKAGE='Polygon_mesh_processing Polyhedron Polyhedron_IO ' + - PACKAGE='Polyline_simplification_2 Polynomial Polytope_distance_d ' + - PACKAGE='Principal_component_analysis Profiling_tools QP_solver ' + - PACKAGE='Random_numbers Ridges_3 Scale_space_reconstruction_3 ' + - PACKAGE='Segment_Delaunay_graph_2 Segment_Delaunay_graph_Linf_2 Set_movable_separability_2 ' + - PACKAGE='Skin_surface_3 Snap_rounding_2 Solver_interface ' + - PACKAGE='Spatial_searching Spatial_sorting STL_Extension ' + - PACKAGE='Straight_skeleton_2 Stream_lines_2 Stream_support ' + - PACKAGE='Subdivision_method_3 Surface_mesh Surface_mesh_deformation ' + - PACKAGE='Surface_mesher Surface_mesh_parameterization Surface_mesh_segmentation ' + - PACKAGE='Surface_mesh_shortest_path Surface_mesh_simplification Surface_mesh_skeletonization ' + - PACKAGE='Sweep_line_2 TDS_2 TDS_3 ' + - PACKAGE='Three Triangulation Triangulation_2 ' + - PACKAGE='Triangulation_3 Union_find Visibility_2 ' + - PACKAGE='Voronoi_diagram_2 ' - PACKAGE='Polyhedron_demo' compiler: - clang-3.6 diff --git a/.travis/packages.txt b/.travis/packages.txt index 9ffea24cd28..b0df6bcb445 100644 --- a/.travis/packages.txt +++ b/.travis/packages.txt @@ -19,6 +19,7 @@ CGAL_ipelets Circular_kernel_2 Circular_kernel_3 Circulator +Classification Combinatorial_map Cone_spanners_2 Convex_decomposition_3 diff --git a/Classification/doc/Classification/Classification.txt b/Classification/doc/Classification/Classification.txt new file mode 100644 index 00000000000..85090025312 --- /dev/null +++ b/Classification/doc/Classification/Classification.txt @@ -0,0 +1,389 @@ +namespace CGAL { +/*! + +\mainpage User Manual +\anchor Chapter_Classification +\cgalAutoToc +\author Simon Giraudot, Florent Lafarge + +This component implements the algorithm described in \cgalCite{cgal:lm-clscm-12} (section 2), generalized to handle different types of data, multiple features and multiple labels. It classifies a data set into a user-defined set of labels, such as _ground_, _vegetation_ and _buildings_. A flexible API is provided so that users can classify any type of data which they can index and for which they can compute relevant features, compute their own local features on the input data set and define their own labels. + +\section Classification_Organization Package Organization + +%Classification of data sets is achieved as follows (see Figure \cgalFigureRef{Classification_organization_fig}): + +- some analysis is performed on the input data set; +- features are computed based on this analysis; +- a set of labels (for example: _ground_, _building_, _vegetation_) is defined by the user; +- a classifier is defined and trained: from the set of values taken by the features at an input item, it measures the likelihood of this item to belong to one label or another; +- classification is computed itemwise using the classifier; +- additional regularization can be used by smoothing either locally or globally through a _Graph Cut_ \cgalCite{Boykov2001FastApproximate} approach. + +\cgalFigureBegin{Classification_organization_fig,organization.svg} +Organization of the package. +\cgalFigureEnd + +This package is designed to be easily extended by users: more specifically, features and labels can be defined by users to handle any data they need to classify. + +Currently, \cgal only provides data structures to handle classification of point sets. + +\section Classification_structures Data Structures + +\subsection Classification_analysis Analysis + +%Classification is based on the computation of local features. These features can take advantage of shared data structures that are precomputed and stored separately. + +\cgal provides the following structures: + +- [Point_set_neighborhood](@ref CGAL::Classification::Point_set_neighborhood) stores spatial searching structures and provides adapted queries for points; +- [Local_eigen_analysis](@ref CGAL::Classification::Local_eigen_analysis) precomputes covariance matrices on local neighborhoods of points and stores the associated eigenvectors and eigenvalues; +- [Planimetric_grid](@ref CGAL::Classification::Planimetric_grid) is a 2D grid used for digital terrain modeling. + +Most of these features depend on a scale parameter. \cgal provides a method to estimate the average spacing based on a number of neighbors (see [CGAL::compute_average_spacing()](@ref compute_average_spacing)), which usually provides satisfying results in the absence of noise. In the presence of noise, [CGAL::estimate_global_range_scale()](@ref estimate_global_range_scale) provides an estimation of the smallest scale such that the point set has the local dimension of a surface (this method is both robust to noise and outliers, see \ref Classification_sowf_result). + +The eigen analysis can be used to estimate normals. Note however that this analysis (based on Principal Component Analysis) might not be robust to a high level of noise. \cgal also provides more robust normal estimation functions (see for example [CGAL::jet_estimate_normals()](@ref jet_estimate_normals)). + +The following code snippet shows how to instantiate such data structures from an input PLY point set (the full example is given at the end of the manual). + +\snippet Classification/example_classification.cpp Analysis + +\subsection Classification_labels Label Set + +A label represents how an item should be classified, for example: _vegetation_, _building_, _road_, etc. In \cgal, a label has a name and is simply identified by a [Label_handle](@ref CGAL::Classification::Label_handle). Note that names are not used for identification: two labels in the same set can have the same name (but not the same handle). + +The following code snippet shows how to add labels to the classification object. + +\snippet Classification/example_classification.cpp Labels + +\subsection Classification_features Feature Set + +Features are defined as scalar fields that associate each input item with a specific value. Note that in order to limit memory consumption, we use the type `float` for these scalar values (as well as for every floating point value in this package). A feature has a name and is identified by a [Feature_handle](@ref CGAL::Classification::Feature_handle). + +\cgal provides some predefined features that are relevant for classification of point sets: + +- [Distance_to_plane](@ref CGAL::Classification::Feature::Distance_to_plane) measures how far away a point is from a locally estimated plane; +- [Elevation](@ref CGAL::Classification::Feature::Elevation) computes the local distance to an estimation of the ground; +- [Vertical_dispersion](@ref CGAL::Classification::Feature::Vertical_dispersion) computes how noisy the point set is on a local Z-cylinder; +- [Verticality](@ref CGAL::Classification::Feature::Verticality) compares the local normal vector to the vertical vector. + +For more details about how these different features can help to identify one label or the other, please refer to their associated reference manual pages. In addition, \cgal also provides features solely based on the eigenvalues \cgalCite{cgal:mbrsh-raofw-11} of the local covariance matrix: + +- [Anisotropy](@ref CGAL::Classification::Feature::Anisotropy) +- [Eigentropy](@ref CGAL::Classification::Feature::Eigentropy) +- [Linearity](@ref CGAL::Classification::Feature::Linearity) +- [Omnivariance](@ref CGAL::Classification::Feature::Omnivariance) +- [Planarity](@ref CGAL::Classification::Feature::Planarity) +- [Sphericity](@ref CGAL::Classification::Feature::Sphericity) +- [Sum_eigenvalues](@ref CGAL::Classification::Feature::Sum_eigenvalues) +- [Surface_variation](@ref CGAL::Classification::Feature::Surface_variation) + +Finally, if the input data set has additional properties, these can also be used as features. For example, \cgal provides the following features: + +- [Echo_scatter](@ref CGAL::Classification::Feature::Echo_scatter) uses the number of returns (echo) provided by most LIDAR scanners if available; +- [Hsv](@ref CGAL::Classification::Feature::Hsv) uses input color information if available. + +In the following code snippet, a subset of these features are instantiated. Note that all the predefined features can also be automatically generated in multiple scales (see \ref Classification_feature_generator). + +\snippet Classification/example_classification.cpp Features + +Users may want to define their own features, especially if the input data set comes with additional properties that were not anticipated by \cgal. A user-defined feature must inherit from [Feature_base](@ref CGAL::Classification::Feature_base) and provide a method [value()](@ref CGAL::Classification::Feature_base::value) that associates a scalar value to each input item. + +The following example shows how to define a feature that discriminates +points that lie inside a 2D box from the others: + +\snippet Classification/example_feature.cpp Feature + +This feature can then be instantiated from the feature set the same way as the others: + +\snippet Classification/example_feature.cpp Addition + +\subsection Classification_feature_generator Point Set Feature Generator + +In standard classification of point sets, users commonly want to use all predefined features to get the best result possible. \cgal provides a class [Point_set_feature_generator](@ref CGAL::Classification::Point_set_feature_generator) that performs the following operations: + +- it estimates the smallest relevant scale; +- it generates all needed analysis structures and provides access to them; +- it generates all possible features (among all the \cgal predefined ones) based on which property maps are available (it uses colors if available, etc.). + +Multiple scales that are sequentially larger can be used to increase the quality of the results \cgalCite{cgal:hws-fsso3-16}. If \cgal is linked with \ref thirdpartyTBB, features can be computed in parallel to increase the overall computation speed. + +Note that using this class in order to generate features is not mandatory, as features and data structures can all be handled by hand. It is mainly provided to make the specific case of point sets simpler to handle. Users can still add their own features within their feature set. + +The following snippet shows how to use the feature generator: + +\snippet Classification/example_generation_and_training.cpp Generator + +\section Classification_classifiers Classifiers + +%Classification relies on a classifier: this classifier is an object that, from the set of values taken by the features at an input item, computes the energy that measures the likelihood of an input item to belong to one label or another. A model of the concept `CGAL::Classification::Classifier` must take the index of an input item and store the energies associated to each label in a vector. If a classifier returns the value 0 for a pair of label and input item, it means that this item belongs to this label with certainty; large values mean that this item is not likely to belong to this label. + +\cgal provides two models for this concept, [Sum_of_weighted_features_classifier](@ref CGAL::Classification::Sum_of_weighted_features_classifier) and [Random_forest_classifier](@ref CGAL::Classification::Random_forest_classifier). + +To perform classification based on these classifiers, please refer to \ref Classification_classification_functions. + +\subsection Classification_sowf Sum of Weighted Features + +This first classifier defines the following attributes: + +- a weight applied to each feature; +- an effect applied to each pair of feature and label. + +For each label, the classifier computes the energy as a sum of features normalized with both their weight and the effect they have on this specific label. + +This classifier can be set up by hand but also embeds a training algorithm. + +\subsubsection Classification_sowf_weights_effects Weights and Effects + +Each feature is assigned a weight that measures its strength with respect to the other features. + +Each pair of feature and label is assigned an effect that can either be: + +- [FAVORING](@ref CGAL::Classification::Sum_of_weighted_features_classifier::FAVORING): the label is favored by high values of the feature; +- [NEUTRAL](@ref CGAL::Classification::Sum_of_weighted_features_classifier::NEUTRAL): the label is not affected by the feature; +- [PENALIZING](@ref CGAL::Classification::Sum_of_weighted_features_classifier::PENALIZING): the label is favored by low values of the feature. + +For example, _vegetation_ is expected to have a high distance to plane and have a color close to green (if colors are available); _facades_ have a low distance to plane and a low verticality; etc. + +Let \f$x=(x_i)_{i=1..N}\f$ be a potential classification result with \f$N\f$ the number of input items and \f$x_i\f$ the class of the \f$i^{th}\f$ item (for example: _vegetation_, _ground_, etc.). Let \f$f_j(i)\f$ be the raw value of the \f$j^{th}\f$ feature at the \f$i^{th}\f$ item and \f$w_j\f$ be the weight of this feature. We define the normalized value \f$F_j(x_i) \in [0:1]\f$ of the \f$j^{th}\f$ feature at the \f$i^{th}\f$ item as follows: + +\f{eqnarray*}{ + F_j(x_i) = & (1 - \min(\max(0,\frac{f_j(i)}{w_j}), 1)) & \mbox{if } f_j \mbox{ favors } x_i \\ + & 0.5 & \mbox{if } f_j \mbox{ is neutral for } x_i \\ + & \min(\max(0,\frac{f_j(i)}{w_j}), 1) & \mbox{if } f_j \mbox{ penalizes } x_i + \f} + +The itemwise energy measures the coherence of the label \f$x_i\f$ at +the \f$i^{th}\f$ item and is defined as: + +\f[ + E_{di}(x_i) = \sum_{j = 1..N_f} F_j(x_i) +\f] + + +The following code snippet shows how to define the weights and effects of features and labels: + +\snippet Classification/example_classification.cpp Weights + +\subsubsection Classification_sowf_training Training + +Each feature has a specific weight and each pair of feature-label has a specific effect. This means that the number of parameters to set up can quickly explode: if 6 features are used to classify between 4 labels, 30 parameters have to be set up (6 weights + 6x4 feature-label relationships). + +Though it is possible to set them up one by one, \cgal also provides a method [train()](@ref CGAL::Classification::Sum_of_weighted_features_classifier::train) that requires a small set of ground truth items provided by users. More specifically, users must provide, for each label they want to classify, a set of known inliers among the input data set (for example, selecting one roof, one tree and one section of the ground). The training algorithm works as follows: + +- for each feature, a range of weights is tested: the effect each feature have on each label is estimated. For a given weight, if a feature has the same effect on each label, it is non-relevant for classification. The range of weights such that the feature is relevant is estimated; + +- for each feature, uniformly