PCTSignatures
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
Member of Xfeatures2d
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Creates PCTSignatures algorithm using sample and seed count. It generates its own sets of sampling points and clusterization seed indexes.
Declaration
Objective-C
+ (nonnull PCTSignatures *)create:(int)initSampleCount initSeedCount:(int)initSeedCount pointDistribution:(int)pointDistribution;
Swift
class func create(initSampleCount: Int32, initSeedCount: Int32, pointDistribution: Int32) -> PCTSignatures
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.
Return Value
Created algorithm.
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Creates PCTSignatures algorithm using sample and seed count. It generates its own sets of sampling points and clusterization seed indexes.
Declaration
Objective-C
+ (nonnull PCTSignatures *)create:(int)initSampleCount initSeedCount:(int)initSeedCount;
Swift
class func create(initSampleCount: Int32, initSeedCount: Int32) -> PCTSignatures
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.
Return Value
Created algorithm.
-
Creates PCTSignatures algorithm using sample and seed count. It generates its own sets of sampling points and clusterization seed indexes.
Declaration
Objective-C
+ (nonnull PCTSignatures *)create:(int)initSampleCount;
Swift
class func create(initSampleCount: Int32) -> PCTSignatures
Parameters
initSampleCount
Number of points used for image sampling. Must be lower or equal to initSampleCount Available: UNIFORM, REGULAR, NORMAL.
Return Value
Created algorithm.
-
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.
Declaration
Objective-C
+ (nonnull PCTSignatures *)create;
Swift
class func create() -> PCTSignatures
Return Value
Created algorithm.
-
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.
Declaration
Objective-C
+ (nonnull PCTSignatures *)create2: (nonnull NSArray<Point2f *> *)initSamplingPoints initSeedCount:(int)initSeedCount;
Swift
class func create(initSamplingPoints: [Point2f], initSeedCount: Int32) -> PCTSignatures
Parameters
initSamplingPoints
Sampling points used in image sampling.
initSeedCount
Number of initial clusterization seeds. Must be lower or equal to initSamplingPoints.size().
Return Value
Created algorithm.
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Creates PCTSignatures algorithm using pre-generated sampling points and clusterization seeds indexes.
Declaration
Parameters
initSamplingPoints
Sampling points used in image sampling.
initClusterSeedIndexes
Indexes of initial clusterization seeds. Its size must be lower or equal to initSamplingPoints.size().
Return Value
Created algorithm.
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Remove centroids in k-means whose weight is lesser or equal to given threshold.
Declaration
Objective-C
- (float)getDropThreshold;
Swift
func getDropThreshold() -> Float
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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.
Declaration
Objective-C
- (float)getJoiningDistance;
Swift
func getJoiningDistance() -> Float
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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)
Declaration
Objective-C
- (float)getWeightA;
Swift
func getWeightA() -> Float
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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)
Declaration
Objective-C
- (float)getWeightB;
Swift
func getWeightB() -> Float
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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)
Declaration
Objective-C
- (float)getWeightContrast;
Swift
func getWeightContrast() -> Float
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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)
Declaration
Objective-C
- (float)getWeightEntropy;
Swift
func getWeightEntropy() -> Float
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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)
Declaration
Objective-C
- (float)getWeightL;
Swift
func getWeightL() -> Float
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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)
Declaration
Objective-C
- (float)getWeightX;
Swift
func getWeightX() -> Float
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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)
Declaration
Objective-C
- (float)getWeightY;
Swift
func getWeightY() -> Float
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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.
Declaration
Objective-C
- (int)getClusterMinSize;
Swift
func getClusterMinSize() -> Int32
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Distance function selector used for measuring distance between two points in k-means.
Declaration
Objective-C
- (int)getDistanceFunction;
Swift
func getDistanceFunction() -> Int32
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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.
Declaration
Objective-C
- (int)getGrayscaleBits;
Swift
func getGrayscaleBits() -> Int32
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Number of initial seeds (initial number of clusters) for the k-means algorithm.
Declaration
Objective-C
- (int)getInitSeedCount;
Swift
func getInitSeedCount() -> Int32
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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).
Declaration
Objective-C
- (int)getIterationCount;
Swift
func getIterationCount() -> Int32
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Maximal number of generated clusters. If the number is exceeded, the clusters are sorted by their weights and the smallest clusters are cropped.
Declaration
Objective-C
- (int)getMaxClustersCount;
Swift
func getMaxClustersCount() -> Int32
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Number of initial samples taken from the image.
Declaration
Objective-C
- (int)getSampleCount;
Swift
func getSampleCount() -> Int32
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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).
