org.bytedeco.opencv.opencv_dnn

Class Model

java.lang.Object

org.bytedeco.javacpp.Pointer

org.bytedeco.opencv.opencv_dnn.Model

All Implemented Interfaces:

AutoCloseable

Direct Known Subclasses:

ClassificationModel, DetectionModel, KeypointsModel, SegmentationModel, TextDetectionModel, TextRecognitionModel

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

\brief This class is presented high-level API for neural networks. Model allows to set params for preprocessing input image. Model creates net from file with trained weights and config, sets preprocessing input and runs forward pass.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	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
Model() Deprecated.
Model(BytePointer model)
Model(BytePointer model, BytePointer config) \brief Create model from deep learning network represented in one of the supported formats.
Model(long size) Native array allocator.
Model(Model arg0)
Model(Net network) \brief Create model from deep learning network.
Model(Pointer p) Pointer cast constructor.
Model(String model)
Model(String model, String config)

Method Summary

Modifier and Type	Method and Description
Net	asNet() Deprecated.
Model	enableWinograd(boolean useWinograd)
Model.Impl	getImpl()
Model.Impl	getImplRef()
Net	getNetwork_()
Model	getPointer(long i)
Model	position(long position)
void	predict(GpuMat frame, GpuMatVector outs)
void	predict(GpuMat frame, MatVector outs)
void	predict(GpuMat frame, UMatVector outs)
void	predict(Mat frame, GpuMatVector outs)
void	predict(Mat frame, MatVector outs) \brief Given the \p input frame, create input blob, run net and return the output \p blobs.
void	predict(Mat frame, UMatVector outs)
void	predict(UMat frame, GpuMatVector outs)
void	predict(UMat frame, MatVector outs)
void	predict(UMat frame, UMatVector outs)
Model	put(Model arg0)
Model	setInputCrop(boolean crop) \brief Set flag crop for frame.
Model	setInputMean(Scalar mean) \brief Set mean value for frame.
void	setInputParams()
void	setInputParams(double scale, Size size, Scalar mean, boolean swapRB, boolean crop) \brief Set preprocessing parameters for frame.
Model	setInputScale(Scalar scale) \brief Set scalefactor value for frame.
Model	setInputSize(int width, int height) \overload
Model	setInputSize(Size size) \brief Set input size for frame.
Model	setInputSwapRB(boolean swapRB) \brief Set flag swapRB for frame.
Model	setOutputNames(StringVector outNames) \brief Set output names for frame.
Model	setPreferableBackend(int backendId)
Model	setPreferableTarget(int targetId)

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, getDirectBufferAddress, 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

Model

public Model(Pointer p)

Pointer cast constructor. Invokes Pointer(Pointer).

Model

public Model(long size)

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

Model

@Deprecated
public Model()

Deprecated.

Model

public Model(@Const @ByRef
             Model arg0)

Model

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

\brief Create model from deep learning 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.

Model

public Model(@opencv_core.Str
             BytePointer model)

Model

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

Model

public Model(@opencv_core.Str
             String model)

Model

public Model(@Const @ByRef
             Net network)

\brief Create model from deep learning network.

Parameters:

network - [in] Net object.

Method Detail

position

public Model position(long position)

Overrides:

position in class Pointer

getPointer

public Model getPointer(long i)

Overrides:

getPointer in class Pointer

put

@ByRef
 @Name(value="operator =")
public Model put(@Const @ByRef
                                                     Model arg0)

setInputSize

@ByRef
public Model setInputSize(@Const @ByRef
                                  Size size)

\brief Set input size for frame.

Parameters:

size - [in] New input size. \note If shape of the new blob less than 0, then frame size not change.

setInputSize

@ByRef
public Model setInputSize(int width,
                                  int height)

\overload

Parameters:

width - [in] New input width.

height - [in] New input height.

setInputMean

@ByRef
public Model setInputMean(@Const @ByRef
                                  Scalar mean)

\brief Set mean value for frame.

Parameters:

mean - [in] Scalar with mean values which are subtracted from channels.

setInputScale

@ByRef
public Model setInputScale(@Const @ByRef
                                   Scalar scale)

\brief Set scalefactor value for frame.

Parameters:

scale - [in] Multiplier for frame values.

setInputCrop

@ByRef
public Model setInputCrop(@Cast(value="bool")
                                  boolean crop)

\brief Set flag crop for frame.

Parameters:

crop - [in] Flag which indicates whether image will be cropped after resize or not.

setInputSwapRB

@ByRef
public Model setInputSwapRB(@Cast(value="bool")
                                    boolean swapRB)

\brief Set flag swapRB for frame.

Parameters:

swapRB - [in] Flag which indicates that swap first and last channels.

setOutputNames

@ByRef
public Model setOutputNames(@Const @ByRef
                                    StringVector outNames)

\brief Set output names for frame.

Parameters:

outNames - [in] Names for output layers.

setInputParams

public void setInputParams(double scale,
                           @Const @ByRef(nullValue="cv::Size()")
                           Size size,
                           @Const @ByRef(nullValue="cv::Scalar()")
                           Scalar mean,
                           @Cast(value="bool")
                           boolean swapRB,
                           @Cast(value="bool")
                           boolean crop)

\brief Set preprocessing parameters for frame.

Parameters:

size - [in] New input size.

mean - [in] Scalar with mean values which are subtracted from channels.

scale - [in] Multiplier for frame values.

swapRB - [in] Flag which indicates that swap first and last channels.

crop - [in] Flag which indicates whether image will be cropped after resize or not. blob(n, c, y, x) = scale * resize( frame(y, x, c) ) - mean(c) )

setInputParams

public void setInputParams()

predict

public void predict(@ByVal
                    Mat frame,
                    @ByVal
                    MatVector outs)

\brief Given the \p input frame, create input blob, run net and return the output \p blobs.

Parameters:

frame - [in] The input image.

outs - [out] Allocated output blobs, which will store results of the computation.

predict

public void predict(@ByVal
                    Mat frame,
                    @ByVal
                    UMatVector outs)

predict

public void predict(@ByVal
                    Mat frame,
                    @ByVal
                    GpuMatVector outs)

predict

public void predict(@ByVal
                    UMat frame,
                    @ByVal
                    MatVector outs)

predict

public void predict(@ByVal
                    UMat frame,
                    @ByVal
                    UMatVector outs)

predict

public void predict(@ByVal
                    UMat frame,
                    @ByVal
                    GpuMatVector outs)

predict

public void predict(@ByVal
                    GpuMat frame,
                    @ByVal
                    MatVector outs)

predict

public void predict(@ByVal
                    GpuMat frame,
                    @ByVal
                    UMatVector outs)

predict

public void predict(@ByVal
                    GpuMat frame,
                    @ByVal
                    GpuMatVector outs)

setPreferableBackend

@ByRef
public Model setPreferableBackend(@Cast(value="cv::dnn::Backend")
                                          int backendId)

See Also:

Net::setPreferableBackend

setPreferableTarget

@ByRef
public Model setPreferableTarget(@Cast(value="cv::dnn::Target")
                                         int targetId)

See Also:

Net::setPreferableTarget

enableWinograd

@ByRef
public Model enableWinograd(@Cast(value="bool")
                                    boolean useWinograd)

See Also:

Net::enableWinograd

asNet

@Deprecated
 @ByRef
 @Name(value="operator cv::dnn::Net&")
public Net asNet()

Deprecated.

getNetwork_

@ByRef
public Net getNetwork_()

getImpl

public Model.Impl getImpl()

getImplRef

@ByRef
public Model.Impl getImplRef()