Class cv::dnn::Model#

This class is presented high-level API for neural networks. View details

#include <opencv2/dnn/dnn.hpp>

Collaboration diagram for cv::dnn::Model:

Detailed Description#

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.

Examples: samples/dnn/classification.cpp, samples/dnn/object_detection.cpp, and samples/dnn/segmentation.cpp.

Constructor & Destructor Documentation#

Model()#

cv::dnn::Model::Model()

Python:

cv.dnn.Model(model[, config]) -> <dnn_Model object>

cv.dnn.Model(network) -> <dnn_Model object>

Model()#

cv::dnn::Model::Model(const Model &)

Python:

cv.dnn.Model(model[, config]) -> <dnn_Model object>

cv.dnn.Model(network) -> <dnn_Model object>

Model()#

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

Python:

cv.dnn.Model(model[, config]) -> <dnn_Model object>

cv.dnn.Model(network) -> <dnn_Model object>

Create model from deep learning network.

Parameters

network — Net object.

Model()#

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

Python:

cv.dnn.Model(model[, config]) -> <dnn_Model object>

cv.dnn.Model(network) -> <dnn_Model object>

Create model from deep learning 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.

Model()#

cv::dnn::Model::Model(Model &&)

Python:

cv.dnn.Model(model[, config]) -> <dnn_Model object>

cv.dnn.Model(network) -> <dnn_Model object>

Member Function Documentation#

enableWinograd()#

Model & cv::dnn::Model::enableWinograd(bool useWinograd)

Python:

cv.dnn.Model.enableWinograd(useWinograd) -> retval

getImpl()#

Impl * cv::dnn::Model::getImpl()

getImplRef()#

Impl & cv::dnn::Model::getImplRef()

getNetwork_()#

Net & cv::dnn::Model::getNetwork_()

Here is the call graph for this function:

getNetwork_()#

Net & cv::dnn::Model::getNetwork_()

operator Net &()#

cv::dnn::Model::operator Net &()

operator=()#

Model & cv::dnn::Model::operator=(const Model &)

operator=()#

Model & cv::dnn::Model::operator=(Model &&)

predict()#

void cv::dnn::Model::predict(
InputArray frame,
OutputArrayOfArrays outs )

Python:

cv.dnn.Model.predict(frame[, outs]) -> outs

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

Parameters

frame — The input image.
outs — Allocated output blobs, which will store results of the computation.

setInputCrop()#

Model & cv::dnn::Model::setInputCrop(bool crop)

Python:

cv.dnn.Model.setInputCrop(crop) -> retval

Set flag crop for frame.

Parameters

crop — Flag which indicates whether image will be cropped after resize or not.

setInputMean()#

Model & cv::dnn::Model::setInputMean(const Scalar & mean)

Python:

cv.dnn.Model.setInputMean(mean) -> retval

Set mean value for frame.

Parameters

mean — Scalar with mean values which are subtracted from channels.

setInputParams()#

void cv::dnn::Model::setInputParams(
double scale = 1.0,
const Size & size = Size(),
const Scalar & mean = Scalar(),
bool swapRB = false,
bool crop = false )

Python:

cv.dnn.Model.setInputParams([, scale[, size[, mean[, swapRB[, crop]]]]])

Set preprocessing parameters for frame.

Parameters

size — New input size.
mean — Scalar with mean values which are subtracted from channels.
scale — Multiplier for frame values.
swapRB — Flag which indicates that swap first and last channels.
crop — 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) )

setInputScale()#

Model & cv::dnn::Model::setInputScale(const Scalar & scale)

Python:

cv.dnn.Model.setInputScale(scale) -> retval

Set scalefactor value for frame.

Parameters

scale — Multiplier for frame values.

setInputSize()#

Model & cv::dnn::Model::setInputSize(const Size & size)

Python:

cv.dnn.Model.setInputSize(size) -> retval

cv.dnn.Model.setInputSize(width, height) -> retval

Set input size for frame.

Note

If shape of the new blob less than 0, then frame size not change.

Parameters

size — New input size.

setInputSize()#

Model & cv::dnn::Model::setInputSize(
int width,
int height )

Python:

cv.dnn.Model.setInputSize(size) -> retval

cv.dnn.Model.setInputSize(width, height) -> retval

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

Parameters

width — New input width.
height — New input height.

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setInputSwapRB()#

Model & cv::dnn::Model::setInputSwapRB(bool swapRB)

Python:

cv.dnn.Model.setInputSwapRB(swapRB) -> retval

Set flag swapRB for frame.

Parameters

swapRB — Flag which indicates that swap first and last channels.

setOutputNames()#

Model & cv::dnn::Model::setOutputNames(const std::vector< String > & outNames)

Python:

cv.dnn.Model.setOutputNames(outNames) -> retval

Set output names for frame.

Parameters

outNames — Names for output layers.

setPreferableBackend()#

Model & cv::dnn::Model::setPreferableBackend(dnn::Backend backendId)

Python:

cv.dnn.Model.setPreferableBackend(backendId) -> retval

setPreferableTarget()#

Model & cv::dnn::Model::setPreferableTarget(dnn::Target targetId)

Python:

cv.dnn.Model.setPreferableTarget(targetId) -> retval

Member Data Documentation#

impl#

Ptr< Impl > cv::dnn::Model::impl

Source file#

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

opencv2/dnn/dnn.hpp