classify_candidates.cpp

/*
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 *  Copyright (c) 2016, Andreas ten Pas
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// ROS
#include <ros/ros.h>

// Grasp Candidates Generator
#include <gpg/cloud_camera.h>

// Custom
#include "../../include/gpd/grasp_detector.h"
#include "../../include/gpd/sequential_importance_sampling.h"


int main(int argc, char* argv[]) 
{
  // initialize ROS
  ros::init(argc, argv, "classify_grasp_candidates");
  ros::NodeHandle node("~");
  
  // Set the position from which the camera sees the point cloud.
  Eigen::Matrix3Xd view_points(3,1);
  view_points << 0.0, 0.0, 0.0;
  std::vector<double> camera_position;
  node.getParam("camera_position", camera_position);
  view_points << camera_position[0], camera_position[1], camera_position[2];

  // Load point cloud from file.
  std::string filename;
  node.param("cloud_file_name", filename, std::string(""));
  CloudCamera cloud_cam(filename, view_points);
  if (cloud_cam.getCloudOriginal()->size() == 0)
  {
    std::cout << "Input point cloud is empty or does not exist!\n";
    return (-1);
  }

  // Detect grasp poses.
  bool use_importance_sampling;
  node.param("use_importance_sampling", use_importance_sampling, false);

  if (use_importance_sampling)
  {
    SequentialImportanceSampling detector(node);

    // Preprocess the point cloud (voxelize, workspace, etc.).
    detector.preprocessPointCloud(cloud_cam);

    // Detect grasps in the point cloud.
    std::vector<Grasp> grasps = detector.detectGrasps(cloud_cam);
  }
  else
  {
    GraspDetector detector(node);

    // Preprocess the point cloud (voxelize, workspace, etc.).
    detector.preprocessPointCloud(cloud_cam);

    // Detect grasps in the point cloud.
    std::vector<Grasp> grasps = detector.detectGrasps(cloud_cam);
  }

  return 0;
}