我是c ++的新手，我不知道为什么我的程序崩溃只是猜测。
以下程序突然开始在第49行的void saveSig(cv::Mat *frame)行自动崩溃
甚至没有踏入自己的功能。
之前运行得很好。
该计划的目的是在某些情况下跟踪视频中的某个人，因为他们还没有被冒犯，所以我不会过去。
我只能猜测我已经没有堆栈了，我不知道为什么，再次它可能是我错过的韭菜，或者我刚刚用完堆栈空间，或者它可能是完全不同的东西而且非常愚蠢。
PS：对不起，如果代码不是“漂亮”，我对C ++和OpenCV真的很陌生，我会很感激任何关于“糟糕的编码实践”的评论。
#include "myCVFunctions.h"
#include <vector>
#define LOADING_VIDEO_ERROR -1
#define LOADING_BACKGROUND_IMAGE_ERROR -2
#define FRAME_BUFFER_SIZE 10
#define SIG_BUFFER_SIZE 6
const cv::string g_c_videoFilePath = "res/tmp.mp4";
const cv::string g_c_bgFilePath = "res/bg.jpg";
const cv::Mat g_c_bg = cv::imread(g_c_bgFilePath);
const cv::Rect g_c_entranceROIRect(869, 999, 345, 80);
const cv::Rect g_c_largeEntranceROIRect(869, 340, 345, 740);
const cv::Rect g_c_sigROI(869,539,345,541);
cv::Mat g_currFrameBackup;
cv::Point g_clickCoords(-1,-1);
cv::Rect g_markedROI;
bool g_trace = false;
bool g_personInside = false;
bool g_useSig = false;
char g_sigCount = 0;
double g_sig[SIG_BUFFER_SIZE];
double g_newSig[SIG_BUFFER_SIZE];
cv::Point g_inSigHeadCoords[SIG_BUFFER_SIZE];
cv::Point g_inNewSigHeadCoords[SIG_BUFFER_SIZE];
long double av1 = 0;
long double av2 = 0;
double minDiff = 9999999999.999999;
void onMouse(int event, int x, int y, int flags, void* userdata){
if(event == CV_EVENT_LBUTTONDOWN){
g_clickCoords.x = x;
g_clickCoords.y = y;
}
if(event == CV_EVENT_MOUSEMOVE && g_clickCoords.x>=0){
g_markedROI = cv::Rect(g_clickCoords, cv::Point(x,y));
g_currFrameBackup.copyTo(*((cv::Mat*)userdata));
cv::rectangle(*((cv::Mat*)userdata), g_markedROI, cv::Scalar(0,255,0));
}
if(event == CV_EVENT_LBUTTONUP){
g_trace = true;
g_useSig = true;
g_clickCoords = cv::Point(-1,-1);
}
}
void saveSig(cv::Mat *frame){ //the crash occurs here
double fftData[512*512];
cv::Mat sigROI, sigHSV, resized;
sigROI = (*frame)(g_c_sigROI);
cv::cvtColor(sigROI, sigHSV, CV_BGR2HSV);
resized = my_cv::resize_zeros(sigHSV, cv::Size(512,512));
cv::MatIterator_<cv::Vec3b> m_it = resized.begin<cv::Vec3b>();
for(int i=0; m_it!=resized.end<cv::Vec3b>(); m_it++, i++){
fftData[i] = (*m_it)[2];
}
my_cv::FFTR fft = my_cv::createFFTR<double>(fftData, 512, 512, FFT_TYPE_2D);
//cv::flip(sigHSV, sigHSV, -1);
//cv::transpose(sigHSV, sigHSV);
//cv::flip(sigHSV, sigHSV, 0);
//cv::imshow("1", sigROI);
//cv::imshow("", sigHSV);
//cv::waitKey();
//resized = my_cv::resize_zeros(sigHSV, cv::Size(512,512));
//m_it = resized.begin<cv::Vec3b>();
//for(int i=0; m_it!=resized.end<cv::Vec3b>(); m_it++, i++){
// fftData[i] = (*m_it)[2];
//}
//my_cv::FFTR fft180 = my_cv::createFFTR<double>(fftData, 512, 512, FFT_TYPE_2D);
my_cv::FFTR multFFT = my_cv::multFFT(fft, fft);
my_cv::m_reverseFFTR(multFFT, FFT_TYPE_2D);
if(g_useSig){
g_newSig[g_sigCount] = my_cv::getFFTAverege(multFFT);
