我正在以下列方式在多个线程上运行OpenCV代码：
std::thread t1(runOnSingleCamera, alphaFile, featureToUse, classifier,0);
std::thread t2(runOnSingleCamera, betaFile, featureToUse, classifier,1);
std::thread t3(runOnSingleCamera, gammaFile, featureToUse, classifier,2);
std::thread t4(runOnSingleCamera, deltaFile, featureToUse, classifier,3);
t1.join();
t2.join();
t3.join();
t4.join();
编译很好，但是当我运行它时会出现各种错误，甚至偶尔也会有效......
以下是我得到的一些错误的示例：
tom@thinkpad:~/Documents/Project/reidThermal/src$ ./main -d=1 -c=0 -f=1
Segmentation fault (core dumped)
tom@thinkpad:~/Documents/Project/reidThermal/src$ ./main -d=1 -c=0 -f=1
(betaInput.webm:8571): GLib-GObject-WARNING **: cannot register existing type 'CvImageWidget'
(betaInput.webm:8571): GLib-GObject-WARNING **: cannot register existing type 'CvImageWidget'
(betaInput.webm:8571): GLib-GObject-WARNING **: cannot register existing type 'CvImageWidget'
(betaInput.webm:8571): GLib-GObject-WARNING **: cannot register existing type 'CvImageWidget'
(betaInput.webm:8571): Gtk-CRITICAL **: IA__gtk_widget_new: assertion 'g_type_is_a (type, GTK_TYPE_WIDGET)' failed
(betaInput.webm:8571): Gtk-CRITICAL **: IA__gtk_widget_new: assertion 'g_type_is_a (type, GTK_TYPE_WIDGET)' failed
Segmentation fault (core dumped)
tom@thinkpad:~/Documents/Project/reidThermal/src$ ./main -d=1 -c=0 -f=1
(alphaInput.webm:8593): GLib-GObject-WARNING **: invalid cast from 'CvImageWidget' to 'CvImageWidget'
** (alphaInput.webm:8593): CRITICAL **: void cvImageWidget_size_allocate(GtkWidget*, GtkAllocation*): assertion 'CV_IS_IMAGE_WIDGET (widget)' failed
** (alphaInput.webm:8593): CRITICAL **: void cvImageWidget_realize(GtkWidget*): assertion 'CV_IS_IMAGE_WIDGET (widget)' failed
**
Gtk:ERROR:/build/gtk+2.0-KsZKkB/gtk+2.0-2.24.30/gtk/gtkwidget.c:8861:gtk_widget_real_map: assertion failed: (gtk_widget_get_realized (widget))
Aborted (core dumped)
tom@thinkpad:~/Documents/Project/reidThermal/src$ ./main -d=1 -c=0 -f=1
/usr/share/themes/Ambiance/gtk-2.0/gtkrc:720: Unable to find include file: "apps/ff.rc"
(betaInput.webm:8615): GLib-GObject-WARNING **: cannot register existing type 'CvImageWidget'
(betaInput.webm:8615): GLib-GObject-WARNING **: cannot register existing type 'CvImageWidget'
(betaInput.webm:8615): Gtk-CRITICAL **: IA__gtk_widget_new: assertion 'g_type_is_a (type, GTK_TYPE_WIDGET)' failed
Segmentation fault (core dumped)
之前有没有人见过/知道出了什么问题以及如何解决？
使用gdb运行提供以下内容：
Thread 4 "main" received signal SIGSEGV, Segmentation fault.
