diff --git a/Classification/doc/Classification/Classification.txt b/Classification/doc/Classification/Classification.txt new file mode 100644 index 00000000000..c494e6623cb --- /dev/null +++ b/Classification/doc/Classification/Classification.txt @@ -0,0 +1,292 @@ +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 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 the user can classify any type of data, compute its own local features on the input data set and define its own labels based on these features. + +\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 +- features are given weights and each pair of feature and label is assigned a relationship. This can either be done by hand or by automatic training if a set of inliers is given for each label (see \ref Classification_trainer) +- classification is computed itemwise by minimizing an energy defined as the sum of the values taken by features on input items (which depend on the feature-label relationship) +- additional regularization can be used by smoothing either locally or globally through an Alpha Expansion approach. + +\cgalFigureBegin{Classification_organization_fig,organization.png} +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 (although \cgal provides a predefined framework for common urban scenes). + +Currently, \cgal 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. + +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). The class `CGAL::Classifier` that handles classification is also instantiated (see \ref Classification_classifier). + +\snippet Classification/example_classifier.cpp Analysis + +\subsection Classification_features Features + +Features are defined as scalar fields that associate each input item with a specific value. 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 `CGAL::Classification::Feature_base` and provide a method [value()](@ref CGAL::Classification::Feature_base::value) that associate a scalar value to each input item. Each feature has a weight that measure its strength with respect to the other features (see \ref Classification_labels). + +Features are accessed through `Handle` objects, `CGAL::Classification::Feature_handle`. + +\cgal provides some predefined features that are relevant for classification of urban 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 and their respective weights are set. Note that these weights can also be automatically set up by training (see \ref Classification_trainer). + +\snippet Classification/example_classifier.cpp Features + +Users can define their own feature classes. Such classes must fulfill the following requirements: + +- they must inherit `CGAL::Classification::Feature_base` +- their constructor(s) must take as first argument the item range used by the classifier object +- they must provide a method [value()](@ref CGAL::Classification::Feature_base::value) that associates each input point to a scalar value + +The following example shows how to define a feature that discriminates +points that lie inside a 2D box from the others: + +\cgalExample{Classification/example_feature.cpp} + + +\subsection Classification_labels Labels + +A label represents how an item should be classified, for example: vegetation, building, road, etc. It is defined by the values the features are expected to take for a specific label. 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. + +\cgal provides a class `CGAL::Classification::Label` to define such a set of feature effects, along with the associated `Handle` object: `CGAL::Classification::Label_handle`. Each label may define how a specific feature affects it: + +- [FAVORING](@ref CGAL::Classification::Feature::FAVORING): the label is favored by high values of the feature +- [NEUTRAL](@ref CGAL::Classification::Feature::NEUTRAL): the label is not affected by the feature +- [PENALIZING](@ref CGAL::Classification::Feature::PENALIZING): the label is favored by low values of the feature + +Let \f$x=(x_i)_{i=1..N_c}\f$ be a potential classification result with \f$N_c\f$ 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$a_j(i)\f$ 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$A_j(x_i) \in [0:1]\f$ of the \f$j^{th}\f$ feature at the \f$i^{th}\f$ item as follows: + +\f{eqnarray*}{ + A_j(x_i) = & (1 - \min(\max(0,\frac{a_j(i)}{w_j}), 1)) & \mbox{if } a_j \mbox{ favors } x_i \\ + & 0.5 & \mbox{if } a_j \mbox{ is neutral for } x_i \\ + & \min(\max(0,\frac{a_j(i)}{w_j}), 1) & \mbox{if } a_j \mbox{ penalizes } x_i +\f} + + +The following code snippet shows how to add labels to the classification object and how to set the effects of the features on these labels. Note that these effects can also be automatically set up by training (see \ref Classification_trainer). + +\snippet Classification/example_classifier.cpp Labels + +\section Classification_classifier Classifier + +%Classification is performed by minizing an energy over the input data set that may include regularization. \cgal provides 3 different methods for classification, ranging from high speed / low quality to low speed / high quality: + +- `CGAL::Classifier::run()` +- `CGAL::Classifier::run_with_local_smoothing()` +- `CGAL::Classifier::run_with_graphcut()` + +On a point set of 3 millions 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_regularized_no No Regularization + +- `CGAL::Classifier::run()`: this is the fastest method +that provides acceptable but usually noisy results (see Figure +\cgalFigureRef{Classification_image}, top-right). + +Let \f$x=(x_i)_{i=1..N_c}\f$ be a potential classification result with \f$N_c\f$ the number of input items and \f$x_i\f$ the class of the \f$i^{th}\f$ item (for example: vegetation, ground, etc.). The classification is performed by minimizing the following energy: + +\f[ + E(x) = \sum_{i = 1..N_c} E_{di}(x_i) +\f] + +This energy is a sum of itemwise energies and involves no regularization. Let \f$A=(A_j)_{j=1..N_a}\f$ be the set of normalized features +(see \ref Classification_labels). The itemwise energy measures the coherence of the class \f$x_i\f$ at the \f$i^{th}\f$ item and is defined as: + +\f[ + E_{di}(x_i) = \sum_{j = 1..N_a} A_j(x_i) +\f] + + + +\subsection Classification_regularized_local Local Smoothing + +- `CGAL::Classifier::run_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_c} E_{si}(x_i) +\f] + +The itemwise energy \f$E_{di}(x_i)\f$ is replaced by +\f$E_{si}(x_i)\f$ defined on a small local neighborhood \f$N(i)\f$ of +the \f$i^{th}\f$ item: + +\f[ + E_{si}(x_i) = \sum_{j = 1..N_a} \frac{\sum_{k \in N(i)} A_j(x_k)}{\left| N(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. + + +\subsection Classification_regularized_graphcut Global Regularization (Graph Cut) + +- `CGAL::Classifier::run_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_c} 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. + + +\section Classification_point_set_classifier Point Set Classifier + +The classification algorithm is designed to be as flexible as possible: users may classify any type of item they need and define their own features and labels. Nevertheless, \cgal provides a predefined specialization of `CGAL::Classifier` to handle common urban point sets: the class `CGAL::Point_set_classifier`. In addition to all the methods inherited from `CGAL::Classifier`, it provides additional features: + +- it takes care of generating all needed analysis structures +- 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 can be used to increase the quality of the results \cgalCite{cgal:hws-fsso3-16} +- input/ouput methods are provided to save and recover a specific configuration (with all features, labels and relationships between them) +- classification can be saved as a PLY format with colors and labels. + +Note that using this class in order to classify point sets is not mandatory, as `CGAL::Classifier` can handle points as well. It is mainly provided to simplify the whole process of defining data structures and features in the specific case of urban point sets. Users can still add their own data structures and features within `CGAL::Point_set_classifier` similarly to what can be done with `CGAL::Classifier`. + +The example \ref Classification_example_training shows how to generate features and save the configuration and classification. + +\section Classification_trainer Trainer + +%Classification is based on relationships between features and labels. 