🤖 AIによる機械翻訳（非公式） — これは OpenCV 5.0.0 公式リファレンス（英語）を AI (Claude) で自動翻訳したものです。訳に誤りを含む場合があります。正確な情報は公式英語版（原文）を参照してください。

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

```
public class PCTSignatures extends Algorithm
```
CITE: KrulisLS16 で説明されている、PCT (position-color-texture) シグネチャ抽出を実装するクラス。アルゴリズムは特徴サンプラとクラスタライザに分かれている。特徴サンプラは与えられた座標集合でサンプルを生成する。次にクラスタライザはk-meansアルゴリズムを用いてこれらのサンプルのクラスタを生成する。得られたクラスタの集合が入力画像のシグネチャとなる。シグネチャはSIGNATURE_DIMENSION次元の点の配列である。使用される次元は、重み、x座標、y位置、lab色、コントラスト、エントロピーである。CITE: KrulisLS16 CITE: BeecksUS10

フィールドの要約

フィールド
Modifier and Type	Field	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`

クラス org.opencv.core.Algorithm から継承されたフィールド
nativeObj

コンストラクタの要約

コンストラクタ
Modifier Constructor Description

protected PCTSignatures(long addr)

メソッドの要約

すべてのメソッド静的メソッドインスタンスメソッド具象メソッド
Modifier and Type	Method	Description
`static PCTSignatures`	`__fromPtr__(long addr)`
`void`	`computeSignature(Mat image, Mat signature)`	Computes signature of given image.
`void`	`computeSignatures(java.util.List<Mat> images, java.util.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.
`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).

クラス org.opencv.core.Algorithm から継承されたメソッド
clear, empty, getDefaultName, getNativeObjAddr, save

クラス java.lang.Object から継承したメソッド
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- フィールドの詳細
  - L0_25
```
public static final int L0_25
```
    関連項目:
    
    定数フィールド値
  - L0_5
```
public static final int L0_5
```
    関連項目:
    
    定数フィールド値
  - L1
```
public static final int L1
```
    関連項目:
    
    定数フィールド値
  - L2
```
public static final int L2
```
    関連項目:
    
    定数フィールド値
  - L2SQUARED
```
public static final int L2SQUARED
```
    関連項目:
    
    定数フィールド値
  - L5
```
public static final int L5
```
    関連項目:
    
    定数フィールド値
  - L_INFINITY
```
public static final int L_INFINITY
```
    関連項目:
    
    定数フィールド値
  - UNIFORM
```
public static final int UNIFORM
```
    関連項目:
    
    定数フィールド値
  - REGULAR
```
public static final int REGULAR
```
    関連項目:
    
    定数フィールド値
  - NORMAL
```
public static final int NORMAL
```
    関連項目:
    
    定数フィールド値
  - MINUS
```
public static final int MINUS
```
    関連項目:
    
    定数フィールド値
  - GAUSSIAN
```
public static final int GAUSSIAN
```
    関連項目:
    
    定数フィールド値
  - HEURISTIC
```
public static final int HEURISTIC
```
    関連項目:
    
    定数フィールド値
- コンストラクタの詳細
  - PCTSignatures
```
protected PCTSignatures(long addr)
```
- メソッドの詳細
  - __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.
    
    引数:
    
    initSampleCount - 画像サンプリングに使用する点の数。
    
    initSeedCount - 初期クラスタリングのシード数。initSampleCount以下でなければならない。
    
    pointDistribution - 生成される点の分布。デフォルト: UNIFORM。指定可能: UNIFORM, REGULAR, NORMAL。
    
    戻り値:
    
    生成されたアルゴリズム。
  - 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.
    
    引数:
    
    initSampleCount - 画像サンプリングに使用する点の数。
    
    initSeedCount - 初期クラスタリングのシード数。initSampleCount以下でなければならない。指定可能: UNIFORM, REGULAR, NORMAL。
    
    戻り値:
    
    生成されたアルゴリズム。
  - 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.
    
    引数:
    
    initSampleCount - 画像サンプリングに使用する点の数。initSampleCount以下でなければならない。指定可能: UNIFORM, REGULAR, NORMAL。
    
    戻り値:
    
    生成されたアルゴリズム。
  - 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.
    
    戻り値:
    
    生成されたアルゴリズム。
  - 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.
    