picked weight values are tested and their effects estimated; + +- each inlier provided by the user is classified using this set of weights and effects; + +- the mean intersection-over-union (see @ref Classification_evaluation) is used to evaluate the quality of this set of weights and effects; + +- the same mechanism is repeated until all features' ranges have been tested. Weights are only changed one by one, the other ones kept to the values that gave the latest best score. + +This usually converges to a satisfying solution (see Figure \cgalFigureRef{Classification_trainer_fig}). The number of trials is user defined, set to 300 by default. Using at least 10 times the number of features is advised (for example, at least 300 iterations if 30 features are used). If the solution is not satisfying, more inliers can be selected, for example, in a region that the user identifies as misclassified with the current configuration. The training algorithm keeps, as initialization, the best weights found at the previous round and carries on trying new weights by taking new inliers into account. + +\cgalFigureBegin{Classification_trainer_fig,classif_training.png} +Example of evolution of the mean intersection-over-union. The purple curve is the score computed at the current iteration, green curve is the best score found so far. +\cgalFigureEnd + +\subsubsection Classification_sowf_result Result + +Figure \cgalFigureRef{Classification_sowf_result_fig} shows an example of output on a defect-laden point set. The accuracy on this example is 0.97 with a mean intersection-over-union of 0.85 (see section \ref Classification_evaluation). + +\cgalFigureBegin{Classification_sowf_result_fig,noise_outliers.png} +Example of classification on a point set with medium noise and outliers (left: input, right: output). _Ground_ is orange, _roofs_ are pink, _vegetation_ is green. Outliers are classified with an additional label _outlier_ in black. +\cgalFigureEnd + + +\subsection Classification_random_forest Random Forest + +This second classifier is [Random_forest_classifier](@ref CGAL::Classification::Random_forest_classifier). +It uses the \ref thirdpartyOpenCV library, more specifically the +[Random Trees](http://docs.opencv.org/2.4/modules/ml/doc/random_trees.html) +package. This classifier uses a ground truth training set to construct several +decision trees that are then used to assign a label to each input +item. + +This classifier cannot be set up by hand and requires a ground truth +training set. The training algorithm is faster but usually requires a +higher number of inliers than the previous classifier. + +An [example](\ref Classification_example_random_forest) shows how to +use this classifier. For more details about the method, please refer +to [the official documentation](http://docs.opencv.org/2.4/modules/ml/doc/random_trees.html) +of OpenCV. + + +\section Classification_classification_functions Classification Functions + +%Classification is performed by minimizing an energy over the input data set that may include regularization. \cgal provides three different methods for classification, ranging from high speed / low quality to low speed / high quality: + +- `CGAL::Classification::classify()` +- `CGAL::Classification::classify_with_local_smoothing()` +- `CGAL::Classification::classify_with_graphcut()` + +On a point set of 3 millions of points, the first method takes about 4 seconds, the second about 40 seconds and the third about 2 minutes. + +\cgalFigureBegin{Classification_image,classif.png} +Top-Left: input point set. Top-Right: raw output classification represented by a set of colors (_ground_ is orange, _facades_ are blue, _roofs_ are pink and _vegetation_ is green). Bottom-Left: output classification using local smoothing. Bottom-Right: output classification using graphcut. +\cgalFigureEnd + + +Mathematical details are provided hereafter. + +\subsection Classification_classify Raw classification + +- `CGAL::Classification::classify()`: this is the fastest method +that provides acceptable but usually noisy results (see Figure +\cgalFigureRef{Classification_image}, top-right). + +Let \f$x=(x_i)_{i=1..N}\f$ be a potential classification result with \f$N\f$ the number of input items and \f$x_i\f$ the label of the \f$i^{th}\f$ item (for example: _vegetation_, _ground_, etc.). The classification is performed by minimizing the following energy: + +\f[ + E(x) = \sum_{i = 1..N} E_{di}(x_i) +\f] + +This energy is a sum of itemwise energies provided by the classifier and involves no regularization. + +The following snippets show how to classify points based on a label +set and a classifier. The result is stored in `label_indices`, +following the same order as the input set and providing for each point +the index (in the label set) of its assigned label. + +\snippet Classification/example_classification.cpp Classify + +\subsection Classification_smoothing Local Regularization + +- `CGAL::Classification::classify_with_local_smoothing()`: this +method is a tradeoff between quality and efficiency (see Figure +\cgalFigureRef{Classification_image}, bottom-left). The minimized +energy is defined as follows: + + +\f[ + E(x) = \sum_{i = 1..N} E_{si}(x_i) +\f] + +The energy \f$E_{si}(x_i)\f$ is defined on a small local neighborhood +\f$Nb(i)\f$ of the \f$i^{th}\f$ item (including itself): + +\f[ + E_{si}(x_i) = \frac{\sum_{k \in Nb(i)} E_{di}(x_k)}{\left| Nb(i) \right|} +\f] + +This allows to eliminate local noisy variations of assigned +labels. Increasing the size of the neighborhood +increases the noise reduction at the cost of higher computation times. + +The following snippets show how to classify points using local +smoothing by providing a model of `CGAL::Classification::NeighborQuery`. + +\snippet Classification/example_classification.cpp Smoothing + +\subsection Classification_graphcut Global Regularization (Graph Cut) + +- `CGAL::Classification::classify_with_graphcut()`: this method +offers the best quality but requires longer computation time (see +Figure \cgalFigureRef{Classification_image}, bottom-right). The +total energy that is minimized is the sum of the partial data term +\f$E_{di}(x_i)\f$ and of a pairwise interaction energy defined by the +standard Potts model \cgalCite{cgal:l-mrfmi-09} : + +\f[ + E(x) = \sum_{i = 1..N} E_{di}(x_i) + \gamma \sum_{i \sim j} \mathbf{1}_{x_i \neq x_j} +\f] + +where \f$\gamma>0\f$ is the parameter of the Potts model that +quantifies the strengh of the regularization, \f$i \sim j\f$ +represents the pairs of neighboring items and +\f$\mathbf{1}_{\{.\}}\f$ the characteristic function. + +A _Graph Cut_ based algorithm (Alpha Expansion) is used to quickly reach +an approximate solution close to the global optimum of this energy. + +This method allows to consistently segment the input data set in +piecewise constant parts and to correct large wrongly classified +clusters. Increasing \f$\gamma\f$ produces more regular result with a +constant computation time. + +To speed up computations, the input domain can be subdivided into +smaller subsets such that several smaller graph cuts are applied +instead of a big one. The computation of these smaller graph cuts can +be done in parallel. Increasing the number of subsets allows for +faster computation times but can also reduce the quality of the +results. + +The following snippet shows how to classify points using a graph cut +regularization providing a model of +`CGAL::Classification::NeighborQuery`, a strengh parameter +\f$\gamma\f$ and a number of subdivisions. + +\snippet Classification/example_classification.cpp Graph_cut + +\section Classification_evaluation Evaluation + +The class [Evaluation](@ref CGAL::Classification::Evaluation) allows +users to evaluate the reliability of the classification with respect +to a provided ground truth. The following measurements are available: + +- [precision()](@ref CGAL::Classification::Evaluation::precision) computes, for one label, the ratio of true positives over the total number of detected positives; +- [recall()](@ref CGAL::Classification::Evaluation::recall) computes, for one label, the ratio of true positives over the total number of provided inliers of this label; +- [f1_score()](@ref CGAL::Classification::Evaluation::f1_score) is the harmonic mean of precision and recall; +- [intersection_over_union()](@ref CGAL::Classification::Evaluation::intersection_over_union) computes the ratio of true positives over the union of the detected positives and of the provided inliers; +- [accuracy()](@ref CGAL::Classification::Evaluation::accuracy) computes the ratio of all true positives over the total number of provided inliers; +- [mean_f1_score()](@ref CGAL::Classification::Evaluation::mean_f1_score); +- [mean_intersection_over_union()](@ref CGAL::Classification::Evaluation::mean_intersection_over_union). + +All these values range from 0 (poor quality) to 1 (perfect quality). + +\section Classification_examples Full Examples + +\subsection Classification_example_general Classification + +The following example: + +- reads an input file (LIDAR point set in PLY format); +- computes useful structures from this input; +- computes features from the input and the precomputed structures; +- defines 3 labels (_vegetation_, _ground_ and _roof_); +- sets up the classification classifier [Sum_of_weighted_features_classifier](@ref CGAL::Classification::Sum_of_weighted_features_classifier); +- classifies the point set with the 3 different methods (this is for +the sake of the example: each method overwrites the previous result, +users should only call one of the methods); +- saves the result in a colored PLY format. + +\cgalExample{Classification/example_classification.cpp} + +\subsection Classification_example_feeature Defining a Custom Feature + +The following example shows how to define a custom feature and how to integrate it in the \cgal classification framework. + +\cgalExample{Classification/example_feature.cpp} + +\subsection Classification_example_training Feature Generation and Training + +The following example: + +- reads a point set with a training set (embedded as a PLY feature _label_); +- automatically generates features on 5 scales; +- trains the classifier [Sum_of_weighted_features_classifier](@ref CGAL::Classification::Sum_of_weighted_features_classifier) using 800 trials; +- runs the algorithm using the graphcut regularization; +- prints some evaluation measurements; +- saves the configuration of the classifier for further use. + +\cgalExample{Classification/example_generation_and_training.cpp} + +\subsection Classification_example_random_forest Random Forest + +The following example shows how to use the classifier [Random_forest_classifier](@ref CGAL::Classification::Random_forest_classifier) using an input training set. + +\cgalExample{Classification/example_random_forest.cpp} + +\section Classification_history History + +This package is based on a research code by [Florent Lafarge](https://www-sop.inria.fr/members/Florent.Lafarge/) that was generalized, extended and packaged by [Simon Giraudot](http://geometryfactory.com/who-we-are/). + + + +*/ +} /* namespace CGAL */ diff --git a/Classification/doc/Classification/Concepts/Classifier.h b/Classification/doc/Classification/Concepts/Classifier.h new file mode 100644 index 00000000000..415672e5046 --- /dev/null +++ b/Classification/doc/Classification/Concepts/Classifier.h @@ -0,0 +1,34 @@ +namespace CGAL +{ + +namespace Classification +{ + +/*! +\ingroup PkgClassificationConcepts +\cgalConcept + +Concept describing a classifier used by classification functions (see +`CGAL::Classification::classify()`, `CGAL::Classification::classify_with_local_smoothing()` and +`CGAL::Classification::classify_with_graphcut()`). + +\cgalHasModel `CGAL::Classification::Sum_of_weighted_features_classifier` +\cgalHasModel `CGAL::Classification::Random_forest_classifier` + +*/ +class Classifier +{ +public: + + /*! + \brief Computes for each label indexed from 0 to `out.size()`, the + energy of this label applied to point at `item_index`. + */ + void operator() (std::size_t item_index, std::vector& out) const; + + +}; + +} // namespace Classification + +} // namespace CGAL diff --git a/Classification/doc/Classification/Concepts/NeighborQuery.h b/Classification/doc/Classification/Concepts/NeighborQuery.h new file mode 100644 index 00000000000..8f9ec8d16f7 --- /dev/null +++ b/Classification/doc/Classification/Concepts/NeighborQuery.h @@ -0,0 +1,40 @@ +namespace CGAL +{ + +namespace Classification +{ + +/*! +\ingroup PkgClassificationConcepts +\cgalConcept + +Concept describing a neighbor query used for classification. + +\cgalHasModel `CGAL::Classification::Point_set_neighborhood::K_neighbor_query` +\cgalHasModel `CGAL::Classification::Point_set_neighborhood::Sphere_neighbor_query` + +*/ +class NeighborQuery +{ +public: + + /*! + \brief Type of the data that is classified. + */ + typedef unspecified_type value_type; + + /*! + + \brief Puts in `output` the indices of the neighbors of `query`. + + \tparam OutputIterator An output iterator accepting `std::size_t` + values. + */ + template + OutputIterator operator() (const value_type& query, OutputIterator output) const; + +}; + +} // namespace Classification + +} // namespace CGAL diff --git a/Classification/doc/Classification/Doxyfile.in b/Classification/doc/Classification/Doxyfile.in new file mode 100644 index 00000000000..b6e5315de01 --- /dev/null +++ b/Classification/doc/Classification/Doxyfile.in @@ -0,0 +1,3 @@ +@INCLUDE = ${CGAL_DOC_PACKAGE_DEFAULTS} +PROJECT_NAME = "CGAL ${CGAL_DOC_VERSION} - Classification" +WARN_IF_UNDOCUMENTED = NO diff --git a/Classification/doc/Classification/PackageDescription.txt b/Classification/doc/Classification/PackageDescription.txt new file mode 100644 index 00000000000..19f53706960 --- /dev/null +++ b/Classification/doc/Classification/PackageDescription.txt @@ -0,0 +1,113 @@ +/*! +\defgroup PkgClassification Classification Reference + +\defgroup PkgClassificationConcepts Concepts +\ingroup PkgClassification + +\defgroup PkgClassificationMain Main Functions +\ingroup PkgClassification + +Functions that perform classification based on a set of labels and a classifier, with or without regularization. + +\defgroup PkgClassificationClassifiers Classifiers +\ingroup PkgClassification + +Classifiers are functors that, given a label set and an input item, associate this input item with an energy for each label. This energy measures the likelihood of the item to belong to this label. + +\defgroup PkgClassificationDataStructures Data Structures +\ingroup PkgClassification + +Useful data structures that are used to compute features (computation of eigenvalues, for example) and to regularize classification (neighborhood). + +\defgroup PkgClassificationLabel Label +\ingroup PkgClassification + +A label represents how an item should be classified, for example: _vegetation_, _building_, _road_, etc. + +\defgroup PkgClassificationFeature Feature +\ingroup PkgClassification + +Features are defined as scalar fields that associates each input item with a specific value. + +\defgroup PkgClassificationFeatures Predefined Features +\ingroup PkgClassification + +\cgal provides some predefined features that are relevant for classification of point sets. + +\addtogroup PkgClassification + +\cgalPkgDescriptionBegin{Classification, PkgClassificationSummary} +\cgalPkgPicture{data_classif.png} + +\cgalPkgSummaryBegin +\cgalPkgAuthors{Simon Giraudot, Florent Lafarge} +\cgalPkgDesc{This component implements an algorithm that classifies a data set into a user-defined set of labels (such as ground, vegetation, buildings, etc.). A flexible API is provided so that users can classify any type of data, compute their own local features on the input data set, and define their own labels.