content: 
  //
  // This file is auto-generated. Please don't modify it!
  //
  package org.opencv.objdetect;
  
  import org.opencv.core.Mat;
  import org.opencv.core.MatOfInt;
  import org.opencv.core.MatOfRect;
  
  public class Objdetect {
  
      public static final int
              CASCADE_DO_CANNY_PRUNING = 1,
              CASCADE_SCALE_IMAGE = 2,
              CASCADE_FIND_BIGGEST_OBJECT = 4,
              CASCADE_DO_ROUGH_SEARCH = 8;
  
  
      //
      // C++:  void drawDataMatrixCodes(Mat& image, vector_string codes, Mat corners)
      //
  
      // Unknown type 'vector_string' (I), skipping the function
  
  
      //
      // C++:  void findDataMatrix(Mat image, vector_string& codes, Mat& corners = Mat(), vector_Mat& dmtx = vector_Mat())
      //
  
      // Unknown type 'vector_string' (O), skipping the function
  
  
      //
      // C++:  void groupRectangles(vector_Rect& rectList, vector_int& weights, int groupThreshold, double eps = 0.2)
      //
  
  /**
   * <p>Groups the object candidate rectangles.</p>
   *
   * <p>The function is a wrapper for the generic function "partition". It clusters
   * all the input rectangles using the rectangle equivalence criteria that
   * combines rectangles with similar sizes and similar locations. The similarity
   * is defined by <code>eps</code>. When <code>eps=0</code>, no clustering is
   * done at all. If <em>eps-> +inf</em>, all the rectangles are put in one
   * cluster. Then, the small clusters containing less than or equal to
   * <code>groupThreshold</code> rectangles are rejected. In each other cluster,
   * the average rectangle is computed and put into the output rectangle list.</p>
   *
   * @param rectList Input/output vector of rectangles. Output vector includes
   * retained and grouped rectangles. (The Python list is not modified in place.)
   * @param weights a weights
   * @param groupThreshold Minimum possible number of rectangles minus 1. The
   * threshold is used in a group of rectangles to retain it.
   * @param eps Relative difference between sides of the rectangles to merge them
   * into a group.
   *
   * @see <a href="http://docs.opencv.org/modules/objdetect/doc/cascade_classification.html#grouprectangles">org.opencv.objdetect.Objdetect.groupRectangles</a>
   */
      public static void groupRectangles(MatOfRect rectList, MatOfInt weights, int groupThreshold, double eps)
      {
          Mat rectList_mat = rectList;
          Mat weights_mat = weights;
          groupRectangles_0(rectList_mat.nativeObj, weights_mat.nativeObj, groupThreshold, eps);
  
          return;
      }
  
  /**
   * <p>Groups the object candidate rectangles.</p>
   *
   * <p>The function is a wrapper for the generic function "partition". It clusters
   * all the input rectangles using the rectangle equivalence criteria that
   * combines rectangles with similar sizes and similar locations. The similarity
   * is defined by <code>eps</code>. When <code>eps=0</code>, no clustering is
   * done at all. If <em>eps-> +inf</em>, all the rectangles are put in one
   * cluster. Then, the small clusters containing less than or equal to
   * <code>groupThreshold</code> rectangles are rejected. In each other cluster,
   * the average rectangle is computed and put into the output rectangle list.</p>
   *
   * @param rectList Input/output vector of rectangles. Output vector includes
   * retained and grouped rectangles. (The Python list is not modified in place.)
   * @param weights a weights
   * @param groupThreshold Minimum possible number of rectangles minus 1. The
   * threshold is used in a group of rectangles to retain it.
   *
   * @see <a href="http://docs.opencv.org/modules/objdetect/doc/cascade_classification.html#grouprectangles">org.opencv.objdetect.Objdetect.groupRectangles</a>
   */
      public static void groupRectangles(MatOfRect rectList, MatOfInt weights, int groupThreshold)
      {
          Mat rectList_mat = rectList;
          Mat weights_mat = weights;
          groupRectangles_1(rectList_mat.nativeObj, weights_mat.nativeObj, groupThreshold);
  
          return;
      }
  
  
  
  
      // C++:  void groupRectangles(vector_Rect& rectList, vector_int& weights, int groupThreshold, double eps = 0.2)
      private static native void groupRectangles_0(long rectList_mat_nativeObj, long weights_mat_nativeObj, int groupThreshold, double eps);
      private static native void groupRectangles_1(long rectList_mat_nativeObj, long weights_mat_nativeObj, int groupThreshold);
  
  }
  