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
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Raising version of clear/1
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Raising version of cluster/1
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Raising version of cluster/2
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Raising version of descriptorsCount/1
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Raising version of getDescriptors/1
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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
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
.
Raising version of cluster/2
.
Raising version of descriptorsCount/1
.
Raising version of getDescriptors/1
.