Micaela Verucchi
77 lines
2.1 KiB
C++
77 lines
2.1 KiB
C++
#ifndef MOBILENETDETECTION_H
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#define MOBILENETDETECTION_H
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#include <opencv2/videoio.hpp>
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#include "opencv2/opencv.hpp"
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#include "DetectionNN.h"
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#define N_COORDS 4
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#define N_SSDSPEC 6
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namespace tk { namespace dnn {
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struct SSDSpec
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{
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int featureSize = 0;
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int shrinkage = 0;
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int boxWidth = 0;
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int boxHeight = 0;
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int ratio1 = 0;
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int ratio2 = 0;
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SSDSpec() {}
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SSDSpec(int feature_size, int shrinkage, int box_width, int box_height, int ratio1, int ratio2) :
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featureSize(feature_size), shrinkage(shrinkage), boxWidth(box_width),
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boxHeight(box_height), ratio1(ratio1), ratio2(ratio2) {}
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void setAll(int feature_size, int shrinkage, int box_width, int box_height, int ratio1, int ratio2)
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{
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this->featureSize = feature_size;
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this->shrinkage = shrinkage;
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this->boxWidth = box_width;
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this->boxHeight = box_height;
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this->ratio1 = ratio1;
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this->ratio2 = ratio2;
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}
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void print()
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{
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std::cout << "fsize: " << featureSize << "\tshrinkage: " << shrinkage <<
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"\t box W:" << boxWidth << "\tbox H: " << boxHeight <<
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"\t x ratio:" << ratio1 << "\t y ratio:" << ratio2 << std::endl;
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}
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};
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class MobilenetDetection : public DetectionNN
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{
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private:
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float IoUThreshold = 0.45;
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float centerVariance = 0.1;
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float sizeVariance = 0.2;
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int imageSize;
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float *priors = nullptr;
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int nPriors = 0;
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float *locations_h, *confidences_h;
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void generate_ssd_priors(const SSDSpec *specs, const int n_specs, bool clamp = true);
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void convert_locatios_to_boxes_and_center();
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float iou(const tk::dnn::box &a, const tk::dnn::box &b);
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public:
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MobilenetDetection() {};
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~MobilenetDetection() {};
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bool init(const std::string& tensor_path, const int n_classes, const int n_batches=1, const float conf_thresh=0.3);
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void preprocess(cv::Mat &frame, const int bi=0);
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void postprocess(const int bi=0,const bool mAP=false);
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};
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} // namespace dnn
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} // namespace tk
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#endif /*MOBILENETDETECTION_H*/ |