org.bytedeco.opencv.opencv_dnn

Class KeypointsModel

java.lang.Object

org.bytedeco.javacpp.Pointer

org.bytedeco.opencv.opencv_dnn.Model

org.bytedeco.opencv.opencv_dnn.KeypointsModel

All Implemented Interfaces:

AutoCloseable

@Namespace(value="cv::dnn")
 @Properties(inherit=opencv_dnn.class)
public class KeypointsModel
extends Model

\brief This class represents high-level API for keypoints models KeypointsModel allows to set params for preprocessing input image. KeypointsModel creates net from file with trained weights and config, sets preprocessing input, runs forward pass and returns the x and y coordinates of each detected keypoint

Nested Class Summary

Nested classes/interfaces inherited from class org.bytedeco.opencv.opencv_dnn.Model

Model.Impl

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
KeypointsModel(BytePointer model)
KeypointsModel(BytePointer model, BytePointer config) \brief Create keypoints model from network represented in one of the supported formats.
KeypointsModel(Net network) \brief Create model from deep learning network.
KeypointsModel(Pointer p) Pointer cast constructor.
KeypointsModel(String model)
KeypointsModel(String model, String config)

Method Summary

Modifier and Type	Method and Description
Point2fVector	estimate(GpuMat frame)
Point2fVector	estimate(GpuMat frame, float thresh)
Point2fVector	estimate(Mat frame)
Point2fVector	estimate(Mat frame, float thresh) \brief Given the \p input frame, create input blob, run net
Point2fVector	estimate(UMat frame)
Point2fVector	estimate(UMat frame, float thresh)

Methods inherited from class org.bytedeco.opencv.opencv_dnn.Model

asNet, getImpl, getImplRef, getNetwork_, getPointer, position, predict, predict, predict, predict, predict, predict, predict, predict, predict, put, setInputCrop, setInputMean, setInputParams, setInputParams, setInputScale, setInputSize, setInputSize, setInputSwapRB, setPreferableBackend, setPreferableTarget

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, getPointer, getPointer, getPointer, hashCode, interruptDeallocatorThread, isNull, isNull, limit, limit, malloc, maxBytes, maxPhysicalBytes, memchr, memcmp, memcpy, memmove, memset, offsetAddress, offsetof, offsetof, parseBytes, physicalBytes, physicalBytesInaccurate, position, put, realloc, referenceCount, releaseReference, retainReference, setNull, sizeof, sizeof, toString, totalBytes, totalCount, totalPhysicalBytes, withDeallocator, zero

Methods inherited from class java.lang.Object

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

Constructor Detail

KeypointsModel

public KeypointsModel(Pointer p)

Pointer cast constructor. Invokes Pointer(Pointer).

KeypointsModel

public KeypointsModel(@opencv_core.Str
                      BytePointer model,
                      @opencv_core.Str
                      BytePointer config)

\brief Create keypoints model from network represented in one of the supported formats. An order of \p model and \p config arguments does not matter.

Parameters:

model - [in] Binary file contains trained weights.

config - [in] Text file contains network configuration.

KeypointsModel

public KeypointsModel(@opencv_core.Str
                      BytePointer model)

KeypointsModel

public KeypointsModel(@opencv_core.Str
                      String model,
                      @opencv_core.Str
                      String config)

KeypointsModel

public KeypointsModel(@opencv_core.Str
                      String model)

KeypointsModel

public KeypointsModel(@Const @ByRef
                      Net network)

\brief Create model from deep learning network.

Parameters:

network - [in] Net object.

Method Detail

estimate

@ByVal
public Point2fVector estimate(@ByVal
                                     Mat frame,
                                     float thresh)

\brief Given the \p input frame, create input blob, run net

Parameters:

frame - [in] The input image.

thresh - minimum confidence threshold to select a keypoint

Returns:

a vector holding the x and y coordinates of each detected keypoint

estimate

@ByVal
public Point2fVector estimate(@ByVal
                                     Mat frame)

estimate

@ByVal
public Point2fVector estimate(@ByVal
                                     UMat frame,
                                     float thresh)

estimate

@ByVal
public Point2fVector estimate(@ByVal
                                     UMat frame)

estimate

@ByVal
public Point2fVector estimate(@ByVal
                                     GpuMat frame,
                                     float thresh)

estimate

@ByVal
public Point2fVector estimate(@ByVal
                                     GpuMat frame)