This class represents high-level API for classification models.
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#include <opencv2/dnn/dnn.hpp>
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| ClassificationModel (const String &model, const String &config="") |
| Create classification model from network represented in one of the supported formats. An order of model and config arguments does not matter. More...
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| ClassificationModel (const Net &network) |
| Create model from deep learning network. More...
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std::pair< int, float > | classify (InputArray frame) |
| Given the input frame, create input blob, run net and return top-1 prediction. More...
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void | classify (InputArray frame, int &classId, float &conf) |
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| Model () |
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| Model (const Model &)=default |
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| Model (Model &&)=default |
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| Model (const String &model, 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. More...
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| Model (const Net &network) |
| Create model from deep learning network. More...
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Impl * | getImpl () const |
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Impl & | getImplRef () const |
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Net & | getNetwork_ () const |
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Net & | getNetwork_ () |
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| operator Net & () const |
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Model & | operator= (const Model &)=default |
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Model & | operator= (Model &&)=default |
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void | predict (InputArray frame, OutputArrayOfArrays outs) const |
| Given the input frame, create input blob, run net and return the output blobs . More...
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Model & | setInputCrop (bool crop) |
| Set flag crop for frame. More...
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Model & | setInputMean (const Scalar &mean) |
| Set mean value for frame. More...
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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. More...
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Model & | setInputScale (double scale) |
| Set scalefactor value for frame. More...
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Model & | setInputSize (const Size &size) |
| Set input size for frame. More...
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Model & | setInputSize (int width, int height) |
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Model & | setInputSwapRB (bool swapRB) |
| Set flag swapRB for frame. More...
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Model & | setPreferableBackend (dnn::Backend backendId) |
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Model & | setPreferableTarget (dnn::Target targetId) |
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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.
◆ ClassificationModel() [1/2]
cv::dnn::ClassificationModel::ClassificationModel |
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const String & |
model, |
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const String & |
config = "" |
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Python: |
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| 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
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[in] | model | Binary file contains trained weights. |
[in] | config | Text file contains network configuration. |
◆ ClassificationModel() [2/2]
cv::dnn::ClassificationModel::ClassificationModel |
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const Net & |
network | ) |
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Python: |
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| cv.dnn_ClassificationModel( | model[, config] | ) -> | <dnn_ClassificationModel object> |
| cv.dnn_ClassificationModel( | network | ) -> | <dnn_ClassificationModel object> |
Create model from deep learning network.
- Parameters
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◆ classify() [1/2]
std::pair<int, float> cv::dnn::ClassificationModel::classify |
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InputArray |
frame | ) |
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Python: |
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| cv.dnn_ClassificationModel.classify( | frame | ) -> | classId, conf |
Given the input
frame, create input blob, run net and return top-1 prediction.
- Parameters
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[in] | frame | The input image. |
◆ classify() [2/2]
void cv::dnn::ClassificationModel::classify |
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InputArray |
frame, |
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int & |
classId, |
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float & |
conf |
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Python: |
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| 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.
The documentation for this class was generated from the following file: