digiKam Developer Documentation
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Digikam::DNNResnetDetector Class Reference
+ Inheritance diagram for Digikam::DNNResnetDetector:

Protected Member Functions

bool loadModels () override
 

Additional Inherited Members

- Public Member Functions inherited from Digikam::DNNBaseDetectorModel
 DNNBaseDetectorModel (float scale, const cv::Scalar &val, const cv::Size &inputImgSize)
 
virtual QHash< QString, QVector< QRect > > detectObjects (const cv::Mat &inputImage)
 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.
 
virtual QList< QHash< QString, QVector< QRect > > > detectObjects (const std::vector< cv::Mat > &inputBatchImages)
 detectObjects in batch images (fixed batch size).
 
QList< QString > generateObjects (const cv::Mat &inputImage)
 generateObjects in one image return just the predicted objects without locations of objects using for the assignment tagging names.
 
QList< QList< QString > > generateObjects (const std::vector< cv::Mat > &inputImage)
 generateObjects in batch images return just the predicted objects without locations of objects using for the assignment tagging names.
 
cv::Size getinputImageSize () const
 Return the input Image Size from Deep NN model.
 
std::vector< cv::String > getOutputsNames () const
 
virtual QList< QString > getPredefinedClasses () const
 Get predefined objects according to selected model.
 
QList< QString > loadDetectionClasses ()
 
QList< QHash< QString, QVector< QRect > > > postprocess (const std::vector< cv::Mat > &inputBatchImages, const std::vector< cv::Mat > &outs) const
 
std::vector< cv::Mat > preprocess (const cv::Mat &inputImage)
 
std::vector< cv::Mat > preprocess (const std::vector< cv::Mat > &inputBatchImages)
 
double showInferenceTime ()
 
- Static Public Attributes inherited from Digikam::DNNBaseDetectorModel
static float nmsThreshold = 0.4F
 Threshold for nms suppression.
 
static float scoreThreshold = 0.45F
 Threshold for class detection score.
 
static int uiConfidenceThreshold = DNN_MODEL_THRESHOLD_NOT_SET
 Threshold for bbox detection. It can be init and changed in the GUI.
 
- Protected Attributes inherited from Digikam::DNNBaseDetectorModel
cv::Size inputImageSize
 
cv::Scalar meanValToSubtract
 
DNNModelBasemodel = nullptr
 
QList< QString > predefinedClasses
 
float scaleFactor = 1.0F
 

Member Function Documentation

◆ loadModels()

bool Digikam::DNNResnetDetector::loadModels ( )
overrideprotectedvirtual