textile_count_alg_node.cpp

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#include "textile_count_alg_node.h"
#include <std_msgs/Int32.h>

using namespace cv;

TextileCountAlgNode::TextileCountAlgNode(void) :
  algorithm_base::IriBaseAlgorithm<TextileCountAlgorithm>()
{
  //init class attributes if necessary
  //this->loop_rate_ = 2;//in [Hz]
  rubbish_threshold_ = 0.01;

  // [init publishers]
  output_img_publisher_     = public_node_handle_.advertise<sensor_msgs::Image>("output_img", 1);
  objects_found_publisher_  = public_node_handle_.advertise<std_msgs::Int32>("num_objects_found", 100);
  // [init subscribers]
  input_raw_image_subscriber_ = this->public_node_handle_.subscribe("input_raw_image", 1, &TextileCountAlgNode::input_raw_image_callback, this);
  
  // [init services]
  
  // [init clients]
  
  // [init action servers]
  
  // [init action clients]
}

TextileCountAlgNode::~TextileCountAlgNode(void)
{
  // [free dynamic memory]
}

void TextileCountAlgNode::mainNodeThread(void)
{
  // [fill msg structures]
  //this->Image_msg_.data = my_var;
  //this->PointCloud2_msg_.data = my_var;
  
  // [fill srv structure and make request to the server]
  
  // [fill action structure and make request to the action server]

  // [publish messages]
//  this->output_img_publisher_.publish(this->Image_msg_);
}

/*  [subscriber callbacks] */
void TextileCountAlgNode::input_raw_image_callback(const sensor_msgs::Image::ConstPtr& msg) 
{ 
        cv_bridge::CvImagePtr cv_ptr;
        
        try
        {
                cv_ptr = cv_bridge::toCvCopy(msg, sensor_msgs::image_encodings::BGR8);
        }
        catch (cv_bridge::Exception& e)
        {
                ROS_ERROR("cv_bridge exception: %s", e.what());
                return;
        }
    
    //ROS_INFO_STREAM("TextileCountAlgNode::input_raw_image_callback: New Message Received"<<cv_ptr->image.cols<<" "<<cv_ptr->image.rows); 

    //Filter out background noise on black images
    int pix_black = 0;
    for (int rr = 0; rr < cv_ptr->image.rows; rr++) {
      for (int cc = 0; cc < cv_ptr->image.cols; cc++) {
            if( cv_ptr->image.at<cv::Vec3b>(rr,cc)[0] == 0 
             && cv_ptr->image.at<cv::Vec3b>(rr,cc)[1] == 0
             && cv_ptr->image.at<cv::Vec3b>(rr,cc)[2] == 0){
                pix_black++;
            }
      }
    }
  this->alg_.lock();
    double rubbish_th = rubbish_threshold_;
  this->alg_.unlock();
    double nonblack_proportion = 1 - ((double)pix_black)/((double)(cv_ptr->image.rows*cv_ptr->image.cols)); 
    if(nonblack_proportion < rubbish_th){
        // Publish 0
        std_msgs::Int32 found;
        found.data = 0;
        objects_found_publisher_.publish(found); 
        ROS_WARN("No objects found!");
    }else{
        // Publish the number of objects found
            histCount(cv_ptr->image);
    }
        
        //ROS_INFO_STREAM("Publishing TextileCountAlgNode");
        
  this->output_img_publisher_.publish(cv_ptr->toImageMsg());
  //use appropiate mutex to shared variables if necessary 
  //this->alg_.lock(); 
  //this->input_raw_image_mutex_.enter(); 

  //std::cout << msg->data << std::endl; 

  //unlock previously blocked shared variables 
  //this->alg_.unlock(); 
  //this->input_raw_image_mutex_.exit(); 
}

void TextileCountAlgNode::histCount(cv::Mat& image)
{
  if( !image.data )
  { return; }

  //No crec que faci res, però...
  GaussianBlur( image, image, Size( 3, 3 ), 0, 0 );


  Mat img_hsv, img_threshold;
  cvtColor(image, img_hsv, CV_BGR2HSV);

  int width = image.cols;
  int height = image.rows;

  // accept only char type matrices
  CV_Assert(img_hsv.depth() != sizeof(uchar));
  int channels = img_hsv.channels();

  Mat segmented;
  for(int i = 0; i < height; ++i)
  {
    uchar *p1 = img_hsv.ptr<uchar>(i);
    for (int j = 0; j < width; ++j)
    {
// cal deixar passar els blancs S<< però no els negres V<<
//TODO: cal tractar els blancs diferent!! el hue pot ser qualsevol!
      if ((p1[j*channels+1]>1)&&(p1[j*channels+2]>10))
      { 
//        segmented.push_back(Vec3b(p1[j*channels],(p1[j*channels+1]+p1[j*channels+2])/2,p1[j*channels+2]));
        segmented.push_back(Vec3b(p1[j*channels],p1[j*channels+1],p1[j*channels+2]));
      }
    }
  }