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 trainer class `CGAL::Classification::Trainer` 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 + +- for each label, the ratio of correctly classified inliers is computed. The minimum of these ratios is used as a score for this set of weights and effects: a ratio of 0.8 means that for each label, at least 80\% of the provided inliers were correctly classified + +- 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 previous value that gave the 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 attributes 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 the best weights found as initialization and carries on trying new weights by taking new inliers into account. + +\cgalFigureBegin{Classification_trainer_fig,classif_training.png} +Example of evolution of training score. The purple curve is the score computed at the current iteration, green curve is the best score found so far. +\cgalFigureEnd + +Tools to evaluate the reliability of the training are provided, namely: + +- [precision()](@ref CGAL::Classification::Trainer::precision) computes, for one label, the ratio of true positives over the total number of detected positives +- [recall()](@ref CGAL::Classification::Trainer::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::Trainer::f1_score) is the harmonic mean of precision and recall +- [intersection_over_union()](@ref CGAL::Classification::Trainer::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::Trainer::accuracy) computes the ratio of all true positives over the total number of provided inliers +- [mean_f1_score()](@ref CGAL::Classification::Trainer::mean_f1_score) +- [mean_intersection_over_union()](@ref CGAL::Classification::Trainer::mean_intersection_over_union) + + +Note that the [Trainer](@ref CGAL::Classification::Trainer) class only uses documented public methods of the [Classifier](@ref CGAL::Classifier), [Label](@ref CGAL::Classification::Label) and [Feature](@ref CGAL::Classification::Feature_base) classes (for example, [set_weight()](@ref CGAL::Classification::Feature_base::set_weight), [set_feature_effect()](@ref CGAL::Classification::Label::set_feature_effect) or [energy_of()](@ref CGAL::Classifier::energy_of)). Users can therefore define their own classes for training using these methods to set up weights and effects and to estimate what are the best parameters. + + + +The example \ref Classification_example_training shows how to set inliers and run the training algorithm. + +\section Classification_examples Examples + +\subsection Classification_example_general General classification + +The following example: + +- reads an input file (LIDAR point set in PLY format) +- computes useful structures from this input +- computes segmentation features from the input and the precomputed structures +- defines 3 labels (vegetation, ground and roof) along with the effects of features on them +- classifies the point set using `CGAL::Classifier` +- saves the result in a colored PLY format. + +\cgalExample{Classification/example_classifier.cpp} + +\subsection Classification_example_training Point set classification with training + +The following example: + +- reads a point set with a training set (embedded as a PLY feature _label_) +- uses `CGAL::Point_set_classifier` to generate features on 5 scales +- trains the algorithm using 800 trials +- runs the algorithm using the graphcut regularization +- saves the output to PLY. + +\cgalExample{Classification/example_point_set_classifier.cpp} + + +\section Classification_history History + +This package is based on a research code by Florent Lafarge that was generalized, extended and packaged by Simon Giraudot. + + + +*/ +} /* namespace CGAL */ diff --git a/Classification/doc/Classification/Concepts/NeighborQuery.h b/Classification/doc/Classification/Concepts/NeighborQuery.h new file mode 100644 index 00000000000..f1a1e185553 --- /dev/null +++ b/Classification/doc/Classification/Concepts/NeighborQuery.h @@ -0,0 +1,35 @@ +namespace CGAL +{ + +/*! +\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::Range_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 CGAL diff --git a/Classification/doc/Classification/Doxyfile.in b/Classification/doc/Classification/Doxyfile.in new file mode 100644 index 00000000000..1e4ae9a525a --- /dev/null +++ b/Classification/doc/Classification/Doxyfile.in @@ -0,0 +1,7 @@ +@INCLUDE = ${CGAL_DOC_PACKAGE_DEFAULTS} +PROJECT_NAME = "CGAL ${CGAL_DOC_VERSION} - Classification" +INPUT = ${CMAKE_SOURCE_DIR}/Classification/doc/Classification/ \ + ${CMAKE_SOURCE_DIR}/Classification/include/CGAL/Classifier.h \ + ${CMAKE_SOURCE_DIR}/Classification/include/CGAL/Point_set_classifier.h \ + ${CMAKE_SOURCE_DIR}/Classification/include/CGAL/Classification/Feature/ \ + ${CMAKE_SOURCE_DIR}/Classification/include/CGAL/Classification/ diff --git a/Classification/doc/Classification/PackageDescription.txt b/Classification/doc/Classification/PackageDescription.txt new file mode 100644 index 00000000000..0e8c0f425ad --- /dev/null +++ b/Classification/doc/Classification/PackageDescription.txt @@ -0,0 +1,80 @@ +/*! +\defgroup PkgClassification Classification Reference + +\defgroup PkgClassificationConcepts Concepts +\ingroup PkgClassification + +\defgroup PkgClassificationDataStructures Data Structures +\ingroup PkgClassification + +\defgroup PkgClassificationAttributes Predefined Attributes +\ingroup PkgClassification + + +\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 the user can classify any type of data, compute its own local features on the data set and define its own labels based on these features.} +\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::NeighborQuery` + +## Classification ## + +- `CGAL::Classifier` +- `CGAL::Point_set_classifier` + +## Data Structures ## + +- `CGAL::Classification::Point_set_neighborhood` +- `CGAL::Classification::Local_eigen_analysis` +- `CGAL::Classification::Planimetric_grid` +- `CGAL::Classification::Trainer` + +## Label ## + +- `CGAL::Classification::Label` +- `CGAL::Classification::Label_handle` + +## Feature ## + +- `CGAL::Classification::Feature` +- `CGAL::Classification::Feature_handle` + +## 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..5b5101754b5 --- /dev/null +++ b/Classification/doc/Classification/dependencies @@ -0,0 +1,7 @@ +Manual +Kernel_23 +STL_Extension +Algebraic_foundations +Circulator +Stream_support +Solver_interface diff --git a/Classification/doc/Classification/examples.txt b/Classification/doc/Classification/examples.txt new file mode 100644 index 00000000000..1307af0c1f1 --- /dev/null +++ b/Classification/doc/Classification/examples.txt @@ -0,0 +1,5 @@ +/*! +\example Classification/example_classifier.cpp +\example Classification/example_feature.cpp +\example Classification/example_point_set_classifier.