[Switching to Thread 0x7fffdb7fe700 (LWP 29317)]
0x0000000000000000 in ?? ()
在使用最新版本的OpenCV时，我正在使用最新版本的Ubuntu。
根据要求提供下面的完整代码，它很长并且由多个元素组成，我想这个问题可能是早期或在声明中，或者可能与imshow函数不兼容：
int runOnSingleCamera(String file, int featureToUse, int classifier, int cameraID)
{
//enable velocity
int timeSteps = 0;
string windowName = file; // window name
Mat img, outputImage, foreground; // image objects
VideoCapture cap;
bool keepProcessing = true; // loop control flag
unsigned char key; // user input
int EVENT_LOOP_DELAY = 40; // delay for GUI window, 40 ms equates to 1000ms/25fps = 40ms per frame
vector<vector<Point> > contours;
vector<Vec4i> hierarchy;
int width = 40;
int height = 100;
int learning = 1000;
int padding = 40;
// if command line arguments are provided try to read image/video_name
// otherwise default to capture from attached H/W camera
if((cap.open(file) == true))
{
// create window object (use flag=0 to allow resize, 1 to auto fix size)
namedWindow(windowName, 1);
// create background / foreground Mixture of Gaussian (MoG) model
Ptr<BackgroundSubtractorMOG2> MoG = createBackgroundSubtractorMOG2(500,25,false);
HOGDescriptor hog;
hog.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector());
CascadeClassifier cascade = CascadeClassifier(CASCADE_TO_USE);
Ptr<SuperpixelSEEDS> seeds;
// start main loop
while(keepProcessing)
{
int64 timeStart = getTickCount();
if (cap.isOpened())
{
cap >> img;
if(img.empty())
{
std::cerr << "End of video file reached" << std::endl;
exit(0);
}
outputImage = img.clone();
cvtColor(img, img, CV_BGR2GRAY);
}
else
{
// if not a capture object set event delay to zero so it waits
// indefinitely (as single image file, no need to loop)
EVENT_LOOP_DELAY = 0;
}
// update background model and get background/foreground
MoG->apply(img, foreground, (double)(1.0/learning));
//imshow("old foreground", foreground);
/////////////////////////////////////////////////////////////////////////////////SUPERPIXELS
int useSuperpixels = 0;
if(useSuperpixels == 1)
{
Mat seedMask, labels, result;
result = img.clone();
int width = img.size().width;
int height = img.size().height;
seeds = createSuperpixelSEEDS(width, height, 1, 2000, 10, 2, 5, true);
seeds->iterate(img, 10);
seeds->getLabels(labels);
vector<int> counter(seeds->getNumberOfSuperpixels(),0);
vector<int> numberOfPixelsPerSuperpixel(seeds->getNumberOfSuperpixels(),0);
vector<bool> useSuperpixel(seeds->getNumberOfSuperpixels(),false);
for(int i = 0; i<foreground.rows; i++)
{
for(int j = 0; j<foreground.cols; j++)
{
numberOfPixelsPerSuperpixel[labels.at<int>(i,j)] += 1;
if(foreground.at<unsigned char>(i,j)==255)
{
counter[labels.at<int>(i,j)] += 1;
}
}
}
for(int i = 0; i<counter.size(); i++)
{
if(counter[i]/numberOfPixelsPerSuperpixel[i] > 0.0001)
{
useSuperpixel[i] = true;
}
}
for(int i = 0; i<foreground.rows; i++)
{
for(int j = 0; j<foreground.cols; j++)
{
if(useSuperpixel[labels.at<int>(i,j)] == true)
{
foreground.at<unsigned char>(i,j) = 255;
}
else
{
foreground.at<unsigned char>(i,j) = 0;
}
}
}
}
/////////////////////////////////////////////////////////////////////////////////
else
{
// perform erosion - removes boundaries of foreground object
erode(foreground, foreground, Mat(),Point(),1);
// perform morphological closing
dilate(foreground, foreground, Mat(),Point(),5);
erode(foreground, foreground, Mat(),Point(),1);
}
//imshow("foreground", foreground);
// get connected components from the foreground
