Class cv::dnn::SegmentationModel#

This class represents high-level API for segmentation models. View details

#include <opencv2/dnn/dnn.hpp>

Collaboration diagram for cv::dnn::SegmentationModel:

Public Member Functions#

Public Member Functions inherited from cv::dnn::Model

Return	Name	Description
	`Model()`
	`Model(const Model &)`
	`Model(const Net & network)`	Create model from deep learning network.
	`Model(` `CV_WRAP_FILE_PATH const String & model,` `CV_WRAP_FILE_PATH const String & config = "" )`	Create model from deep learning network represented in one of the supported formats. An order of model and config arguments does not matter.
	`Model(Model &&)`
`Model &`	`enableWinograd(bool useWinograd)`
`Impl *`	`getImpl()`
`Impl &`	`getImplRef()`
`Net &`	`getNetwork_()`
`Net &`	`getNetwork_()`
	`operator Net &()`
`Model &`	`operator=(const Model &)`
`Model &`	`operator=(Model &&)`
`void`	`predict(` `InputArray frame,` `OutputArrayOfArrays outs )`	Given the input frame, create input blob, run net and return the output blobs.
`Model &`	`setInputCrop(bool crop)`	Set flag crop for frame.
`Model &`	`setInputMean(const Scalar & mean)`	Set mean value for frame.
`void`	`setInputParams(` `double scale = 1.0,` `const Size & size = Size(),` `const Scalar & mean = Scalar(),` `bool swapRB = false,` `bool crop = false )`	Set preprocessing parameters for frame.
`Model &`	`setInputScale(const Scalar & scale)`	Set scalefactor value for frame.
`Model &`	`setInputSize(const Size & size)`	Set input size for frame.
`Model &`	`setInputSize(` `int width,` `int height )`
`Model &`	`setInputSwapRB(bool swapRB)`	Set flag swapRB for frame.
`Model &`	`setOutputNames(const std::vector< String > & outNames)`	Set output names for frame.
`Model &`	`setPreferableBackend(dnn::Backend backendId)`
`Model &`	`setPreferableTarget(dnn::Target targetId)`

Additional Inherited Members#

Detailed Description#

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.

Constructor & Destructor Documentation#

SegmentationModel()#

cv::dnn::SegmentationModel::SegmentationModel(const Net & network)

Python:

cv.dnn.SegmentationModel(model[, config]) -> <dnn_SegmentationModel object>

cv.dnn.SegmentationModel(network) -> <dnn_SegmentationModel object>

Create model from deep learning network.

Parameters

network — Net object.

SegmentationModel()#

cv::dnn::SegmentationModel::SegmentationModel(
CV_WRAP_FILE_PATH const String & model,
CV_WRAP_FILE_PATH const String & config = “” )

Python:

cv.dnn.SegmentationModel(model[, config]) -> <dnn_SegmentationModel object>

cv.dnn.SegmentationModel(network) -> <dnn_SegmentationModel object>

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

Parameters

model — Binary file contains trained weights.
config — Text file contains network configuration.

Member Function Documentation#

segment()#

void cv::dnn::SegmentationModel::segment(
InputArray frame,
OutputArray mask )

Python:

cv.dnn.SegmentationModel.segment(frame[, mask]) -> mask

Given the input frame, create input blob, run net.

Parameters

frame — The input image.
mask — Allocated class prediction for each pixel

Source file#

The documentation for this class was generated from the following file:

opencv2/dnn/dnn.hpp