detectMultiScale with outputRejectLevels true doesn't return anything?

edit

if you want to use the overload with rejectLevels and weights, take care to get all other default args right, like minSize and maxSize. (you don't show your code, but i rather guess, some problem with the params is the reason for your failure)

#include <iostream>
#include <opencv2/opencv.hpp>

using namespace std;
using namespace cv;

int main()
{
    Mat im = imread("c:/p/data/img/people.jpg");
    // resize(im,im,Size(), 4,4); // my demo img is very small

    cv::imshow("src", im);
    cv::waitKey(1);

    String face_cascade_name = "c:/p/opencv/data/haarcascades/haarcascade_frontalface_alt.xml";
    CascadeClassifier face_cascade(face_cascade_name);

    std::vector<Rect> faces;
    Mat frame_gray;

    cvtColor( im, frame_gray, COLOR_BGR2GRAY );
    equalizeHist( frame_gray, frame_gray );

    //-- Detect faces
    face_cascade.detectMultiScale( frame_gray, faces, 1.1, 2, 0|CASCADE_SCALE_IMAGE, Size(30, 30) );
    cerr << faces.size() << " objects detected." << endl;

    for ( size_t i = 0; i < faces.size(); i++ )
    {
        rectangle( im, faces[i], Scalar( 255, 0, 255 ), 4, 8, 0 );
    }

    cv::imshow("dst", im);
    cv::waitKey(1);

    //-- Detect faces, now with weights
    vector<int> rejectLevels;
    vector<double> rejectWeights;
    face_cascade.detectMultiScale( frame_gray, faces, rejectLevels, rejectWeights,  1.1, 2, 0|CASCADE_SCALE_IMAGE, Size(30, 30), Size(90, 90), true );
    cerr << faces.size() << " objects detected." << endl;

    for ( size_t i = 0; i < faces.size(); i++ )
    {
        rectangle( im, faces[i], Scalar( 0, 255, 0 ), 2, 8, 0 );
        cerr << rejectLevels[i] << " " << rejectWeights[i] << "\t" << faces[i] << endl;
    }

    cv::imshow("dst_weights", im);
    cv::waitKey();
    return 0;
}
35 objects detected.
35 objects detected.
22 108.387      [67 x 67 from (39, 163)]
22 108.109      [60 x 60 from (36, 300)]
22 108.413      [61 x 61 from (568, 29)]
22 107.246      [48 x 48 from (766, 37)]
22 108.523      [54 x 54 from (858, 154)]
22 108.57       [68 x 68 from (705, 161)]
22 107.547      [65 x 65 from (563, 155)]
22 107.765      [65 x 65 from (315, 161)]
22 107.261      [66 x 66 from (179, 297)]
22 107.699      [66 x 66 from (426, 310)]
22 108.767      [72 x 72 from (292, 299)]
22 106.998      [72 x 72 from (829, 303)]
22 107.397      [74 x 74 from (24, 24)]
22 108.513      [71 x 71 from (159, 23)]
22 108.235      [72 x 72 from (433, 25)]
22 108.465      [74 x 74 from (699, 25)]
22 108.23       [74 x 74 from (305, 31)]
22 107.797      [84 x 84 from (698, 299)]
22 107.872      [58 x 58 from (156, 429)]
22 107.958      [63 x 63 from (638, 412)]
22 106.754      [64 x 64 from (345, 418)]
22 108.119      [63 x 63 from (298, 432)]
22 107.301      [59 x 59 from (299, 560)]
22 106.449      [66 x 66 from (724, 432)]
22 108.637      [68 x 68 from (566, 564)]
22 107.188      [68 x 68 from (838, 441)]
22 108.433      [69 x 69 from (700, 561)]
22 109.369      [68 x 68 from (572, 425)]
22 108.901      [67 x 67 from (164, 564)]
22 108.015      [74 ...