findContours(foreground, contours, hierarchy, CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE);
// iterate through all the top-level contours,
// and get bounding rectangles for them (if larger than given value)
for(int idx = 0; idx >=0; idx = hierarchy[idx][0])
{
Rect r = boundingRect(contours[idx]);
// adjust bounding rectangle to be padding% larger
// around the object
r.x = max(0, r.x - (int) (padding/100.0 * (double) r.width));
r.y = max(0, r.y - (int) (padding/100.0 * (double) r.height));
r.width = min(img.cols - 1, (r.width + 2 * (int) (padding/100.0 * (double) r.width)));
r.height = min(img.rows - 1, (r.height + 2 * (int) (padding/100.0 * (double) r.height)));
// draw rectangle if greater than width/height constraints and if
// also still inside image
if ((r.width >= width) && (r.height >= height) && (r.x + r.width < img.cols) && (r.y + r.height < img.rows))
{
vector<Rect> found, found_filtered;
Mat roi = outputImage(r);
if (classifier == 1)
{
//changing last parameter helps deal with multiple rectangles per person
if (cameraID == 3)
{
hog.detectMultiScale(roi, found, 0, Size(8,8), Size(32,32), 1.05, 5);
}
else
{
hog.detectMultiScale(roi, found, 0, Size(8,8), Size(64,64), 1.05, 5);
}
}
else
{
if (cameraID == 3)
{
cascade.detectMultiScale(roi, found, 1.1, 4, CV_HAAR_DO_CANNY_PRUNING, cvSize(32,32));
}
else
{
cascade.detectMultiScale(roi, found, 1.1, 4, CV_HAAR_DO_CANNY_PRUNING, cvSize(64,64));
}
}
for(size_t i = 0; i < found.size(); i++ )
{
Rect rec = found[i];
rec.x += r.x;
rec.y += r.y;
size_t j;
// Do not add small detections inside a bigger detection.
for ( j = 0; j < found.size(); j++ )
{
if ( j != i && (rec & found[j]) == rec )
{
break;
}
}
if (j == found.size())
{
found_filtered.push_back(rec);
}
}
for (size_t i = 0; i < found_filtered.size(); i++)
{
Rect rec = found_filtered[i];
// The HOG/Cascade detector returns slightly larger rectangles than the real objects,
// so we slightly shrink the rectangles to get a nicer output.
rec.x += rec.width*0.1;
rec.width = rec.width*0.8;
rec.y += rec.height*0.1;
rec.height = rec.height*0.8;
// rectangle(img, rec.tl(), rec.br(), cv::Scalar(0,255,0), 3);
Point2f center = Point2f(float(rec.x + rec.width/2.0), float(rec.y + rec.height/2.0));
Mat regionOfInterest;
Mat regionOfInterestOriginal = img(rec);
//Mat regionOfInterestOriginal = img(r);
Mat regionOfInterestForeground = foreground(rec);
//Mat regionOfInterestForeground = foreground(r);
bitwise_and(regionOfInterestOriginal, regionOfInterestForeground, regionOfInterest);
Mat clone = regionOfInterest.clone();
resize(clone, regionOfInterest, Size(64,128), CV_INTER_CUBIC);
imshow("roi", regionOfInterest);
double huMoments[7];
vector<double> hu(7);
Mat hist;
vector<float> descriptorsValues;
Mat feature;
if(featureToUse == 1) //HuMoments
{
vector<vector<Point> > contoursHu;
vector<Vec4i> hierarchyHu;
findContours(regionOfInterest, contoursHu, hierarchyHu, CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE);
double largestSize,size;
int largestContour;
for(int i = 0; i < contoursHu.size(); i++)
{
size = contoursHu[i].size();
if(size > largestSize)
{
largestSize = size;
largestContour = i;
}
}
Moments contourMoments;
contourMoments = moments(contoursHu[largestContour]);
HuMoments(contourMoments, huMoments);
hu.assign(huMoments,huMoments+7);
feature = Mat(hu);
feature = feature.t();
}
else if(featureToUse == 2) //HistogramOfIntensities
