[−][src]Trait opencv::features2d::BOWTrainer

pub trait BOWTrainer {
    fn as_raw_BOWTrainer(&self) -> *mut c_void;

    fn add(&mut self, descriptors: &Mat) -> Result<()> { ... }

    fn get_descriptors(&self) -> Result<VectorOfMat> { ... }

    fn descriptors_count(&self) -> Result<i32> { ... }

    fn clear(&mut self) -> Result<()> { ... }

    fn cluster(&self) -> Result<Mat> { ... }

    fn cluster_with_descriptors(&self, descriptors: &Mat) -> Result<Mat> { ... }
}

Abstract base class for training the bag of visual words vocabulary from a set of descriptors.

For details, see, for example, Visual Categorization with Bags of Keypoints by Gabriella Csurka, Christopher R. Dance, Lixin Fan, Jutta Willamowski, Cedric Bray, 2004. :

Required methods

fn as_raw_BOWTrainer(&self) -> *mut c_void

Loading content...

Provided methods

fn add(&mut self, descriptors: &Mat) -> Result<()>

Adds descriptors to a training set.

Parameters

  • descriptors: Descriptors to add to a training set. Each row of the descriptors matrix is a descriptor.

The training set is clustered using clustermethod to construct the vocabulary.

fn get_descriptors(&self) -> Result<VectorOfMat>

Returns a training set of descriptors.

fn descriptors_count(&self) -> Result<i32>

Returns the count of all descriptors stored in the training set.

fn clear(&mut self) -> Result<()>

fn cluster(&self) -> Result<Mat>

fn cluster_with_descriptors(&self, descriptors: &Mat) -> Result<Mat>

Clusters train descriptors.

Parameters

  • descriptors: Descriptors to cluster. Each row of the descriptors matrix is a descriptor. Descriptors are not added to the inner train descriptor set.

The vocabulary consists of cluster centers. So, this method returns the vocabulary. In the first variant of the method, train descriptors stored in the object are clustered. In the second variant, input descriptors are clustered.

Loading content...

Implementors

Loading content...