roi out of bounds

I have the following error i don't have any idea how to fix it. can you please help me to fix this error? The full code is below. #include "opencv2/core/core.hpp" #include "opencv2/imgproc/imgproc.hpp" #include "opencv2/highgui/highgui.hpp" #include "math.h" #include #include #include #include using namespace cv; using namespace std; // Gradient Checking #define G_CHECKING 0 // Conv2 parameter #define CONV_FULL 0 #define CONV_SAME 1 #define CONV_VALID 2 // Pooling methods #define POOL_MAX 0 #define POOL_MEAN 1 #define POOL_MAX 2 #define POOL_STOCHASTIC 1 #define ATD at #define elif else if int NumHiddenNeurons = 50; //200 int NumHiddenLayers = 2; int nclasses = 4; //10 int KernelSize = 3; //13 int KernelAmount = 8; int PoolingDim = 2; //4 int batch; int Pooling_Methed = POOL_STOCHASTIC; typedef struct ConvKernel{ Mat W; double b; Mat Wgrad; double bgrad; }ConvK; typedef struct ConvLayer{ vector layer; int kernelAmount; }Cvl; typedef struct Network{ Mat W; Mat b; Mat Wgrad; Mat bgrad; }Ntw; typedef struct SoftmaxRegession{ Mat Weight; Mat Wgrad; Mat b; Mat bgrad; double cost; }SMR; Mat concatenateMat(vector>&vec){ int subFeatures = vec[0][0].rows * vec[0][0].cols; int height = vec[0].size() * subFeatures; int width = vec.size(); Mat res = Mat::zeros(height, width, CV_64FC1); for(int i=0; i&vec){ int height = vec[0].rows; int width = vec[0].cols; Mat res = Mat::zeros(height * width, vec.size(), CV_64FC1); for(int i=0; i>&vec, int vsize){ int sqDim = M.rows / vsize; int Dim = sqrt ((double) sqDim); for(int i=0; i oneColumn; for(int j=0; j> 8) & 255; ch3 = (i >> 16) & 255; ch4 = (i >> 24) & 255; return((int) ch1 << 24) + ((int)ch2 << 16) + ((int)ch3 << 8) + ch4; } void read_Mnist(string filename, vector&vec){ ifstream file(filename, ios::binary); if (file.is_open()){ int magic_number = 0; int number_of_images = 0; int n_rows = 0; int n_cols = 0; file.read((char*) &magic_number, sizeof(magic_number)); magic_number = ReverseInt(magic_number); file.read((char*) &number_of_images,sizeof(number_of_images)); number_of_images = ReverseInt(number_of_images); file.read((char*) &n_rows, sizeof(n_rows)); n_rows = ReverseInt(n_rows); file.read((char*) &n_cols, sizeof(n_cols)); n_cols = ReverseInt(n_cols); for(int i = 0; i < number_of_images; ++i){ Mat tpmat = Mat::zeros(n_rows, n_cols, CV_8UC1); for(int r = 0; r < n_rows; ++r){ for(int c = 0; c < n_cols; ++c){ unsigned char temp = 0; file.read((char*) &temp, sizeof(temp)); tpmat.at(r, c) = (int) temp; } } vec.push_back(tpmat); } } } void read_Mnist_Label(string filename, Mat &mat) { ifstream file(filename, ios::binary); if (file.is_open()){ int magic_number = 0; int number_of_images = 0; int n_rows = 0; int n_cols = 0; file.read((char*) &magic_number, sizeof(magic_number)); magic_number = ReverseInt(magic_number); file.read((char*) &number_of_images,sizeof(number_of_images)); number_of_images = ReverseInt(number_of_images); for(int i = 0; i < number_of_images; ++i){ unsigned char temp = 0; file.read((char*) &temp, sizeof(temp)); mat.ATD(0, i) = (double)temp; } } } Mat sigmoid(Mat &M){ Mat temp; exp(-M, temp); return 1.0 / (temp + 1.0); } Mat dsigmoid(Mat &a){ Mat res = 1.0 - a; res = res.mul(a); return res; } Mat