    引数:
    
    initSamplingPoints - 画像サンプリングで使用するサンプリング点。
    
    initSeedCount - 初期クラスタリングのシード数。initSamplingPoints.size()以下でなければならない。
    
    戻り値:
    
    生成されたアルゴリズム。
  - create
```
public static PCTSignatures create(MatOfPoint2f initSamplingPoints,
                                   MatOfInt initClusterSeedIndexes)
```
    Creates PCTSignatures algorithm using pre-generated sampling points and clusterization seeds indexes.
    
    引数:
    
    initSamplingPoints - 画像サンプリングで使用するサンプリング点。
    
    initClusterSeedIndexes - クラスタリングの初期シードのインデックス。そのサイズは initSamplingPoints.size() 以下でなければならない。
    
    戻り値:
    
    生成されたアルゴリズム。
  - computeSignature
```
public void computeSignature(Mat image,
                             Mat signature)
```
    Computes signature of given image.
    
    引数:
    
    image - CV_8U 型の入力画像。
    
    signature - 計算されたシグネチャの出力。
  - computeSignatures
```
public void computeSignatures(java.util.List<Mat> images,
                              java.util.List<Mat> signatures)
```
    Computes signatures for multiple images in parallel.
    
    引数:
    
    images - CV_8U 型の入力画像のベクトル。
    
    signatures - 計算されたシグネチャのベクトル。
  - 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.
    
    引数:
    
    source - 元画像。
    
    signature - 画像のシグネチャ。
    
    result - 出力結果。
    
    radiusToShorterSideRatio - 出力画像におけるシグネチャの最大半径を決定する。
    
    borderThickness - 可視化されたシグネチャの境界の太さ。
  - 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.
    
    引数:
    
    source - 元画像。
    
    signature - 画像のシグネチャ。
    
    result - 出力結果。
    
    radiusToShorterSideRatio - 出力画像におけるシグネチャの最大半径を決定する。
  - 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.
    
    引数:
    
    source - 元画像。
    
    signature - 画像のシグネチャ。
    
    result - 出力結果。
  - generateInitPoints
```
public static void generateInitPoints(MatOfPoint2f initPoints,
                                      int count,
                                      int pointDistribution)
```
    Generates initial sampling points according to selected point distribution.
    
    引数:
    
    initPoints - 生成された点が保存される出力ベクトル。
    
    count - 生成する点の数。
    
    pointDistribution - 点の分布セレクタ。利用可能: UNIFORM, REGULAR, NORMAL。覚え書き: 生成される座標は範囲 [0..1) 内である
  - getSampleCount
```
public int getSampleCount()
```
    Number of initial samples taken from the image.
    
    戻り値:
    
    自動生成
  - 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.
    
    戻り値:
    
    自動生成
  - 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.
    
    引数:
    
    grayscaleBits - 自動生成
  - 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).
    
    戻り値:
    
    自動生成
  - 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).
    
    引数:
    
    radius - 自動生成
  - 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)
    
    戻り値:
    
    自動生成
  - 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)
    
    引数:
    
    weight - 自動生成
  - 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)
    
    戻り値:
    
    自動生成
  - 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)
    
    引数:
    
    weight - 自動生成
  - 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)
    
    戻り値:
    
    自動生成
  - 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)
    
    引数:
    
    weight - 自動生成
  - 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)
    
    戻り値:
    
    自動生成
  - 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)
    
    引数:
    
    weight - 自動生成
  - 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)
    
    戻り値:
    
    自動生成
  - 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)
    
    引数:
    
    weight - 自動生成
  - 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)
    
    戻り値:
    
    自動生成
  - 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)
    
    引数:
    
    weight - 自動生成
  - 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)
    
    戻り値:
    
    自動生成
  - 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)
    
    引数:
    
    weight - 自動生成
  - getSamplingPoints
```
public MatOfPoint2f getSamplingPoints()
```
    Initial samples taken from the image. These sampled features become the input for clustering.
    
    戻り値:
    
    自動生成
  - setWeight
```
public void setWeight(int idx,
                      float value)
```
    Weights (multiplicative constants) that linearly stretch individual axes of the feature space.
    
    引数:
    
    idx - 重みの ID
    
    value - 重みの値 覚え書き: WEIGHT_IDX = 0; X_IDX = 1; Y_IDX = 2; L_IDX = 3; A_IDX = 4; B_IDX = 5; CONTRAST_IDX = 6; ENTROPY_IDX = 7;
  - setWeights
```
public void setWeights(MatOfFloat weights)
```
    Weights (multiplicative constants) that linearly stretch individual axes of the feature space.
    