} +\cgalPkgManuals{Chapter_Classification, PkgClassification} +\cgalPkgSummaryEnd + +\cgalPkgShortInfoBegin +\cgalPkgSince{4.9} +\cgalPkgBib{cgal:lm-clscm-12} +\cgalPkgLicense{\ref licensesGPL "GPL"} +\cgalPkgDemo{Operations on Polyhedra,polyhedron_3.zip} +\cgalPkgShortInfoEnd + +\cgalPkgDescriptionEnd + +\cgalClassifedRefPages + +## Concepts ## + +- `CGAL::Classification::Classifier` +- `CGAL::Classification::NeighborQuery` + +## Main Functions ## + +- `CGAL::Classification::classify()` +- `CGAL::Classification::classify_with_local_smoothing()` +- `CGAL::Classification::classify_with_graphcut()` + +## Classifiers ## + +- `CGAL::Classification::Sum_of_weighted_features_classifier` +- `CGAL::Classification::Random_forest_classifier` + +## Data Structures ## + +- `CGAL::Classification::Local_eigen_analysis` +- `CGAL::Classification::Planimetric_grid` +- `CGAL::Classification::Point_set_feature_generator` +- `CGAL::Classification::Point_set_neighborhood` +- `CGAL::Classification::Evaluation` + +## Label ## + +- `CGAL::Classification::Label` +- `CGAL::Classification::Label_handle` +- `CGAL::Classification::Label_set` + +## Feature ## + +- `CGAL::Classification::Feature_base` +- `CGAL::Classification::Feature_handle` +- `CGAL::Classification::Feature_set` + +## Predefined Features ## + +- `CGAL::Classification::Feature::Anisotropy` +- `CGAL::Classification::Feature::Distance_to_plane` +- `CGAL::Classification::Feature::Echo_scatter` +- `CGAL::Classification::Feature::Eigentropy` +- `CGAL::Classification::Feature::Elevation` +- `CGAL::Classification::Feature::Hsv` +- `CGAL::Classification::Feature::Linearity` +- `CGAL::Classification::Feature::Omnivariance` +- `CGAL::Classification::Feature::Planarity` +- `CGAL::Classification::Feature::Sphericity` +- `CGAL::Classification::Feature::Sum_eigenvalues` +- `CGAL::Classification::Feature::Surface_variation` +- `CGAL::Classification::Feature::Vertical_dispersion` +- `CGAL::Classification::Feature::Verticality` + + +*/ diff --git a/Classification/doc/Classification/dependencies b/Classification/doc/Classification/dependencies new file mode 100644 index 00000000000..92bb60cefa1 --- /dev/null +++ b/Classification/doc/Classification/dependencies @@ -0,0 +1,8 @@ +Manual +Kernel_23 +STL_Extension +Algebraic_foundations +Circulator +Stream_support +Solver_interface +Point_set_processing_3 diff --git a/Classification/doc/Classification/examples.txt b/Classification/doc/Classification/examples.txt new file mode 100644 index 00000000000..2ef96507c17 --- /dev/null +++ b/Classification/doc/Classification/examples.txt @@ -0,0 +1,6 @@ +/*! +\example Classification/example_classification.cpp +\example Classification/example_feature.cpp +\example Classification/example_generation_and_training.cpp +\example Classification/example_random_forest.cpp +*/ diff --git a/Classification/doc/Classification/fig/classif.png b/Classification/doc/Classification/fig/classif.png new file mode 100644 index 00000000000..b87c6990642 Binary files /dev/null and b/Classification/doc/Classification/fig/classif.png differ diff --git a/Classification/doc/Classification/fig/classif2.png b/Classification/doc/Classification/fig/classif2.png new file mode 100644 index 00000000000..df96dd832d0 Binary files /dev/null and b/Classification/doc/Classification/fig/classif2.png differ diff --git a/Classification/doc/Classification/fig/classif_training.png b/Classification/doc/Classification/fig/classif_training.png new file mode 100644 index 00000000000..7625c934e9b Binary files /dev/null and b/Classification/doc/Classification/fig/classif_training.png differ diff --git a/Classification/doc/Classification/fig/data_classif.png b/Classification/doc/Classification/fig/data_classif.png new file mode 100644 index 00000000000..79c35e960ef Binary files /dev/null and b/Classification/doc/Classification/fig/data_classif.png differ diff --git a/Classification/doc/Classification/fig/noise_outliers.png b/Classification/doc/Classification/fig/noise_outliers.png new file mode 100644 index 00000000000..b1d0a430859 Binary files /dev/null and b/Classification/doc/Classification/fig/noise_outliers.png differ diff --git a/Classification/doc/Classification/fig/organization.svg b/Classification/doc/Classification/fig/organization.svg new file mode 100644 index 00000000000..aece403903a --- /dev/null +++ b/Classification/doc/Classification/fig/organization.svg @@ -0,0 +1,1235 @@ + + + + + + image/svg+xml + + + + + + + + + + + + + Classified Data Set + + + + + + + + + + + + + + + + processed by + + + refined and + combined into + + + generates + + + + + + + + + + + + + + + used as + input by + + + used as + input by + + + + + + + + + + + + + + + + + + + + + + + + used for + training by + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + INPUT + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Neighborhood + + + + Local Eigen + Analysis + + + + Planimetric + Grid + + + + Bounding Box + + + Analysis + + + + + + + + Classifier + + + + + + + + + + + + Planarity + + + + Verticality + + + + Elevation + + + + + + Feature_set + + + + (Etc.) + + + + + + + + + + + + + + + classify() + classify_with_local_smoothing() + classify_with_graphcut() + + + + + + + + used as + input by + + + applied and + regularized by + + + + + + + + Data Set + + + + + + Ground Truth Subsets + + + + + + + + + + + + + + + + + + + + + + + + + + + + "vegetation" + + + + "building" + + + + + + + + + + "ground" + + + + + + + + + Label_set + + + + (Etc.) + + + + + + + + + + + + + + + + + OUTPUT + + + + + + diff --git a/Classification/doc/Classification/fig/trees.png b/Classification/doc/Classification/fig/trees.png new file mode 100644 index 00000000000..24dac59c235 Binary files /dev/null and b/Classification/doc/Classification/fig/trees.png differ diff --git a/Classification/dont_submit b/Classification/dont_submit new file mode 100644 index 00000000000..e69de29bb2d diff --git a/Classification/examples/Classification/CMakeLists.txt b/Classification/examples/Classification/CMakeLists.txt new file mode 100644 index 00000000000..58170a221f0 --- /dev/null +++ b/Classification/examples/Classification/CMakeLists.txt @@ -0,0 +1,67 @@ +# Created by the script cgal_create_CMakeLists +# This is the CMake script for compiling a set of CGAL applications. + +project( Classification_Examples ) + + +cmake_minimum_required(VERSION 2.8.11) + +# CGAL and its components +find_package( CGAL QUIET COMPONENTS ) + +if ( NOT CGAL_FOUND ) + + message(STATUS "This project requires the CGAL library, and will not be compiled.") + return() + +endif() + +# include helper file +include( ${CGAL_USE_FILE} ) + + +# Boost and its components +find_package( Boost REQUIRED ) + +if ( NOT Boost_FOUND ) + + message(STATUS "This project requires the Boost library, and will not be compiled.") + + return() + +endif() + +find_package( TBB ) +if( TBB_FOUND ) + include(${TBB_USE_FILE}) + list(APPEND CGAL_3RD_PARTY_LIBRARIES ${TBB_LIBRARIES}) +endif() + + +find_package(OpenCV QUIET) + +# include for local directory +include_directories( BEFORE include ) + +# include for local package +include_directories( BEFORE ../../include ) + + +# Creating entries for all C++ files with "main" routine +# ########################################################## + +include( CGAL_CreateSingleSourceCGALProgram ) + +set(needed_cxx_features cxx_rvalue_references cxx_variadic_templates) +create_single_source_cgal_program( "example_classification.cpp" CXX_FEATURES ${needed_cxx_features} ) +create_single_source_cgal_program( "example_generation_and_training.cpp" CXX_FEATURES ${needed_cxx_features} ) +create_single_source_cgal_program( "example_feature.cpp" CXX_FEATURES ${needed_cxx_features} ) + +if( OpenCV_FOUND ) 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+#include +#include +#include +#include + +#include +#include +#include + +#include + +#include + +#ifdef CGAL_LINKED_WITH_TBB +typedef CGAL::Parallel_tag Concurrency_tag; +#else +typedef CGAL::Sequential_tag Concurrency_tag; +#endif + +typedef CGAL::Simple_cartesian Kernel; +typedef Kernel::Point_3 Point; +typedef Kernel::Iso_cuboid_3 Iso_cuboid_3; +typedef std::vector Point_range; +typedef CGAL::Identity_property_map Pmap; + +namespace Classification = CGAL::Classification; + +typedef Classification::Sum_of_weighted_features_classifier Classifier; + +typedef Classification::Planimetric_grid Planimetric_grid; +typedef Classification::Point_set_neighborhood Neighborhood; +typedef Classification::Local_eigen_analysis Local_eigen_analysis; + +typedef Classification::Label_handle Label_handle; +typedef Classification::Feature_handle Feature_handle; +typedef Classification::Label_set Label_set; +typedef Classification::Feature_set Feature_set; + +typedef Classification::Feature::Distance_to_plane Distance_to_plane; +typedef Classification::Feature::Linearity Linearity; +typedef Classification::Feature::Omnivariance Omnivariance; +typedef Classification::Feature::Planarity Planarity; +typedef Classification::Feature::Surface_variation Surface_variation; +typedef Classification::Feature::Elevation Elevation; +typedef Classification::Feature::Vertical_dispersion Dispersion; + + +/////////////////////////////////////////////////////////////////// +//! [Analysis] + +int main (int argc, char** argv) +{ + std::string filename (argc > 1 ? argv[1] : "data/b9.ply"); + std::ifstream in (filename.c_str()); + std::vector pts; + + std::cerr << "Reading input" << std::endl; + if (!in + || !(CGAL::read_ply_points (in, std::back_inserter (pts)))) + { + std::cerr << "Error: cannot read " << filename << std::endl; + return EXIT_FAILURE; + } + + float grid_resolution = 0.34f; + float radius_neighbors = 1.7f; + float radius_dtm = 15.0f; + + std::cerr << "Computing useful structures" << std::endl; + + Iso_cuboid_3 bbox = CGAL::bounding_box (pts.begin(), pts.end()); + + Planimetric_grid grid (pts, Pmap(), bbox, grid_resolution); + Neighborhood neighborhood (pts, Pmap()); + Local_eigen_analysis eigen (pts, Pmap(), neighborhood.k_neighbor_query(6)); + + //! [Analysis] + /////////////////////////////////////////////////////////////////// + + /////////////////////////////////////////////////////////////////// + //! [Features] + + std::cerr << "Computing features" << std::endl; + Feature_set features; + Feature_handle distance_to_plane = features.add (pts, Pmap(), eigen); + Feature_handle linearity = features.add (pts, eigen); + Feature_handle omnivariance = features.add (pts, eigen); + Feature_handle planarity = features.add (pts, eigen); + Feature_handle surface_variation = features.add (pts, eigen); + Feature_handle dispersion = features.add (pts, Pmap(), grid, + radius_neighbors); + Feature_handle elevation = features.add (pts, Pmap(), grid, + radius_dtm); + + //! [Features] + /////////////////////////////////////////////////////////////////// + + /////////////////////////////////////////////////////////////////// + //! [Labels] + + Label_set labels; + Label_handle ground = labels.add ("ground"); + Label_handle vegetation = labels.add ("vegetation"); + Label_handle roof = labels.add ("roof"); + + //! [Labels] + /////////////////////////////////////////////////////////////////// + + /////////////////////////////////////////////////////////////////// + //! [Weights] + + std::cerr << "Setting weights" << std::endl; + Classifier classifier (labels, features); + classifier.set_weight (distance_to_plane, 6.75e-2f); + classifier.set_weight (linearity, 1.19f); + classifier.set_weight (omnivariance, 1.34e-1f); + classifier.set_weight (planarity, 7.32e-1f); + classifier.set_weight (surface_variation, 1.36e-1f); + classifier.set_weight (dispersion, 5.45e-1f); + classifier.set_weight (elevation, 1.47e1f); + + std::cerr << "Setting effects" << std::endl; + classifier.set_effect (ground, distance_to_plane, Classifier::NEUTRAL); + classifier.set_effect (ground, linearity, Classifier::PENALIZING); + classifier.set_effect (ground, omnivariance, Classifier::NEUTRAL); + classifier.set_effect (ground, planarity, Classifier::FAVORING); + classifier.set_effect (ground, surface_variation, Classifier::PENALIZING); + classifier.set_effect (ground, dispersion, Classifier::NEUTRAL); + classifier.set_effect (ground, elevation, Classifier::PENALIZING); + + classifier.set_effect (vegetation, distance_to_plane, Classifier::FAVORING); + classifier.set_effect (vegetation, linearity, Classifier::NEUTRAL); + classifier.set_effect (vegetation, omnivariance, Classifier::FAVORING); + classifier.set_effect (vegetation, planarity, Classifier::NEUTRAL); + classifier.set_effect (vegetation, surface_variation, Classifier::NEUTRAL); + classifier.set_effect (vegetation, dispersion, Classifier::FAVORING); + classifier.set_effect (vegetation, elevation, Classifier::NEUTRAL); + + classifier.set_effect (roof, distance_to_plane, Classifier::NEUTRAL); + classifier.set_effect (roof, linearity, Classifier::PENALIZING); + classifier.set_effect (roof, omnivariance, Classifier::FAVORING); + classifier.set_effect (roof, planarity, Classifier::FAVORING); + classifier.set_effect (roof, surface_variation, Classifier::PENALIZING); + classifier.set_effect (roof, dispersion, Classifier::NEUTRAL); + classifier.set_effect (roof, elevation, Classifier::FAVORING); + + //! [Weights] + /////////////////////////////////////////////////////////////////// + + // Run classification + std::cerr << "Classifying" << std::endl; + + /////////////////////////////////////////////////////////////////// + //! [Classify] + std::vector label_indices; + + CGAL::Real_timer t; + t.start(); + Classification::classify (pts, labels, classifier, label_indices); + t.stop(); + std::cerr << "Raw classification performed in " << t.time() << " second(s)" << std::endl; + t.reset(); + //! [Classify] + /////////////////////////////////////////////////////////////////// + + /////////////////////////////////////////////////////////////////// + //! [Smoothing] + t.start(); + Classification::classify_with_local_smoothing + (pts, Pmap(), labels, classifier, + neighborhood.sphere_neighbor_query(radius_neighbors), + label_indices); + t.stop(); + std::cerr << "Classification with local smoothing performed in " << t.time() << " second(s)" << std::endl; + t.reset(); + //! [Smoothing] + /////////////////////////////////////////////////////////////////// + + /////////////////////////////////////////////////////////////////// + //! [Graph_cut] + t.start(); + Classification::classify_with_graphcut + (pts, Pmap(), labels, classifier, + neighborhood.k_neighbor_query(12), + 0.2f, 4, label_indices); + t.stop(); + std::cerr << "Classification with graphcut performed in " << t.time() << " second(s)" << std::endl; + //! [Graph_cut] + /////////////////////////////////////////////////////////////////// + + // Save the output in a colored PLY format + + std::ofstream f ("classification.ply"); + f << "ply" << std::endl + << "format ascii 1.0" << std::endl + << "element vertex " << pts.size() << std::endl + << "property float x" << std::endl + << "property float y" << std::endl + << "property float z" << std::endl + << "property uchar red" << std::endl + << "property uchar green" << std::endl + << "property uchar blue" << std::endl + << "end_header" << std::endl; + + for (std::size_t i = 0; i < pts.size(); ++ i) + { + f << pts[i] << " "; + + Label_handle label = labels[label_indices[i]]; + if (label == ground) + f << "245 180 0" << std::endl; + else if (label == vegetation) + f << "0 255 27" << std::endl; + else if (label == roof) + f << "255 0 170" << std::endl; + else + { + f << "0 0 0" << std::endl; + std::cerr << "Error: unknown classification label" << std::endl; + } + } + + std::cerr << "All done" << std::endl; + return EXIT_SUCCESS; +} diff --git a/Classification/examples/Classification/example_feature.cpp b/Classification/examples/Classification/example_feature.cpp new file mode 100644 index 00000000000..dae2833edf3 --- /dev/null +++ b/Classification/examples/Classification/example_feature.cpp @@ -0,0 +1,124 @@ +#if defined (_MSC_VER) && !defined (_WIN64) +#pragma warning(disable:4244) // boost::number_distance::distance() + // converts 64 to 32 bits integers +#endif + +#include +#include +#include +#include + +#include +#include +#include + +typedef CGAL::Simple_cartesian Kernel; +typedef Kernel::Point_3 Point; +typedef Kernel::Iso_cuboid_3 Iso_cuboid_3; +typedef std::vector Point_range; +typedef CGAL::Identity_property_map Pmap; + +namespace Classification = CGAL::Classification; + +typedef Classification::Sum_of_weighted_features_classifier Classifier; + +typedef Classification::Point_set_neighborhood Neighborhood; +typedef Classification::Local_eigen_analysis Local_eigen_analysis; + +typedef Classification::Label_handle Label_handle; +typedef Classification::Feature_handle Feature_handle; +typedef Classification::Label_set Label_set; +typedef Classification::Feature_set Feature_set; + +typedef Classification::Feature::Sphericity Sphericity; + +/////////////////////////////////////////////////////////////////// +//! [Feature] + +// User-defined feature that identifies a specific area of the 3D +// space. This feature takes value 1 for points that lie inside the +// area and 0 for the others. +class My_feature : public CGAL::Classification::Feature_base +{ + const Point_range& range; + double xmin, xmax, ymin, ymax; +public: + My_feature (const Point_range& range, + double xmin, double xmax, double ymin, double ymax) + : range (range), xmin(xmin), xmax(xmax), ymin(ymin), ymax(ymax) + { + this->set_name ("my_feature"); + } + + float value (std::size_t pt_index) + { + if (xmin < range[pt_index].x() && range[pt_index].x() < xmax && + ymin < range[pt_index].y() && range[pt_index].y() < ymax) + return 1.f; + else + return 0.f; + } + +}; + +//! [Feature] +/////////////////////////////////////////////////////////////////// + +int main (int argc, char** argv) +{ + std::string filename (argc > 1 ? argv[1] : "data/b9.ply"); + std::ifstream in (filename.c_str()); + std::vector pts; + + std::cerr << "Reading input" << std::endl; + if (!in + || !(CGAL::read_ply_points (in, std::back_inserter (pts)))) + { + std::cerr << "Error: cannot read " << filename << std::endl; + return EXIT_FAILURE; + } + + Neighborhood neighborhood (pts, Pmap()); + Local_eigen_analysis eigen (pts, Pmap(), neighborhood.k_neighbor_query(6)); + + Label_set labels; + Label_handle a = labels.add ("label_A"); + Label_handle b = labels.add ("label_B"); + + /////////////////////////////////////////////////////////////////// + //! [Addition] + + std::cerr << "Computing features" << std::endl; + Feature_set features; + + // Feature that identifies points whose x coordinate is between -20 + // and 20 and whose y coordinate is between -15 and 15 + Feature_handle my_feature = features.add (pts, -20., 20., -15., 15.); + + //! [Addition] + /////////////////////////////////////////////////////////////////// + + Feature_handle sphericity = features.add (pts, eigen); + + Classifier classifier (labels, features); + + std::cerr << "Setting weights" << std::endl; + classifier.set_weight(sphericity, 0.5); + classifier.set_weight(my_feature, 0.25); + + std::cerr << "Setting up labels" << std::endl; + classifier.set_effect (a, sphericity, Classifier::FAVORING); + classifier.set_effect (a, my_feature, Classifier::FAVORING); + classifier.set_effect (b, sphericity, Classifier::PENALIZING); + classifier.set_effect (b, my_feature, Classifier::PENALIZING); + + std::cerr << "Classifying" << std::endl; + std::vector label_indices(pts.size(), -1); + Classification::classify_with_graphcut + (pts, Pmap(), labels, classifier, + neighborhood.k_neighbor_query(12), + 0.5, 1, label_indices); + + std::cerr << "All done" << std::endl; + return EXIT_SUCCESS; +} diff --git a/Classification/examples/Classification/example_generation_and_training.cpp b/Classification/examples/Classification/example_generation_and_training.cpp new file mode 100644 index 00000000000..5f2e2edf61c --- /dev/null +++ b/Classification/examples/Classification/example_generation_and_training.cpp @@ -0,0 +1,127 @@ +#if defined (_MSC_VER) && !defined (_WIN64) +#pragma warning(disable:4244) // boost::number_distance::distance() + // converts 64 to 32 bits integers +#endif + +#include +#include +#include +#include + +#include +#include +#include +#include + +#include + +typedef CGAL::Simple_cartesian Kernel; +typedef Kernel::Point_3 Point; +typedef CGAL::Point_set_3 Point_set; +typedef Kernel::Iso_cuboid_3 Iso_cuboid_3; + +typedef Point_set::Point_map Pmap; +typedef Point_set::Property_map Imap; + +namespace Classification = CGAL::Classification; + +typedef Classification::Label_handle Label_handle; +typedef Classification::Feature_handle Feature_handle; +typedef Classification::Label_set Label_set; +typedef Classification::Feature_set Feature_set; + +typedef Classification::Sum_of_weighted_features_classifier Classifier; + +typedef Classification::Point_set_feature_generator Feature_generator; + + +int main (int argc, char** argv) +{ + std::string filename (argc > 1 ? argv[1] : "data/b9_training.ply"); + std::ifstream in (filename.c_str(), std::ios::binary); + Point_set pts; + + std::cerr << "Reading input" << std::endl; + in >> pts; + + Imap label_map; + bool lm_found = false; + boost::tie (label_map, lm_found) = pts.property_map ("label"); + if (!lm_found) + { + std::cerr << "Error: \"label\" property not found in input file." << std::endl; + return EXIT_FAILURE; + } + + std::vector ground_truth; + ground_truth.reserve (pts.size()); + std::copy (pts.range(label_map).begin(), pts.range(label_map).end(), + std::back_inserter (ground_truth)); + + /////////////////////////////////////////////////////////////////// + //! [Generator] + + Feature_set features; + + std::cerr << "Generating features" << std::endl; + CGAL::Real_timer t; + t.start(); + Feature_generator generator (features, pts, pts.point_map(), + 5); // using 5 scales + t.stop(); + std::cerr << features.size() << " feature(s) generated in " << t.time() << " second(s)" << std::endl; + + //! [Generator] + /////////////////////////////////////////////////////////////////// + + // Add types + Label_set labels; + Label_handle ground = labels.add ("ground"); + Label_handle vegetation = labels.add ("vegetation"); + Label_handle roof = labels.add ("roof"); + + Classifier classifier (labels, features); + + std::cerr << "Training" << std::endl; + t.reset(); + t.start(); + classifier.train (ground_truth, 800); + t.stop(); + std::cerr << "Done in " << t.time() << " second(s)" << std::endl; + + t.reset(); + t.start(); + std::vector label_indices(pts.size(), -1); + Classification::classify_with_graphcut + (pts, pts.point_map(), labels, classifier, + generator.neighborhood().k_neighbor_query(12), + 0.2f, 10, label_indices); + t.stop(); + std::cerr << "Classification with graphcut done in " << t.time() << " second(s)" << std::endl; + + std::cerr << "Precision, recall, F1 scores and IoU:" << std::endl; + Classification::Evaluation evaluation (labels, ground_truth, label_indices); + + for (std::size_t i = 0; i < labels.size(); ++ i) + { + std::cerr << " * " << labels[i]->name() << ": " + << evaluation.precision(labels[i]) << " ; " + << evaluation.recall(labels[i]) << " ; " + << evaluation.f1_score(labels[i]) << " ; " + << evaluation.intersection_over_union(labels[i]) << std::endl; + } + + std::cerr << "Accuracy = " << evaluation.accuracy() << std::endl + << "Mean F1 score = " << evaluation.mean_f1_score() << std::endl + << "Mean IoU = " << evaluation.mean_intersection_over_union() << std::endl; + + + /// Save the configuration to be able to reload it later + std::ofstream fconfig ("config.xml"); + classifier.save_configuration (fconfig); + fconfig.close(); + + std::cerr << "All done" << std::endl; + + return EXIT_SUCCESS; +} diff --git a/Classification/examples/Classification/example_random_forest.cpp b/Classification/examples/Classification/example_random_forest.cpp new file mode 100644 index 00000000000..adb13e03a23 --- /dev/null +++ b/Classification/examples/Classification/example_random_forest.cpp @@ -0,0 +1,147 @@ +#if defined (_MSC_VER) && !defined (_WIN64) +#pragma warning(disable:4244) // boost::number_distance::distance() + // converts 64 to 32 bits integers +#endif + +#include +#include +#include +#include + +#include +#include +#include +#include +#include + +#include + +typedef CGAL::Simple_cartesian Kernel; +typedef Kernel::Point_3 Point; +typedef CGAL::Point_set_3 Point_set; +typedef Kernel::Iso_cuboid_3 Iso_cuboid_3; + +typedef typename Point_set::Point_map Pmap; +typedef typename Point_set::Property_map Imap; +typedef typename Point_set::Property_map UCmap; + +namespace Classification = CGAL::Classification; + +typedef Classification::Label_handle Label_handle; +typedef Classification::Feature_handle Feature_handle; +typedef Classification::Label_set Label_set; +typedef Classification::Feature_set Feature_set; + +typedef Classification::Random_forest_classifier Classifier; + +typedef Classification::Point_set_feature_generator Feature_generator; + + +int main (int argc, char** argv) +{ + std::string filename (argc > 1 ? argv[1] : "data/b9_training.ply"); + std::ifstream in (filename.c_str()); + Point_set pts; + + std::cerr << "Reading input" << std::endl; + in >> pts; + + Imap label_map; + bool lm_found = false; + boost::tie (label_map, lm_found) = pts.property_map ("label"); + if (!lm_found) + { + std::cerr << "Error: \"label\" property not found in input file." << std::endl; + return EXIT_FAILURE; + } + + std::vector ground_truth; + ground_truth.reserve (pts.size()); + std::copy (pts.range(label_map).begin(), pts.range(label_map).end(), + std::back_inserter (ground_truth)); + + Feature_set features; + + std::cerr << "Generating features" << std::endl; + CGAL::Real_timer t; + t.start(); + Feature_generator generator (features, pts, pts.point_map(), + 5); // using 5 scales + t.stop(); + std::cerr << "Done in " << t.time() << " second(s)" << std::endl; + + // Add types + Label_set labels; + Label_handle ground = labels.add ("ground"); + Label_handle vegetation = labels.add ("vegetation"); + Label_handle roof = labels.add ("roof"); + + Classifier classifier (labels, features); + + std::cerr << "Training" << std::endl; + t.reset(); + t.start(); + classifier.train (ground_truth); + t.stop(); + std::cerr << "Done in " << t.time() << " second(s)" << std::endl; + + t.reset(); + t.start(); + std::vector label_indices(pts.size(), -1); + Classification::classify_with_graphcut + (pts, pts.point_map(), labels, classifier, + generator.neighborhood().k_neighbor_query(12), + 0.2, 1, label_indices); + t.stop(); + std::cerr << "Classification with graphcut done in " << t.time() << " second(s)" << std::endl; + + std::cerr << "Precision, recall, F1 scores and IoU:" << std::endl; + Classification::Evaluation evaluation (labels, ground_truth, label_indices); + + for (std::size_t i = 0; i < labels.size(); ++ i) + { + std::cerr << " * " << labels[i]->name() << ": " + << evaluation.precision(labels[i]) << " ; " + << evaluation.recall(labels[i]) << " ; " + << evaluation.f1_score(labels[i]) << " ; " + << evaluation.intersection_over_union(labels[i]) << std::endl; + } + + std::cerr << "Accuracy = " << evaluation.accuracy() << std::endl + << "Mean F1 score = " << evaluation.mean_f1_score() << std::endl + << "Mean IoU = " << evaluation.mean_intersection_over_union() << std::endl; + + // Color point set according to class + UCmap red = pts.add_property_map("red", 0).first; + UCmap green = pts.add_property_map("green", 0).first; + UCmap blue = pts.add_property_map("blue", 0).first; + + for (std::size_t i = 0; i < label_indices.size(); ++ i) + { + label_map[i] = label_indices[i]; // update label map with computed classification + + Label_handle label = labels[label_indices[i]]; + + if (label == ground) + { + red[i] = 245; green[i] = 180; blue[i] = 0; + } + else if (label == vegetation) + { + red[i] = 0; green[i] = 255; blue[i] = 27; + } + else if (label == roof) + { + red[i] = 255; green[i] = 0; blue[i] = 170; + } + } + + // Write result + std::ofstream f ("classification.ply"); + f.precision(18); + f << pts; + + std::cerr << "All done" << std::endl; + + return EXIT_SUCCESS; +} diff --git a/Classification/include/CGAL/Classification.h b/Classification/include/CGAL/Classification.h new file mode 100644 index 00000000000..3ed9cc8ea14 --- /dev/null +++ b/Classification/include/CGAL/Classification.h @@ -0,0 +1,51 @@ +// Copyright (c) 2017 GeometryFactory Sarl (France). +// All rights reserved. +// +// This file is part of CGAL (www.cgal.org). +// You can redistribute it and/or modify it under the terms of the GNU +// General Public License as published by the Free Software Foundation, +// either version 3 