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 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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..c20e05a4ca0 --- /dev/null +++ b/Classification/examples/Classification/CMakeLists.txt @@ -0,0 +1,55 @@ +# Created by the script cgal_create_CMakeLists +# This is the CMake script for compiling a set of CGAL applications. + +project( Classification ) + + +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( ${CGAL_USE_FILE} ) + + +# Boost and its components +find_package( Boost REQUIRED ) + +if ( NOT Boost_FOUND ) + + message(STATUS "This project requires the Boost library, and will not be compiled.") + + return() + +endif() + +find_package( TBB REQUIRED ) +if( TBB_FOUND ) + include(${TBB_USE_FILE}) + list(APPEND CGAL_3RD_PARTY_LIBRARIES ${TBB_LIBRARIES}) +endif() + + +# 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 ) + +create_single_source_cgal_program( "example_classifier.cpp" ) +create_single_source_cgal_program( "example_point_set_classifier.cpp" ) +create_single_source_cgal_program( "example_feature.cpp" ) diff --git a/Classification/examples/Classification/data/b9.ply b/Classification/examples/Classification/data/b9.ply new file mode 100644 index 00000000000..ff81600a08f --- /dev/null +++ b/Classification/examples/Classification/data/b9.ply @@ -0,0 +1,22310 @@ +ply +format ascii 1.0 +comment VCGLIB generated +element vertex 22300 +property float x +property float y +property float z +element face 0 +property list uchar int vertex_indices +end_header +45.375 52.1094 -10.6268 +45.25 52.8281 -10.723 +45.125 53.5156 -10.6896 +45.125 54.1563 -9.33412 +45 54.8594 -9.32715 +44.875 55.5781 -9.29468 +44.3125 55.8438 -9.29655 +44.4375 55.1406 -9.29178 +44.5625 54.4375 -9.30634 +44.625 53.7969 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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; + +typedef CGAL::Classifier Classifier; + +typedef CGAL::Classification::Planimetric_grid Planimetric_grid; +typedef CGAL::Classification::Point_set_neighborhood Neighborhood; +typedef CGAL::Classification::Local_eigen_analysis Local_eigen_analysis; + +typedef CGAL::Classification::Label_handle Label_handle; +typedef CGAL::Classification::Feature_handle Feature_handle; + +typedef CGAL::Classification::Feature::Distance_to_plane Distance_to_plane; +typedef CGAL::Classification::Feature::Linearity Linearity; +typedef CGAL::Classification::Feature::Omnivariance Omnivariance; +typedef CGAL::Classification::Feature::Planarity Planarity; +typedef CGAL::Classification::Feature::Surface_variation Surface_variation; +typedef CGAL::Classification::Feature::Elevation Elevation; +typedef CGAL::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; + } + + double grid_resolution = 0.34; + double radius_neighbors = 1.7; + double radius_dtm = 15.0; + + 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)); + + Classifier classifier (pts, Pmap()); + + //! [Analysis] + /////////////////////////////////////////////////////////////////// + + /////////////////////////////////////////////////////////////////// + //! [Features] + + std::cerr << "Computing features" << std::endl; + Feature_handle d2p = classifier.add_feature (Pmap(), eigen); + Feature_handle lin = classifier.add_feature (eigen); + Feature_handle omni = classifier.add_feature (eigen); + Feature_handle plan = classifier.add_feature (eigen); + Feature_handle surf = classifier.add_feature (eigen); + Feature_handle disp = classifier.add_feature (Pmap(), grid, + grid_resolution, + radius_neighbors); + Feature_handle elev = classifier.add_feature (Pmap(), grid, + grid_resolution, + radius_dtm); + + std::cerr << "Setting weights" << std::endl; + d2p->set_weight(6.75e-2); + lin->set_weight(1.19); + omni->set_weight(1.34e-1); + plan->set_weight(7.32e-1); + surf->set_weight(1.36e-1); + disp->set_weight(5.45e-1); + elev->set_weight(1.47e1); + + + //! [Features] + /////////////////////////////////////////////////////////////////// + + /////////////////////////////////////////////////////////////////// + //! [Labels] + + std::cerr << "Setting up labels" << std::endl; + + // Create label and define how features affect them + Label_handle ground = classifier.add_label ("ground"); + ground->set_feature_effect (d2p, CGAL::Classification::Feature::NEUTRAL); + ground->set_feature_effect (lin, CGAL::Classification::Feature::PENALIZING); + ground->set_feature_effect (omni, CGAL::Classification::Feature::NEUTRAL); + ground->set_feature_effect (plan, CGAL::Classification::Feature::FAVORING); + ground->set_feature_effect (surf, CGAL::Classification::Feature::PENALIZING); + ground->set_feature_effect (disp, CGAL::Classification::Feature::NEUTRAL); + ground->set_feature_effect (elev, CGAL::Classification::Feature::PENALIZING); + + Label_handle vege = classifier.add_label ("vegetation"); + vege->set_feature_effect (d2p, CGAL::Classification::Feature::FAVORING); + vege->set_feature_effect (lin, CGAL::Classification::Feature::NEUTRAL); + vege->set_feature_effect (omni, CGAL::Classification::Feature::FAVORING); + vege->set_feature_effect (plan, CGAL::Classification::Feature::NEUTRAL); + vege->set_feature_effect (surf, CGAL::Classification::Feature::NEUTRAL); + vege->set_feature_effect (disp, CGAL::Classification::Feature::FAVORING); + vege->set_feature_effect (elev, CGAL::Classification::Feature::NEUTRAL); + + Label_handle roof = classifier.add_label ("roof"); + roof->set_feature_effect (d2p, CGAL::Classification::Feature::NEUTRAL); + roof->set_feature_effect (lin, CGAL::Classification::Feature::PENALIZING); + roof->set_feature_effect (omni, CGAL::Classification::Feature::FAVORING); + roof->set_feature_effect (plan, CGAL::Classification::Feature::FAVORING); + roof->set_feature_effect (surf, CGAL::Classification::Feature::PENALIZING); + roof->set_feature_effect (disp, CGAL::Classification::Feature::NEUTRAL); + roof->set_feature_effect (elev, CGAL::Classification::Feature::FAVORING); + + //! [Labels] + /////////////////////////////////////////////////////////////////// + + // Run classification + CGAL::Real_timer t; + t.start(); + classifier.run (); + t.stop(); + std::cerr << "Raw classification performed in " << t.time() << " second(s)" << std::endl; + t.reset(); + t.start(); + classifier.run_with_local_smoothing (neighborhood.range_neighbor_query(radius_neighbors)); + t.stop(); + std::cerr << "Classification with local smoothing performed in " << t.time() << " second(s)" << std::endl; + t.reset(); + t.start(); + classifier.run_with_graphcut (neighborhood.k_neighbor_query(12), 0.2); + t.stop(); + std::cerr << "Classification with graphcut performed in " << t.time() << " second(s)" << std::endl; + + // 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 = classifier.label_of (i); + if (label == ground) + f << "245 180 0" << std::endl; + else if (label == vege) + 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..c105684ace1 --- /dev/null +++ b/Classification/examples/Classification/example_feature.cpp @@ -0,0 +1,99 @@ +#include +#include +#include +#include + +#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; + +typedef CGAL::Classifier Classifier; + +typedef CGAL::Classification::Point_set_neighborhood Neighborhood; +typedef CGAL::Classification::Local_eigen_analysis Local_eigen_analysis; + +typedef CGAL::Classification::Label_handle Label_handle; +typedef CGAL::Classification::Feature_handle Feature_handle; + +typedef CGAL::Classification::Feature::Sphericity Sphericity; + + +// 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, // constructor should start with item range + double xmin, double xmax, double ymin, double ymax) + : range (range), xmin(xmin), xmax(xmax), ymin(ymin), ymax(ymax) + { } + + double 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.; + else + return 0.; + } + +}; + +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)); + + Classifier classifier (pts, Pmap()); + + std::cerr << "Computing features" << std::endl; + Feature_handle sphericity = classifier.add_feature (eigen); + + // 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 = classifier.add_feature (-20., 20., -15., 15.); + + std::cerr << "Setting weights" << std::endl; + sphericity->set_weight(0.5); + my_feature->set_weight(0.25); + + std::cerr << "Setting up labels" << std::endl; + Label_handle a = classifier.add_label ("label_A"); + a->set_feature_effect (sphericity, CGAL::Classification::Feature::FAVORING); + a->set_feature_effect (my_feature, CGAL::Classification::Feature::FAVORING); + + Label_handle b = classifier.add_label ("label_B"); + b->set_feature_effect (sphericity, CGAL::Classification::Feature::PENALIZING); + b->set_feature_effect (my_feature, CGAL::Classification::Feature::PENALIZING); + + classifier.run_with_graphcut (neighborhood.k_neighbor_query(12), 0.2); + + std::cerr << "All done" << std::endl; + return EXIT_SUCCESS; +} diff --git a/Classification/examples/Classification/example_point_set_classifier.cpp b/Classification/examples/Classification/example_point_set_classifier.cpp new file mode 100644 index 00000000000..391c45a62ac --- /dev/null +++ b/Classification/examples/Classification/example_point_set_classifier.cpp @@ -0,0 +1,184 @@ +#include +#include +#include +#include + +//#define CGAL_CLASSIFICATION_VERBOSE + +#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; + +typedef CGAL::Point_set_classifier Point_set_classifier; +typedef CGAL::Classification::Trainer Trainer; + +/* + This interpreter is used to read a PLY input that contains training + attributes (with the PLY "label" property). +*/ +class My_ply_interpreter +{ + std::vector& points; + std::vector& labels; + +public: + My_ply_interpreter (std::vector& points, + std::vector& labels) + : points (points), labels (labels) + { } + + // Init and test if input file contains the right properties + bool is_applicable (CGAL::Ply_reader& reader) + { + return reader.does_tag_exist ("x") + && reader.does_tag_exist ("y") + && reader.does_tag_exist ("z") + && reader.does_tag_exist ("label"); + } + + // Describes how to process one line (= one point object) + void process_line (CGAL::Ply_reader& reader) + { + double x = 0., y = 0., z = 0.; + int l = 0; + + reader.assign (x, "x"); + reader.assign (y, "y"); + reader.assign (z, "z"); + reader.assign (l, "label"); + + points.push_back (Point (x, y, z)); + labels.push_back(l); + } + +}; + + +int main (int argc, char** argv) +{ + std::string filename (argc > 1 ? argv[1] : "data/b9_training.ply"); + std::ifstream in (filename.c_str()); + std::vector pts; + std::vector labels; + + std::cerr << "Reading input" << std::endl; + My_ply_interpreter interpreter (pts, labels); + if (!in + || !(CGAL::read_ply_custom_points (in, interpreter, Kernel()))) + { + std::cerr << "Error: cannot read " << filename << std::endl; + return EXIT_FAILURE; + } + + Point_set_classifier psc (pts, Pmap()); + + std::cerr << "Generating features" << std::endl; + CGAL::Real_timer t; + t.start(); + psc.generate_features (5); // Using 5 scales + t.stop(); + std::cerr << "Done in " << t.time() << " second(s)" << std::endl; + + // Add types to PSC + CGAL::Classification::Label_handle ground + = psc.add_label ("ground"); + CGAL::Classification::Label_handle vege + = psc.add_label ("vegetation"); + CGAL::Classification::Label_handle roof + = psc.add_label ("roof"); + CGAL::Classification::Label_handle facade + = psc.add_label ("facade"); + + Trainer trainer (psc); + + // Set training sets + std::size_t nb_inliers = 0; + for (std::size_t i = 0; i < labels.size(); ++ i) + { + switch (labels[i]) + { + case 0: + trainer.set_inlier(vege, i); + ++ nb_inliers; + break; + case 1: + trainer.set_inlier(ground, i); + ++ nb_inliers; + break; + case 2: + trainer.set_inlier(roof, i); + ++ nb_inliers; + break; + case 3: + trainer.set_inlier(facade, i); + ++ nb_inliers; + break; + default: + break; + } + } + + std::cerr << "Training using " << nb_inliers << " inliers" << std::endl; + t.reset(); + t.start(); + trainer.train (400); // 800 trials + t.stop(); + std::cerr << "Done in " << t.time() << " second(s)" << std::endl; + + std::cerr << "Precision, recall, F1 scores and IoU:" << std::endl; + for (std::size_t i = 0; i < psc.number_of_labels(); ++ i) + { + std::cerr << " * " << psc.label(i)->name() << ": " + << trainer.precision(psc.label(i)) << " ; " + << trainer.recall(psc.label(i)) << " ; " + << trainer.f1_score(psc.label(i)) << " ; " + << trainer.intersection_over_union(psc.label(i)) << std::endl; + } + + std::cerr << "Accuracy = " << trainer.accuracy() << std::endl + << "Mean F1 score = " << trainer.mean_f1_score() << std::endl + << "Mean IoU = " << trainer.mean_intersection_over_union() << std::endl; + + t.reset(); + t.start(); + psc.run_with_graphcut (psc.neighborhood().k_neighbor_query(12), 0.5); + t.stop(); + std::cerr << "One graphcut done in " << t.time() << " second(s)" << std::endl; + + // Save the output in a colored PLY format + { + std::ofstream f ("classification_one.ply"); + f.precision(18); + psc.write_classification_to_ply (f); + } + + t.reset(); + t.start(); + psc.run_with_graphcut (psc.neighborhood().k_neighbor_query(12), 0.5, 30); + t.stop(); + std::cerr << std::size_t(pts.size() / 25000) << " graphcuts done in " << t.time() << " second(s)" << std::endl; + + // Save the output in a colored PLY format + { + std::ofstream f ("classification_several.ply"); + f.precision(18); + psc.write_classification_to_ply (f); + } + + /// Save the configuration to be able to reload it later + std::ofstream fconfig ("config.xml"); + psc.save_configuration (fconfig); + + std::cerr << "All done" << std::endl; + + return EXIT_SUCCESS; +} diff --git a/Classification/include/CGAL/Classification/Color.h b/Classification/include/CGAL/Classification/Color.h new file mode 100644 index 00000000000..ec8a72f5923 --- /dev/null +++ b/Classification/include/CGAL/Classification/Color.h @@ -0,0 +1,136 @@ +// 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 + +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 ( + */ +typedef CGAL::cpp11::array RGB_Color; + /*! + \ingroup PkgClassificationColor + + Color described in hue/saturation/value space. Each component is stored + as a double: + + - `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 = {{ H, S, 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/Feature/Distance_to_plane.h