View Source Evision.BOWTrainer (Evision v0.1.8)

Link to this section Summary

cv

Adds descriptors to a training set.

Python prototype (for reference):

Has overloading in C++

Clusters train descriptors.

Returns the count of all descriptors stored in the training set.

Returns a training set of descriptors.

Functions

Raising version of add/2.

Raising version of clear/1.

Raising version of cluster/1.

Link to this section cv

Adds descriptors to a training set.

Positional Arguments
  • descriptors: Evision.Mat.

    Descriptors to add to a training set. Each row of the descriptors matrix is a descriptor.

The training set is clustered using clustermethod to construct the vocabulary.

Python prototype (for reference):

add(descriptors) -> None

Python prototype (for reference):

clear() -> None

Has overloading in C++

Python prototype (for reference):

cluster() -> retval
Link to this function

cluster(self, descriptors)

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Clusters train descriptors.

Positional Arguments
  • descriptors: Evision.Mat.

    Descriptors to cluster. Each row of the descriptors matrix is a descriptor. Descriptors are not added to the inner train descriptor set.

The vocabulary consists of cluster centers. So, this method returns the vocabulary. In the first variant of the method, train descriptors stored in the object are clustered. In the second variant, input descriptors are clustered.

Python prototype (for reference):

cluster(descriptors) -> retval

Returns the count of all descriptors stored in the training set.

Python prototype (for reference):

descriptorsCount() -> retval

Returns a training set of descriptors.

Python prototype (for reference):

getDescriptors() -> retval

Link to this section Functions

Raising version of add/2.

Raising version of clear/1.

Raising version of cluster/1.

Link to this function

cluster!(self, descriptors)

View Source

Raising version of cluster/2.

Raising version of descriptorsCount/1.

Raising version of getDescriptors/1.