This class is presented high-level API for neural networks.
More...
#include <opencv2/dnn/dnn.hpp>
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.
◆ Model() [1/5]
cv::dnn::Model::Model |
( |
| ) |
|
Python: |
---|
| cv.dnn.Model( | model[, config] | ) -> | <dnn_Model object> |
| cv.dnn.Model( | network | ) -> | <dnn_Model object> |
◆ Model() [2/5]
cv::dnn::Model::Model |
( |
const Model & |
| ) |
|
|
default |
Python: |
---|
| cv.dnn.Model( | model[, config] | ) -> | <dnn_Model object> |
| cv.dnn.Model( | network | ) -> | <dnn_Model object> |
◆ Model() [3/5]
cv::dnn::Model::Model |
( |
Model && |
| ) |
|
|
default |
Python: |
---|
| cv.dnn.Model( | model[, config] | ) -> | <dnn_Model object> |
| cv.dnn.Model( | network | ) -> | <dnn_Model object> |
◆ Model() [4/5]
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
-
[in] | model | Binary file contains trained weights. |
[in] | config | Text file contains network configuration. |
◆ Model() [5/5]
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
-
◆ enableWinograd()
Model & cv::dnn::Model::enableWinograd |
( |
bool |
useWinograd | ) |
|
Python: |
---|
| cv.dnn.Model.enableWinograd( | useWinograd | ) -> | retval |
◆ getImpl()
Impl * cv::dnn::Model::getImpl |
( |
| ) |
const |
|
inline |
◆ getImplRef()
Impl & cv::dnn::Model::getImplRef |
( |
| ) |
const |
|
inline |
◆ getNetwork_() [1/2]
Net & cv::dnn::Model::getNetwork_ |
( |
| ) |
|
|
inline |
◆ getNetwork_() [2/2]
Net & cv::dnn::Model::getNetwork_ |
( |
| ) |
const |
◆ operator Net &()
cv::dnn::Model::operator Net & |
( |
| ) |
const |
|
inline |
◆ operator=() [1/2]
Model & cv::dnn::Model::operator= |
( |
const Model & |
| ) |
|
|
default |
◆ operator=() [2/2]
◆ predict()
Python: |
---|
| cv.dnn.Model.predict( | frame[, outs] | ) -> | outs |
Given the input
frame, create input blob, run net and return the output blobs
.
- Parameters
-
[in] | frame | The input image. |
[out] | 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
-
[in] | 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
-
[in] | 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]]]]] | ) -> | None |
Set preprocessing parameters for frame.
- Parameters
-
[in] | size | New input size. |
[in] | mean | Scalar with mean values which are subtracted from channels. |
[in] | scale | Multiplier for frame values. |
[in] | swapRB | Flag which indicates that swap first and last channels. |
[in] | 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
-
[in] | scale | Multiplier for frame values. |
◆ setInputSize() [1/2]
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.
- Parameters
-
- Note
- If shape of the new blob less than 0, then frame size not change.
◆ setInputSize() [2/2]
Model & cv::dnn::Model::setInputSize |
( |
int |
width, |
|
|
int |
height |
|
) |
| |
|
inline |
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
-
[in] | width | New input width. |
[in] | height | New input height. |
◆ setInputSwapRB()
Model & cv::dnn::Model::setInputSwapRB |
( |
bool |
swapRB | ) |
|
Python: |
---|
| cv.dnn.Model.setInputSwapRB( | swapRB | ) -> | retval |
Set flag swapRB for frame.
- Parameters
-
[in] | 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
-
[in] | outNames | Names for output layers. |
◆ setPreferableBackend()
Python: |
---|
| cv.dnn.Model.setPreferableBackend( | backendId | ) -> | retval |
◆ setPreferableTarget()
Python: |
---|
| cv.dnn.Model.setPreferableTarget( | targetId | ) -> | retval |
◆ impl
Ptr<Impl> cv::dnn::Model::impl |
|
protected |
The documentation for this class was generated from the following file: