Struct opencv::xfeatures2d::PCTSignatures

pub struct PCTSignatures { /* private fields */ }

Class implementing PCT (position-color-texture) signature extraction as described in KrulisLS16. The algorithm is divided to a feature sampler and a clusterizer. Feature sampler produces samples at given set of coordinates. Clusterizer then produces clusters of these samples using k-means algorithm. Resulting set of clusters is the signature of the input image.

  A signature is an array of SIGNATURE_DIMENSION-dimensional points.
  Used dimensions are:
  weight, x, y position; lab color, contrast, entropy.

KrulisLS16 BeecksUS10

Implementations

impl PCTSignatures

pub fn create( init_sample_count: i32, init_seed_count: i32, point_distribution: i32 ) -> Result<Ptr<PCTSignatures>>

Creates PCTSignatures algorithm using sample and seed count. It generates its own sets of sampling points and clusterization seed indexes.

Parameters

• initSampleCount: Number of points used for image sampling.
• initSeedCount: Number of initial clusterization seeds. Must be lower or equal to initSampleCount
• pointDistribution: Distribution of generated points. Default: UNIFORM. Available: UNIFORM, REGULAR, NORMAL.

Returns

Created algorithm.

C++ default parameters

• init_sample_count: 2000
• init_seed_count: 400
• point_distribution: 0

pub fn create_def() -> Result<Ptr<PCTSignatures>>

Creates PCTSignatures algorithm using sample and seed count. It generates its own sets of sampling points and clusterization seed indexes.

Parameters

• initSampleCount: Number of points used for image sampling.
• initSeedCount: Number of initial clusterization seeds. Must be lower or equal to initSampleCount
• pointDistribution: Distribution of generated points. Default: UNIFORM. Available: UNIFORM, REGULAR, NORMAL.

Returns

Created algorithm.

Note

This alternative version of PCTSignatures::create function uses the following default values for its arguments:

• init_sample_count: 2000
• init_seed_count: 400
• point_distribution: 0

pub fn create_1( init_sampling_points: &Vector<Point2f>, init_seed_count: i32 ) -> Result<Ptr<PCTSignatures>>

Creates PCTSignatures algorithm using pre-generated sampling points and number of clusterization seeds. It uses the provided sampling points and generates its own clusterization seed indexes.

Parameters

• initSamplingPoints: Sampling points used in image sampling.
• initSeedCount: Number of initial clusterization seeds. Must be lower or equal to initSamplingPoints.size().

Returns

Created algorithm.

pub fn create_2( init_sampling_points: &Vector<Point2f>, init_cluster_seed_indexes: &Vector<i32> ) -> Result<Ptr<PCTSignatures>>

Creates PCTSignatures algorithm using pre-generated sampling points and clusterization seeds indexes.

Parameters

• initSamplingPoints: Sampling points used in image sampling.
• initClusterSeedIndexes: Indexes of initial clusterization seeds. Its size must be lower or equal to initSamplingPoints.size().

Returns

Created algorithm.

pub fn draw_signature( source: &impl ToInputArray, signature: &impl ToInputArray, result: &mut impl ToOutputArray, radius_to_shorter_side_ratio: f32, border_thickness: i32 ) -> Result<()>

Draws signature in the source image and outputs the result. Signatures are visualized as a circle with radius based on signature weight and color based on signature color. Contrast and entropy are not visualized.

Parameters

• source: Source image.
• signature: Image signature.
• result: Output result.
• radiusToShorterSideRatio: Determines maximal radius of signature in the output image.
• borderThickness: Border thickness of the visualized signature.

C++ default parameters

• radius_to_shorter_side_ratio: 1.0/8
• border_thickness: 1

pub fn draw_signature_def( source: &impl ToInputArray, signature: &impl ToInputArray, result: &mut impl ToOutputArray ) -> Result<()>

Draws signature in the source image and outputs the result. Signatures are visualized as a circle with radius based on signature weight and color based on signature color. Contrast and entropy are not visualized.

Parameters

• source: Source image.
• signature: Image signature.
• result: Output result.
• radiusToShorterSideRatio: Determines maximal radius of signature in the output image.
• borderThickness: Border thickness of the visualized signature.

Note

This alternative version of PCTSignatures::draw_signature function uses the following default values for its arguments:

• radius_to_shorter_side_ratio: 1.0/8
• border_thickness: 1

pub fn generate_init_points( init_points: &mut Vector<Point2f>, count: i32, point_distribution: i32 ) -> Result<()>

Generates initial sampling points according to selected point distribution.

Parameters

• initPoints: Output vector where the generated points will be saved.
• count: Number of points to generate.
• pointDistribution: Point distribution selector. Available: UNIFORM, REGULAR, NORMAL.

Note: Generated coordinates are in range [0..1)

Trait Implementations

impl AlgorithmTrait for PCTSignatures

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

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

Clears the algorithm state

fn read(&mut self, fn_: &impl FileNodeTraitConst) -> Result<()>

Reads algorithm parameters from a file storage

impl AlgorithmTraitConst for PCTSignatures

fn as_raw_Algorithm(&self) -> *const c_void

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

Stores algorithm parameters in a file storage

fn write_1(&self, fs: &mut impl FileStorageTrait, name: &str) -> Result<()>

Stores algorithm parameters in a file storage

fn write_with_name(&self, fs: &Ptr<FileStorage>, name: &str) -> Result<()>

@deprecated

fn write_with_name_def(&self, fs: &Ptr<FileStorage>) -> Result<()>

👎Deprecated:

Note

Deprecated: ## Note This alternative version of AlgorithmTraitConst::write_with_name function uses the following default values for its arguments:

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 Boxed for PCTSignatures

unsafe fn from_raw( ptr: <PCTSignatures as OpenCVFromExtern>::ExternReceive ) -> Self

Wrap the specified raw pointer

fn into_raw(self) -> <PCTSignatures as OpenCVTypeExternContainer>::ExternSendMut

Return the underlying raw pointer while consuming this wrapper.

fn as_raw(&self) -> <PCTSignatures as OpenCVTypeExternContainer>::ExternSend

Return the underlying raw pointer.

fn as_raw_mut( &mut self ) -> <PCTSignatures as OpenCVTypeExternContainer>::ExternSendMut

Return the underlying mutable raw pointer

impl Debug for PCTSignatures

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Drop for PCTSignatures

fn drop(&mut self)

Executes the destructor for this type.

impl From<PCTSignatures> for Algorithm

fn from(s: PCTSignatures) -> Self

Converts to this type from the input type.

impl PCTSignaturesTrait for PCTSignatures

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

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.

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

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

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

Translations of the individual axes of the feature space.

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

Translations of the individual axes of the feature space.

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

Sets sampling points used to sample the input image.

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.

impl PCTSignaturesTraitConst for PCTSignatures

fn as_raw_PCTSignatures(&self) -> *const c_void

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

Computes signature of given image.

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

Computes signatures for multiple images in parallel.

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 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 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 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 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 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 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 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 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 get_sampling_points(&self) -> Result<Vector<Point2f>>

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

fn get_init_seed_indexes(&self) -> Result<Vector<i32>>

** clusterizer ***

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 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 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 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 get_drop_threshold(&self) -> Result<f32>

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.

impl Send for PCTSignatures