b/Classification/include/CGAL/Classification/Feature/Distance_to_plane.h new file mode 100644 index 00000000000..52e53e720ad --- /dev/null +++ b/Classification/include/CGAL/Classification/Feature/Distance_to_plane.h @@ -0,0 +1,115 @@ +// 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. + + \tparam Geom_traits model of \cgal Kernel. + \tparam PointRange model of `ConstRange`. Its iterator type + is `RandomAccessIterator`. + \tparam PointMap model of `ReadablePropertyMap` whose key + type is the value type of the iterator of `PointRange` and value type + is `Geom_traits::Point_3`. + \tparam DiagonalizeTraits model of `DiagonalizeTraits` used + for matrix diagonalization. + */ +template > +class Distance_to_plane : public Feature_base +{ + typedef Classification::Local_eigen_analysis Local_eigen_analysis; +#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 input 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_weight(1.); +#ifndef CGAL_CLASSIFICATION_PRECOMPUTE_FEATURES + std::vector distance_to_plane_feature; +#endif + + 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)))); + + this->compute_mean_max (distance_to_plane_feature, this->mean, this->max); + // max *= 2; + } + + /// \cond SKIP_IN_MANUAL + virtual double value (std::size_t pt_index) + { +#ifdef CGAL_CLASSIFICATION_PRECOMPUTE_FEATURES + return distance_to_plane_feature[pt_index]; +#else + return CGAL::sqrt (CGAL::squared_distance + (get(point_map, *(input.begin()+pt_index)), eigen.plane(pt_index))); +#endif + } + + virtual std::string name() { return "distance_to_plane"; } + /// \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..116f828fc33 --- /dev/null +++ b/Classification/include/CGAL/Classification/Feature/Echo_scatter.h @@ -0,0 +1,152 @@ +// 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 + + +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. + + \tparam Geom_traits model of \cgal Kernel. + \tparam PointRange model of `ConstRange`. Its iterator type + is `RandomAccessIterator`. + \tparam PointMap model of `ReadablePropertyMap` whose key + type is the value type of the iterator of `PointRange` and value type + is `Geom_traits::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 input range. + \param echo_map property map to access the echo values of the input points. + \param grid precomputed `Planimetric_grid`. + \param grid_resolution resolution of the planimetric grid. + \param radius_neighbors radius of local neighborhoods. + */ + Echo_scatter (const PointRange& input, + EchoMap echo_map, + const Grid& grid, + const double grid_resolution, + double radius_neighbors = 1.) + { + this->set_weight(1.); + 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.mask(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((double)k-i,2)+pow((double)l-j,2))<=(double)0.5*radius_neighbors/grid_resolution){ + + if(grid.indices(k,l).size()>0){ + + for(std::size_t t=0; t 1) + NB_echo_sup++; + } + + NB_echo_total=NB_echo_total+grid.indices(k,l).size(); + + } + + } + + } + + } + + 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((double)Scatter(I,J)); + } + this->compute_mean_max (echo_scatter, this->mean, this->max); + } + + /// \cond SKIP_IN_MANUAL + virtual double value (std::size_t pt_index) + { + return echo_scatter[pt_index]; + } + + virtual std::string name() { return "echo_scatter"; } + /// \endcond +}; + +} // namespace Feature + +} // namespace Classification + +} // namespace CGAL + +#endif // CGAL_CLASSIFICATION_FEATURE_ECHO_SCATTER_H diff --git a/Classification/include/CGAL/Classification/Feature/Effect.h b/Classification/include/CGAL/Classification/Feature/Effect.h new file mode 100644 index 00000000000..3a158656f0f --- /dev/null +++ b/Classification/include/CGAL/Classification/Feature/Effect.h @@ -0,0 +1,42 @@ +// 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_EFFECT_H +#define CGAL_CLASSIFICATION_FEATURE_EFFECT_H + +namespace CGAL { + +namespace Classification { + +namespace Feature { + +enum Effect /// Defines the effect of an feature on a type. + { + FAVORING = 0, ///< High values of the feature favor this type + NEUTRAL = 1, ///< The feature has no effect on this type + PENALIZING = 2 ///< Low values of the feature favor this type + }; + +} // Feature + +} // Classification + +} // CGAL + +#endif diff --git a/Classification/include/CGAL/Classification/Feature/Eigen.h b/Classification/include/CGAL/Classification/Feature/Eigen.h new file mode 100644 index 00000000000..d61391e2688 --- /dev/null +++ b/Classification/include/CGAL/Classification/Feature/Eigen.h @@ -0,0 +1,504 @@ +// 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 + +namespace CGAL { + +namespace Classification { + +namespace Feature { + +template > +class Eigen_feature : public Feature_base +{ +protected: + typedef Classification::Local_eigen_analysis Local_eigen_analysis; + +#ifdef CGAL_CLASSIFICATION_PRECOMPUTE_FEATURES + std::vector attrib; +#else + const Local_eigen_analysis& eigen; +#endif + +public: + Eigen_feature (const PointRange&, + const Local_eigen_analysis& eigen) +#ifndef CGAL_CLASSIFICATION_PRECOMPUTE_FEATURES + : eigen (eigen) +#endif + { + } + + virtual void init (const PointRange& input, const Local_eigen_analysis& eigen) + { + this->set_weight(1.); +#ifndef CGAL_CLASSIFICATION_PRECOMPUTE_FEATURES + std::vector attrib; +#endif + attrib.reserve (input.size()); + for (std::size_t i = 0; i < input.size(); ++ i) + attrib.push_back (get_value (eigen, i)); + + this->compute_mean_max (attrib, mean, this->max); + } + + virtual double get_value (const Local_eigen_analysis& eigen, std::size_t i) = 0; + virtual double value (std::size_t pt_index) + { +#ifdef CGAL_CLASSIFICATION_PRECOMPUTE_FEATURES + return attrib[pt_index]; +#else + return get_value(eigen, pt_index); +#endif + } + virtual std::string name() { return "eigen_feature"; } + +}; + + /*! + \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] + \tparam Geom_traits model of \cgal Kernel. + \tparam PointRange model of `ConstRange`. Its iterator type + is `RandomAccessIterator`. + \tparam PointMap model of `ReadablePropertyMap` whose key + type is the value type of the iterator of `PointRange` and value type + is `Geom_traits::Point_3`. + \tparam DiagonalizeTraits model of `DiagonalizeTraits` used + for matrix diagonalization. + */ +template > +class Linearity : public Eigen_feature +{ + typedef Classification::Local_eigen_analysis Local_eigen_analysis; + typedef Eigen_feature Base; +public: + /*! + Constructs the feature. + + \param input input range. + \param eigen class with precomputed eigenvectors and eigenvalues. + */ + Linearity (const PointRange& input, + const Local_eigen_analysis& eigen) : Base (input, eigen) + { + this->init(input, eigen); + } + + /// \cond SKIP_IN_MANUAL + virtual double get_value (const Local_eigen_analysis& eigen, std::size_t i) + { + const typename Local_eigen_analysis::Eigenvalues& ev = eigen.eigenvalue(i); + if (ev[2] < 1e-15) + return 0.; + else + return ((ev[2] - ev[1]) / ev[2]); + } + virtual std::string name() { return "linearity"; } + /// \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] + \tparam Geom_traits model of \cgal Kernel. + \tparam PointRange model of `ConstRange`. Its iterator type + is `RandomAccessIterator`. + \tparam PointMap model of `ReadablePropertyMap` whose key + type is the value type of the iterator of `PointRange` and value type + is `Geom_traits::Point_3`. + \tparam