Trait PCTSignaturesTrait 

pub trait PCTSignaturesTrait: AlgorithmTrait + PCTSignaturesTraitConst {
    // Required method
    fn as_raw_mut_PCTSignatures(&mut self) -> *mut c_void;

    // Provided methods
    fn set_grayscale_bits(&mut self, grayscale_bits: i32) -> Result<()> { ... }
    fn set_window_radius(&mut self, radius: i32) -> Result<()> { ... }
    fn set_weight_x(&mut self, weight: f32) -> Result<()> { ... }
    fn set_weight_y(&mut self, weight: f32) -> Result<()> { ... }
    fn set_weight_l(&mut self, weight: f32) -> Result<()> { ... }
    fn set_weight_a(&mut self, weight: f32) -> Result<()> { ... }
    fn set_weight_b(&mut self, weight: f32) -> Result<()> { ... }
    fn set_weight_contrast(&mut self, weight: f32) -> Result<()> { ... }
    fn set_weight_entropy(&mut self, weight: f32) -> Result<()> { ... }
    fn set_weight(&mut self, idx: i32, value: f32) -> Result<()> { ... }
    fn set_weights(&mut self, weights: &Vector<f32>) -> Result<()> { ... }
    fn set_translation(&mut self, idx: i32, value: f32) -> Result<()> { ... }
    fn set_translations(&mut self, translations: &Vector<f32>) -> Result<()> { ... }
    fn set_sampling_points(
        &mut self,
        sampling_points: Vector<Point2f>,
    ) -> Result<()> { ... }
    fn set_init_seed_indexes(
        &mut self,
        init_seed_indexes: Vector<i32>,
    ) -> Result<()> { ... }
    fn set_iteration_count(&mut self, iteration_count: i32) -> Result<()> { ... }
    fn set_max_clusters_count(&mut self, max_clusters_count: i32) -> Result<()> { ... }
    fn set_cluster_min_size(&mut self, cluster_min_size: i32) -> Result<()> { ... }
    fn set_joining_distance(&mut self, joining_distance: f32) -> Result<()> { ... }
    fn set_drop_threshold(&mut self, drop_threshold: f32) -> Result<()> { ... }
    fn set_distance_function(&mut self, distance_function: i32) -> Result<()> { ... }
}

Mutable methods for crate::xfeatures2d::PCTSignatures

Required Methods

fn as_raw_mut_PCTSignatures(&mut self) -> *mut c_void

Provided Methods

fn set_grayscale_bits(&mut self, grayscale_bits: i32) -> Result<()>

Color resolution of the greyscale bitmap represented in allocated bits (i.e., value 4 means that 16 shades of grey are used). The greyscale bitmap is used for computing contrast and entropy values.

fn set_window_radius(&mut self, radius: i32) -> Result<()>

Size of the texture sampling window used to compute contrast and entropy (center of the window is always in the pixel selected by x,y coordinates of the corresponding feature sample).

fn set_weight_x(&mut self, weight: f32) -> Result<()>

Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)

fn set_weight_y(&mut self, weight: f32) -> Result<()>

Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)

fn set_weight_l(&mut self, weight: f32) -> Result<()>

Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)

fn set_weight_a(&mut self, weight: f32) -> Result<()>

Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)

fn set_weight_b(&mut self, weight: f32) -> Result<()>

Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)

fn set_weight_contrast(&mut self, weight: f32) -> Result<()>

Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)

fn set_weight_entropy(&mut self, weight: f32) -> Result<()>

Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)

fn set_weight(&mut self, idx: i32, value: f32) -> Result<()>

Weights (multiplicative constants) that linearly stretch individual axes of the feature space.

Parameters

• idx: ID of the weight
• value: Value of the weight

Note: WEIGHT_IDX = 0; X_IDX = 1; Y_IDX = 2; L_IDX = 3; A_IDX = 4; B_IDX = 5; CONTRAST_IDX = 6; ENTROPY_IDX = 7;

fn set_weights(&mut self, weights: &Vector<f32>) -> Result<()>

Weights (multiplicative constants) that linearly stretch individual axes of the feature space.

Parameters

• weights: Values of all weights.

Note: WEIGHT_IDX = 0; X_IDX = 1; Y_IDX = 2; L_IDX = 3; A_IDX = 4; B_IDX = 5; CONTRAST_IDX = 6; ENTROPY_IDX = 7;

fn set_translation(&mut self, idx: i32, value: f32) -> Result<()>

Translations of the individual axes of the feature space.

Parameters

• idx: ID of the translation
• value: Value of the translation

Note: WEIGHT_IDX = 0; X_IDX = 1; Y_IDX = 2; L_IDX = 3; A_IDX = 4; B_IDX = 5; CONTRAST_IDX = 6; ENTROPY_IDX = 7;

fn set_translations(&mut self, translations: &Vector<f32>) -> Result<()>

Translations of the individual axes of the feature space.

Parameters

• translations: Values of all translations.

Note: WEIGHT_IDX = 0; X_IDX = 1; Y_IDX = 2; L_IDX = 3; A_IDX = 4; B_IDX = 5; CONTRAST_IDX = 6; ENTROPY_IDX = 7;

fn set_sampling_points( &mut self, sampling_points: Vector<Point2f>, ) -> Result<()>

Sets sampling points used to sample the input image.

Parameters

• samplingPoints: Vector of sampling points in range [0..1)

Note: Number of sampling points must be greater or equal to clusterization seed count.

fn set_init_seed_indexes( &mut self, init_seed_indexes: Vector<i32>, ) -> Result<()>

Initial seed indexes for the k-means algorithm.

fn set_iteration_count(&mut self, iteration_count: i32) -> Result<()>

Number of iterations of the k-means clustering. We use fixed number of iterations, since the modified clustering is pruning clusters (not iteratively refining k clusters).

fn set_max_clusters_count(&mut self, max_clusters_count: i32) -> Result<()>

Maximal number of generated clusters. If the number is exceeded, the clusters are sorted by their weights and the smallest clusters are cropped.

fn set_cluster_min_size(&mut self, cluster_min_size: i32) -> Result<()>

This parameter multiplied by the index of iteration gives lower limit for cluster size. Clusters containing fewer points than specified by the limit have their centroid dismissed and points are reassigned.

fn set_joining_distance(&mut self, joining_distance: f32) -> Result<()>

Threshold euclidean distance between two centroids. If two cluster centers are closer than this distance, one of the centroid is dismissed and points are reassigned.

fn set_drop_threshold(&mut self, drop_threshold: f32) -> Result<()>

Remove centroids in k-means whose weight is lesser or equal to given threshold.

fn set_distance_function(&mut self, distance_function: i32) -> Result<()>

Distance function selector used for measuring distance between two points in k-means. Available: L0_25, L0_5, L1, L2, L2SQUARED, L5, L_INFINITY.

Dyn Compatibility

This trait is not dyn compatible.

In older versions of Rust, dyn compatibility was called "object safety", so this trait is not object safe.

Implementors

impl PCTSignaturesTrait for BoxedRefMut<'_, PCTSignatures>

impl PCTSignaturesTrait for Ptr<PCTSignatures>

impl PCTSignaturesTrait for PCTSignatures