of the License, or (at your option) any later version. +// +// Licensees holding a valid commercial license may use this file in +// accordance with the commercial license agreement provided with the software. +// +// This file is provided AS IS with NO WARRANTY OF ANY KIND, INCLUDING THE +// WARRANTY OF DESIGN, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. +// +// $URL$ +// $Id$ +// +// Author(s) : Simon Giraudot + +#ifndef CGAL_CLASSIFICATION_H +#define CGAL_CLASSIFICATION_H + +#include + +#include +#include + +#ifdef CGAL_LINKED_WITH_OPENCV +#include +#endif + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include + +#endif // CGAL_CLASSIFICATION_H diff --git a/Classification/include/CGAL/Classification/Color.h b/Classification/include/CGAL/Classification/Color.h new file mode 100644 index 00000000000..7d30c3eb9cf --- /dev/null +++ b/Classification/include/CGAL/Classification/Color.h @@ -0,0 +1,138 @@ +// Copyright (c) 2017 GeometryFactory Sarl (France). +// All rights reserved. +// +// This file is part of CGAL (www.cgal.org). +// You can redistribute it and/or modify it under the terms of the GNU +// General Public License as published by the Free Software Foundation, +// either version 3 of the License, or (at your option) any later version. +// +// Licensees holding a valid commercial license may use this file in +// accordance with the commercial license agreement provided with the software. +// +// This file is provided AS IS with NO WARRANTY OF ANY KIND, INCLUDING THE +// WARRANTY OF DESIGN, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. +// +// $URL$ +// $Id$ +// +// Author(s) : Simon Giraudot + +#ifndef CGAL_CLASSIFICATION_COLOR_H +#define CGAL_CLASSIFICATION_COLOR_H + +#include + +#include + +namespace CGAL { +namespace Classification { + + /*! + \ingroup PkgClassificationColor + + %Color described in red/green/blue space. Each component is stored + as an unsigned char ranging from 0 (no color) to 255 (full color). + */ +typedef CGAL::cpp11::array RGB_Color; + /*! + \ingroup PkgClassificationColor + + %Color described in hue/saturation/value space. Each component is stored + as a float: + + - `hue` ranges from 0° to 360° (corresponding to the color tint) + - `saturation` ranges from 0.0 (gray) to 100.0 (full saturation) + - `value` ranges from 0.0 (black) to 100.0 (white) + */ +typedef CGAL::cpp11::array HSV_Color; + + + /// \cond SKIP_IN_MANUAL +inline HSV_Color rgb_to_hsv (const RGB_Color& c) +{ + double r = (double)(c[0]) / 255.; + double g = (double)(c[1]) / 255.; + double b = (double)(c[2]) / 255.; + double Cmax = (std::max) (r, (std::max) (g, b)); + double Cmin = (std::min) (r, (std::min) (g, b)); + double delta = Cmax - Cmin; + double H = 0.; + + if (delta != 0.) + { + if (Cmax == r) + H = 60. * ((g - b) / delta); + else if (Cmax == g) + H = 60. * (((b - r) / delta) + 2.); + else + H = 60. * (((r - g) / delta) + 4.); + } + if (H < 0.) H += 360.; + double S = (Cmax == 0. ? 0. : 100. * (delta / Cmax)); + double V = 100. * Cmax; + HSV_Color out = {{ float(H), float(S), float(V) }}; + return out; +} + +inline RGB_Color hsv_to_rgb (const HSV_Color& c) +{ + double h = c[0]; + double s = c[1]; + double v = c[2]; + + s /= 100.; + v /= 100.; + double C = v*s; + int hh = (int)(h/60.); + double X = C * (1-CGAL::abs (hh % 2 - 1)); + double r = 0, g = 0, b = 0; + + if( hh>=0 && hh<1 ) + { + r = C; + g = X; + } + else if( hh>=1 && hh<2 ) + { + r = X; + g = C; + } + else if( hh>=2 && hh<3 ) + { + g = C; + b = X; + } + else if( hh>=3 && hh<4 ) + { + g = X; + b = C; + } + else if( hh>=4 && hh<5 ) + { + r = X; + b = C; + } + else + { + r = C; + b = X; + } + double m = v-C; + r += m; + g += m; + b += m; + r *= 255.0; + g *= 255.0; + b *= 255.0; + + RGB_Color out = {{ (unsigned char)r, (unsigned char)g, (unsigned char)b }}; + return out; +} + /// \endcond + +} // namespace Classification +} // namespace CGAL + + + +#endif // CGAL_CLASSIFICATION_COLOR_H diff --git a/Classification/include/CGAL/Classification/Evaluation.h b/Classification/include/CGAL/Classification/Evaluation.h new file mode 100644 index 00000000000..caadd32dbe8 --- /dev/null +++ b/Classification/include/CGAL/Classification/Evaluation.h @@ -0,0 +1,224 @@ +// Copyright (c) 2017 GeometryFactory Sarl (France). +// All rights reserved. +// +// This file is part of CGAL (www.cgal.org). +// You can redistribute it and/or modify it under the terms of the GNU +// General Public License as published by the Free Software Foundation, +// either version 3 of the License, or (at your option) any later version. +// +// Licensees holding a valid commercial license may use this file in +// accordance with the commercial license agreement provided with the software. +// +// This file is provided AS IS with NO WARRANTY OF ANY KIND, INCLUDING THE +// WARRANTY OF DESIGN, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. +// +// $URL$ +// $Id$ +// +// Author(s) : Simon Giraudot + +#ifndef CGAL_CLASSIFICATION_EVALUATION_H +#define CGAL_CLASSIFICATION_EVALUATION_H + +#include + +#include +#include + +namespace CGAL { + +namespace Classification { + +/*! + \ingroup PkgClassificationDataStructures + + \brief Class to compute several measurements to evaluate the quality + of a classification output. +*/ +class Evaluation +{ + mutable std::map m_map_labels; + + std::vector m_precision; + std::vector m_recall; + std::vector m_iou; // intersection over union + float m_accuracy; + float m_mean_iou; + float m_mean_f1; + +public: + + /// \name Constructor + /// @{ + + +/*! + + \brief Instantiates an evaluation object and computes all + measurements. + + \param labels labels used. + + \param ground_truth vector of label indices: it should contain the + index of the corresponding label in the `Label_set` provided in the + constructor. Input items that do not have a ground truth information + should be given the value `-1`. + + \param result similar to `ground_truth` but contained the result of + a classification. + +*/ + template + Evaluation (const Label_set& labels, + const LabelIndexRange& ground_truth, + const LabelIndexRange& result) + : m_precision (labels.size()), + m_recall (labels.size()), + m_iou (labels.size()) + { + for (std::size_t i = 0; i < labels.size(); ++ i) + m_map_labels[labels[i]] = i; + + std::vector true_positives (labels.size()); + std::vector false_positives (labels.size()); + std::vector false_negatives (labels.size()); + + std::size_t sum_true_positives = 0; + std::size_t total = 0; + + for (std::size_t j = 0; j < ground_truth.size(); ++ j) + { + int gt = static_cast(ground_truth[j]); + int res = static_cast(result[j]); + if (gt == -1 || res == -1) + continue; + ++ total; + if (gt == res) + { + ++ true_positives[gt]; + ++ sum_true_positives; + continue; + } + ++ false_positives[res]; + ++ false_negatives[gt]; + } + + m_mean_iou = 0.; + m_mean_f1 = 0.; + + for (std::size_t j = 0; j < labels.size(); ++ j) + { + m_precision[j] = true_positives[j] / float(true_positives[j] + false_positives[j]); + m_recall[j] = true_positives[j] / float(true_positives[j] + false_negatives[j]); + m_iou[j] = true_positives[j] / float(true_positives[j] + false_positives[j] + false_negatives[j]); + + m_mean_iou += m_iou[j]; + m_mean_f1 += 2.f * (m_precision[j] * m_recall[j]) + / (m_precision[j] + m_recall[j]); + } + + m_mean_iou /= labels.size(); + m_mean_f1 /= labels.size(); + m_accuracy = sum_true_positives / float(total); + } + + /// @} + + /// \name Label Evaluation + /// @{ + + + /*! + + \brief Returns the precision of the training for the given label. + + Precision is the number of true positives divided by the sum of + the true positives and the false positives. + + */ + float precision (Label_handle label) const + { + return m_precision[m_map_labels[label]]; + } + + /*! + + \brief Returns the recall of the training for the given label. + + Recall is the number of true positives divided by the sum of + the true positives and the false negatives. + + */ + float recall (Label_handle label) const + { + return m_recall[m_map_labels[label]]; + } + + /*! + + \brief Returns the \f$F_1\f$ score of the training for the given label. + + \f$F_1\f$ score is the harmonic mean of `precision()` and `recall()`: + + \f[ + F_1 = 2 \times \frac{precision \times recall}{precision + recall} + \f] + + */ + float f1_score (Label_handle label) const + { + std::size_t label_idx = m_map_labels[label]; + return 2.f * (m_precision[label_idx] * m_recall[label_idx]) + / (m_precision[label_idx] + m_recall[label_idx]); + } + + /*! + \brief Returns the intersection over union of the training for the + given label. + + Intersection over union is the number of true positives divided by + the sum of the true positives, of the false positives and of the + false negatives. + */ + float intersection_over_union (Label_handle label) const + { + return m_iou[m_map_labels[label]]; + } + + /// @} + + /// \name Global Evaluation + /// @{ + + + /*! + \brief Returns the accuracy of the training. + + Accuracy is the total number of true positives divided by the + total number of provided inliers. + */ + float accuracy() const { return m_accuracy; } + + /*! + \brief Returns the mean \f$F_1\f$ score of the training over all + labels (see `f1_score()`). + */ + float mean_f1_score() const { return m_mean_f1; } + + /*! + \brief Returns the mean intersection over union of the training + over all labels (see `intersection_over_union()`). + */ + float mean_intersection_over_union() const { return m_mean_iou; } + + /// @} + +}; + + +} // namespace Classification + +} // namespace CGAL + +#endif // CGAL_CLASSIFICATION_EVALUATION_H + diff --git a/Classification/include/CGAL/Classification/Feature/Distance_to_plane.h b/Classification/include/CGAL/Classification/Feature/Distance_to_plane.h new file mode 100644 index 00000000000..b1afde104d5 --- /dev/null +++ b/Classification/include/CGAL/Classification/Feature/Distance_to_plane.h @@ -0,0 +1,109 @@ +// Copyright (c) 2012 INRIA Sophia-Antipolis (France). +// Copyright (c) 2017 GeometryFactory Sarl (France). +// All rights reserved. +// +// This file is part of CGAL (www.cgal.org). +// You can redistribute it and/or modify it under the terms of the GNU +// General Public License as published by the Free Software Foundation, +// either version 3 of the License, or (at your option) any later version. +// +// Licensees holding a valid commercial license may use this file in +// accordance with the commercial license agreement provided with the software. +// +// This file is provided AS IS with NO WARRANTY OF ANY KIND, INCLUDING THE +// WARRANTY OF DESIGN, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. +// +// $URL$ +// $Id$ +// +// Author(s) : Simon Giraudot, Florent Lafarge + +#ifndef CGAL_CLASSIFICATION_FEATURE_DISTANCE_TO_PLANE_H +#define CGAL_CLASSIFICATION_FEATURE_DISTANCE_TO_PLANE_H + +#include + +#include + +namespace CGAL { + +namespace Classification { + +namespace Feature { + + /*! + \ingroup PkgClassificationFeatures + + %Feature based on local distance to a fitted + plane. Characterizing a level of non-planarity can help to + identify noisy parts of the input such as vegetation. This + feature computes the distance of a point to a locally fitted + plane. + + Its default name is "distance_to_plane". + + \tparam PointRange model of `ConstRange`. Its iterator type + is `RandomAccessIterator` and its value type is the key type of + `PointMap`. + \tparam PointMap model of `ReadablePropertyMap` whose key + type is the value type of the iterator of `PointRange` and value type + is `CGAL::Point_3`. + */ +template +class Distance_to_plane : public Feature_base +{ + + typedef typename Kernel_traits::Kernel Kernel; + +#ifdef CGAL_CLASSIFICATION_PRECOMPUTE_FEATURES + std::vector distance_to_plane_feature; +#else + const PointRange& input; + PointMap point_map; + const Local_eigen_analysis& eigen; +#endif + +public: + /*! + \brief Constructs the feature. + + \param input point range. + \param point_map property map to access the input points. + \param eigen class with precomputed eigenvectors and eigenvalues. + */ + Distance_to_plane (const PointRange& input, + PointMap point_map, + const Local_eigen_analysis& eigen) +#ifndef CGAL_CLASSIFICATION_PRECOMPUTE_FEATURES + : input(input), point_map(point_map), eigen(eigen) +#endif + { + this->set_name ("distance_to_plane"); +#ifdef CGAL_CLASSIFICATION_PRECOMPUTE_FEATURES + for(std::size_t i = 0; i < input.size(); i++) + distance_to_plane_feature.push_back + (CGAL::sqrt (CGAL::squared_distance (get(point_map, *(input.begin()+i)), eigen.plane(i)))); +#endif + } + + /// \cond SKIP_IN_MANUAL + virtual float value (std::size_t pt_index) + { +#ifdef CGAL_CLASSIFICATION_PRECOMPUTE_FEATURES + return distance_to_plane_feature[pt_index]; +#else + return float(CGAL::sqrt (CGAL::squared_distance + (get(point_map, *(input.begin()+pt_index)), + eigen.plane(pt_index)))); +#endif + } + /// \endcond +}; + +} // namespace Feature + +} // namespace Classification + +} // namespace CGAL + +#endif // CGAL_CLASSIFICATION_FEATURE_DISTANCE_TO_PLANE_H diff --git a/Classification/include/CGAL/Classification/Feature/Echo_scatter.h b/Classification/include/CGAL/Classification/Feature/Echo_scatter.h new file mode 100644 index 00000000000..a8613d74c4a --- /dev/null +++ b/Classification/include/CGAL/Classification/Feature/Echo_scatter.h @@ -0,0 +1,150 @@ +// Copyright (c) 2012 INRIA Sophia-Antipolis (France). +// Copyright (c) 2017 GeometryFactory Sarl (France). +// All rights reserved. +// +// This file is part of CGAL (www.cgal.org). +// You can redistribute it and/or modify it under the terms of the GNU +// General Public License as published by the Free Software Foundation, +// either version 3 of the License, or (at your option) any later version. +// +// Licensees holding a valid commercial license may use this file in +// accordance with the commercial license agreement provided with the software. +// +// This file is provided AS IS with NO WARRANTY OF ANY KIND, INCLUDING THE +// WARRANTY OF DESIGN, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. +// +// $URL$ +// $Id$ +// +// Author(s) : Simon Giraudot, Florent Lafarge + +#ifndef CGAL_CLASSIFICATION_FEATURE_ECHO_SCATTER_H +#define CGAL_CLASSIFICATION_FEATURE_ECHO_SCATTER_H + +#include + +#include + + +namespace CGAL { + +namespace Classification { + +namespace Feature { + + /*! + \ingroup PkgClassificationFeatures + + %Feature based on echo scatter. The number of returns (echo + number) is a useful information provided by most LIDAR sensors. It + can help to identify trees. + + Its default name is "echo_scatter". + + \tparam GeomTraits model of \cgal Kernel. + \tparam PointRange model of `ConstRange`. Its iterator type + is `RandomAccessIterator` and its value type is the key type of + `PointMap`. + \tparam PointMap model of `ReadablePropertyMap` whose key + type is the value type of the iterator of `PointRange` and value type + is `GeomTraits::Point_3`. + \tparam EchoMap model of `ReadablePropertyMap` whose key + type is the value type of the iterator of `PointRange` and value type + is `std::size_t`. + */ +template +class Echo_scatter : public Feature_base +{ +public: + typedef Classification::Planimetric_grid Grid; +private: + typedef Classification::Image Image_float; + + std::vector echo_scatter; + +public: + /*! + \brief Constructs the feature. + + \param input point range. + \param echo_map property map to access the echo values of the input points. + \param grid precomputed `Planimetric_grid`. + \param radius_neighbors radius of local neighborhoods. + */ + Echo_scatter (const PointRange& input, + EchoMap echo_map, + const Grid& grid, + float radius_neighbors = 1.) + { + this->set_name ("echo_scatter"); + Image_float Scatter(grid.width(), grid.height()); + for (std::size_t j = 0; j < grid.height(); j++) + for (std::size_t i = 0; i < grid.width(); i++) + Scatter(i,j)=0; + + std::size_t square = (std::size_t)(0.5 * radius_neighbors / grid.resolution()) + 1; + + for (std::size_t j = 0; j < grid.height(); j++){ + for (std::size_t i = 0; i < grid.width(); i++){ + + if(grid.has_points(i,j)){ + + std::size_t squareXmin = (i < square ? 0 : i - square); + std::size_t squareXmax = (std::min) (grid.width()-1, i + square); + std::size_t squareYmin = (j < square ? 0 : j - square); + std::size_t squareYmax = (std::min) (grid.height()-1, j + square); + + std::size_t NB_echo_sup=0; + std::size_t NB_echo_total=0; + + for(std::size_t k = squareXmin; k <= squareXmax; k++){ + for(std::size_t l = squareYmin; l <= squareYmax; l++){ + + if(CGAL::sqrt(pow((float)k-i,2)+pow((float)l-j,2))<=(float)0.5*radius_neighbors/grid.resolution()) + { + typename Grid::iterator end = grid.indices_end(k,l); + std::size_t nb = 0; + for (typename Grid::iterator it = grid.indices_begin(k,l); it != end; ++ it) + { + ++ nb; + if(get(echo_map, *(input.begin()+(*it))) > 1) + NB_echo_sup++; + } + + NB_echo_total=NB_echo_total+nb; + + } + + } + + } + + Scatter(i,j)=(float)NB_echo_sup/NB_echo_total; + + } + + } + + } + for(std::size_t i = 0; i < input.size(); i++){ + std::size_t I= grid.x(i); + std::size_t J= grid.y(i); + echo_scatter.push_back((float)Scatter(I,J)); + } + } + + /// \cond SKIP_IN_MANUAL + virtual float value (std::size_t pt_index) + { + return echo_scatter[pt_index]; + } + /// \endcond +}; + +} // namespace Feature + +} // namespace Classification + +} // namespace CGAL + +#endif // CGAL_CLASSIFICATION_FEATURE_ECHO_SCATTER_H diff --git a/Classification/include/CGAL/Classification/Feature/Eigen.h b/Classification/include/CGAL/Classification/Feature/Eigen.h new file mode 100644 index 00000000000..ef6531ac08b --- /dev/null +++ b/Classification/include/CGAL/Classification/Feature/Eigen.h @@ -0,0 +1,465 @@ +// Copyright (c) 2017 GeometryFactory Sarl (France). +// All rights reserved. +// +// This file is part of CGAL (www.cgal.org). +// You can redistribute it and/or modify it under the terms of the GNU +// General Public License as published by the Free Software Foundation, +// either version 3 of the License, or (at your option) any later version. +// +// Licensees holding a valid commercial license may use this file in +// accordance with the commercial license agreement provided with the software. +// +// This file is provided AS IS with NO WARRANTY OF ANY KIND, INCLUDING THE +// WARRANTY OF DESIGN, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. +// +// $URL$ +// $Id$ +// +// Author(s) : Simon Giraudot + +#ifndef CGAL_CLASSIFICATION_FEATURES_EIGEN_H +#define CGAL_CLASSIFICATION_FEATURES_EIGEN_H + +#include + +#include + +#include + +namespace CGAL { + +namespace Classification { + +namespace Feature { + +/// \cond SKIP_IN_MANUAL +class Eigen_feature : public Feature_base +{ +protected: +#ifdef CGAL_CLASSIFICATION_PRECOMPUTE_FEATURES + std::vector attrib; +#else + const Classification::Local_eigen_analysis& eigen; +#endif + +public: + template + Eigen_feature (const InputRange&, + const Classification::Local_eigen_analysis& eigen) +#ifndef CGAL_CLASSIFICATION_PRECOMPUTE_FEATURES + : eigen (eigen) +#endif + { + } + +#ifdef CGAL_CLASSIFICATION_PRECOMPUTE_FEATURES + virtual void init (std::size_t size, const Classification::Local_eigen_analysis& eigen) + { + attrib.reserve (size); + for (std::size_t i = 0; i < size; ++ i) + attrib.push_back (get_value (eigen, i)); + } +#else + virtual void init (std::size_t, const Classification::Local_eigen_analysis&) + { + } +#endif + + virtual float get_value (const Classification::Local_eigen_analysis& eigen, std::size_t i) = 0; + virtual float value (std::size_t pt_index) + { +#ifdef CGAL_CLASSIFICATION_PRECOMPUTE_FEATURES + return attrib[pt_index]; +#else + return get_value(eigen, pt_index); +#endif + } + +}; +/// \endcond + + /*! + \ingroup PkgClassificationFeatures + + %Feature based on the eigenvalues of the covariance matrix of a + local neighborhood. Linearity is defined, for the 3 eigenvalues + \f$\lambda_1 \ge \lambda_2 \ge \lambda_3 \ge 0\f$, as: + + \f[ + \frac{\lambda_1 - \lambda_2}{\lambda_1} + \f] + + Its default name is "linearity". + */ +class Linearity +#ifdef DOXYGEN_RUNNING + : public Feature_base +#else + : public Eigen_feature +#endif +{ +public: + /*! + Constructs the feature. + + \tparam Input model of `ConstRange`. Its iterator type + is `RandomAccessIterator`. + \param input point range. + \param eigen class with precomputed eigenvectors and eigenvalues. + */ + template + Linearity (const InputRange& input, + const Local_eigen_analysis& eigen) : Eigen_feature (input, eigen) + { + this->set_name("linearity"); + this->init(input.size(), eigen); + } + + /// \cond SKIP_IN_MANUAL + virtual float get_value (const Local_eigen_analysis& eigen, std::size_t i) + { + const Local_eigen_analysis::Eigenvalues& ev = eigen.eigenvalue(i); + if (ev[2] < 1e-15) + return 0.; + else + return ((ev[2] - ev[1]) / ev[2]); + } + /// \endcond +}; + + /*! + \ingroup PkgClassificationFeatures + + %Feature based on the eigenvalues of the covariance matrix of a + local neighborhood. Planarity is defined, for the 3 eigenvalues + \f$\lambda_1 \ge \lambda_2 \ge \lambda_3 \ge 0\f$, as: + + \f[ + \frac{\lambda_2 - \lambda_3}{\lambda_1} + \f] + + Its default name is "planarity". + */ +class Planarity +#ifdef DOXYGEN_RUNNING + : public Feature_base +#else + : public Eigen_feature +#endif +{ +public: + /*! + Constructs the feature. + + \param input point range. + \param eigen class with precomputed eigenvectors and eigenvalues. + */ + template + Planarity (const InputRange& input, + const Local_eigen_analysis& eigen) + : Eigen_feature(input, eigen) + { + this->set_name("planarity"); + this->init(input.size(), eigen); + } + /// \cond SKIP_IN_MANUAL + virtual float get_value (const Local_eigen_analysis& eigen, std::size_t i) + { + const Local_eigen_analysis::Eigenvalues& ev = eigen.eigenvalue(i); + if (ev[2] < 1e-15) + return 0.; + else + return ((ev[1] - ev[0]) / ev[2]); + } + /// \endcond + +}; + + /*! + \ingroup PkgClassificationFeatures + + %Feature based on the eigenvalues of the covariance matrix of a + local neighborhood. Sphericity is defined, for the 3 eigenvalues + \f$\lambda_1 \ge \lambda_2 \ge \lambda_3 \ge 0\f$, as: + + \f[ + \frac{\lambda_3}{\lambda_1} + \f] + + Its default name is "sphericity". + */ +class Sphericity +#ifdef DOXYGEN_RUNNING + : public Feature_base +#else + : public Eigen_feature +#endif +{ +public: + /*! + Constructs the feature. + + \param input point range. + \param eigen class with precomputed eigenvectors and eigenvalues. + */ + template + Sphericity (const InputRange& input, + const Local_eigen_analysis& eigen) + : Eigen_feature(input, eigen) + { + this->set_name("sphericity"); + this->init(input.size(), eigen); + } + /// \cond SKIP_IN_MANUAL + virtual float get_value (const Local_eigen_analysis& eigen, std::size_t i) + { + const Local_eigen_analysis::Eigenvalues& ev = eigen.eigenvalue(i); + if (ev[2] < 1e-15) + return 0.; + else + return (ev[0] / ev[2]); + } + /// \endcond +}; + + /*! + \ingroup PkgClassificationFeatures + + %Feature based on the eigenvalues of the covariance matrix of a + local neighborhood. Omnivariance is defined, for the 3 eigenvalues + \f$\lambda_1 \ge \lambda_2 \ge \lambda_3 \ge 0\f$, as: + + \f[ + (\lambda_1 \times \lambda_2 \times \lambda_3)^{\frac{1}{3}} + \f] + + Its default name is "omnivariance". + */ +class Omnivariance +#ifdef DOXYGEN_RUNNING + : public Feature_base +#else + : public Eigen_feature +#endif +{ +public: + /*! + Constructs the feature. + + \param input point range. + \param eigen class with precomputed eigenvectors and eigenvalues. + */ + template + Omnivariance (const InputRange& input, + const Local_eigen_analysis& eigen) + : Eigen_feature(input, eigen) + { + this->set_name("omnivariance"); + this->init(input.size(), eigen); + } + /// \cond SKIP_IN_MANUAL + virtual float get_value (const Local_eigen_analysis& eigen, std::size_t i) + { + const Local_eigen_analysis::Eigenvalues& ev = eigen.eigenvalue(i); + return (std::pow (CGAL::abs(ev[0] * ev[1] * ev[2]), 0.333333333f)); + } + /// \endcond +}; + + /*! + \ingroup PkgClassificationFeatures + + %Feature based on the eigenvalues of the covariance matrix of a + local neighborhood. Anisotropy is defined, for the 3 eigenvalues + \f$\lambda_1 \ge \lambda_2 \ge \lambda_3 \ge 0\f$, as: + + \f[ + \frac{\lambda_1 - \lambda_3}{\lambda_1} + \f] + + Its default name is "anisotropy". + */ +class Anisotropy +#ifdef DOXYGEN_RUNNING + : public Feature_base +#else + : public Eigen_feature +#endif +{ +public: + /*! + Constructs the feature. + + \param input point range. + \param eigen class with precomputed eigenvectors and eigenvalues. + */ + template + Anisotropy (const InputRange& input, + const Local_eigen_analysis& eigen) + : Eigen_feature(input, eigen) + { + this->set_name("anisotropy"); + this->init(input.size(), eigen); + } + /// \cond SKIP_IN_MANUAL + virtual float get_value (const Local_eigen_analysis& eigen, std::size_t i) + { + const Local_eigen_analysis::Eigenvalues& ev = eigen.eigenvalue(i); + if (ev[2] < 1e-15) + return 0.; + else + return ((ev[2] - ev[0]) / ev[2]); + } + /// \endcond +}; + + /*! + \ingroup PkgClassificationFeatures + + %Feature based on the eigenvalues of the covariance matrix of a + local neighborhood. Eigentropy is defined, for the 3 eigenvalues + \f$\lambda_1 \ge \lambda_2 \ge \lambda_3 \ge 0\f$, as: + + \f[ + - \sum_{i=1}^3 \lambda_i \times \log{\lambda_i} + \f] + + Its default name is "eigentropy". + */ +class Eigentropy +#ifdef DOXYGEN_RUNNING + : public Feature_base +#else + : public Eigen_feature +#endif +{ +public: + /*! + Constructs the feature. + + \param input point range. + \param eigen class with precomputed eigenvectors and eigenvalues. + */ + template + Eigentropy (const InputRange& input, + const Local_eigen_analysis& eigen) + : Eigen_feature(input, eigen) + { + this->set_name("eigentropy"); + this->init(input.size(), eigen); + } + /// \cond SKIP_IN_MANUAL + virtual float get_value (const Local_eigen_analysis& eigen, std::size_t i) + { + const Local_eigen_analysis::Eigenvalues& ev = eigen.eigenvalue(i); + if (ev[0] < 1e-15 + || ev[1] < 1e-15 + || ev[2] < 1e-15) + return 0.; + else + return (- ev[0] * std::log(ev[0]) + - ev[1] * std::log(ev[1]) + - ev[2] * std::log(ev[2])); + } + /// \endcond +}; + + /*! + \ingroup PkgClassificationFeatures + + %Feature based on the eigenvalues of the covariance matrix of a + local neighborhood. The sum of the eigenvalues is defined, for the + 3 eigenvalues \f$\lambda_1 \ge \lambda_2 \ge \lambda_3 \ge 0\f$, + as: + + \f[ + \lambda_1 + \lambda_2 + \lambda_3 + \f] + + Its default name is "sum_eigen". + */ +class Sum_eigenvalues +#ifdef DOXYGEN_RUNNING + : public Feature_base +#else + : public Eigen_feature +#endif +{ +public: + /*! + Constructs the feature. + + \param input point range. + \param eigen class with precomputed eigenvectors and eigenvalues. + */ + template + Sum_eigenvalues (const InputRange& input, + const Local_eigen_analysis& eigen) + : Eigen_feature(input, eigen) + { + this->set_name("sum_eigen"); + this->init(input.size(), eigen); + } + /// \cond SKIP_IN_MANUAL + virtual float get_value (const Local_eigen_analysis& eigen, std::size_t i) + { + return eigen.sum_of_eigenvalues(i); + } + /// \endcond +}; + + /*! + \ingroup PkgClassificationFeatures + + %Feature based on the eigenvalues of the covariance + matrix of a local neighborhood. Surface variation is defined, for + the 3 eigenvalues \f$\lambda_1 \ge \lambda_2 \ge \lambda_3 \ge + 0\f$, as: + + \f[ + \frac{\lambda_3}{\lambda_1 + \lambda_2 + \lambda_3} + \f] + + Its default name is "surface_variation". + */ +class Surface_variation +#ifdef DOXYGEN_RUNNING + : public Feature_base +#else + : public Eigen_feature +#endif +{ +public: + /*! + Constructs the feature. + + \param input point range. + \param eigen class with precomputed eigenvectors and eigenvalues. + */ + template + Surface_variation (const InputRange& input, + const Local_eigen_analysis& eigen) + : Eigen_feature(input, eigen) + { + this->set_name("surface_variation"); + this->init(input.size(), eigen); + } + /// \cond SKIP_IN_MANUAL + virtual float get_value (const Local_eigen_analysis& eigen, std::size_t i) + { + const Local_eigen_analysis::Eigenvalues& ev = eigen.eigenvalue(i); + if (ev[0] + ev[1] + ev[2] < 1e-15) + return 0.; + else + return (ev[0] / (ev[0] + ev[1] + ev[2])); + } + /// \endcond +}; + +} // namespace Feature + +} // namespace Classification + +} // namespace CGAL + +#endif // CGAL_CLASSIFICATION_FEATURES_EIGEN_H diff --git a/Classification/include/CGAL/Classification/Feature/Elevation.h b/Classification/include/CGAL/Classification/Feature/Elevation.h new file mode 100644 index 00000000000..bafcab57abf --- /dev/null +++ b/Classification/include/CGAL/Classification/Feature/Elevation.h @@ -0,0 +1,198 @@ +// Copyright (c) 2012 INRIA Sophia-Antipolis (France). +// Copyright (c) 2017 GeometryFactory Sarl (France). +// All rights reserved. +// +// This file is part of CGAL (www.cgal.org). +// You can redistribute it and/or modify it under the terms of the GNU +// General Public License as published by the Free Software Foundation, +// either version 3 of the License, or (at your option) any later version. +// +// Licensees holding a valid commercial license may use this file in +// accordance with the commercial license agreement provided with the software. +// +// This file is provided AS IS with NO WARRANTY OF ANY KIND, INCLUDING THE +// WARRANTY OF DESIGN, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. +// +// $URL$ +// $Id$ +// +// Author(s) : Florent Lafarge, Simon Giraudot + +#ifndef CGAL_CLASSIFICATION_FEATURE_ELEVATION_H +#define CGAL_CLASSIFICATION_FEATURE_ELEVATION_H + +#include + +#include + +#include +#include +#include + +namespace CGAL { + +namespace Classification { + +namespace Feature { + + /*! + \ingroup PkgClassificationFeatures + + %Feature based on local elevation. The local position of the + ground can be computed for urban scenes. This feature computes + the distance to the local estimation of the ground. It is useful + to discriminate the ground from horizontal roofs. + + Its default name is "elevation". + + \tparam GeomTraits model of \cgal Kernel. + \tparam PointRange model of `ConstRange`. Its iterator type + is `RandomAccessIterator` and its value type is the key type of + `PointMap`. + \tparam PointMap model of `ReadablePropertyMap` whose key + type is the value type of the iterator of `PointRange` and value type + is `GeomTraits::Point_3`. + + */ +template +class Elevation : public Feature_base +{ + typedef typename GeomTraits::Iso_cuboid_3 Iso_cuboid_3; + + typedef Image Image_float; + typedef Planimetric_grid Grid; + +#ifdef CGAL_CLASSIFICATION_PRECOMPUTE_FEATURES + std::vector elevation_feature; +#else + const PointRange& input; + PointMap point_map; + const Grid& grid; + Image_float dtm; +#endif + +public: + /*! + \brief Constructs the feature. + + \param input point range. + \param point_map property map to access the input points. + \param grid precomputed `Planimetric_grid`. + \param radius_dtm radius for digital terrain modeling (should be + larger than the width and length of the largest building). + */ + Elevation (const PointRange& input, + PointMap point_map, + const Grid& grid, + float radius_dtm = -1.) +#ifndef CGAL_CLASSIFICATION_PRECOMPUTE_FEATURES + : input(input), point_map(point_map), grid(grid) +#endif + { + this->set_name ("elevation"); + if (radius_dtm < 0.) + radius_dtm = 100.f * grid.resolution(); + + //DEM + Image_float dem(grid.width(),grid.height()); + + for (std::size_t j = 0; j < grid.height(); ++ j) + for (std::size_t i = 0; i < grid.width(); ++ i) + { + float mean = 0.; + std::size_t nb = 0; + typename Grid::iterator end = grid.indices_end(i,j); + for (typename Grid::iterator it = grid.indices_begin(i,j); it != end; ++ it) + { + mean += float(get(point_map, *(input.begin()+(*it))).z()); + ++ nb; + } + if (nb == 0) + continue; + mean /= nb; + dem(i,j) = mean; + } + + std::size_t square = (std::size_t)(0.5 * radius_dtm / grid.resolution()) + 1; + + Image_float dtm_x(grid.width(),grid.height()); + + for (std::size_t j = 0; j < grid.height(); ++ j) + { + for (std::size_t i = 0; i < grid.width(); ++ i) + { + std::size_t squareXmin = (i < square ? 0 : i - square); + std::size_t squareXmax = (std::min)(grid.width() - 1, i + square); + + std::vector z; + z.reserve(squareXmax - squareXmin +1 ); + for(std::size_t k = squareXmin; k <= squareXmax; k++) + if (dem(k,j) != 0.) + z.push_back (dem(k,j)); + if (z.empty()) + continue; + std::nth_element (z.begin(), z.begin() + (z.size() / 10), z.end()); + dtm_x(i,j) = z[z.size() / 10]; + } + } + dem.free(); + +#ifdef CGAL_CLASSIFICATION_PRECOMPUTE_FEATURES + Image_float dtm(grid.width(),grid.height()); +#else + dtm = Image_float(grid.width(),grid.height()); +#endif + + for (std::size_t i = 0; i < grid.width(); ++ i) + { + for (std::size_t j = 0; j < grid.height(); ++ j) + { + std::size_t squareYmin = (j < square ? 0 : j - square); + std::size_t squareYmax = (std::min)(grid.height() - 1, j + square); + std::vector z; + z.reserve(squareYmax - squareYmin +1 ); + for(std::size_t l = squareYmin; l <= squareYmax; l++) + if (dtm_x(i,l) != 0.) + z.push_back (dtm_x(i,l)); + if (z.empty()) + continue; + std::nth_element (z.begin(), z.begin() + (z.size() / 10), z.end()); + dtm(i,j) = z[z.size() / 10]; + } + } + dtm_x.free(); + +#ifdef CGAL_CLASSIFICATION_PRECOMPUTE_FEATURES + elevation_feature.reserve(input.size()); + for (std::size_t i = 0; i < input.size(); i++){ + std::size_t I = grid.x(i); + std::size_t J = grid.y(i); + elevation_feature.push_back ((float)(get(point_map, *(input.begin()+i)).z()-dtm(I,J))); + } +#endif + + } + + /// \cond SKIP_IN_MANUAL + virtual float value (std::size_t pt_index) + { +#ifdef CGAL_CLASSIFICATION_PRECOMPUTE_FEATURES + return elevation_feature[pt_index]; +#else + std::size_t I = grid.x(pt_index); + std::size_t J = grid.y(pt_index); + return ((float)(get(point_map, *(input.begin()+pt_index)).z()-dtm(I,J))); +#endif + } + + /// \endcond +}; + +} // namespace Feature + +} // namespace Classification + + +} // namespace CGAL + +#endif // CGAL_CLASSIFICATION_FEATURE_ELEVATION_H diff --git a/Classification/include/CGAL/Classification/Feature/Hsv.h b/Classification/include/CGAL/Classification/Feature/Hsv.h new file mode 100644 index 00000000000..c269deb4dde --- /dev/null +++ b/Classification/include/CGAL/Classification/Feature/Hsv.h @@ -0,0 +1,169 @@ +// Copyright (c) 2017 GeometryFactory Sarl (France). +// All rights reserved. +// +// This file is part of CGAL (www.cgal.org). +// You can redistribute it and/or modify it under the terms of the GNU +// General Public License as published by the Free Software Foundation, +// either version 3 of the License, or (at your option) any later version. +// +// Licensees holding a valid commercial license may use this file in +// accordance with the commercial license agreement provided with the software. +// +// This file is provided AS IS with NO WARRANTY OF ANY KIND, INCLUDING THE +// WARRANTY OF DESIGN, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. +// +// $URL$ +// $Id$ +// +// Author(s) : Simon Giraudot + +#ifndef CGAL_CLASSIFICATION_FEATURE_HSV_H +#define CGAL_CLASSIFICATION_FEATURE_HSV_H + +#include + +#include + +#include + +namespace CGAL { + +namespace Classification { + +namespace Feature { + + /*! + \ingroup PkgClassificationFeatures + + %Feature based on HSV colorimetric information. If the input + point cloud has colorimetric information, it can be used for + classification purposes. This feature is based on a Gaussian + probabilistic model on one of the three HSV channels (hue, + saturation or value). It computes the probability of the color of + the input point to match this specific color channel defined by a + mean and a standard deviation. + + The HSV channels are defined this way: + + - Hue ranges from 0 to 360 and measures the general "tint" of the + color (green, blue, pink, etc.) + + - Saturation ranges from 0 to 100 and measures the "strength" of the + color (0 is gray and 100 is the fully saturated color) + + - Value ranges from 0 to 100 and measures the "brightness" of the + color (0 is black and 100 is the fully bright color) + + For example, such an feature using the channel 0 (hue) with a + mean of 90 (which corresponds to a green hue) can help to identify + trees. + + \image html trees.png + +
Left: input point set with colors. Right: HSV feature on hue with + a mean of 90 (from low values in white to high values in dark + red).
+ + Its default name is the channel followed by the mean value (for + example: "hue_180", "saturation_20" or "value_98"). + + \note The user only needs to provide a map to standard (and more common) + RGB colors, the conversion to HSV is done internally. + + \tparam GeomTraits model of \cgal Kernel. + \tparam PointRange model of `ConstRange`. Its iterator type + is `RandomAccessIterator` and its value type is the key type of + `ColorMap`. + \tparam ColorMap model of `ReadablePropertyMap` whose key + type is the value type of the iterator of `PointRange` and value type + is `CGAL::Classification::RGB_Color`. + */ +template +class Hsv : public Feature_base +{ +public: + + /// Selected channel. + enum Channel + { + HUE = 0, ///< 0 + SATURATION = 1, ///< 1 + VALUE = 2 ///< 2 + }; + +private: + + typedef typename Classification::RGB_Color RGB_Color; + typedef typename Classification::HSV_Color HSV_Color; + +#ifdef CGAL_CLASSIFICATION_PRECOMPUTE_FEATURES + std::vector color_feature; +#else + const PointRange& input; + ColorMap color_map; + Channel m_channel; + float m_mean; + float m_sd; +#endif + +public: + + /*! + + \brief Constructs a feature based on the given color channel, + mean and standard deviation. + + \param input point range. + \param color_map property map to access the colors of the input points. + \param channel chosen HSV channel. + \param mean mean value of the specified channel. + \param sd standard deviation of the specified channel. + */ + Hsv (const PointRange& input, + ColorMap color_map, + Channel channel, + float mean, float sd) +#ifndef CGAL_CLASSIFICATION_PRECOMPUTE_FEATURES + : input(input), color_map(color_map), m_channel(channel), m_mean(mean), m_sd(sd) +#endif + { + +#ifdef CGAL_CLASSIFICATION_PRECOMPUTE_FEATURES + for(std::size_t i = 0; i < input.size();i++) + { + HSV_Color c = Classification::rgb_to_hsv (get(color_map, *(input.begin()+i))); + color_feature.push_back (std::exp (-(c[std::size_t(channel)] - mean) + * (c[std::size_t(channel)] - mean) / (2. * sd * sd))); + } +#endif + + std::ostringstream oss; + if (channel == HUE) oss << "hue"; + else if (channel == SATURATION) oss << "saturation"; + else if (channel == VALUE) oss << "value"; + oss << "_" << mean; + this->set_name (oss.str()); + } + + /// \cond SKIP_IN_MANUAL + virtual float value (std::size_t pt_index) + { +#ifdef CGAL_CLASSIFICATION_PRECOMPUTE_FEATURES + return color_feature[pt_index]; +#else + HSV_Color c = Classification::rgb_to_hsv (get(color_map, *(input.begin()+pt_index))); + return std::exp (-(c[std::size_t(m_channel)] - m_mean) + * (c[std::size_t(m_channel)] - m_mean) / (2.f * m_sd * m_sd)); +#endif + } + + /// \endcond +}; + +} // namespace Feature + +} // namespace Classification + +} // namespace CGAL + +#endif // CGAL_CLASSIFICATION_FEATURE_HSV_H diff --git a/Classification/include/CGAL/Classification/Feature/Vertical_dispersion.h b/Classification/include/CGAL/Classification/Feature/Vertical_dispersion.h new file mode 100644 index 00000000000..cdd26db9265 --- /dev/null +++ b/Classification/include/CGAL/Classification/Feature/Vertical_dispersion.h @@ -0,0 +1,187 @@ +// Copyright (c) 2017 GeometryFactory Sarl (France). +// All rights reserved. +// +// This file is part of CGAL (www.cgal.org). +// You can redistribute it and/or modify it under the terms of the GNU +// General Public License as published by the Free Software Foundation, +// either version 3 of the License, or (at your option) any later version. +// +// Licensees holding a valid commercial license may use this file in +// accordance with the commercial license agreement provided with the software. +// +// This file is provided AS IS with NO WARRANTY OF ANY KIND, INCLUDING THE +// WARRANTY OF DESIGN, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. +// +// $URL$ +// $Id$ +// +// Author(s) : Simon Giraudot + +#ifndef CGAL_CLASSIFICATION_FEATURE_VERTICAL_DISPERSION_H +#define CGAL_CLASSIFICATION_FEATURE_VERTICAL_DISPERSION_H + +#include + +#include + +#include +#include +#include +#include + +namespace CGAL { + +namespace Classification { + +namespace Feature { + + /*! + \ingroup PkgClassificationFeatures + + %Feature based on local vertical dispersion of points. Urban + scenes can often be decomposed as a set of 2D regions with + different heights. While these heights are usually piecewise + constant or piecewise linear, on some specific parts of the scene + such as vegetation, they can become extremely unstable. This + feature quantifies the vertical dispersion of the points on a + local Z-cylinder around the points. + + Its default name is "vertical_dispersion". + + \tparam GeomTraits model of \cgal Kernel. + \tparam PointRange model of `ConstRange`. Its iterator type + is `RandomAccessIterator` and its value type is the key type of + `PointMap`. + \tparam PointMap model of `ReadablePropertyMap` whose key + type is the value type of the iterator of `PointRange` and value type + is `GeomTraits::Point_3`. + */ +template +class Vertical_dispersion : public Feature_base +{ + typedef Classification::Image Image_float; + typedef Classification::Planimetric_grid Grid; + +#ifdef CGAL_CLASSIFICATION_PRECOMPUTE_FEATURES + std::vector vertical_dispersion; +#else + const Grid& grid; + Image_float Dispersion; +#endif + +public: + /*! + \brief Constructs the feature. + + \param input point range. + \param point_map property map to access the input points. + \param grid precomputed `Planimetric_grid`. + \param radius_neighbors radius of local neighborhoods. + */ + Vertical_dispersion (const PointRange& input, + PointMap point_map, + const Grid& grid, + float radius_neighbors = -1.) +#ifndef CGAL_CLASSIFICATION_PRECOMPUTE_FEATURES + : grid (grid) +#endif + { + this->set_name ("vertical_dispersion"); + if (radius_neighbors < 0.) + radius_neighbors = 5.f * grid.resolution(); + +#ifdef CGAL_CLASSIFICATION_PRECOMPUTE_FEATURES + Image_float Dispersion(grid.width(), grid.height()); +#else + Dispersion = Image_float(grid.width(), grid.height()); +#endif + + for (std::size_t j = 0; j < grid.height(); j++) + for (std::size_t i = 0; i < grid.width(); i++) + Dispersion(i,j)=0; + + std::size_t square = (std::size_t)(0.5 * radius_neighbors / grid.resolution()) + 1; + typename GeomTraits::Vector_3 verti (0., 0., 1.); + + std::vector hori; + + for (std::size_t j = 0; j < grid.height(); j++){ + for (std::size_t i = 0; i < grid.width(); i++){ + + if(!(grid.has_points(i,j))) + continue; + hori.clear(); + + std::size_t squareXmin = (i < square ? 0 : i - square); + std::size_t squareXmax = (std::min) (grid.width()-1, i + square); + std::size_t squareYmin = (j < square ? 0 : j - square); + std::size_t squareYmax = (std::min) (grid.height()-1, j + square); + + float bound = (float)0.5*radius_neighbors/grid.resolution(); + bound = CGAL::square(bound); + for(std::size_t k = squareXmin; k <= squareXmax; k++) + for(std::size_t l = squareYmin; l <= squareYmax; l++) + { + if(CGAL::square((float)(k-i))+ CGAL::square((float)(l-j)) + <= bound) + { + for (typename Grid::iterator it = grid.indices_begin(k,l); it != grid.indices_end(k,l); ++ it) + hori.push_back (float(get(point_map, *(input.begin()+(*it))).z())); + } + } + + if (hori.empty()) + continue; + + std::vector::iterator min_it, max_it; + boost::tie(min_it, max_it) + = boost::minmax_element (hori.begin(), hori.end()); + + std::vector occupy (1 + (std::size_t)((*max_it - *min_it) / grid.resolution()), false); + + for (std::size_t k = 0; k < hori.size(); ++ k) + { + std::size_t index = (std::size_t)((hori[k] - *min_it) / grid.resolution()); + occupy[index] = true; + } + + std::size_t nb_occ = 0; + for (std::size_t k = 0; k < occupy.size(); ++ k) + if (occupy[k]) + ++ nb_occ; + + Dispersion(i,j)= 1.f - (nb_occ / (float)(occupy.size())); + + } + + } +#ifdef CGAL_CLASSIFICATION_PRECOMPUTE_FEATURES + for (std::size_t i = 0; i < input.size(); i++) + { + std::size_t I= grid.x(i); + std::size_t J= grid.y(i); + vertical_dispersion.push_back((float)Dispersion(I,J)); + } +#endif + } + /// \cond SKIP_IN_MANUAL + virtual float value (std::size_t pt_index) + { +#ifdef CGAL_CLASSIFICATION_PRECOMPUTE_FEATURES + return vertical_dispersion[pt_index]; +#else + std::size_t I = grid.x(pt_index); + std::size_t J = grid.y(pt_index); + return ((float)Dispersion(I,J)); +#endif + } + /// \endcond +}; + +} + +} + +} + +#endif // CGAL_CLASSIFICATION_FEATURE_VERTICAL_DISPERSION_H diff --git a/Classification/include/CGAL/Classification/Feature/Verticality.h b/Classification/include/CGAL/Classification/Feature/Verticality.h new file mode 100644 index 00000000000..93bf008d323 --- /dev/null +++ b/Classification/include/CGAL/Classification/Feature/Verticality.h @@ -0,0 +1,139 @@ +// Copyright (c) 2017 GeometryFactory Sarl (France). +// All rights reserved. +// +// This file is part of CGAL (www.cgal.org). +// You can redistribute it and/or modify it under the terms of the GNU +// General Public License as published by the Free Software Foundation, +// either version 3 of the License, or (at your option) any later version. +// +// Licensees holding a valid commercial license may use this file in +// accordance with the commercial license agreement provided with the software. +// +// This file is provided AS IS with NO WARRANTY OF ANY KIND, INCLUDING THE +// WARRANTY OF DESIGN, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. +// +// $URL$ +// $Id$ +// +// Author(s) : Simon Giraudot + +#ifndef CGAL_CLASSIFICATION_FEATURE_VERTICALITY_H +#define CGAL_CLASSIFICATION_FEATURE_VERTICALITY_H + +#include + +#include + +#include + +namespace CGAL { + +namespace Classification { + +namespace Feature { + + /*! + \ingroup PkgClassificationFeatures + + %Feature based on local verticality. The orientation of the + local tangent plane of the considered point can be useful to + discriminate facades from the ground. This feature can use + normal vectors if available or eigen analysis that estimates + tangent planes from local neighborhoods. + + Its default name is "verticality". + + \tparam GeomTraits model of \cgal Kernel. + */ +template +class Verticality : public Feature_base +{ + const typename GeomTraits::Vector_3 vertical; + std::vector verticality_feature; + const Local_eigen_analysis* eigen; + +public: + /*! + \brief Constructs the feature using local eigen analysis. + + \tparam InputRange model of `ConstRange`. Its iterator type + is `RandomAccessIterator`. + \param input point range. + \param eigen class with precomputed eigenvectors and eigenvalues. + */ +#if defined(CGAL_CLASSIFICATION_PRECOMPUTE_FEATURES) || defined(DOXYGEN_RUNNING) + template + Verticality (const InputRange& input, + const Local_eigen_analysis& eigen) + : vertical (0., 0., 1.), eigen(NULL) + { + this->set_name ("verticality"); + + for (std::size_t i = 0; i < input.size(); i++) + { + typename GeomTraits::Vector_3 normal = eigen.normal_vector(i); + normal = normal / CGAL::sqrt (normal * normal); + verticality_feature.push_back (1. - CGAL::abs(normal * vertical)); + } + } +#else + template + Verticality (const InputRange&, + const Local_eigen_analysis& eigen) + : vertical (0., 0., 1.), eigen (&eigen) + { + this->set_name ("verticality"); + } +#endif + + /*! + \brief Constructs the feature using provided normals of points. + + \tparam PointRange model of `ConstRange`. Its iterator type + is `RandomAccessIterator` and its value type is the key type of + `VectorMap`. + \tparam VectorMap model of `ReadablePropertyMap` whose key + type is the value type of the iterator of `PointRange` and value type + is `GeomTraits::Vector_3`. + \param input point range. + \param normal_map property map to access the normal vectors of the input points. + */ + template + Verticality (const PointRange& input, + VectorMap normal_map) + : vertical (0., 0., 1.), eigen(NULL) + { + this->set_name ("verticality"); + for (std::size_t i = 0; i < input.size(); i++) + { + typename GeomTraits::Vector_3 normal = get(normal_map, *(input.begin()+i)); + normal = normal / CGAL::sqrt (normal * normal); + verticality_feature.push_back (1.f - float(CGAL::abs(normal * vertical))); + } + } + + + /// \cond SKIP_IN_MANUAL + virtual float value (std::size_t pt_index) + { +#ifndef CGAL_CLASSIFICATION_PRECOMPUTE_FEATURES + if (eigen != NULL) + { + typename GeomTraits::Vector_3 normal = eigen->normal_vector(pt_index); + normal = normal / CGAL::sqrt (normal * normal); + return (1.f - float(CGAL::abs(normal * vertical))); + } + else +#endif + return verticality_feature[pt_index]; + } + /// \endcond +}; + +} // namespace Feature + +} // namespace Classification + +} // namespace CGAL + +#endif // CGAL_CLASSIFICATION_FEATURE_VERTICALITY_H diff --git a/Classification/include/CGAL/Classification/Feature_base.h b/Classification/include/CGAL/Classification/Feature_base.h new file mode 100644 index 00000000000..c68414d23cc --- /dev/null +++ b/Classification/include/CGAL/Classification/Feature_base.h @@ -0,0 +1,91 @@ +// Copyright (c) 2017 GeometryFactory Sarl (France). +// All rights reserved. +// +// This file is part of CGAL (www.cgal.org). +// You can redistribute it and/or modify it under the terms of the GNU +// General Public License as published by the Free Software Foundation, +// either version 3 of the License, or (at your option) any later version. +// +// Licensees holding a valid commercial license may use this file in +// accordance with the commercial license agreement provided with the software. +// +// This file is provided AS IS with NO WARRANTY OF ANY KIND, INCLUDING THE +// WARRANTY OF DESIGN, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. +// +// $URL$ +// $Id$ +// +// Author(s) : Simon Giraudot + +#ifndef CGAL_CLASSIFICATION_FEATURE_BASE_H +#define CGAL_CLASSIFICATION_FEATURE_BASE_H + +#include + +#include + +#include + +namespace CGAL { + +namespace Classification { + +/*! + \ingroup PkgClassificationFeature + + \brief Abstract class describing a classification feature that + associates a scalar value to each item of the classification input. +*/ + + +class Feature_base +{ + std::string m_name; + +public: + + /// \cond SKIP_IN_MANUAL + Feature_base() : m_name ("abstract_feature") { } + virtual ~Feature_base() { } + /// \endcond + + /*! + \brief Returns the name of the feature (initialized to + `abstract_feature` for `Feature_base`). + */ + const std::string& name() const { return m_name; } + + /*! + \brief Changes the name of the feature. + */ + void set_name (const std::string& name) { m_name = name; } + + /*! + \brief Returns the value taken by the feature for at the item for + the item at position `index`. This method must be implemented by + inherited classes. + */ + virtual float value (std::size_t index) = 0; + +}; + + +#ifdef DOXYGEN_RUNNING +/*! + \ingroup PkgClassificationFeature + + \brief %Handle to a `Feature_base`. + + \cgalModels Handle +*/ +class Feature_handle { }; +#else +typedef boost::shared_ptr Feature_handle; +#endif + + +} // namespace Classification + +} // namespace CGAL + +#endif // CGAL_CLASSIFICATION_FEATURE_BASE_H diff --git a/Classification/include/CGAL/Classification/Feature_set.h b/Classification/include/CGAL/Classification/Feature_set.h new file mode 100644 index 00000000000..dd91f165e3f --- /dev/null +++ b/Classification/include/CGAL/Classification/Feature_set.h @@ -0,0 +1,148 @@ +// Copyright (c) 2017 GeometryFactory Sarl (France). +// All rights reserved. +// +// This file is part of CGAL (www.cgal.org). +// You can redistribute it and/or modify it under the terms of the GNU +// General Public License as published by the Free Software Foundation, +// either version 3 of the License, or (at your option) any later version. +// +// Licensees holding a valid commercial license may use this file in +// accordance with the commercial license agreement provided with the software. +// +// This file is provided AS IS with NO WARRANTY OF ANY KIND, INCLUDING THE +// WARRANTY OF DESIGN, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. +// +// $URL$ +// $Id$ +// +// Author(s) : Simon Giraudot + +#ifndef CGAL_CLASSIFICATION_FEATURE_SET_H +#define CGAL_CLASSIFICATION_FEATURE_SET_H + +#include + +#include + +#ifdef CGAL_LINKED_WITH_TBB +#include +#endif // CGAL_LINKED_WITH_TBB + +#include + +namespace CGAL { + +namespace Classification { + +/*! +\ingroup PkgClassificationFeature + +\brief Set of features (see `Feature_base`) used as input by +classification algorithms. This class handles both the instantiation, +the addition and the deletion of features. + +*/ +class Feature_set +{ + typedef std::vector Base; + Base m_features; + +#ifdef CGAL_LINKED_WITH_TBB + tbb::mutex m_mutex; + void mutex_lock() { m_mutex.lock(); } + void mutex_unlock() { m_mutex.unlock(); } +#else // CGAL_LINKED_WITH_TBB + void mutex_lock() { } + void mutex_unlock() { } +#endif // CGAL_LINKED_WITH_TBB + +public: + + Feature_set() { } + + /// \cond SKIP_IN_MANUAL + virtual ~Feature_set() { } + /// \endcond + + /*! + \brief Instantiates a new feature and adds it to the set. + + \tparam Feature type of the feature, inherited from + `Feature_base`. + + \tparam T types of the parameters of the feature's constructor. + + \param t parameters of the feature's constructor. + + \return a handle to the newly added feature. + */ + template + Feature_handle add (T&& ... t) + { + Feature_handle fh (new Feature(std::forward(t)...)); + mutex_lock(); + m_features.push_back (fh); + mutex_unlock(); + return fh; + } + + /*! + \brief Removes a feature. + + \param feature the handle to feature type that must be removed. + + \return `true` if the feature was correctly removed, `false` if + its handle was not found. + */ + bool remove (Feature_handle feature) + { + for (std::size_t i = 0; i < m_features.size(); ++ i) + if (m_features[i] == feature) + { + m_features.erase (m_features.begin() + i); + return true; + } + return false; + } + + /*! + \brief Returns how many features are defined. + */ + std::size_t size() const + { + return m_features.size(); + } + + + /*! + \brief Returns the \f$i^{th}\f$ feature. + */ + Feature_handle operator[](std::size_t i) const + { + return m_features[i]; + } + + /*! + \brief Removes all features. + */ + void clear () + { + m_features.clear(); + } + + /// \cond SKIP_IN_MANUAL + void free_memory(std::size_t i) + { + m_features[i] = Feature_handle(); + } + /// \endcond + +}; + + + +} // namespace Classification + +} // namespace CGAL + +#endif // CGAL_CLASSIFICATION_FEATURE_SET_H diff --git a/Classification/include/CGAL/Classification/Image.h b/Classification/include/CGAL/Classification/Image.h new file mode 100644 index 00000000000..a79ce0d6266 --- /dev/null +++ b/Classification/include/CGAL/Classification/Image.h @@ -0,0 +1,120 @@ +// Copyright (c) 2017 GeometryFactory Sarl (France). +// All rights reserved. +// +// This file is part of CGAL (www.cgal.org). +// You can redistribute it and/or modify it under the terms of the GNU +// General Public License as published by the Free Software Foundation, +// either version 3 of the License, or (at your option) any later version. +// +// Licensees holding a valid commercial license may use this file in +// accordance with the commercial license agreement provided with the software. +// +// This file is provided AS IS with NO WARRANTY OF ANY KIND, INCLUDING THE +// WARRANTY OF DESIGN, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. +// +// $URL$ +// $Id$ +// +// Author(s) : Simon Giraudot + +#ifndef CGAL_CLASSIFICATION_IMAGE_H +#define CGAL_CLASSIFICATION_IMAGE_H + +#include + +namespace CGAL { +namespace Classification { + + /// \cond SKIP_IN_MANUAL + +template +class Image +{ + std::size_t m_width; + std::size_t m_height; + Type* m_raw; + + +public: + + Image () : m_width(0), m_height(0), m_raw (NULL) + { + } + + Image (std::size_t width, std::size_t height) + : m_width (width), + m_height (height) + { + if (m_width * m_height > 0) + m_raw = new Type[width * height](); + else + m_raw = NULL; + } + + ~Image () + { + free(); + } + + void free() + { + if (m_raw != NULL) + delete[] m_raw; + m_raw = NULL; + } + + Image (const Image& other) + : m_width (other.width()), + m_height (other.height()) + + { + if (m_width * m_height > 0) + { + m_raw = new Type[m_width * m_height]; + std::copy (other.m_raw, other.m_raw + (m_width * m_height), this->m_raw); + } + else + m_raw = NULL; + } + Image& operator= (const Image& other) + { + if (m_raw != NULL) + delete[] m_raw; + + m_raw = NULL; + m_width = other.width(); + m_height = other.height(); + if (m_width * m_height > 0) + { + m_raw = new Type[m_width * m_height]; + std::copy (other.m_raw, other.m_raw + (m_width * m_height), this->m_raw); + } + + return *this; + } + + std::size_t width() const { return m_width; } + std::size_t height() const { return m_height; } + + Type& operator() (const std::size_t& x, const std::size_t& y) + { + // return m_raw[y * m_width + x]; + return m_raw[x * m_height + y]; + } + const Type& operator() (const std::size_t& x, const std::size_t& y) const + { + // return m_raw[y * m_width + x]; + return m_raw[x * m_height + y]; + } + + +}; + + /// \endcond + +} // namespace Classification +} // namespace CGAL + + + +#endif // CGAL_CLASSIFICATION_IMAGE_H diff --git a/Classification/include/CGAL/Classification/Label.h b/Classification/include/CGAL/Classification/Label.h new file mode 100644 index 00000000000..57bc6297f03 --- /dev/null +++ b/Classification/include/CGAL/Classification/Label.h @@ -0,0 +1,71 @@ +// Copyright (c) 2017 GeometryFactory Sarl (France). +// All rights reserved. +// +// This file is part of CGAL (www.cgal.org). +// You can redistribute it and/or modify it under the terms of the GNU +// General Public License as published by the Free Software Foundation, +// either version 3 of the License, or (at your option) any later version. +// +// Licensees holding a valid commercial license may use this file in +// accordance with the commercial license agreement provided with the software. +// +// This file is provided AS IS with NO WARRANTY OF ANY KIND, INCLUDING THE +// WARRANTY OF DESIGN, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. +// +// $URL$ +// $Id$ +// +// Author(s) : Simon Giraudot + +#ifndef CGAL_CLASSIFICATION_LABEL_H +#define CGAL_CLASSIFICATION_LABEL_H + +#include + +#include + +namespace CGAL { + +namespace Classification { + +/*! +\ingroup PkgClassificationLabel + +\brief %Classification label (for example: vegetation, ground, etc.) +defined as a set of relationships with classification features. + +*/ +class Label +{ +private: + + std::string m_name; + +public: + + /*! + \param name name of the classification label (e.g. vegetation). + */ + Label (std::string name) : m_name (name) { } + + const std::string& name() const { return m_name; } +}; + +#ifdef DOXYGEN_RUNNING +/*! + \ingroup PkgClassificationLabel + + \brief %Handle to a classification `Label`. + + \cgalModels Handle +*/ +class Label_handle { }; +#else +typedef boost::shared_ptr