}else{
g_sig[g_sigCount] = my_cv::getFFTAverege(multFFT);
}
g_sigCount++;
if(g_sigCount>=SIG_BUFFER_SIZE&&g_useSig){
av1 = ((g_sig[0]+g_sig[1]+g_sig[2]+g_sig[3]+g_sig[4]+g_sig[5])/6)/1000000.0;
av2 = ((g_newSig[0]+g_newSig[1]+g_newSig[2]+g_newSig[3]+g_newSig[4]+g_newSig[5])/6)/1000000.0;
/*for(int i=0; i<SIG_BUFFER_SIZE; i++){
for(int j=0; j<SIG_BUFFER_SIZE; j++){
double diff = abs(g_newSig[i]-g_sig[j]);
minDiff = (diff<minDiff ? diff : minDiff);
}
}*/
my_cv::deleteFFTR(fft);
//my_cv::deleteFFTR(fft180);
my_cv::deleteFFTR(multFFT);
}
}
void proccesFrame(cv::Mat *frame){
cv::Mat grayFrame, negativeFrame, bwFrame, entranceROI;
negativeFrame = g_c_bg - *frame;
cv::cvtColor(negativeFrame, grayFrame, CV_BGR2GRAY);
cv::threshold(grayFrame, bwFrame, 30, 255, cv::THRESH_BINARY);
cv::Mat erode = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(7,7));
cv::Mat dilate = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(10,10));
cv::erode(bwFrame, bwFrame, erode);
cv::dilate(bwFrame, bwFrame, dilate);
entranceROI = bwFrame(g_c_largeEntranceROIRect);
cv::MatIterator_<uchar> m_it = entranceROI.begin<uchar>();
for(g_personInside = false; m_it!=entranceROI.end<uchar>(); m_it++){
if(*m_it==255){
g_personInside = true;
break;
}
}
if(!g_personInside){
g_trace = false;
g_sigCount = 0;
av1 = 0;
av2 = 0;
minDiff = 9999999999.999999;
}else{
if(g_sigCount<SIG_BUFFER_SIZE){
cv::Mat ROI = bwFrame(g_c_entranceROIRect);
cv::MatIterator_<uchar> bw_it = bwFrame.begin<uchar>();
if(!g_useSig){
for(int i=0; bw_it!=bwFrame.end<uchar>(); bw_it++, i++){
if(*bw_it==255){
g_inSigHeadCoords[g_sigCount] = cv::Point(i%bwFrame.cols, i/bwFrame.cols);
break;
}
}
}else{
for(int i=0; bw_it!=bwFrame.end<uchar>(); bw_it++, i++){
if(*bw_it==255){
g_inNewSigHeadCoords[g_sigCount] = cv::Point(i%bwFrame.cols, i/bwFrame.cols);
break;
}
}
}
saveSig(frame);
}
cv::putText(*frame, "Person inside", cv::Point(20,120), CV_FONT_HERSHEY_PLAIN, 3.0, cv::Scalar(0,255,0), 2);
if(g_useSig&&g_sigCount>=SIG_BUFFER_SIZE){
g_sig;
g_newSig;
g_sigCount++;
//g_trace = true;
}
if(g_trace){
std::vector<std::vector<cv::Point>> contours;
std::vector<cv::Vec4i> hierarchy;
findContours(bwFrame, contours, hierarchy, CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE);
std::vector<std::vector<cv::Point>>::iterator o_it = contours.begin();
for(; o_it!=contours.end(); o_it++){
std::vector<cv::Point>::iterator i_it = (*o_it).begin();
for(; i_it!=(*o_it).end()-1; i_it++){
cv::line(*frame, *i_it, *(i_it+1), cv::Scalar(0,255,0) , 3);
}
}
}
}
}
int main(int argc, char* argv[]){
//init//////////////////////////////////////////////////////////////////////
cv::VideoCapture videoBuffer(g_c_videoFilePath);
if(!videoBuffer.isOpened()){
