A class to upscale images via convolutional neural networks. The following four models are implemented:
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#include <opencv2/dnn_superres.hpp>
A class to upscale images via convolutional neural networks. The following four models are implemented:
◆ DnnSuperResImpl() [1/2]
cv::dnn_superres::DnnSuperResImpl::DnnSuperResImpl |
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◆ DnnSuperResImpl() [2/2]
cv::dnn_superres::DnnSuperResImpl::DnnSuperResImpl |
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const String & |
algo, |
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int |
scale |
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) |
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Constructor which immediately sets the desired model.
- Parameters
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algo | String containing one of the desired models:
|
scale | Integer specifying the upscale factor |
◆ create()
Python: |
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| cv.dnn_superres.DnnSuperResImpl.create( | | ) -> | retval |
| cv.dnn_superres.DnnSuperResImpl_create( | | ) -> | retval |
Empty constructor for python.
◆ getAlgorithm()
String cv::dnn_superres::DnnSuperResImpl::getAlgorithm |
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Python: |
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| cv.dnn_superres.DnnSuperResImpl.getAlgorithm( | | ) -> | retval |
Returns the scale factor of the model:
- Returns
- Current algorithm.
◆ getScale()
int cv::dnn_superres::DnnSuperResImpl::getScale |
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Python: |
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| cv.dnn_superres.DnnSuperResImpl.getScale( | | ) -> | retval |
Returns the scale factor of the model:
- Returns
- Current scale factor.
◆ readModel() [1/2]
void cv::dnn_superres::DnnSuperResImpl::readModel |
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const String & |
path | ) |
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Python: |
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| cv.dnn_superres.DnnSuperResImpl.readModel( | path | ) -> | None |
Read the model from the given path.
- Parameters
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path | Path to the model file. |
◆ readModel() [2/2]
void cv::dnn_superres::DnnSuperResImpl::readModel |
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const String & |
weights, |
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const String & |
definition |
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) |
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Python: |
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| cv.dnn_superres.DnnSuperResImpl.readModel( | path | ) -> | None |
Read the model from the given path.
- Parameters
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weights | Path to the model weights file. |
definition | Path to the model definition file. |
◆ setModel()
void cv::dnn_superres::DnnSuperResImpl::setModel |
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const String & |
algo, |
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int |
scale |
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) |
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Python: |
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| cv.dnn_superres.DnnSuperResImpl.setModel( | algo, scale | ) -> | None |
Set desired model.
- Parameters
-
algo | String containing one of the desired models:
|
scale | Integer specifying the upscale factor |
◆ setPreferableBackend()
void cv::dnn_superres::DnnSuperResImpl::setPreferableBackend |
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int |
backendId | ) |
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Python: |
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| cv.dnn_superres.DnnSuperResImpl.setPreferableBackend( | backendId | ) -> | None |
◆ setPreferableTarget()
void cv::dnn_superres::DnnSuperResImpl::setPreferableTarget |
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int |
targetId | ) |
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Python: |
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| cv.dnn_superres.DnnSuperResImpl.setPreferableTarget( | targetId | ) -> | None |
◆ upsample()
Python: |
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| cv.dnn_superres.DnnSuperResImpl.upsample( | img[, result] | ) -> | result |
Upsample via neural network.
- Parameters
-
img | Image to upscale |
result | Destination upscaled image |
◆ upsampleMultioutput()
void cv::dnn_superres::DnnSuperResImpl::upsampleMultioutput |
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InputArray |
img, |
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std::vector< Mat > & |
imgs_new, |
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const std::vector< int > & |
scale_factors, |
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const std::vector< String > & |
node_names |
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) |
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Python: |
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| cv.dnn_superres.DnnSuperResImpl.upsampleMultioutput( | img, imgs_new, scale_factors, node_names | ) -> | None |
Upsample via neural network of multiple outputs.
- Parameters
-
img | Image to upscale |
imgs_new | Destination upscaled images |
scale_factors | Scaling factors of the output nodes |
node_names | Names of the output nodes in the neural network |
The documentation for this class was generated from the following file: