org.bytedeco.opencv.opencv_dnn

Class SegmentationModel

java.lang.Object

org.bytedeco.javacpp.Pointer

org.bytedeco.opencv.opencv_dnn.Model

org.bytedeco.opencv.opencv_dnn.SegmentationModel

All Implemented Interfaces:

AutoCloseable

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

\brief This class represents high-level API for segmentation models SegmentationModel allows to set params for preprocessing input image. SegmentationModel creates net from file with trained weights and config, sets preprocessing input, runs forward pass and returns the class prediction for each pixel.

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

Method Summary

Modifier and Type	Method and Description
void	segment(GpuMat frame, GpuMat mask)
void	segment(Mat frame, Mat mask) \brief Given the \p input frame, create input blob, run net
void	segment(UMat frame, UMat mask)

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, isNull, isNull, limit, limit, malloc, maxBytes, maxPhysicalBytes, memchr, memcmp, memcpy, memmove, memset, offsetAddress, offsetof, offsetof, parseBytes, physicalBytes, 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

SegmentationModel

public SegmentationModel(Pointer p)

Pointer cast constructor. Invokes Pointer(Pointer).

SegmentationModel

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

\brief Create segmentation 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.

SegmentationModel

public SegmentationModel(@opencv_core.Str
                         BytePointer model)

SegmentationModel

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

SegmentationModel

public SegmentationModel(@opencv_core.Str
                         String model)

SegmentationModel

public SegmentationModel(@Const @ByRef
                         Net network)

\brief Create model from deep learning network.

Parameters:

network - [in] Net object.

Method Detail

segment

public void segment(@ByVal
                    Mat frame,
                    @ByVal
                    Mat mask)

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

Parameters:

frame - [in] The input image.

mask - [out] Allocated class prediction for each pixel

segment

public void segment(@ByVal
                    UMat frame,
                    @ByVal
                    UMat mask)

segment

public void segment(@ByVal
                    GpuMat frame,
                    @ByVal
                    GpuMat mask)