std::cerr << "Can't load video please check the paths\n";
return LOADING_VIDEO_ERROR;
}
if(!g_c_bg.data){
std::cerr << "Can't load background image please check the paths\n";
return LOADING_BACKGROUND_IMAGE_ERROR;
}
std::vector<cv::Mat> frameBuffer;
frameBuffer.resize(FRAME_BUFFER_SIZE);
const std::vector<cv::Mat>::iterator currFrame = frameBuffer.begin();
const cv::string mainWindow = "Object Tracker";
cv::namedWindow(mainWindow, CV_WINDOW_AUTOSIZE);
cv::setMouseCallback(mainWindow, onMouse, (void*)&(*currFrame));
//init end/////////////////////////////////////////////////////////////////////////////
//video loop///////////////////////////////////////////////////////////////////////////
for(char paused = 0;;){
paused = (cv::waitKey(20)==' ' ? 1 : 0);
while(paused){
cv::resize(*currFrame, *currFrame, cv::Size(900, 540));
cv::imshow(mainWindow, *currFrame);
paused = (cv::waitKey(20)==' ' ? 0 : 1);
}
cv::Mat frame;
videoBuffer.read(frame);
frame.copyTo(g_currFrameBackup);
frameBuffer.pop_back();
frameBuffer.insert(frameBuffer.begin(), frame);
std::stringstream ss;
ss << "Frame: " << videoBuffer.get(CV_CAP_PROP_POS_FRAMES);
cv::putText(*currFrame, ss.str().c_str(), cv::Point(20,70), CV_FONT_HERSHEY_PLAIN, 3.0, cv::Scalar(0,255,0), 2);
proccesFrame(&(*currFrame));
/*if(g_personInside){
cv::resize(*currFrame, *currFrame, cv::Size(900, 540));
while(cv::waitKey(40)!=' ')
cv::imshow(mainWindow, *currFrame);
}*/
cv::resize(*currFrame, *currFrame, cv::Size(900, 540));
cv::imshow(mainWindow, *currFrame);
}
//video loop end///////////////////////////////////////////////////////////////////////
return 0;
}
和“myCVFunctions.h”文件：
#pragma once
#include "opencv\cv.h"
#include "opencv\highgui.h"
#include "fftw3.h"
#define FFT_TYPE_1D 1
#define FFT_TYPE_2D 2
namespace my_cv{
struct myComplex{
double real;
double imag;
};
struct FFTR{
myComplex** data;
int cols;
int rows;
};
struct ENTROPR{
double** data;
int cols;
int rows;
};
void printFFTR(FFTR fft);
FFTR createFFTR(cv::Mat mGrey, int type){
FFTR result;
result.rows = mGrey.rows, result.cols = mGrey.cols;
result.data = new myComplex*[result.cols];
for(int i = 0; i<result.cols; i++)
result.data[i] = new myComplex[result.rows];
fftw_complex *in, *out;
fftw_plan p;
in = (fftw_complex*) fftw_malloc(sizeof(fftw_complex) * result.rows * result.cols);
out = (fftw_complex*) fftw_malloc(sizeof(fftw_complex) * result.rows * result.cols);
switch(type){
case FFT_TYPE_1D:
p = fftw_plan_dft_1d(result.rows*result.cols, in, out, FFTW_FORWARD, FFTW_ESTIMATE);
break;
case FFT_TYPE_2D:
p = fftw_plan_dft_2d(result.rows, result.cols, in, out, FFTW_FORWARD, FFTW_ESTIMATE);
break;
}
cv::MatIterator_<uchar> mGrey_it = mGrey.begin<uchar>();
