graft_cv/grab_point_rs.h

#pragma once

#include <pcl\point_types.h>
#include <pcl\point_cloud.h>
#include "data_def_api.h"
#include "data_def.h"
#include "logger.h"
#include "imstorage_manager.h"

namespace graft_cv {
	class CRootStockGrabPoint {
	public:
		CRootStockGrabPoint(ConfigParam&c, CGcvLogger*pLog = 0);
		~CRootStockGrabPoint();
		int grab_point_detect( int dtype, PositionInfo& posinfo);
		float* get_raw_point_cloud(int &data_size);
		int load_data(float*pPoint, int pixel_size, int pt_size, const char* fn = 0);
		void set_image_saver(CImStoreManager** ppis) { m_ppImgSaver = ppis; }
	private:
		//global configure parameters
		ConfigParam& m_cparam;
		CGcvLogger * m_pLogger;
		int m_dtype;
		string m_pcdId;

		CImStoreManager** m_ppImgSaver;

		pcl::PointCloud<pcl::PointXYZ>::Ptr m_raw_cloud;


		int read_ply_file(const char* fn);
		double compute_nearest_neighbor_distance(pcl::PointCloud<pcl::PointXYZ>::Ptr);

		////////////////////////////////////////////////
		//---> 2023-8-8优化，seedling order识别错误问题
		/*void CRootStockGrabPoint::find_tray_top_edge(
			pcl::PointCloud<pcl::PointXYZ>::Ptr in_cloud,
			float & tray_top_edge_y
		);
		void CRootStockGrabPoint::find_seedling_position_line_based(
			pcl::PointCloud<pcl::PointXYZ>::Ptr in_cloud,
			std::vector<int> &first_seedling_cloud_idx,
			pcl::PointXYZ&xz_center
		);
		void segement_line(
			pcl::PointCloud<pcl::PointXYZ>::Ptr in_cloud
		);*/
		//<---------
		/////////////////////////////////////////////////
		void find_seedling_position(
			pcl::PointCloud<pcl::PointXYZ>::Ptr in_cloud,
			std::vector<int> &first_seedling_cloud_idx,
			pcl::PointXYZ&xz_center
		);
		void crop_nn_analysis(
			pcl::PointCloud<pcl::PointXYZ>::Ptr in_cloud,
			pcl::PointCloud<pcl::PointXYZ>::Ptr seed_cloud,
			double dist_mean,
			std::vector<double>&mass_indices,
			std::vector<int>& idx
		);

		void euclidean_clustering_ttsas(
			pcl::PointCloud<pcl::PointXYZ>::Ptr in_cloud,
			double d1, double d2,
			std::vector<pcl::PointXYZ>&cluster_center,
			std::vector<std::vector<int>> &clustr_member
		);
		void cal_obb_2d(
			pcl::PointCloud<pcl::PointXYZ>::Ptr in_cloud,
			int axis,
			double &dx_obb,
			double &dy_obb,
			double &angle_obb);

		void get_optimal_seed(
			pcl::PointCloud<pcl::PointXYZ>::Ptr,
			pcl::PointXYZ&pt,
			int &pt_idx);
		//在一株苗的空间范围内找出直线（茎，假设茎是直线分布的），并返回直线上的点index
		void find_line_in_cube(
			pcl::PointCloud<pcl::PointXYZ>::Ptr,	//输入，整体点云
			pcl::PointXYZ& min_pt_ex,				//输入，
			pcl::PointXYZ& max_pt_ex,				//输入，
			std::vector<int>& out_idx				//输出，直线上点的index， 基于输入整体点云
			);

		void viewer_cloud(pcl::PointCloud<pcl::PointXYZ>::Ptr, std::string&winname);
		void viewer_cloud(pcl::PointCloud<pcl::PointXYZRGB>::Ptr, std::string&winname);

		void viewer_cloud_debug(
			pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud, 
			pcl::PointXYZ&p,
			pcl::PointXYZ &root_pt,
			std::string&winname);

		void viewer_cloud_cluster(pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud, std::vector<pcl::PointXYZ>cluster_center, std::string&winname);
		void viewer_cloud_cluster_box(
			pcl::PointCloud<pcl::PointXYZ>::Ptr cloud,
			std::vector<pcl::PointXYZ>&cluster_center,
			std::vector<pcl::PointXYZ>&cluster_box,
			std::vector<std::vector<int> >& clt_line_idx,
			std::string&winname);
	};

};