  // Quantize the hue to 180/5 levels
  // and the saturation to 256/5 levels
  //int hbins = 45, sbins = 64;
  int hbins = 30, sbins = 43;
  int histSize[] = {hbins, sbins};
  // hue varies from 0 to 179
  float hranges[] = { 0, 180 };
  // saturation varies from 0 (black-gray-white) to
  // 255 (pure spectrum color)
  float sranges[] = { 0, 256 };
  const float* ranges[] = { hranges, sranges };
  MatND hist;
  int channelsSeg[] = {0, 1};

  calcHist( &segmented, 1, channelsSeg, Mat(), // do not use mask
            hist, 2, histSize, ranges,
            true, // the histogram is uniform
            false );

  double maxVal=0;
  minMaxLoc(hist, 0, &maxVal, 0, 0);
  int scale = 10;
  Mat histImg = Mat::zeros(sbins*scale, hbins*10, CV_8UC3);
  for( int h = 0; h < hbins; h++ )
    for( int s = 0; s < sbins; s++ )
    {
      float binVal = hist.at<float>(h, s);
      int intensity = cvRound(binVal*255/maxVal);
      if (intensity>20) intensity=255;
      else intensity = 0;
      rectangle( histImg, Point(h*scale, s*scale),
      Point( (h+1)*scale - 1, (s+1)*scale - 1),
             Scalar::all(intensity),
             CV_FILLED );
  }
/*
//filtrem els punts espuris. Amb erode ens carreguem massa coses, (no?)
double m[3][3] = {{1, 1, 1}, {1, 0, 1}, {1, 1, 1}};
Mat kernel = Mat(3, 3, CV_64F, m);
Point anchor = Point( -1, -1 );
double delta = 0;
int ddepth = -1;

//filter2D(histImg, histImg, ddepth , kernel, anchor, delta, BORDER_DEFAULT );
erode(histImg, histImg, Mat(),anchor, 2);
*/
  //per comptar els blobs podem fer servir moments
  int thresh = 100;
  RNG rng(12345);

  Mat canny_output;
  Mat src_gray;
  vector<vector<Point> > contours;
  vector<Vec4i> hierarchy;
  cvtColor( histImg, src_gray, CV_BGR2GRAY );
  Canny( src_gray, canny_output, thresh, thresh*2, 5 );
  findContours( canny_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0) );
  vector<Moments> mu(contours.size() );
  for(unsigned int i = 0; i < contours.size(); i++ )
  { mu[i] = moments( contours[i], false ); }

  vector<Point2f> mc( contours.size() );
  for(unsigned int i = 0; i < contours.size(); i++ )
  { mc[i] = Point2f( mu[i].m10/mu[i].m00 , mu[i].m01/mu[i].m00 ); }
  Mat drawing = Mat::zeros( canny_output.size(), CV_8UC3 );
  
  //per treure els repetits
  int oldx=0, oldy=0;
  
  int iTrobats=0;
  for(unsigned int i = 0; i< contours.size(); i++ )
  {
    if ((oldx!=(int)mc[i].x)&&(oldy!=(int)mc[i].y))
    {
      Scalar color = Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
      //drawContours( drawing, contours, i, color, 2, 8, hierarchy, 0, Point() );
      circle( histImg, mc[i], 4, color, -1, 8, 0 );
      ++iTrobats;oldx=(int)mc[i].x;oldy=(int)mc[i].y;
      //ROS_INFO_STREAM("Posició: "<<mc[i]);
    }
  }

  // Publish the number of objects found
  std_msgs::Int32 found;
  found.data = iTrobats;
  objects_found_publisher_.publish(found);

  ROS_INFO_STREAM("Objects found: " << found.data);

  image=histImg;
}




/*  [service callbacks] */

/*  [action callbacks] */

/*  [action requests] */

void TextileCountAlgNode::node_config_update(Config &config, uint32_t level)
{
  this->alg_.lock();
  ROS_INFO("Reconfig rubbish threshold: %f", config.rubbish_threshold);
  rubbish_threshold_ = config.rubbish_threshold;

  this->alg_.unlock();
}


void TextileCountAlgNode::addNodeDiagnostics(void)
{
}

/* main function */
int main(int argc,char *argv[])
{
  return algorithm_base::main<TextileCountAlgNode>(argc, argv, "textile_count_alg_node");
}