Cannot detect a simple rectangle

edit

I am using code inspired by https://github.com/opencv/opencv/blob.... My code is barely any different, and yet cannot detect ANYTHING. Any help would be appreciated. When running the program, it says (as I have instructed it to if this happens) that the list of detections is empty...

#include <opencv2/core.hpp>
#include <opencv2/imgproc.hpp>
#include "opencv2/imgcodecs.hpp"
#include <opencv2/highgui.hpp>
#include <opencv2/ml.hpp>
#include <opencv2/objdetect.hpp>
#include <string>
#include <iostream>
#include <fstream>
#include <vector>

#include <time.h>

using namespace cv;
using namespace cv::ml;
using namespace std;

void getSvmDetector(Ptr<SVM> & svm, vector < float > & detector);
void draw_locations(Mat & img, const vector < Rect > & locations, const Scalar & color);
void load_images(string dir, vector< Mat > & images, int numFiles);
void compute_hog(vector < Mat > & mats, vector < Mat > & gradient, Size & s);
void train_svm(const vector < Mat > & gradient_list, const vector < int > & labels);
void convert_to_ml(const vector< Mat > & train_samples, Mat & trainData);

int main(int, char**)
{
    int numPos = 6;
    int numNeg = 20;

    vector< Mat > pos_lst;
    vector< Mat > neg_lst;
    vector< Mat > gradient_list;
    vector < int > labels;

    string posDir = "C:/Users/danekstein/Desktop/rectangle/";
    string negDir = "C:/Users/danekstein/Desktop/nr/";

    // load and label positive images
    cout << "positive images loading..." << endl;
    load_images(posDir, pos_lst, numPos);
    labels.assign(pos_lst.size(), +1);

    // size with only positives
    const unsigned int old = (unsigned)labels.size();

    // load and label positive images
    cout << "negative images loading..." << endl;
    load_images(negDir, neg_lst, numNeg);   
    labels.insert(labels.end(), neg_lst.size(), -1);

    // check the labels were successfully added
    CV_Assert(old < labels.size());

    cout << "computing hog for positives" << endl;
    compute_hog(pos_lst, gradient_list, Size(256, 128));

    cout << "computing hog for negatives" << endl;
    compute_hog(neg_lst, gradient_list, Size(256, 128));


    train_svm(gradient_list, labels);

    Scalar border_color(0, 255, 0);
    Mat img, draw;
    Ptr<SVM> svm;
    HOGDescriptor hog = HOGDescriptor::HOGDescriptor();
    hog.winSize = Size(256, 128);


    // locations where a plane is detected
    vector< Rect > locations;

    // loading the trained svm
    svm = StatModel::load<SVM>("C:/Users/danekstein/Desktop/r.yml");

    // set the trained svm to the hog 
    vector< float > hog_detector;

    getSvmDetector(svm, hog_detector);

    // set the detector
    hog.setSVMDetector(hog_detector);

    Mat image = imread("C:/Users/danekstein/Desktop/rectangle/1.jpg");

    locations.clear();

    cout << "detecting features from sample" << endl;

    hog.detectMultiScale(image,locations);

    draw = image.clone();
    draw_locations(draw, locations, border_color);

    imshow("Image", draw);
    waitKey(0);
    destroyAllWindows();

}

void load_images(string dir, vector< Mat > & images, int numImages) {
    for (int i = 0; i < numImages; i++) {

        Mat im = imread(dir + to_string(i) + ".jpg");
        if (im.empty()) cout << "not good";

#ifdef _DEBUG

#endif
        images.push_back(im.clone());
    }
}

void compute_hog(vector < Mat > & mats, vector < Mat > & gradients, Size & size) {
    HOGDescriptor hog;
    hog.winSize = size;
    Mat gray;
    vector< Point > location;
    vector< float > descriptors;

    vector< Mat >::const_iterator img = mats.begin();
    vector< Mat >::const_iterator end = mats.end();
    for (; img != end; ++img) {
        cvtColor(*img, gray, COLOR_BGR2GRAY);
        hog.compute(gray, descriptors, Size(1,1), Size(0, 0), location);
        gradients.push_back(Mat(descriptors).clone());
    }
}

void getSvmDetector(Ptr<SVM> & svm, vector< float>  & detector) {
    // grab the support vectors... yay!
    Mat sv = svm->getSupportVectors();
    const int sv_total = sv.rows;

    // get the decision function
    Mat alpha, svidx;
    double rho = svm->getDecisionFunction ...