Trait opencv::hub_prelude::PCTSignaturesConst

source · 
pub trait PCTSignaturesConst: AlgorithmTraitConst {

Show 22 methods    fn as_raw_PCTSignatures(&self) -> *const c_void;

    fn compute_signature(
        &self,
        image: &dyn ToInputArray,
        signature: &mut dyn ToOutputArray
    ) -> Result<()> { ... }
    fn compute_signatures(
        &self,
        images: &Vector<Mat>,
        signatures: &mut Vector<Mat>
    ) -> Result<()> { ... }
    fn get_sample_count(&self) -> Result<i32> { ... }
    fn get_grayscale_bits(&self) -> Result<i32> { ... }
    fn get_window_radius(&self) -> Result<i32> { ... }
    fn get_weight_x(&self) -> Result<f32> { ... }
    fn get_weight_y(&self) -> Result<f32> { ... }
    fn get_weight_l(&self) -> Result<f32> { ... }
    fn get_weight_a(&self) -> Result<f32> { ... }
    fn get_weight_b(&self) -> Result<f32> { ... }
    fn get_weight_contrast(&self) -> Result<f32> { ... }
    fn get_weight_entropy(&self) -> Result<f32> { ... }
    fn get_sampling_points(&self) -> Result<Vector<Point2f>> { ... }
    fn get_init_seed_indexes(&self) -> Result<Vector<i32>> { ... }
    fn get_init_seed_count(&self) -> Result<i32> { ... }
    fn get_iteration_count(&self) -> Result<i32> { ... }
    fn get_max_clusters_count(&self) -> Result<i32> { ... }
    fn get_cluster_min_size(&self) -> Result<i32> { ... }
    fn get_joining_distance(&self) -> Result<f32> { ... }
    fn get_drop_threshold(&self) -> Result<f32> { ... }
    fn get_distance_function(&self) -> Result<i32> { ... }

}
Expand description

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

Required Methods§

Provided Methods§

Computes signature of given image.

Parameters
  • image: Input image of CV_8U type.
  • signature: Output computed signature.

Computes signatures for multiple images in parallel.

Parameters
  • images: Vector of input images of CV_8U type.
  • signatures: Vector of computed signatures.

Number of initial samples taken from the image.

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.

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).

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)

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)

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)

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)

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)

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)

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)

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

** clusterizer ***

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

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

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).

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

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.

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.

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

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

Implementors§