OpenCV for Unity
2.6.3
Enox Software / Please refer to OpenCV official document ( http://docs.opencv.org/4.10.0/index.html ) for the details of the argument of the method.
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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. More...
Public Member Functions | |
void | computeSignature (Mat image, Mat signature) |
Computes signature of given image. More... | |
void | computeSignatures (List< Mat > images, List< Mat > signatures) |
Computes signatures for multiple images in parallel. More... | |
int | getSampleCount () |
Number of initial samples taken from the image. More... | |
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. More... | |
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. More... | |
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). More... | |
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). More... | |
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) More... | |
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) More... | |
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) More... | |
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) More... | |
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) More... | |
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) More... | |
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) More... | |
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) More... | |
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) More... | |
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) More... | |
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) More... | |
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) More... | |
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) More... | |
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) More... | |
MatOfPoint2f | getSamplingPoints () |
Initial samples taken from the image. These sampled features become the input for clustering. More... | |
void | setWeight (int idx, float value) |
Weights (multiplicative constants) that linearly stretch individual axes of the feature space. More... | |
void | setWeights (MatOfFloat weights) |
Weights (multiplicative constants) that linearly stretch individual axes of the feature space. More... | |
void | setTranslation (int idx, float value) |
Translations of the individual axes of the feature space. More... | |
void | setTranslations (MatOfFloat translations) |
Translations of the individual axes of the feature space. More... | |
void | setSamplingPoints (MatOfPoint2f samplingPoints) |
Sets sampling points used to sample the input image. More... | |
MatOfInt | getInitSeedIndexes () |
Initial seeds (initial number of clusters) for the k-means algorithm. More... | |
void | setInitSeedIndexes (MatOfInt initSeedIndexes) |
Initial seed indexes for the k-means algorithm. More... | |
int | getInitSeedCount () |
Number of initial seeds (initial number of clusters) for the k-means algorithm. More... | |
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). More... | |
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). More... | |
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. More... | |
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. More... | |
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. More... | |
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. More... | |
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. More... | |
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. More... | |
float | getDropThreshold () |
Remove centroids in k-means whose weight is lesser or equal to given threshold. More... | |
void | setDropThreshold (float dropThreshold) |
Remove centroids in k-means whose weight is lesser or equal to given threshold. More... | |
int | getDistanceFunction () |
Distance function selector used for measuring distance between two points in k-means. More... | |
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. More... | |
Public Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm | |
IntPtr | getNativeObjAddr () |
virtual void | clear () |
Clears the algorithm state. More... | |
virtual bool | empty () |
Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read. More... | |
void | save (string filename) |
virtual string | getDefaultName () |
Public Member Functions inherited from OpenCVForUnity.DisposableObject | |
void | Dispose () |
void | ThrowIfDisposed () |
Static Public Member Functions | |
static new PCTSignatures | __fromPtr__ (IntPtr addr) |
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. More... | |
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. More... | |
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. More... | |
static PCTSignatures | create () |
Creates PCTSignatures algorithm using sample and seed count. It generates its own sets of sampling points and clusterization seed indexes. More... | |
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. More... | |
static PCTSignatures | create (MatOfPoint2f initSamplingPoints, MatOfInt initClusterSeedIndexes) |
Creates PCTSignatures algorithm using pre-generated sampling points and clusterization seeds indexes. More... | |
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. More... | |
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. More... | |
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. More... | |
static void | generateInitPoints (MatOfPoint2f initPoints, int count, int pointDistribution) |
Generates initial sampling points according to selected point distribution. More... | |
Static Public Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm | |
static Algorithm | __fromPtr__ (IntPtr addr) |
Static Public Member Functions inherited from OpenCVForUnity.DisposableObject | |
static IntPtr | ThrowIfNullIntPtr (IntPtr ptr) |
Public Attributes | |
const int | L0_25 = 0 |
const int | L0_5 = 1 |
const int | L1 = 2 |
const int | L2 = 3 |
const int | L2SQUARED = 4 |
const int | L5 = 5 |
const int | L_INFINITY = 6 |
const int | UNIFORM = 0 |
const int | REGULAR = 1 |
const int | NORMAL = 2 |
const int | MINUS = 0 |
const int | GAUSSIAN = 1 |
const int | HEURISTIC = 2 |
Protected Member Functions | |
override void | Dispose (bool disposing) |
Protected Member Functions inherited from OpenCVForUnity.DisposableOpenCVObject | |
DisposableOpenCVObject () | |
DisposableOpenCVObject (IntPtr ptr) | |
DisposableOpenCVObject (bool isEnabledDispose) | |
DisposableOpenCVObject (IntPtr ptr, bool isEnabledDispose) | |
Protected Member Functions inherited from OpenCVForUnity.DisposableObject | |
DisposableObject () | |
DisposableObject (bool isEnabledDispose) | |
Additional Inherited Members | |
Properties inherited from OpenCVForUnity.DisposableObject | |
bool | IsDisposed [get, protected set] |
bool | IsEnabledDispose [get, set] |
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]
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Computes signature of given image.
image | Input image of CV_8U type. |
signature | Output computed signature. |
void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.computeSignatures | ( | List< Mat > | images, |
List< Mat > | signatures | ||
) |
Computes signatures for multiple images in parallel.
images | Vector of input images of CV_8U type. |
signatures | Vector of computed signatures. |
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static |
Creates PCTSignatures algorithm using sample and seed count. It generates its own sets of sampling points and clusterization seed indexes.
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. |
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static |
Creates PCTSignatures algorithm using sample and seed count. It generates its own sets of sampling points and clusterization seed indexes.
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. |
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static |
Creates PCTSignatures algorithm using sample and seed count. It generates its own sets of sampling points and clusterization seed indexes.
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. |
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static |
Creates PCTSignatures algorithm using sample and seed count. It generates its own sets of sampling points and clusterization seed indexes.
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. |
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static |
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.
initSamplingPoints | Sampling points used in image sampling. |
initSeedCount | Number of initial clusterization seeds. Must be lower or equal to initSamplingPoints.size(). |
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static |
Creates PCTSignatures algorithm using pre-generated sampling points and clusterization seeds indexes.
initSamplingPoints | Sampling points used in image sampling. |
initClusterSeedIndexes | Indexes of initial clusterization seeds. Its size must be lower or equal to initSamplingPoints.size(). |
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protectedvirtual |
Reimplemented from OpenCVForUnity.CoreModule.Algorithm.
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static |
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.
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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static |
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.
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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static |
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.
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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static |
Generates initial sampling points according to selected point distribution.
initPoints | Output vector where the generated points will be saved. |
count | Number of points to generate. |
pointDistribution | Point distribution selector. Available: UNIFORM, REGULAR, NORMAL. |
int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.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.
int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.getDistanceFunction | ( | ) |
Distance function selector used for measuring distance between two points in k-means.
float OpenCVForUnity.Xfeatures2dModule.PCTSignatures.getDropThreshold | ( | ) |
Remove centroids in k-means whose weight is lesser or equal to given threshold.
int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.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.
int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.getInitSeedCount | ( | ) |
Number of initial seeds (initial number of clusters) for the k-means algorithm.
MatOfInt OpenCVForUnity.Xfeatures2dModule.PCTSignatures.getInitSeedIndexes | ( | ) |
Initial seeds (initial number of clusters) for the k-means algorithm.
int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.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).
float OpenCVForUnity.Xfeatures2dModule.PCTSignatures.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.
int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.getMaxClustersCount | ( | ) |
Maximal number of generated clusters. If the number is exceeded, the clusters are sorted by their weights and the smallest clusters are cropped.
int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.getSampleCount | ( | ) |
Number of initial samples taken from the image.
MatOfPoint2f OpenCVForUnity.Xfeatures2dModule.PCTSignatures.getSamplingPoints | ( | ) |
Initial samples taken from the image. These sampled features become the input for clustering.
float OpenCVForUnity.Xfeatures2dModule.PCTSignatures.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)
float OpenCVForUnity.Xfeatures2dModule.PCTSignatures.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)
float OpenCVForUnity.Xfeatures2dModule.PCTSignatures.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)
float OpenCVForUnity.Xfeatures2dModule.PCTSignatures.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)
float OpenCVForUnity.Xfeatures2dModule.PCTSignatures.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)
float OpenCVForUnity.Xfeatures2dModule.PCTSignatures.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)
float OpenCVForUnity.Xfeatures2dModule.PCTSignatures.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)
int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.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 OpenCVForUnity.Xfeatures2dModule.PCTSignatures.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.
void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.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.
void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.setDropThreshold | ( | float | dropThreshold | ) |
Remove centroids in k-means whose weight is lesser or equal to given threshold.
void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.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.
void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.setInitSeedIndexes | ( | MatOfInt | initSeedIndexes | ) |
Initial seed indexes for the k-means algorithm.
void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.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).
void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.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.
void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.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.
void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.setSamplingPoints | ( | MatOfPoint2f | samplingPoints | ) |
Sets sampling points used to sample the input image.
samplingPoints | Vector of sampling points in range [0..1) |
void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.setTranslation | ( | int | idx, |
float | value | ||
) |
Translations of the individual axes of the feature space.
idx | ID of the translation |
value | Value of the translation |
void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.setTranslations | ( | MatOfFloat | translations | ) |
Translations of the individual axes of the feature space.
translations | Values of all translations. |
void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.setWeight | ( | int | idx, |
float | value | ||
) |
Weights (multiplicative constants) that linearly stretch individual axes of the feature space.
idx | ID of the weight |
value | Value of the weight |
void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.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)
void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.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)
void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.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)
void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.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)
void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.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)
void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.setWeights | ( | MatOfFloat | weights | ) |
Weights (multiplicative constants) that linearly stretch individual axes of the feature space.
weights | Values of all weights. |
void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.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)
void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.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)
void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.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).
const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.GAUSSIAN = 1 |
const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.HEURISTIC = 2 |
const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.L0_25 = 0 |
const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.L0_5 = 1 |
const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.L1 = 2 |
const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.L2 = 3 |
const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.L2SQUARED = 4 |
const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.L5 = 5 |
const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.L_INFINITY = 6 |
const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.MINUS = 0 |
const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.NORMAL = 2 |
const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.REGULAR = 1 |
const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.UNIFORM = 0 |