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| DNNYoloDetector (YoloVersions modelVersion=YoloVersions::YOLOV5NANO) |
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| DNNBaseDetectorModel (float scale, const cv::Scalar &val, const cv::Size &inputImgSize) |
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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.
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virtual QList< QHash< QString, QVector< QRect > > > | detectObjects (const std::vector< cv::Mat > &inputBatchImages) |
| detectObjects in batch images (fixed batch size).
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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.
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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.
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cv::Size | getinputImageSize () const |
| Return the input Image Size from Deep NN model.
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std::vector< cv::String > | getOutputsNames () const |
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virtual QList< QString > | getPredefinedClasses () const |
| Get predefined objects according to selected model.
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QList< QString > | loadDetectionClasses () |
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QList< QHash< QString, QVector< QRect > > > | postprocess (const std::vector< cv::Mat > &inputBatchImages, const std::vector< cv::Mat > &outs) const |
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std::vector< cv::Mat > | preprocess (const cv::Mat &inputImage) |
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std::vector< cv::Mat > | preprocess (const std::vector< cv::Mat > &inputBatchImages) |
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double | showInferenceTime () |
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