DiagonalizeTraits model of `DiagonalizeTraits` used + for matrix diagonalization. + */ +template > +class Planarity : public Eigen_feature +{ + typedef Classification::Local_eigen_analysis Local_eigen_analysis; + typedef Eigen_feature Base; +public: + /*! + Constructs the feature. + + \param input input range. + \param eigen class with precomputed eigenvectors and eigenvalues. + */ + Planarity (const PointRange& input, + const Local_eigen_analysis& eigen) + : Base(input, eigen) + { + this->init(input, eigen); + } + /// \cond SKIP_IN_MANUAL + virtual double get_value (const Local_eigen_analysis& eigen, std::size_t i) + { + const typename Local_eigen_analysis::Eigenvalues& ev = eigen.eigenvalue(i); + if (ev[2] < 1e-15) + return 0.; + else + return ((ev[1] - ev[0]) / ev[2]); + } + virtual std::string name() { return "planarity"; } + /// \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] + \tparam Geom_traits model of \cgal Kernel. + \tparam PointRange model of `ConstRange`. Its iterator type + is `RandomAccessIterator`. + \tparam PointMap model of `ReadablePropertyMap` whose key + type is the value type of the iterator of `PointRange` and value type + is `Geom_traits::Point_3`. + \tparam DiagonalizeTraits model of `DiagonalizeTraits` used + for matrix diagonalization. + */ +template > +class Sphericity : public Eigen_feature +{ + typedef Classification::Local_eigen_analysis Local_eigen_analysis; + typedef Eigen_feature Base; +public: + /*! + Constructs the feature. + + \param input input range. + \param eigen class with precomputed eigenvectors and eigenvalues. + */ + Sphericity (const PointRange& input, + const Local_eigen_analysis& eigen) + : Base(input, eigen) + { + this->init(input, eigen); + } + /// \cond SKIP_IN_MANUAL + virtual double get_value (const Local_eigen_analysis& eigen, std::size_t i) + { + const typename Local_eigen_analysis::Eigenvalues& ev = eigen.eigenvalue(i); + if (ev[2] < 1e-15) + return 0.; + else + return (ev[0] / ev[2]); + } + virtual std::string name() { return "sphericity"; } + /// \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] + \tparam Geom_traits model of \cgal Kernel. + \tparam PointRange model of `ConstRange`. Its iterator type + is `RandomAccessIterator`. + \tparam PointMap model of `ReadablePropertyMap` whose key + type is the value type of the iterator of `PointRange` and value type + is `Geom_traits::Point_3`. + \tparam DiagonalizeTraits model of `DiagonalizeTraits` used + for matrix diagonalization. + */ +template > +class Omnivariance : public Eigen_feature +{ + typedef Classification::Local_eigen_analysis Local_eigen_analysis; + typedef Eigen_feature Base; +public: + /*! + Constructs the feature. + + \param input input range. + \param eigen class with precomputed eigenvectors and eigenvalues. + */ + Omnivariance (const PointRange& input, + const Local_eigen_analysis& eigen) + : Base(input, eigen) + { + this->init(input, eigen); + } + /// \cond SKIP_IN_MANUAL + virtual double get_value (const Local_eigen_analysis& eigen, std::size_t i) + { + const typename Local_eigen_analysis::Eigenvalues& ev = eigen.eigenvalue(i); + return (std::pow (std::fabs(ev[0] * ev[1] * ev[2]), 0.333333333)); + } + virtual std::string name() { return "omnivariance"; } + /// \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] + \tparam Geom_traits model of \cgal Kernel. + \tparam PointRange model of `ConstRange`. Its iterator type + is `RandomAccessIterator`. + \tparam PointMap model of `ReadablePropertyMap` whose key + type is the value type of the iterator of `PointRange` and value type + is `Geom_traits::Point_3`. + \tparam DiagonalizeTraits model of `DiagonalizeTraits` used + for matrix diagonalization. + */ +template > +class Anisotropy : public Eigen_feature +{ + typedef Classification::Local_eigen_analysis Local_eigen_analysis; + typedef Eigen_feature Base; +public: + /*! + Constructs the feature. + + \param input input range. + \param eigen class with precomputed eigenvectors and eigenvalues. + */ + Anisotropy (const PointRange& input, + const Local_eigen_analysis& eigen) + : Base(input, eigen) + { + this->init(input, eigen); + } + /// \cond SKIP_IN_MANUAL + virtual double get_value (const Local_eigen_analysis& eigen, std::size_t i) + { + const typename Local_eigen_analysis::Eigenvalues& ev = eigen.eigenvalue(i); + if (ev[2] < 1e-15) + return 0.; + else + return ((ev[2] - ev[0]) / ev[2]); + } + virtual std::string name() { return "anisotropy"; } + /// \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] + \tparam Geom_traits model of \cgal Kernel. + \tparam PointRange model of `ConstRange`. Its iterator type + is `RandomAccessIterator`. + \tparam PointMap model of `ReadablePropertyMap` whose key + type is the value type of the iterator of `PointRange` and value type + is `Geom_traits::Point_3`. + \tparam DiagonalizeTraits model of `DiagonalizeTraits` used + for matrix diagonalization. + */ +template > +class Eigentropy : public Eigen_feature +{ + typedef Classification::Local_eigen_analysis Local_eigen_analysis; + typedef Eigen_feature Base; +public: + /*! + Constructs the feature. + + \param input input range. + \param eigen class with precomputed eigenvectors and eigenvalues. + */ + Eigentropy (const PointRange& input, + const Local_eigen_analysis& eigen) + : Base(input, eigen) + { + this->init(input, eigen); + } + /// \cond SKIP_IN_MANUAL + virtual double get_value (const Local_eigen_analysis& eigen, std::size_t i) + { + const typename 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])); + } + virtual std::string name() { return "eigentropy"; } + /// \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] + \tparam Geom_traits model of \cgal Kernel. + \tparam PointRange model of `ConstRange`. Its iterator type + is `RandomAccessIterator`. + \tparam PointMap model of `ReadablePropertyMap` whose key + type is the value type of the iterator of `PointRange` and value type + is `Geom_traits::Point_3`. + \tparam DiagonalizeTraits model of `DiagonalizeTraits` used + for matrix diagonalization. + */ +template > +class Sum_eigenvalues : public Eigen_feature +{ + typedef Classification::Local_eigen_analysis Local_eigen_analysis; + typedef Eigen_feature Base; +public: + /*! + Constructs the feature. + + \param input input range. + \param eigen class with precomputed eigenvectors and eigenvalues. + */ + Sum_eigenvalues (const PointRange& input, + const Local_eigen_analysis& eigen) + : Base(input, eigen) + { + this->init(input, eigen); + } + /// \cond SKIP_IN_MANUAL + virtual double get_value (const Local_eigen_analysis& eigen, std::size_t i) + { + return eigen.sum_of_eigenvalues(i); + } + virtual std::string name() { return "sum_eigen"; } + /// \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] + \tparam Geom_traits model of \cgal Kernel. + \tparam PointRange model of `ConstRange`. Its iterator type + is `RandomAccessIterator`. + \tparam PointMap model of `ReadablePropertyMap` whose key + type is the value type of the iterator of `PointRange` and value type + is `Geom_traits::Point_3`. + \tparam DiagonalizeTraits model of `DiagonalizeTraits` used + for matrix diagonalization. + */ +template > +class Surface_variation : public Eigen_feature +{ + typedef Classification::Local_eigen_analysis Local_eigen_analysis; + typedef Eigen_feature Base; +public: + /*! + Constructs the feature. + + \param input input