org.opencv.xfeatures2d

## Class PCTSignatures

- java.lang.Object
- - org.opencv.core.Algorithm
  - - org.opencv.xfeatures2d.PCTSignatures

-     public class PCTSignatures
      extends Algorithm

  Class implementing PCT (position-color-texture) signature extraction as described in CITE: 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. CITE: KrulisLS16 CITE: BeecksUS10

- - ### Field Summary

    Fields
    Modifier and Type | Field and Description
    --- | ---
    `static int` | `GAUSSIAN`
    `static int` | `HEURISTIC`
    `static int` | `L_INFINITY`
    `static int` | `L0_25`
    `static int` | `L0_5`
    `static int` | `L1`
    `static int` | `L2`
    `static int` | `L2SQUARED`
    `static int` | `L5`
    `static int` | `MINUS`
    `static int` | `NORMAL`
    `static int` | `REGULAR`
    `static int` | `UNIFORM`

    - ### Fields inherited from class org.opencv.core.Algorithm

      `nativeObj`

  - ### Constructor Summary

    Constructors
    Modifier | Constructor and Description
    --- | ---
    `protected` | `PCTSignatures(long addr)`

  - ### Method Summary

    All Methods Static Methods Instance Methods Concrete Methods
    Modifier and Type | Method and Description
    --- | ---
    `static PCTSignatures` | `__fromPtr__(long addr)`
    `void` | `computeSignature(Mat image, Mat signature)` Computes signature of given image.
    `void` | `computeSignatures(List<Mat> images, List<Mat> signatures)` Computes signatures for multiple images in parallel.
    `static PCTSignatures` | `create()` Creates PCTSignatures algorithm using sample and seed count.
    `static PCTSignatures` | `create(int initSampleCount)` Creates PCTSignatures algorithm using sample and seed count.
    `static PCTSignatures` | `create(int initSampleCount, int initSeedCount)` Creates PCTSignatures algorithm using sample and seed count.
    `static PCTSignatures` | `create(int initSampleCount, int initSeedCount, int pointDistribution)` Creates PCTSignatures algorithm using sample and seed count.
    `static PCTSignatures` | `create(MatOfPoint2f initSamplingPoints, int initSeedCount)` Creates PCTSignatures algorithm using pre-generated sampling points and number of clusterization seeds.
    `static PCTSignatures` | `create(MatOfPoint2f initSamplingPoints, MatOfInt initClusterSeedIndexes)` Creates PCTSignatures algorithm using pre-generated sampling points and clusterization seeds indexes.
    `static void` | `drawSignature(Mat source, Mat signature, Mat result)` Draws signature in the source image and outputs the result.
    `static void` | `drawSignature(Mat source, Mat signature, Mat result, float radiusToShorterSideRatio)` Draws signature in the source image and outputs the result.
    `static void` | `drawSignature(Mat source, Mat signature, Mat result, float radiusToShorterSideRatio, int borderThickness)` Draws signature in the source image and outputs the result.
    `protected void` | `finalize()`
    `static void` | `generateInitPoints(MatOfPoint2f initPoints, int count, int pointDistribution)` Generates initial sampling points according to selected point distribution.
    `int` | `getClusterMinSize()` This parameter multiplied by the index of iteration gives lower limit for cluster size.
    `int` | `getDistanceFunction()` Distance function selector used for measuring distance between two points in k-means.
    `float` | `getDropThreshold()` Remove centroids in k-means whose weight is lesser or equal to given threshold.
    `int` | `getGrayscaleBits()` Color resolution of the greyscale bitmap represented in allocated bits (i.e., value 4 means that 16 shades of grey are used).
    `int` | `getInitSeedCount()` Number of initial seeds (initial number of clusters) for the k-means algorithm.
    `MatOfInt` | `getInitSeedIndexes()` Initial seeds (initial number of clusters) for the k-means algorithm.
    `int` | `getIterationCount()` Number of iterations of the k-means clustering.
    `float` | `getJoiningDistance()` Threshold euclidean distance between two centroids.
    `int` | `getMaxClustersCount()` Maximal number of generated clusters.
    `int` | `getSampleCount()` Number of initial samples taken from the image.
    `MatOfPoint2f` | `getSamplingPoints()` Initial samples taken from the image.
    `float` | `getWeightA()` 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.
    `float` | `getWeightB()` 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.
    `float` | `getWeightContrast()` 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.
    `float` | `getWeightEntropy()` 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.
    `float` | `getWeightL()` 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.
    `float` | `getWeightX()` 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.
    `float` | `getWeightY()` 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.
    `int` | `getWindowRadius()` 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).
    `void` | `setClusterMinSize(int clusterMinSize)` This parameter multiplied by the index of iteration gives lower limit for cluster size.
    `void` | `setDistanceFunction(int distanceFunction)` Distance function selector used for measuring distance between two points in k-means.
    `void` | `setDropThreshold(float dropThreshold)` Remove centroids in k-means whose weight is lesser or equal to given threshold.
    `void` | `setGrayscaleBits(int grayscaleBits)` Color resolution of the greyscale bitmap represented in allocated bits (i.e., value 4 means that 16 shades of grey are used).
    `void` | `setInitSeedIndexes(MatOfInt initSeedIndexes)` Initial seed indexes for the k-means algorithm.
    `void` | `setIterationCount(int iterationCount)` Number of iterations of the k-means clustering.
    `void` | `setJoiningDistance(float joiningDistance)` Threshold euclidean distance between two centroids.
    `void` | `setMaxClustersCount(int maxClustersCount)` Maximal number of generated clusters.
    `void` | `setSamplingPoints(MatOfPoint2f samplingPoints)` Sets sampling points used to sample the input image.
    `void` | `setTranslation(int idx, float value)` Translations of the individual axes of the feature space.
    `void` | `setTranslations(MatOfFloat translations)` Translations of the individual axes of the feature space.
    `void` | `setWeight(int idx, float value)` Weights (multiplicative constants) that linearly stretch individual axes of the feature space.
    `void` | `setWeightA(float weight)` 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.
    `void` | `setWeightB(float weight)` 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.
    `void` | `setWeightContrast(float weight)` 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.
    `void` | `setWeightEntropy(float weight)` 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.
    `void` | `setWeightL(float weight)` 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.
    `void` | `setWeights(MatOfFloat weights)` Weights (multiplicative constants) that linearly stretch individual axes of the feature space.
    `void` | `setWeightX(float weight)` 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.
    `void` | `setWeightY(float weight)` 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.
    `void` | `setWindowRadius(int radius)` 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).