    引数:
    
    weights - すべての重みの値。覚え書き: WEIGHT_IDX = 0; X_IDX = 1; Y_IDX = 2; L_IDX = 3; A_IDX = 4; B_IDX = 5; CONTRAST_IDX = 6; ENTROPY_IDX = 7;
  - setTranslation
```
public void setTranslation(int idx,
                           float value)
```
    Translations of the individual axes of the feature space.
    
    引数:
    
    idx - 平行移動の ID
    
    value - 平行移動の値 覚え書き: 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.
    
    引数:
    
    translations - すべての平行移動の値。覚え書き: WEIGHT_IDX = 0; X_IDX = 1; Y_IDX = 2; L_IDX = 3; A_IDX = 4; B_IDX = 5; CONTRAST_IDX = 6; ENTROPY_IDX = 7;
  - setSamplingPoints
```
public void setSamplingPoints(MatOfPoint2f samplingPoints)
```
    Sets sampling points used to sample the input image.
    
    引数:
    
    samplingPoints - 範囲 [0..1) 内のサンプリング点のベクトル 覚え書き: サンプリング点の数はクラスタリングのシード数以上でなければならない。
  - getInitSeedIndexes
```
public MatOfInt getInitSeedIndexes()
```
    Initial seeds (initial number of clusters) for the k-means algorithm.
    
    戻り値:
    
    自動生成
  - setInitSeedIndexes
```
public void setInitSeedIndexes(MatOfInt initSeedIndexes)
```
    Initial seed indexes for the k-means algorithm.
    
    引数:
    
    initSeedIndexes - 自動生成
  - getInitSeedCount
```
public int getInitSeedCount()
```
    Number of initial seeds (initial number of clusters) for the k-means algorithm.
    
    戻り値:
    
    自動生成
  - 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).
    
    戻り値:
    
    自動生成
  - 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).
    
    引数:
    
    iterationCount - 自動生成
  - 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.
    
    戻り値:
    
    自動生成
  - 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.
    
    引数:
    
    maxClustersCount - 自動生成
  - 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.
    
    戻り値:
    
    自動生成
  - 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.
    
    引数:
    
    clusterMinSize - 自動生成
  - 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.
    
    戻り値:
    
    自動生成
  - 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.
    
    引数:
    
    joiningDistance - 自動生成
  - getDropThreshold
```
public float getDropThreshold()
```
    Remove centroids in k-means whose weight is lesser or equal to given threshold.
    
    戻り値:
    
    自動生成
  - setDropThreshold
```
public void setDropThreshold(float dropThreshold)
```
    Remove centroids in k-means whose weight is lesser or equal to given threshold.
    
    引数:
    
    dropThreshold - 自動生成
  - getDistanceFunction
```
public int getDistanceFunction()
```
    Distance function selector used for measuring distance between two points in k-means.
    
    戻り値:
    
    自動生成
  - 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.
    
    引数:
    
    distanceFunction - 自動生成

クラス PCTSignatures

フィールドの要約

クラス org.opencv.core.Algorithm から継承されたフィールド

コンストラクタの要約

メソッドの要約

クラス org.opencv.core.Algorithm から継承されたメソッド

クラス java.lang.Object から継承したメソッド

フィールドの詳細

L0_25

L0_5

L1

L2

L2SQUARED

L5

L_INFINITY

UNIFORM

REGULAR

NORMAL

MINUS

GAUSSIAN

HEURISTIC

コンストラクタの詳細

PCTSignatures

メソッドの詳細

__fromPtr__

create

create

create

create

create

create

computeSignature

computeSignatures

drawSignature

drawSignature

drawSignature

generateInitPoints

getSampleCount

getGrayscaleBits

setGrayscaleBits

getWindowRadius

setWindowRadius

getWeightX

setWeightX

getWeightY

setWeightY

getWeightL

setWeightL

getWeightA

setWeightA

getWeightB

setWeightB

getWeightContrast

setWeightContrast

getWeightEntropy

setWeightEntropy

getSamplingPoints

setWeight

setWeights

setTranslation

setTranslations

setSamplingPoints

getInitSeedIndexes

setInitSeedIndexes

getInitSeedCount

getIterationCount

setIterationCount

getMaxClustersCount

setMaxClustersCount

getClusterMinSize

setClusterMinSize

getJoiningDistance

setJoiningDistance

getDropThreshold

setDropThreshold

getDistanceFunction

setDistanceFunction

fromPtr