Struct opencv::types::PtrOfPCTSignatures

pub struct PtrOfPCTSignatures { /* fields omitted */ }

Methods

impl PtrOfPCTSignatures

pub fn as_raw_PtrOfPCTSignatures(&self) -> *mut c_void

pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self

Trait Implementations

impl AlgorithmTrait for PtrOfPCTSignatures

fn as_raw_Algorithm(&self) -> *mut c_void

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

Clears the algorithm state

fn write(&self, fs: &mut FileStorage) -> Result<()>

Stores algorithm parameters in a file storage

fn write_1(&self, fs: &PtrOfFileStorage, name: &str) -> Result<()>

simplified API for language bindings

fn read(&mut self, _fn: &FileNode) -> Result<()>

Reads algorithm parameters from a file storage

fn empty(&self) -> Result<bool>

Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read

fn save(&self, filename: &str) -> Result<()>

Saves the algorithm to a file. In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).

fn get_default_name(&self) -> Result<String>

Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.

impl PCTSignatures for PtrOfPCTSignatures

fn as_raw_PCTSignatures(&self) -> *mut c_void

fn compute_signature(
    &self,
    image: &dyn ToInputArray,
    signature: &mut dyn ToOutputArray
) -> Result<()>

Computes signature of given image. ## Parameters * image: Input image of CV_8U type. * signature: Output computed signature.

fn compute_signatures(
    &self,
    images: &VectorOfMat,
    signatures: &mut VectorOfMat
) -> Result<()>

Computes signatures for multiple images in parallel. ## Parameters * images: Vector of input images of CV_8U type. * signatures: Vector of computed signatures.

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

Number of initial samples taken from the image.

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

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_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 get_window_radius(&self) -> Result<i32>

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_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 get_weight_x(&self) -> Result<f32>

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_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 get_weight_y(&self) -> Result<f32>

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 get_weight_l(&self) -> Result<f32>

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 get_weight_a(&self) -> Result<f32>

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 get_weight_b(&self) -> Result<f32>

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 get_weight_contrast(&self) -> Result<f32>

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 get_weight_entropy(&self) -> Result<f32>

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 get_sampling_points(&self) -> Result<VectorOfPoint2f>

Initial samples taken from the image. These sampled features become the input for clustering.

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

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

Weights (multiplicative constants) that linearly stretch individual axes of the feature space. ## Parameters * weights: Values of all weights.

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

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

Translations of the individual axes of the feature space. ## Parameters * translations: Values of all translations.

fn set_sampling_points(
    &mut self,
    sampling_points: &VectorOfPoint2f
) -> Result<()>

Sets sampling points used to sample the input image. ## Parameters * samplingPoints: Vector of sampling points in range [0..1)

fn get_init_seed_indexes(&self) -> Result<VectorOfint>

Initial seeds (initial number of clusters) for the k-means algorithm.

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

Initial seed indexes for the k-means algorithm.

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

Number of initial seeds (initial number of clusters) for the k-means algorithm.

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

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_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 get_max_clusters_count(&self) -> Result<i32>

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_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 get_cluster_min_size(&self) -> Result<i32>

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_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 get_joining_distance(&self) -> Result<f32>

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_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 get_drop_threshold(&self) -> Result<f32>

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

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 get_distance_function(&self) -> Result<i32>

Distance function selector used for measuring distance between two points in k-means.

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.

impl Send for PtrOfPCTSignatures

impl Drop for PtrOfPCTSignatures

fn drop(&mut self)

Executes the destructor for this type.