#include <dnnbasedetectormodel.h>
◆ DNNBaseDetectorModel() [1/2]
Digikam::DNNBaseDetectorModel::DNNBaseDetectorModel |
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◆ DNNBaseDetectorModel() [2/2]
Digikam::DNNBaseDetectorModel::DNNBaseDetectorModel |
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float |
scale, |
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const cv::Scalar & |
val, |
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const cv::Size & |
inputImgSize |
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◆ ~DNNBaseDetectorModel()
virtual Digikam::DNNBaseDetectorModel::~DNNBaseDetectorModel |
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virtualdefault |
◆ detectObjects() [1/2]
virtual QHash< QString, QVector< QRect > > Digikam::DNNBaseDetectorModel::detectObjects |
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const cv::Mat & |
inputImage | ) |
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detectObjects return the predicted objects and localization as well (if we use deeplearning for object detection like YOLO, etc) otherwise the map whose the key is the objects name and their values are empty.
Referenced by generateObjects(), and generateObjects().
◆ detectObjects() [2/2]
QList< QHash< QString, QVector< QRect > > > Digikam::DNNBaseDetectorModel::detectObjects |
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const std::vector< cv::Mat > & |
inputBatchImages | ) |
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◆ generateObjects() [1/2]
QList< QString > Digikam::DNNBaseDetectorModel::generateObjects |
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const cv::Mat & |
inputImage | ) |
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◆ generateObjects() [2/2]
QList< QList< QString > > Digikam::DNNBaseDetectorModel::generateObjects |
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const std::vector< cv::Mat > & |
inputImage | ) |
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generateObjects in batch images return just the predicted objects without locations of objects using for the assignment tagging names.
References detectObjects().
◆ getinputImageSize()
cv::Size Digikam::DNNBaseDetectorModel::getinputImageSize |
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◆ getOutputsNames()
std::vector< cv::String > Digikam::DNNBaseDetectorModel::getOutputsNames |
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◆ getPredefinedClasses()
QList< QString > Digikam::DNNBaseDetectorModel::getPredefinedClasses |
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◆ loadDetectionClasses()
QList< QString > Digikam::DNNBaseDetectorModel::loadDetectionClasses |
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◆ loadModels()
virtual bool Digikam::DNNBaseDetectorModel::loadModels |
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◆ postprocess() [1/2]
virtual QHash< QString, QVector< QRect > > Digikam::DNNBaseDetectorModel::postprocess |
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const cv::Mat & |
inputImage, |
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const cv::Mat & |
out |
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◆ postprocess() [2/2]
QList< QHash< QString, QVector< QRect > > > Digikam::DNNBaseDetectorModel::postprocess |
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const std::vector< cv::Mat > & |
inputBatchImages, |
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const std::vector< cv::Mat > & |
outs |
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◆ preprocess() [1/2]
std::vector< cv::Mat > Digikam::DNNBaseDetectorModel::preprocess |
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const cv::Mat & |
inputImage | ) |
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◆ preprocess() [2/2]
std::vector< cv::Mat > Digikam::DNNBaseDetectorModel::preprocess |
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const std::vector< cv::Mat > & |
inputBatchImages | ) |
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◆ showInferenceTime()
double Digikam::DNNBaseDetectorModel::showInferenceTime |
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◆ inputImageSize
cv::Size Digikam::DNNBaseDetectorModel::inputImageSize |
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◆ meanValToSubtract
cv::Scalar Digikam::DNNBaseDetectorModel::meanValToSubtract |
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◆ model
DNNModelBase* Digikam::DNNBaseDetectorModel::model = nullptr |
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◆ nmsThreshold
float Digikam::DNNBaseDetectorModel::nmsThreshold = 0.4F |
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Threshold for nms suppression.
◆ predefinedClasses
QList<QString> Digikam::DNNBaseDetectorModel::predefinedClasses |
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◆ scaleFactor
float Digikam::DNNBaseDetectorModel::scaleFactor = 1.0F |
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◆ scoreThreshold
float Digikam::DNNBaseDetectorModel::scoreThreshold = 0.45F |
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Threshold for class detection score.
◆ uiConfidenceThreshold
Threshold for bbox detection. It can be init and changed in the GUI.
setting 1000 will use the value from dnnmodels.conf if passed in
The documentation for this class was generated from the following files: