MeanMapper.java

package org.hipi.tools.covar;

import org.hipi.image.FloatImage;
import org.hipi.image.HipiImageHeader;
import org.hipi.opencv.OpenCVMatWritable;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.mapreduce.Mapper;
import org.bytedeco.javacpp.opencv_core;
import org.bytedeco.javacpp.opencv_core.Mat;
import org.bytedeco.javacpp.opencv_core.Rect;
import org.bytedeco.javacpp.opencv_core.Scalar;

import java.io.IOException;

public class MeanMapper extends 
  Mapper<HipiImageHeader, FloatImage, IntWritable, OpenCVMatWritable> {

  @Override
  public void map(HipiImageHeader header, FloatImage image, Context context) throws IOException,
      InterruptedException {
    
    /////
    // Perform conversion to OpenCV
    /////
    
    Mat cvImage = new Mat(image.getHeight(), image.getWidth(), opencv_core.CV_32FC1);
    
    // if unable to convert input FloatImage to grayscale Mat, skip image and move on
    if(!Covariance.convertFloatImageToGrayscaleMat(image, cvImage)) {
      System.out.println("MeanMapper is skipping image with invalid color space.");
      return;
    }
    
    /////
    // Compute mean using OpenCV
    /////
    
    //patch dimensions (N X N)
    int N = Covariance.patchSize;
 
    Mat mean = new Mat(N, N, opencv_core.CV_32FC1, new Scalar(0.0));
    
    //specify number of patches to use in mean patch computation (iMax * jMax patches)
    int iMax = 10;
    int jMax = 10;

    //collect patches and add their values to mean patch mat
    for (int i = 0; i < iMax; i++) {
      int x = ((cvImage.cols() - N) * i) / iMax;
      for (int j = 0; j < jMax; j++) {
        int y = ((cvImage.rows() - N) * j) / jMax;
        Mat patch = cvImage.apply(new Rect(x, y, N, N));
        opencv_core.add(patch, mean, mean);
      }
    }
    
    //scale mean patch mat based on total number of patches
    mean = opencv_core.divide(mean, ((double) (iMax * jMax))).asMat();
    
    context.write(new IntWritable(0), new OpenCVMatWritable(mean));
  }
}