Creates PCTSignatures algorithm

obj = cv.PCTSignatures()
obj = cv.PCTSignatures('OptionName',optionValue, ...)

obj = cv.PCTSignatures(initSamplingPoints, initSeedCount)

obj = cv.PCTSignatures(initSamplingPoints, initClusterSeedIndexes)

Input

• initSamplingPoints Sampling points used in image sampling.
• initSeedCount Number of initial clusterization seeds. Must be lower or equal to length(initSamplingPoints).
• initClusterSeedIndexes Indexes of initial clusterization seeds. Its size must be lower or equal to length(initSamplingPoints).

Options (first variant)

• InitSampleCount Number of points used for image sampling. default 2000
• InitSeedCount Number of initial clusterization seeds. Must be lower or equal to InitSampleCount. default 400
• PointDistribution Distribution of generated points. Available:
  • Uniform (default) Generate numbers uniformly.
  • Regular Generate points in a regular grid.
  • Normal Generate points with normal (gaussian) distribution.

In the first variant, it creates PCTSignatures algorithm using sample and seed count. It generates its own sets of sampling points and clusterization seed indexes.

In the second variant, it 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.

In the third variant, it creates PCTSignatures algorithm using pre-generated sampling points and clusterization seeds indexes.