org.bytedeco.opencv.opencv_face

Class FacemarkKazemi.Params

java.lang.Object

org.bytedeco.javacpp.Pointer

org.bytedeco.opencv.opencv_face.FacemarkKazemi.Params

All Implemented Interfaces:

AutoCloseable

Enclosing class:

FacemarkKazemi

@NoOffset
public static class FacemarkKazemi.Params
extends Pointer

Nested Class Summary

Nested classes/interfaces inherited from class org.bytedeco.javacpp.Pointer

Pointer.CustomDeallocator, Pointer.Deallocator, Pointer.NativeDeallocator, Pointer.ReferenceCounter

Field Summary

Fields inherited from class org.bytedeco.javacpp.Pointer

address, capacity, limit, position

Constructor Summary

Constructors

Constructor and Description
Params() \brief Constructor
Params(long size) Native array allocator.
Params(Pointer p) Pointer cast constructor.

Method Summary

Modifier and Type	Method and Description
long	cascade_depth() cascade_depth This stores the deapth of cascade used for training.
FacemarkKazemi.Params	cascade_depth(long setter)
BytePointer	configfile() configfile stores the name of the file containing the values of training parameters
FacemarkKazemi.Params	configfile(BytePointer setter)
float	lambda() lambda stores a value to calculate probability of closeness of two coordinates.
FacemarkKazemi.Params	lambda(float setter)
float	learning_rate() learning_rate stores the learning rate in gradient boosting, also referred as shrinkage.
FacemarkKazemi.Params	learning_rate(float setter)
long	num_test_coordinates() num_test_coordinates stores number of test coordinates.
FacemarkKazemi.Params	num_test_coordinates(long setter)
long	num_test_splits() num_test_splits stores number of random test splits generated.
FacemarkKazemi.Params	num_test_splits(long setter)
long	num_trees_per_cascade_level() num_trees_per_cascade_level This stores number of trees fit per cascade level.
FacemarkKazemi.Params	num_trees_per_cascade_level(long setter)
long	oversampling_amount() oversampling_amount stores number of initialisations used to create training samples.
FacemarkKazemi.Params	oversampling_amount(long setter)
FacemarkKazemi.Params	position(long position)
long	tree_depth() tree_depth This stores the max height of the regression tree built.
FacemarkKazemi.Params	tree_depth(long setter)

Methods inherited from class org.bytedeco.javacpp.Pointer

address, asBuffer, asByteBuffer, availablePhysicalBytes, calloc, capacity, capacity, close, deallocate, deallocate, deallocateReferences, deallocator, deallocator, equals, fill, formatBytes, free, hashCode, isNull, isNull, limit, limit, malloc, maxBytes, maxPhysicalBytes, memchr, memcmp, memcpy, memmove, memset, offsetof, parseBytes, physicalBytes, position, put, realloc, referenceCount, releaseReference, retainReference, setNull, sizeof, toString, totalBytes, totalPhysicalBytes, withDeallocator, zero

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Constructor Detail

Params

public Params(Pointer p)

Pointer cast constructor. Invokes Pointer.Pointer(Pointer).

Params

public Params(long size)

Native array allocator. Access with Pointer.position(long).

Params

public Params()

\brief Constructor

Method Detail

position

public FacemarkKazemi.Params position(long position)

Overrides:

position in class Pointer

cascade_depth

@Cast(value="unsigned long")
public long cascade_depth()

cascade_depth This stores the deapth of cascade used for training.

cascade_depth

public FacemarkKazemi.Params cascade_depth(long setter)

tree_depth

@Cast(value="unsigned long")
public long tree_depth()

tree_depth This stores the max height of the regression tree built.

tree_depth

public FacemarkKazemi.Params tree_depth(long setter)

num_trees_per_cascade_level

@Cast(value="unsigned long")
public long num_trees_per_cascade_level()

num_trees_per_cascade_level This stores number of trees fit per cascade level.

num_trees_per_cascade_level

public FacemarkKazemi.Params num_trees_per_cascade_level(long setter)

learning_rate

public float learning_rate()

learning_rate stores the learning rate in gradient boosting, also referred as shrinkage.

learning_rate

public FacemarkKazemi.Params learning_rate(float setter)

oversampling_amount

@Cast(value="unsigned long")
public long oversampling_amount()

oversampling_amount stores number of initialisations used to create training samples.

oversampling_amount

public FacemarkKazemi.Params oversampling_amount(long setter)

num_test_coordinates

@Cast(value="unsigned long")
public long num_test_coordinates()

num_test_coordinates stores number of test coordinates.

num_test_coordinates

public FacemarkKazemi.Params num_test_coordinates(long setter)

lambda

public float lambda()

lambda stores a value to calculate probability of closeness of two coordinates.

lambda

public FacemarkKazemi.Params lambda(float setter)

num_test_splits

@Cast(value="unsigned long")
public long num_test_splits()

num_test_splits stores number of random test splits generated.

num_test_splits

public FacemarkKazemi.Params num_test_splits(long setter)

configfile

@opencv_core.Str
public BytePointer configfile()

configfile stores the name of the file containing the values of training parameters

configfile

public FacemarkKazemi.Params configfile(BytePointer setter)