    - ### Methods inherited from class org.opencv.core.Algorithm

      `clear, empty, getDefaultName, getNativeObjAddr, save`

    - ### Methods inherited from class java.lang.Object

      `clone, equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

- - ### Field Detail

    - #### L0_25

          public static final int L0_25

      See Also:
        Constant Field Values

    - #### L0_5

          public static final int L0_5

      See Also:
        Constant Field Values

    - #### L1

          public static final int L1

      See Also:
        Constant Field Values

    - #### L2

          public static final int L2

      See Also:
        Constant Field Values

    - #### L2SQUARED

          public static final int L2SQUARED

      See Also:
        Constant Field Values

    - #### L5

          public static final int L5

      See Also:
        Constant Field Values

    - #### L_INFINITY

          public static final int L_INFINITY

      See Also:
        Constant Field Values

    - #### MINUS

          public static final int MINUS

      See Also:
        Constant Field Values

    - #### GAUSSIAN

          public static final int GAUSSIAN

      See Also:
        Constant Field Values

    - #### HEURISTIC

          public static final int HEURISTIC

      See Also:
        Constant Field Values

    - #### UNIFORM

          public static final int UNIFORM

      See Also:
        Constant Field Values

    - #### REGULAR

          public static final int REGULAR

      See Also:
        Constant Field Values

    - #### NORMAL

          public static final int NORMAL

      See Also:
        Constant Field Values

  - ### Constructor Detail

    - #### PCTSignatures

          protected PCTSignatures(long addr)

  - ### Method Detail

    - #### __fromPtr__

          public static PCTSignatures __fromPtr__(long addr)

    - #### create

          public static PCTSignatures create(int initSampleCount,
                                             int initSeedCount,
                                             int pointDistribution)

      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.

    - #### create

          public static PCTSignatures create(int initSampleCount,
                                             int initSeedCount)

      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 Available: UNIFORM, REGULAR, NORMAL.
      Returns:
        Created algorithm.

    - #### create

          public static PCTSignatures create(int initSampleCount)

      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. Must be lower or equal to initSampleCount Available: UNIFORM, REGULAR, NORMAL.
      Returns:
        Created algorithm.

    - #### create

          public static PCTSignatures create()

      Creates PCTSignatures algorithm using sample and seed count. It generates its own sets of sampling points and clusterization seed indexes. Must be lower or equal to initSampleCount Available: UNIFORM, REGULAR, NORMAL.
      Returns:
        Created algorithm.

    - #### create

          public static PCTSignatures create(MatOfPoint2f initSamplingPoints,
                                             int initSeedCount)

      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.

    - #### create

          public static PCTSignatures create(MatOfPoint2f initSamplingPoints,
                                             MatOfInt initClusterSeedIndexes)

      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.

    - #### getDropThreshold

          public float getDropThreshold()

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

    - #### getJoiningDistance

          public float getJoiningDistance()

      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.
      Returns:
        automatically generated

    - #### getWeightA

          public float getWeightA()

      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)
      Returns:
        automatically generated

    - #### getWeightB

          public float getWeightB()

      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)
      Returns:
        automatically generated

    - #### getWeightContrast

          public float getWeightContrast()

      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)
      Returns:
        automatically generated

    - #### getWeightEntropy

          public float getWeightEntropy()

      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)
      Returns:
        automatically generated

    - #### getWeightL

          public float getWeightL()

      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)
      Returns:
        automatically generated

    - #### getWeightX

          public float getWeightX()

      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)
      Returns:
        automatically generated

    - #### getWeightY

          public float getWeightY()

      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)
      Returns:
        automatically generated

    - #### getClusterMinSize

          public int getClusterMinSize()

      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.
      Returns:
        automatically generated

    - #### getDistanceFunction

          public int getDistanceFunction()

      Distance function selector used for measuring distance between two points in k-means.
      Returns:
        automatically generated

    - #### getGrayscaleBits

          public int getGrayscaleBits()

      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.
      Returns:
        automatically generated

    - #### getInitSeedCount

          public int getInitSeedCount()

      Number of initial seeds (initial number of clusters) for the k-means algorithm.
      Returns:
        automatically generated

    - #### getIterationCount

          public int getIterationCount()

      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).
      Returns:
        automatically generated

    - #### getMaxClustersCount

          public int getMaxClustersCount()

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

    - #### getSampleCount

          public int getSampleCount()

      Number of initial samples taken from the image.
      Returns:
        automatically generated

    - #### getWindowRadius

          public int getWindowRadius()

      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).
      Returns:
        automatically generated

    - #### getSamplingPoints

          public MatOfPoint2f getSamplingPoints()

      Initial samples taken from the image. These sampled features become the input for clustering.
      Returns:
        automatically generated

    - #### getInitSeedIndexes

          public MatOfInt getInitSeedIndexes()

      Initial seeds (initial number of clusters) for the k-means algorithm.
      Returns:
        automatically generated

    - #### computeSignature

          public void computeSignature(Mat image,
                                       Mat signature)

      Computes signature of given image.
      Parameters:
        `image` - Input image of CV_8U type.
        `signature` - Output computed signature.

