org.opencv.objdetect.Objdetect Maven / Gradle / Ivy
//
// 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)
//
/**
* Groups the object candidate rectangles.
*
* 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 eps. When eps=0, no clustering is
* done at all. If eps-> +inf, all the rectangles are put in one
* cluster. Then, the small clusters containing less than or equal to
* groupThreshold rectangles are rejected. In each other cluster,
* the average rectangle is computed and put into the output rectangle list.
*
* @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 org.opencv.objdetect.Objdetect.groupRectangles
*/
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;
}
/**
* Groups the object candidate rectangles.
*
* 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 eps. When eps=0, no clustering is
* done at all. If eps-> +inf, all the rectangles are put in one
* cluster. Then, the small clusters containing less than or equal to
* groupThreshold rectangles are rejected. In each other cluster,
* the average rectangle is computed and put into the output rectangle list.
*
* @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 org.opencv.objdetect.Objdetect.groupRectangles
*/
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);
}