range. + \param eigen class with precomputed eigenvectors and eigenvalues. + */ + Surface_variation (const PointRange& input, + const Local_eigen_analysis& eigen) + : Base(input, eigen) + { + this->init(input, eigen); + } + /// \cond SKIP_IN_MANUAL + virtual double get_value (const Local_eigen_analysis& eigen, std::size_t i) + { + const typename 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])); + } + virtual std::string name() { return "surface_variation"; } + /// \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..ec3616f9db0 --- /dev/null +++ b/Classification/include/CGAL/Classification/Feature/Elevation.h @@ -0,0 +1,170 @@ +// 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 + +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. + + \tparam Geom_traits model of \cgal Kernel. + \tparam PointRange model of `ConstRange`. Its iterator type + is `RandomAccessIterator`. + \tparam PointMap model of `ReadablePropertyMap` whose key + type is the value type of the iterator of `PointRange` and value type + is `Geom_traits::Point_3`. + + */ +template +class Elevation : public Feature_base +{ + typedef typename Geom_traits::Iso_cuboid_3 Iso_cuboid_3; + + typedef Image Image_float; + typedef Planimetric_grid Grid; + + std::vector elevation_feature; + +public: + /*! + \brief Constructs the feature. + + \param input input range. + \param point_map property map to access the input points. + \param grid precomputed `Planimetric_grid`. + \param grid_resolution resolution of the 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, + const double grid_resolution, + double radius_dtm = -1.) + { + this->set_weight(1.); + if (radius_dtm < 0.) + radius_dtm = 100. * 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) + { + if (grid.indices(i,j).empty()) + continue; + double mean = 0.; + for (std::size_t k = 0; k < grid.indices(i,j).size(); ++ k) + mean += get(point_map, *(input.begin()+grid.indices(i,j)[k])).z(); + mean /= grid.indices(i,j).size(); + 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(); + + Image_float dtm(grid.width(),grid.height()); + + 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(); + + 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 ((double)(get(point_map, *(input.begin()+i)).z()-dtm(I,J))); + } + + this->compute_mean_max (elevation_feature, this->mean, this->max); + } + + /// \cond SKIP_IN_MANUAL + virtual double value (std::size_t pt_index) + { + return elevation_feature[pt_index]; + } + + virtual std::string name() { return "elevation"; } + /// \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..13cacf3ba76 --- /dev/null +++ b/Classification/include/CGAL/Classification/Feature/Hsv.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_HSV_H +#define CGAL_CLASSIFICATION_FEATURE_HSV_H + +#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. + + \note The user only needs to provide a map to standard (and more common) + RGB colors, the conversion to HSV is done internally. + + \tparam Geom_traits model of \cgal Kernel. + \tparam PointRange model of `ConstRange`. Its iterator type + is `RandomAccessIterator`. + \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 +{ + 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; + std::size_t channel; + double mean; + double sd; +#endif + std::string m_name; + +public: + + /*! + + \brief Constructs an feature based on the given color channel, + mean and standard deviation. + + \param input input range. + \param color_map property map to access the colors of the input points. + \param channel chosen HSV channel (0 for hue, 1 for saturation, 2 for value). + \param mean mean value of the specified channel. + \param sd standard deviation of the specified channel. + */ + Hsv (const PointRange& input, + ColorMap color_map, + std::size_t channel, + double mean, double sd) +#ifndef CGAL_CLASSIFICATION_PRECOMPUTE_FEATURES + : input(input), color_map(color_map), channel(channel), mean(mean), sd(sd) +#endif + { + this->set_weight(1.); + +#ifndef CGAL_CLASSIFICATION_PRECOMPUTE_FEATURES + std::vector color_feature; +#endif + 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[channel] - mean) * (c[channel] - mean) / (2. * sd * sd))); + } + this->compute_mean_max (color_feature, this->mean, this->max); + + std::ostringstream oss; + if (channel == 0) oss << "hue"; + else if (channel == 1) oss << "saturation"; + else if (channel == 2) oss << "value"; + oss << "_" << mean; + m_name = oss.str(); + } + + /// \cond SKIP_IN_MANUAL + virtual double 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[channel] - mean) * (c[channel] - mean) / (2. * sd * sd)); +#endif + } + + virtual std::string name() { return m_name; } + /// \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..62642edd3b2 --- /dev/null +++ b/Classification/include/CGAL/Classification/Feature/Vertical_dispersion.h @@ -0,0 +1,163 @@ +// 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 + +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. + + \tparam Geom_traits model of \cgal Kernel. + \tparam PointRange model of `ConstRange`. Its iterator type + is `RandomAccessIterator`. + \tparam PointMap model of `ReadablePropertyMap` whose key + type is the value type of the iterator of `PointRange` and value type + is `Geom_traits::Point_3`. + */ +template +class Vertical_dispersion : public Feature_base +{ + typedef Classification::Image Image_float; + typedef Classification::Planimetric_grid Grid; + std::vector vertical_dispersion; + +public: + /*! + \brief Constructs the feature. + + \param input input range. + \param point_map property map to access the input points. + \param grid precomputed `Planimetric_grid`. + \param grid_resolution resolution of the planimetric grid. + \param radius_neighbors radius of local neighborhoods. + */ + Vertical_dispersion (const PointRange& input, + PointMap point_map, + const Grid& grid, + const double grid_resolution, + double radius_neighbors = -1.) + { + this->set_weight(1.); + if (radius_neighbors < 0.) + radius_neighbors = 5. * grid_resolution; + + Image_float Dispersion(grid.width(), grid.height()); + 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 Geom_traits::Vector_3 verti (0., 0., 1.); + + for (std::size_t j = 0; j < grid.height(); j++){ + for (std::size_t i = 0; i < grid.width(); i++){ + + if(!