    - #### computeSignatures

          public void computeSignatures(List<Mat> images,
                                        List<Mat> signatures)

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

    - #### drawSignature

          public static void drawSignature(Mat source,
                                           Mat signature,
                                           Mat result,
                                           float radiusToShorterSideRatio,
                                           int borderThickness)

      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.

    - #### drawSignature

          public static void drawSignature(Mat source,
                                           Mat signature,
                                           Mat result,
                                           float radiusToShorterSideRatio)

      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.

    - #### drawSignature

          public static void drawSignature(Mat source,
                                           Mat signature,
                                           Mat 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.

    - #### generateInitPoints

          public static void generateInitPoints(MatOfPoint2f initPoints,
                                                int count,
                                                int pointDistribution)

      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)

    - #### setClusterMinSize

          public void setClusterMinSize(int clusterMinSize)

      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.
      Parameters:
        `clusterMinSize` - automatically generated

    - #### setDistanceFunction

          public void setDistanceFunction(int distanceFunction)

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

    - #### setDropThreshold

          public void setDropThreshold(float dropThreshold)

      Remove centroids in k-means whose weight is lesser or equal to given threshold.
      Parameters:
        `dropThreshold` - automatically generated

    - #### setGrayscaleBits

          public void setGrayscaleBits(int grayscaleBits)

      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.
      Parameters:
        `grayscaleBits` - automatically generated

    - #### setInitSeedIndexes

          public void setInitSeedIndexes(MatOfInt initSeedIndexes)

      Initial seed indexes for the k-means algorithm.
      Parameters:
        `initSeedIndexes` - automatically generated

    - #### setIterationCount

          public void setIterationCount(int iterationCount)

      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).
      Parameters:
        `iterationCount` - automatically generated

    - #### setJoiningDistance

          public void setJoiningDistance(float joiningDistance)

      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.
      Parameters:
        `joiningDistance` - automatically generated

    - #### setMaxClustersCount

          public void setMaxClustersCount(int maxClustersCount)

      Maximal number of generated clusters. If the number is exceeded, the clusters are sorted by their weights and the smallest clusters are cropped.
      Parameters:
        `maxClustersCount` - automatically generated

    - #### setSamplingPoints

          public void setSamplingPoints(MatOfPoint2f samplingPoints)

      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.

    - #### setTranslation

          public void setTranslation(int idx,
                                     float value)

      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;

    - #### setTranslations

          public void setTranslations(MatOfFloat translations)

      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;

    - #### setWeight

          public void setWeight(int idx,
                                float value)

      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;

    - #### setWeightA

          public void setWeightA(float weight)

      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)
      Parameters:
        `weight` - automatically generated

    - #### setWeightB

          public void setWeightB(float weight)

      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)
      Parameters:
        `weight` - automatically generated

    - #### setWeightContrast

          public void setWeightContrast(float weight)

      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)
      Parameters:
        `weight` - automatically generated

    - #### setWeightEntropy

          public void setWeightEntropy(float weight)

      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)
      Parameters:
        `weight` - automatically generated

    - #### setWeightL

          public void setWeightL(float weight)

      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)
      Parameters:
        `weight` - automatically generated

    - #### setWeightX

          public void setWeightX(float weight)

      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)
      Parameters:
        `weight` - automatically generated

    - #### setWeightY

          public void setWeightY(float weight)

      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)
      Parameters:
        `weight` - automatically generated

    - #### setWeights

          public void setWeights(MatOfFloat weights)

      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;

    - #### setWindowRadius

          public void setWindowRadius(int radius)

      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).
      Parameters:
        `radius` - automatically generated

    - #### finalize

          protected void finalize()
                           throws Throwable

      Overrides:
        `finalize` in class `Algorithm`
      Throws:
        `Throwable`

Generated on Thu Jul 25 2019 21:39:44 UTC / OpenCV 3.4.7