ReLU(Mat& M){ Mat res(M); for(int i=0; i 0.0) res.ATD(i, j) = 1.0; } } return res; } // Mimic rot90() in Matlab/GNU Octave. Mat rot90(Mat &M, int k){ Mat res; if(k == 0) return M; elif(k == 1){ flip(M.t(), res, 0); }else{ flip(rot90(M, k - 1).t(), res, 0); } return res; } // A Matlab/Octave style 2-d convolution function. // from http://blog.timmlinder.com/2011/07/opencv-equivalent-to-matlabs-conv2-function/ Mat conv2(Mat &img, Mat &kernel, int convtype) { Mat dest; Mat source = img; if(CONV_FULL == convtype) { source = Mat(); int additionalRows = kernel.rows-1, additionalCols = kernel.cols-1; copyMakeBorder(img, source, (additionalRows+1)/2, additionalRows/2, (additionalCols+1)/2, additionalCols/2, BORDER_CONSTANT, Scalar(0)); } Point anchor(kernel.cols - kernel.cols/2 - 1, kernel.rows - kernel.rows/2 - 1); int borderMode = BORDER_CONSTANT; Mat fkernal; flip(kernel, fkernal, -1); filter2D(source, dest, img.depth(), fkernal, anchor, 0, borderMode); if(CONV_VALID == convtype) { dest = dest.colRange((kernel.cols-1)/2, dest.cols - kernel.cols/2) .rowRange((kernel.rows-1)/2, dest.rows - kernel.rows/2); } return dest; } // get KroneckerProduct // for upsample // see function kron() in Matlab/Octave Mat kron(Mat &a, Mat &b){ Mat res = Mat::zeros(a.rows * b.rows, a.cols * b.cols, CV_64FC1); for(int i=0; i= M.ATD(i, j) && (val - M.ATD(i, j) < minDiff)){ minDiff = val - M.ATD(i, j); res.x = j; res.y = i; } } } return res; } Mat Pooling(Mat &M, int pVert, int pHori, int poolingMethod, vector&locat, bool isTest){ int remX = M.cols % pHori; int remY = M.rows % pVert; Mat newM; if(remX == 0 && remY == 0) M.copyTo(newM); else{ Rect roi = Rect(remX, remY, M.cols - remX, M.rows - remY); M(roi).copyTo(newM); } Mat res = Mat::zeros(newM.rows / pVert, newM.cols / pHori, CV_64FC1); for(int i=0; i&locat){ Mat res; if(POOL_MEAN == poolingMethod){ Mat one = Mat::ones(pVert, pHori, CV_64FC1); res = kron(M, one) / (pVert * pHori); }elif(POOL_MAX == poolingMethod || POOL_STOCHASTIC == poolingMethod){ res = Mat::zeros(M.rows * pVert, M.cols * pHori, CV_64FC1); for(int i=0; i(i); for(int j=0; j(); } } convk.W = convk.W * (2 * epsilon) - epsilon; convk.b = 0; convk.Wgrad = Mat::zeros(width, width, CV_64FC1); convk.bgrad = 0; } void weightRandomInit(Ntw &ntw, int inputsize, int hiddensize, int nsamples){ double epsilon = sqrt((double)6) / sqrt((double)(hiddensize + inputsize + 1)); double *pData; ntw.W = Mat::ones(hiddensize, inputsize, CV_64FC1); for(int i=0; i(i); for(int j=0; j(); } } ntw.W = ntw.W * (2 * epsilon) - epsilon; ntw.b = Mat::zeros(hiddensize, 1, CV_64FC1); ntw.Wgrad = Mat::zeros(hiddensize, inputsize, CV_64FC1); ntw.bgrad = Mat::zeros(hiddensize, 1, CV_64FC1); } void weightRandomInit(SMR &smr, int nclasses, int nfeatures){ double epsilon = 0.01; smr.Weight = Mat::ones(nclasses, nfeatures, CV_64FC1); double *pData; for(int i = 0; i(i); for(int j=0; j(); } } smr.Weight = smr.Weight * (2 * epsilon) - epsilon; smr.b = Mat::zeros(nclasses, 1, CV_64FC1); smr.cost = 0.0; smr.Wgrad = Mat::zeros(nclasses, nfeatures, CV_64FC1); smr.bgrad = Mat::zeros(nclasses, 1, CV_64FC1); } void ConvNetInitPrarms(Cvl &cvl, vector&HiddenLayers, SMR &smr, int imgDim, int nsamples){ // Init Conv layers for(int j=0; j