(grid.mask(i,j))) + continue; + std::vector hori; + + 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); + + for(std::size_t k = squareXmin; k <= squareXmax; k++) + for(std::size_t l = squareYmin; l <= squareYmax; l++) + if(CGAL::sqrt(pow((double)k-i,2)+pow((double)l-j,2)) + <=(double)0.5*radius_neighbors/grid_resolution + && (grid.indices(k,l).size()>0)) + for(std::size_t t=0; t occupy (1 + (std::size_t)((hori.back() - hori.front()) / grid_resolution), false); + + std::size_t last_index = 0; + for (std::size_t k = 0; k < hori.size(); ++ k) + { + std::size_t index = (std::size_t)((hori[k] - hori.front()) / grid_resolution); + occupy[index] = true; + if (index > last_index + 1) + ++ nb_layers; + last_index = index; + } + + 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())); + + } + + } + 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((double)Dispersion(I,J)); + } + + this->compute_mean_max (vertical_dispersion, this->mean, this->max); + } + /// \cond SKIP_IN_MANUAL + virtual double value (std::size_t pt_index) + { + return vertical_dispersion[pt_index]; + } + + virtual std::string name() { return "vertical_dispersion"; } + /// \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..69474ad2a76 --- /dev/null +++ b/Classification/include/CGAL/Classification/Feature/Verticality.h @@ -0,0 +1,128 @@ +// 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 + +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. + + \tparam Geom_traits model of \cgal Kernel. + \tparam PointRange model of `ConstRange`. Its iterator type + is `RandomAccessIterator`. + \tparam PointMap model of `ReadablePropertyMap` whose key + type is the value type of the iterator of `PointRange` and value type + is `Geom_traits::Point_3`. + \tparam DiagonalizeTraits model of `DiagonalizeTraits` used + for matrix diagonalization. + */ +template > +class Verticality : public Feature_base +{ + typedef Classification::Local_eigen_analysis Local_eigen_analysis; + + std::vector verticality_feature; + +public: + /*! + \brief Constructs the feature using local eigen analysis. + + \param input input range. + \param eigen class with precomputed eigenvectors and eigenvalues. + */ + Verticality (const PointRange& input, + const Local_eigen_analysis& eigen) + { + this->set_weight(1.); + typename Geom_traits::Vector_3 vertical (0., 0., 1.); + + for (std::size_t i = 0; i < input.size(); i++) + { + typename Geom_traits::Vector_3 normal = eigen.normal_vector(i); + normal = normal / CGAL::sqrt (normal * normal); + verticality_feature.push_back (1. - CGAL::abs(normal * vertical)); + } + + this->compute_mean_max (verticality_feature, this->mean, this->max); + // max *= 2; + } + + /*! + \brief Constructs the feature using provided normals of points. + + \tparam VectorMap model of `ReadablePropertyMap` whose key + type is the value type of the iterator of `PointRange` and value type + is `Geom_traits::Vector_3`. + \param input input range. + \param normal_map property map to access the normal vectors of the input points. + */ + template + Verticality (const PointRange& input, + VectorMap normal_map) + { + this->set_weight(1.); + typename Geom_traits::Vector_3 vertical (0., 0., 1.); + + for (std::size_t i = 0; i < input.size(); i++) + { + typename Geom_traits::Vector_3 normal = get(normal_map, *(input.begin()+i)); + normal = normal / CGAL::sqrt (normal * normal); + verticality_feature.push_back (1. - std::fabs(normal * vertical)); + } + + this->compute_mean_max (verticality_feature, this->mean, this->max); + // max *= 2; + } + + + /// \cond SKIP_IN_MANUAL + virtual double value (std::size_t pt_index) + { + return verticality_feature[pt_index]; + } + + virtual std::string name() { return "verticality"; } + /// \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..93b61dab7b1 --- /dev/null +++ b/Classification/include/CGAL/Classification/Feature_base.h @@ -0,0 +1,131 @@ +// 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 + +namespace CGAL { + +namespace Classification { + +/*! + \ingroup PkgClassification + + \brief Abstract class describing a classification feature that + associates a scalar value to each item of the classification input. + + User-defined features must inherit this class. Feature objects are + generated by `CGAL::Classifier` objects by the method + `CGAL::Classifier::add_feature()`. For this method to work, the + first argument of the feature's constructors must be of type + `ItemRange&` (where `ItemRange` is the first template argument of + `CGAL::Classifier`). +*/ + + +class Feature_base +{ + double m_weight; +public: + + /// \cond SKIP_IN_MANUAL + double mean; + double max; + + virtual ~Feature_base() { } + /// \endcond + + /*! + \brief Returns the weight of the feature. + */ + double weight() const { return m_weight; } + + /*! + \brief Sets the weight of the feature (`weight` must be positive). + */ + void set_weight (double weight) { m_weight = weight; } + + /*! + \brief Returns `abstract_feature` and should be overloaded + by an inherited class with a specific name. + */ + virtual std::string name() { return "abstract_feature"; } + + /*! + \brief Returns the value taken by the feature for at the item at + position `index`. This method must be implemented by inherited + classes. + */ + virtual double value (std::size_t index) = 0; + + /// \cond SKIP_IN_MANUAL + virtual double normalized (std::size_t index) + { + return (std::max) (0., (std::min) (1., value(index) / m_weight)); + } + virtual double favored (std::size_t index) { return (1. - normalized (index)); } + virtual double penalized (std::size_t index) { return normalized (index); } + // virtual double ignored (std::size_t index) { return (std::min) (favored(index), penalized(index)); } + virtual double ignored (std::size_t) { return 0.5; } + + void compute_mean_max (std::vector& vect, double& mean, double& max) + { + mean = 0.; + max = -std::numeric_limits::max(); + double min = std::numeric_limits::max(); + + for (std::size_t i = 0; i < vect.size(); ++ i) + { + mean += vect[i]; + if (vect[i] > max) + max = vect[i]; + if (vect[i] < min) + min = vect[i]; + } + // std::cerr << name() << " Min/max = " << min << " / " << max << std::endl; + mean /= vect.size(); + + } + /// \endcond +}; + + +#ifdef DOXYGEN_RUNNING +/*! + \ingroup PkgClassification + + \brief %Handle to an `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/Image.h b/Classification/include/CGAL/Classification/Image.h new file mode 100644 index 00000000000..2c784d87707 --- /dev/null +++ b/Classification/include/CGAL/Classification/Image.h @@ -0,0 +1,118 @@ +// 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 + +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..e81e24b2c2a --- /dev/null +++ b/Classification/include/CGAL/Classification/Label.h @@ -0,0 +1,112 @@ +// 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 PkgClassification + +\brief %Classification label (for example: vegetation, ground, etc.) +defined as a set of relationships with classification features. + +*/ +class Label +{ +public: + +private: + /// \cond SKIP_IN_MANUAL + std::string m_name; + std::map m_feature_effects; + /// \endcond + +public: + + /*! + \param name name of the classification label (e.g. vegetation). + */ + Label (std::string name) : m_name (name) { } + + /*! + \brief Sets the effect of feature `att` on the classification label. + */ + void set_feature_effect (Feature_handle att, Feature::Effect effect) + { + m_feature_effects[att] = effect; + } + + /*! + \brief Returns the effect of feature `att` on the classification label. + */ + Feature::Effect feature_effect (Feature_handle att) + { + std::map::iterator + search = m_feature_effects.find (att); + return (search == m_feature_effects.end () ? Feature::NEUTRAL : search->second); + } + + const std::string& name() const { return m_name; } + + /// \cond SKIP_IN_MANUAL + void info() + { + std::cerr << "Feature " << m_name << ": "; + for (std::map::iterator it = m_feature_effects.begin(); + it != m_feature_effects.end(); ++ it) + { + if (it->second == Feature::NEUTRAL) + continue; + + std::cerr << it->first; + if (it->second == Feature::FAVORING) std::cerr << " (favored), "; + else if (it->second == Feature::PENALIZING) std::cerr << " (penalized), "; + } + std::cerr << std::endl; + } + /// \endcond + +}; + +#ifdef DOXYGEN_RUNNING +/*! + \ingroup PkgClassification + + \brief %Handle to a classification `Label`. + + \cgalModels Handle +*/ +class Label_handle { }; +#else +typedef boost::shared_ptr