{
int histSize = 16; // bin size - need to determine which pixel threshold to use
float range[] = {0,255};
const float *ranges[] = {range};
int channels[] = {0, 1};
calcHist(®ionOfInterest, 1, channels, Mat(), hist, 1, &histSize, ranges, true, false);
feature = hist.clone();
feature = feature.t();
}
else if(featureToUse == 3) //HOG
{
//play with these parameters to change HOG size
cv::HOGDescriptor descriptor(Size(64, 128), Size(16, 16), Size(16, 16), Size(16, 16), 4, -1, 0.2, true, 64);
descriptor.compute(regionOfInterest, descriptorsValues);
feature = Mat(descriptorsValues);
feature = feature.t();
}
else if(featureToUse == 4) //Correlogram
{
Mat correlogram(8,8,CV_64F);
Mat occurances(8,8,CV_8U);
int xIntensity, yIntensity;
for(int i = 0; i<regionOfInterest.rows; i++)
{
for(int j = 0; j<regionOfInterest.cols; j++)
{
xIntensity = floor(regionOfInterest.at<unsigned char>(i,j)/32);
for(int k = i; k<regionOfInterest.rows; k++)
{
for(int l = 0; l<regionOfInterest.cols; l++)
{
if((k == i && l > j) || k > i)
{
yIntensity = floor(regionOfInterest.at<unsigned char>(k,l)/32);
correlogram.at<double>(xIntensity,yIntensity) += (norm(Point(i,j)-Point(k,l)));
correlogram.at<double>(yIntensity,xIntensity) += (norm(Point(i,j)-Point(k,l)));
occurances.at<unsigned char>(xIntensity,yIntensity) += 1;
occurances.at<unsigned char>(yIntensity,xIntensity) += 1;
}
}
}
}
}
//average it out
for(int i = 0; i<correlogram.rows; i++)
{
for(int j = 0; j<correlogram.cols; j++)
{
correlogram.at<double>(i,j) = occurances.at<unsigned char>(i,j);
}
}
feature = correlogram.reshape(1,1);
}
else if(featureToUse == 5) //Flow
{
}
feature.convertTo(feature, CV_64F);
normalize(feature, feature, 1, 0, NORM_L1, -1, Mat());
cout << "New Feature" << endl << feature << endl;
//classify first target
if(targets.size() == 0) //if first target found
{
Person person(0, center.x, center.y, timeSteps, rec.width, rec.height);
person.kalmanCorrect(center.x, center.y, timeSteps, rec.width, rec.height);
Rect p = person.kalmanPredict();
person.updateFeatures(feature);
person.setCurrentCamera(cameraID);
rectangle(outputImage, p.tl(), p.br(), cv::Scalar(255,0,0), 3);
char str[200];
sprintf(str,"Person %d",person.getIdentifier());
putText(outputImage, str, center, FONT_HERSHEY_SIMPLEX,1,(0,0,0));
targets.push_back(person);
}
else
{
vector<double> mDistances;
bool singleEntry = false;
for(int i = 0; i<targets.size(); i++)
{
if(targets[i].getFeatures().rows == 1)
{
singleEntry = true;
}
}
for(int i = 0; i<targets.size(); i++)
{
Mat covar, mean;
Mat data = targets[i].getFeatures();
calcCovarMatrix(data,covar,mean,CV_COVAR_NORMAL|CV_COVAR_ROWS);
// cout << i << " data" << endl << data << endl;
// cout << i << " Covar" << endl << covar << endl;
// cout << i << " mean" << endl << mean << endl;
double mDistance;
if(singleEntry == false)
{
Mat invCovar;
invert(covar,invCovar,DECOMP_SVD);
mDistance = Mahalanobis(feature,mean,invCovar);
cout << i << " Mahalanobis Distance" << endl << mDistance << endl;
}
else
{
mDistance = norm(feature,mean,NORM_L1);
cout << i << " Norm Distance" << endl << mDistance << endl;
}
mDistances.push_back(mDistance);
}
Mat test = Mat(mDistances);
cout << "Distances" << endl << test << endl;
double sum = 0.0;
for(int i = 0; i<mDistances.size(); i++)
{
sum += mDistances[i];
}
for(int i = 0; i<mDistances.size(); i++)
{
mDistances[i] = sum/mDistances[i];
}
normalize(mDistances,mDistances,1,0,NORM_L1,-1,Mat());
Mat probabilities = Mat(mDistances);
cout << "Probabilities" << endl << probabilities << endl;
//special case to classify second target
if(targets.size() == 1)
{
if(fabs(center.x-targets[0].getLastPosition().x)<100 and fabs(center.y-targets[0].getLastPosition().y)<100)
{
targets[0].kalmanCorrect(center.x, center.y, timeSteps, rec.width, rec.height);
Rect p = targets[0].kalmanPredict();
targets[0].updateFeatures(feature);
targets[0].setCurrentCamera(cameraID);
rectangle(outputImage, p.tl(), p.br(), cv::Scalar(255,0,0), 3);
char str[200];
sprintf(str,"Person %d",targets[0].getIdentifier());
putText(outputImage, str, center, FONT_HERSHEY_SIMPLEX,1,(0,0,0));
}
else
{
Person person(1, center.x, center.y, timeSteps, rec.width, rec.height);
person.kalmanCorrect(center.x, center.y, timeSteps, rec.width, rec.height);
Rect p = person.kalmanPredict();
person.updateFeatures(feature);
person.setCurrentCamera(cameraID);
rectangle(outputImage, p.tl(), p.br(), cv::Scalar(255,0,0), 3);
char str[200];
sprintf(str,"Person %d",person.getIdentifier());
putText(outputImage, str, center, FONT_HERSHEY_SIMPLEX,1,(0,0,0));
targets.push_back(person);
}
}
else
{
double greatestProbability = 0.0;
int identifier = 0;
double min, max;
Point min_loc, max_loc;
minMaxLoc(probabilities, &min, &max, &min_loc, &max_loc);
greatestProbability = max;
identifier = max_loc.y;
cout << greatestProbability << " at " << identifier << endl;
if(greatestProbability >= 0.5)
{
targets[identifier].kalmanCorrect(center.x, center.y, timeSteps, rec.width, rec.height);
Rect p = targets[identifier].kalmanPredict();
targets[identifier].updateFeatures(feature);
targets[identifier].setCurrentCamera(cameraID);
rectangle(outputImage, p.tl(), p.br(), cv::Scalar(255,0,0), 3);
char str[200];
sprintf(str,"Person %d",targets[identifier].getIdentifier());
putText(outputImage, str, center, FONT_HERSHEY_SIMPLEX,1,(0,0,0));
}
else
{
int identifier = targets.size();
Person person(identifier, center.x, center.y, timeSteps, rec.width, rec.height);
person.kalmanCorrect(center.x, center.y, timeSteps, rec.width, rec.height);
Rect p = person.kalmanPredict();
person.updateFeatures(feature);
person.setCurrentCamera(cameraID);
rectangle(outputImage, p.tl(), p.br(), cv::Scalar(255,0,0), 3);
char str[200];
sprintf(str,"Person %d",person.getIdentifier());
putText(outputImage, str, center, FONT_HERSHEY_SIMPLEX,1,(0,0,0));
targets.push_back(person);
}
}
}
}
rectangle(outputImage, r, Scalar(0,0,255), 2, 8, 0);
}
}
// display image in window
imshow(windowName, outputImage);
key = waitKey((int) std::max(2.0, EVENT_LOOP_DELAY - (((getTickCount() - timeStart) / getTickFrequency())*1000)));
if (key == 'x')
{
// if user presses "x" then exit
std::cout << "Keyboard exit requested : exiting now - bye!" << std::endl;
keepProcessing = false;
}
timeSteps += 1;
}
// the camera will be deinitialized automatically in VideoCapture destructor
// all OK : main returns 0
return 0;
}
// not OK : main returns -1
return -1;
}
答案 0 :(得分：0)
正在发生的事情是你滥用OpenCV的一些功能，它不支持在多线程环境中运行，否则你就没有正确使用互斥和监视器等控制机制来限制对关键的访问代码的一部分一次一个线程。除非你分享更多代码，否则我们无法告诉你你做错了什么。从您的日志中可以看出，某种初始化似乎不止一次。
从您的代码和日志中，我会想到两件事：
如果这些都没有帮助，请尝试在代码中添加一些日志输出，以便我们确定导致错误的是哪一行。
毕竟它是＃2。要修复它，您必须将所有namedWindow调用移动到主线程。之后，如果在imshow调用中仍然失败，您还必须将其移动到主线程。你需要为每个线程提供一个条件变量，以及线程写入的全局变量和主线程用于更新窗口的全局变量。我提供了代码，但我对c ++并发性知之甚少。您可以在此处详细了解此任务：waiting thread until a condition has been occurred