Class cv::dnn::ClassificationModel#

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

#include <opencv2/dnn/dnn.hpp>

Collaboration diagram for cv::dnn::ClassificationModel:

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 classification models.

ClassificationModel allows to set params for preprocessing input image. ClassificationModel creates net from file with trained weights and config, sets preprocessing input, runs forward pass and return top-1 prediction.

Constructor & Destructor Documentation#

ClassificationModel()#

cv::dnn::ClassificationModel::ClassificationModel()

Python:

cv.dnn.ClassificationModel(model[, config]) -> <dnn_ClassificationModel object>

cv.dnn.ClassificationModel(network) -> <dnn_ClassificationModel object>

ClassificationModel()#

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

Python:

cv.dnn.ClassificationModel(model[, config]) -> <dnn_ClassificationModel object>

cv.dnn.ClassificationModel(network) -> <dnn_ClassificationModel object>

Create model from deep learning network.

Parameters

network — Net object.

ClassificationModel()#

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

Python:

cv.dnn.ClassificationModel(model[, config]) -> <dnn_ClassificationModel object>

cv.dnn.ClassificationModel(network) -> <dnn_ClassificationModel object>

Create classification 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#

classify()#

std::pair< int, float > cv::dnn::ClassificationModel::classify(InputArray frame)

Python:

cv.dnn.ClassificationModel.classify(frame) -> classId, conf

Given the input frame, create input blob, run net and return top-1 prediction.

Parameters

frame — The input image.

classify()#

void cv::dnn::ClassificationModel::classify(
InputArray frame,
int & classId,
float & conf )

Python:

cv.dnn.ClassificationModel.classify(frame) -> classId, conf

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

getEnableSoftmaxPostProcessing()#

bool cv::dnn::ClassificationModel::getEnableSoftmaxPostProcessing()

Python:

cv.dnn.ClassificationModel.getEnableSoftmaxPostProcessing() -> retval

Get enable/disable softmax post processing option.

This option defaults to false, softmax post processing is not applied within the classify() function.

setEnableSoftmaxPostProcessing()#

ClassificationModel & cv::dnn::ClassificationModel::setEnableSoftmaxPostProcessing(bool enable)

Python:

cv.dnn.ClassificationModel.setEnableSoftmaxPostProcessing(enable) -> retval

Set enable/disable softmax post processing option.

If this option is true, softmax is applied after forward inference within the classify() function to convert the confidences range to [0.0-1.0]. This function allows you to toggle this behavior. Please turn true when not contain softmax layer in model.

Parameters

enable — Set enable softmax post processing within the classify() function.

Source file#

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

opencv2/dnn/dnn.hpp