for(int i=0; mGrey_it != mGrey.end<uchar>(); mGrey_it++, i++){
in[i][0] = *mGrey_it;
in[i][1] = 0;
}
fftw_execute(p);
for(int i=0; i<result.rows*result.cols; i++){
int x = i%result.cols, y = i/result.cols;
result.data[x][y].real = out[i][0];
result.data[x][y].imag = out[i][1];
}
fftw_destroy_plan(p);
fftw_free(in);
fftw_free(out);
return result;
}
template<class T> FFTR createFFTR(const T* const mat, int cols, int rows, int type){
FFTR result;
result.rows = rows, result.cols = cols;
result.data = new myComplex*[result.cols];
for(int i = 0; i<result.cols; i++)
result.data[i] = new myComplex[result.rows];
fftw_complex *in, *out;
fftw_plan p;
in = (fftw_complex*) fftw_malloc(sizeof(fftw_complex) * result.rows * result.cols);
out = (fftw_complex*) fftw_malloc(sizeof(fftw_complex) * result.rows * result.cols);
switch(type){
case FFT_TYPE_1D:
p = fftw_plan_dft_1d(result.rows*result.cols, in, out, FFTW_FORWARD, FFTW_ESTIMATE);
break;
case FFT_TYPE_2D:
p = fftw_plan_dft_2d(result.rows, result.cols, in, out, FFTW_FORWARD, FFTW_ESTIMATE);
break;
}
for(int i=0; i<cols*rows; i++){
in[i][0] = mat[i];
in[i][1] = 0;
}
fftw_execute(p);
for(int i=0; i<result.rows*result.cols; i++){
int x = i%result.cols, y = i/result.cols;
result.data[x][y].real = out[i][0];
result.data[x][y].imag = out[i][1];
}
fftw_destroy_plan(p);
fftw_free(in);
fftw_free(out);
return result;
}
void m_reverseFFTR(FFTR fft, int type){
fftw_complex *in, *out;
fftw_plan p;
int scaleFactor = fft.cols*fft.rows;
in = (fftw_complex*) fftw_malloc(sizeof(fftw_complex) * fft.rows * fft.cols);
out = (fftw_complex*) fftw_malloc(sizeof(fftw_complex) * fft.rows * fft.cols);
switch(type){
case FFT_TYPE_1D:
p = fftw_plan_dft_1d(fft.rows*fft.cols, in, out, FFTW_BACKWARD, FFTW_ESTIMATE);
break;
case FFT_TYPE_2D:
p = fftw_plan_dft_2d(fft.rows, fft.cols, in, out, FFTW_BACKWARD, FFTW_ESTIMATE);
break;
}
for(int j=0; j<fft.rows; j++)
for(int i=0; i<fft.cols; i++){
int index = j*fft.cols+i;
in[index][0] = fft.data[i][j].real;
in[index][1] = fft.data[i][j].imag;
}
fftw_execute(p);
for(int i=0; i<fft.rows*fft.cols; i++){
int x = i%fft.cols, y = i/fft.cols;
fft.data[x][y].real = out[i][0]/scaleFactor;
fft.data[x][y].imag = out[i][1]/scaleFactor;
}
fftw_destroy_plan(p);
fftw_free(in);
fftw_free(out);
}
FFTR multFFT(const FFTR fft1, const FFTR fft2){
FFTR result;
result.cols = fft1.cols;
result.rows = fft1.rows;
result.data = new myComplex*[result.cols];
for(int i=0; i<result.cols; i++)
result.data[i] = new myComplex[result.rows];
for(int i=0; i<result.cols; i++){
for(int j=0; j<result.rows; j++){
result.data[i][j].real = (fft1.data[i][j].real*fft2.data[i][j].real)-(fft1.data[i][j].imag*fft2.data[i][j].imag);
result.data[i][j].imag = (fft1.data[i][j].real*fft2.data[i][j].imag)+(fft1.data[i][j].imag*fft2.data[i][j].real);
}
}
return result;
}
long double getFFTAverege(FFTR fft){
long double result = 0;
for(int i=0; i<fft.cols; i++){
long double sum=0;
for(int j=0; j<fft.rows; j++){
sum += fft.data[i][j].real;
}
result += sum/fft.rows;
}
return result/fft.rows;
}
void deleteFFTR(FFTR fftr){
for(int i=0; i<fftr.cols; i++)
if(fftr.data[i]) delete [] fftr.data[i];
if(fftr.data) delete [] fftr.data;
}
void printFFTR(FFTR fft){
for(int j=0; j<fft.rows; j++){
for(int i=0; i<fft.cols; i++){
printf("%f%si%f\n", fft.data[i][j].real, (fft.data[i][j].imag<0 ? "-" : "+"), abs(fft.data[i][j].imag));
}
}
}
cv::Mat resize_zeros(const cv::Mat src, cv::Size newSize){
cv::Mat srcROI, result, resultROI;
result.create(newSize, src.type());
srcROI = src(cv::Rect(0,0,(src.cols>result.cols ? result.cols : src.cols), (src.rows>result.rows ? result.rows : src.rows)));
result = 0;
resultROI = result(cv::Rect(0,0, srcROI.cols, srcROI.rows));
srcROI.copyTo(resultROI);
return result;
}
//otsu's threshhold
template<class T> T getThreshold(cv::Mat mGrey){
uchar* image = mGrey.data;
int columns = mGrey.cols;
int rows = mGrey.rows;
const T SIGMA = 0.000001;
const int num_bins = 257;
int counts[num_bins] = {0};
T p[num_bins] = {0};
T mu[num_bins] = {0};
T omega[num_bins] = {0};
T sigma_b_squared[num_bins] = {0};
int sumC;
// calculate histogram
for(int i = 0; i < rows*columns; i++)
counts[image[i]]++;
sumC = 0;
for(int i = 0; i < num_bins; i++)
sumC += counts[i];
for(int i = 0; i < num_bins; i++)
p[i] = ((T)counts[i])/sumC;
mu[0] = omega[0] = p[0];
for(int i = 1; i < num_bins; i++){
omega[i] = omega[i-1] + p[i];
mu[i] = mu[i-1] + p[i]*(i+1);
}
T mu_t = mu[num_bins-1];
T maxval = -1.0;
for(int i = 0; i < num_bins; i++){
T v = mu_t * omega[i] - mu[i];
if (omega[i] > SIGMA && abs(1.0-omega[i]) > SIGMA){
sigma_b_squared[i] = v*v/(omega[i]* (1.0 - omega[i]));
maxval = std::max(maxval,sigma_b_squared[i]);
}
}
// Find the location of the maximum value of sigma_b_squared.
// The maximum may extend over several bins, so average together the
// locations.
// If maxval == -1, sigma_b_squared is not defined, then return 0.
T level = 0;
if (maxval > 0){
T idx = 0;
int maxNumbers = 0;
for(int i = 0; i < num_bins; i++){
if (sigma_b_squared[i] == maxval){
idx += i;
maxNumbers++;
}
}
if (maxNumbers >= 0){
idx /= maxNumbers;
// Normalize the threshold to the range [0, 1].
// level = (idx - 1) / (num_bins - 1);
level = idx / (num_bins - 1);
}
}
return level;
}
}
答案 0 :(得分：3)
double fftData[512*512];
那可能是2MB的数据，这可能（可能）太大而无法放入堆栈。最简单的解决方法是使用动态数组：
std::vector<double> fftData(512*512);
或者，如果动态分配过于昂贵，则可以使用静态或全局数组。这通常是个坏主意，因为它使得函数在多线程程序中不可重入且难以使用;但是，你已经有了这么多的全局变量，而且还有一个可能不会受到伤害。