Declaration
Objective-C
- (int)getWindowRadius;
Swift
func getWindowRadius() -> Int32
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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.
Declaration
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.
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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.
Declaration
Parameters
source
Source image.
signature
Image signature.
result
Output result.
radiusToShorterSideRatio
Determines maximal radius of signature in the output image.
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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.
Declaration
Parameters
source
Source image.
signature
Image signature.
result
Output result.
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Generates initial sampling points according to selected point distribution.
Note
Generated coordinates are in range [0..1)Declaration
Objective-C
+ (void)generateInitPoints:(nonnull NSArray<Point2f *> *)initPoints count:(int)count pointDistribution:(int)pointDistribution;
Swift
class func generateInitPoints(initPoints: [Point2f], count: Int32, pointDistribution: Int32)
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.
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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.
Declaration
Objective-C
- (void)setClusterMinSize:(int)clusterMinSize;
Swift
func setClusterMinSize(clusterMinSize: Int32)
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Distance function selector used for measuring distance between two points in k-means. Available: L0_25, L0_5, L1, L2, L2SQUARED, L5, L_INFINITY.
Declaration
Objective-C
- (void)setDistanceFunction:(int)distanceFunction;
Swift
func setDistanceFunction(distanceFunction: Int32)
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Remove centroids in k-means whose weight is lesser or equal to given threshold.
Declaration
Objective-C
- (void)setDropThreshold:(float)dropThreshold;
Swift
func setDropThreshold(dropThreshold: Float)
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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.
Declaration
Objective-C
- (void)setGrayscaleBits:(int)grayscaleBits;
Swift
func setGrayscaleBits(grayscaleBits: Int32)
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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).
Declaration
Objective-C
- (void)setIterationCount:(int)iterationCount;
Swift
func setIterationCount(iterationCount: Int32)
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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.
Declaration
Objective-C
- (void)setJoiningDistance:(float)joiningDistance;
Swift
func setJoiningDistance(joiningDistance: Float)
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Maximal number of generated clusters. If the number is exceeded, the clusters are sorted by their weights and the smallest clusters are cropped.
Declaration
Objective-C
- (void)setMaxClustersCount:(int)maxClustersCount;
Swift
func setMaxClustersCount(maxClustersCount: Int32)
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Sets sampling points used to sample the input image.
Note
Number of sampling points must be greater or equal to clusterization seed count.Declaration
Objective-C
- (void)setSamplingPoints:(nonnull NSArray<Point2f *> *)samplingPoints;
Swift
func setSamplingPoints(samplingPoints: [Point2f])
Parameters
samplingPoints
Vector of sampling points in range [0..1)
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Translations of the individual axes of the feature space.
Declaration
Objective-C
- (void)setTranslation:(int)idx value:(float)value;
Swift
func setTranslation(idx: Int32, value: Float)
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;
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Translations of the individual axes of the feature space.
Declaration
Objective-C
- (void)setTranslations:(nonnull FloatVector *)translations;
Swift
func setTranslations(translations: FloatVector)
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;
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Weights (multiplicative constants) that linearly stretch individual axes of the feature space.
Declaration
Objective-C
- (void)setWeight:(int)idx value:(float)value;
Swift
func setWeight(idx: Int32, value: Float)
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;
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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)
Declaration
Objective-C
- (void)setWeightA:(float)weight;
Swift
func setWeightA(weight: Float)
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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)
Declaration
Objective-C
- (void)setWeightB:(float)weight;
Swift
func setWeightB(weight: Float)
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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)
Declaration
Objective-C
- (void)setWeightContrast:(float)weight;
Swift
func setWeightContrast(weight: Float)
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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)
Declaration
Objective-C
- (void)setWeightEntropy:(float)weight;
Swift
func setWeightEntropy(weight: Float)
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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)
Declaration
Objective-C
- (void)setWeightL:(float)weight;
Swift
func setWeightL(weight: Float)
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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)
Declaration
Objective-C
- (void)setWeightX:(float)weight;
Swift
func setWeightX(weight: Float)
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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)
Declaration
Objective-C
- (void)setWeightY:(float)weight;
Swift
func setWeightY(weight: Float)
-
Weights (multiplicative constants) that linearly stretch individual axes of the feature space.
Declaration
Objective-C
- (void)setWeights:(nonnull FloatVector *)weights;
Swift
func setWeights(weights: FloatVector)
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;
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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).
Declaration
Objective-C
- (void)setWindowRadius:(int)radius;
Swift
func setWindowRadius(radius: Int32)