[−][src]Trait opencv::prelude::PPF3DDetectorTrait

pub trait PPF3DDetectorTrait {
    pub fn as_raw_PPF3DDetector(&self) -> *const c_void;

    pub fn as_raw_mut_PPF3DDetector(&mut self) -> *mut c_void;

    pub fn set_search_params(
        &mut self, 
        position_threshold: f64, 
        rotation_threshold: f64, 
        use_weighted_clustering: bool
    ) -> Result<()> { ... }

    pub fn train_model(&mut self, model: &Mat) -> Result<()> { ... }

    pub fn match_(
        &mut self, 
        scene: &Mat, 
        results: &mut Vector<Pose3DPtr>, 
        relative_scene_sample_step: f64, 
        relative_scene_distance: f64
    ) -> Result<()> { ... }
}

Class, allowing the load and matching 3D models. Typical Use:

ⓘ
// Train a model
ppf_match_3d::PPF3DDetector detector(0.05, 0.05);
detector.trainModel(pc);
// Search the model in a given scene
vector<Pose3DPtr> results;
detector.match(pcTest, results, 1.0/5.0,0.05);

Required methods

pub fn as_raw_PPF3DDetector(&self) -> *const c_void[src]

pub fn as_raw_mut_PPF3DDetector(&mut self) -> *mut c_void[src]

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Provided methods

pub fn set_search_params(
    &mut self,
    position_threshold: f64,
    rotation_threshold: f64,
    use_weighted_clustering: bool
) -> Result<()>[src]

Set the parameters for the search

Parameters

  • positionThreshold: Position threshold controlling the similarity of translations. Depends on the units of calibration/model.
  • rotationThreshold: Position threshold controlling the similarity of rotations. This parameter can be perceived as a threshold over the difference of angles
  • useWeightedClustering: The algorithm by default clusters the poses without weighting. A non-zero value would indicate that the pose clustering should take into account the number of votes as the weights and perform a weighted averaging instead of a simple one.

C++ default parameters

  • position_threshold: -1
  • rotation_threshold: -1
  • use_weighted_clustering: false

pub fn train_model(&mut self, model: &Mat) -> Result<()>[src]

\brief Trains a new model.

Parameters

  • Model: The input point cloud with normals (Nx6)

\details Uses the parameters set in the constructor to downsample and learn a new model. When the model is learnt, the instance gets ready for calling "match".

pub fn match_(
    &mut self,
    scene: &Mat,
    results: &mut Vector<Pose3DPtr>,
    relative_scene_sample_step: f64,
    relative_scene_distance: f64
) -> Result<()>[src]

\brief Matches a trained model across a provided scene.

Parameters

  • scene: Point cloud for the scene
  • results:[out] List of output poses
  • relativeSceneSampleStep: The ratio of scene points to be used for the matching after sampling with relativeSceneDistance. For example, if this value is set to 1.0/5.0, every 5th point from the scene is used for pose estimation. This parameter allows an easy trade-off between speed and accuracy of the matching. Increasing the value leads to less points being used and in turn to a faster but less accurate pose computation. Decreasing the value has the inverse effect.
  • relativeSceneDistance: Set the distance threshold relative to the diameter of the model. This parameter is equivalent to relativeSamplingStep in the training stage. This parameter acts like a prior sampling with the relativeSceneSampleStep parameter.

C++ default parameters

  • relative_scene_sample_step: 1.0/5.0
  • relative_scene_distance: 0.03
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Implementors

impl PPF3DDetectorTrait for PPF3DDetector[src]

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