#pragma once
#include "NCNNBytekalmanFilter.h"
#include <unordered_map>
namespace ByteTrackNCNN {
	enum TrackState { New = 0, Tracked, Lost, Removed };
	class STrack
	{
	public:
		STrack(std::vector<float> tlwh_, float _score,
			int _class_id, float _left, float _top,
			float _right, float _bottom, std::string _object_id);
		~STrack();

		std::vector<float> static tlbr_to_tlwh(std::vector<float>& tlbr);
		void static multi_predict(std::vector<STrack*>& stracks, ByteTrackNCNN::ByteKalmanFilter& kalman_filter);
		void static_tlwh();
		void static_tlbr();
		std::vector<float> tlwh_to_xyah(std::vector<float> tlwh_tmp);
		std::vector<float> to_xyah();
		void mark_lost();
		void mark_removed();
		int next_id();
		int end_frame();
		void reset_count();

		void activate(ByteTrackNCNN::ByteKalmanFilter& kalman_filter, int frame_id);
		void activate(ByteTrackNCNN::ByteKalmanFilter& kalman_filter, int frame_id, int track_id_count);
		void re_activate(STrack& new_track, int frame_id, bool new_id = false);
		void update(STrack& new_track, int frame_id);

	public:
		bool is_activated;
		int track_id;
		int state;
		int count;

		std::vector<float> _tlwh;
		std::vector<float> tlwh;
		std::vector<float> tlbr;
		float left; // left, top, right, bottom (original bounding of detected object) 
		float top;
		float right;
		float bottom;
		int frame_id;
		int tracklet_len;
		int start_frame;
		std::string object_id;

		KAL_MEAN mean;
		KAL_COVA covariance;
		float score;
		int class_id;

	private:
		ByteTrackNCNN::ByteKalmanFilter kalman_filter;
		std::unordered_map<int, float> class_id_scores_;
		int detection_count_;
		bool class_id_locked_;
		static const int CLASS_ID_LOCK_FRAMES = 10;
		void voteClassId(int new_class_id, float score);
	};
}