如何调查随机Android原生函数调用错误（）

知识就是力量，时间就是生命。这篇文章主要讲述如何调查随机Android原生函数调用错误？相关的知识，希望能为你提供帮助。
首先，我很抱歉问题的标题，我面临一个问题，我找不到任何关于我得到的日志错误。
我正在开发一个使用Opencv进行图像处理和匹配的android应用程序。
主要的Opencv代码使用c ++完成，并使用JNI函数导出到java。
申请过程是这样的，首先我拍摄相机图像，然后打开Opencv相机并开始将每个帧与参考图像匹配。
在每个帧上，我调用一个名为detectFeatures的本机方法，它返回一个double，表示匹配的两个图像的百分比。
有时应用程序工作，有时它崩溃，我得到这个错误是日志

/data/app/com.grimg.coffretpfe-2/lib/arm/libnative-lib.so (_Z6toGrayN2cv3MatES0_+1577) /data/app/com.grimg.coffretpfe-2/lib/arm/libnative-lib.so (Java_com_grimg_coffretpfe_Activities_CompareActivity_detectFeatures+100)

在c ++代码中，我有一个名为toGray的函数，这是它的签名

double toGray(Mat captured, Mat target)

【如何调查随机Android原生函数调用错误（）】我在java中调用的jni方法是

extern "C" jdouble JNICALL Java_com_grimg_coffretpfe_Activities_CompareActivity_detectFeatures( JNIEnv *env, jclass type, jlong addrRgba, jlong addrGray /* this */) {Mat & mRgb = *(Mat *) addrRgba; Mat & mGray = *(Mat *) addrGray; jdouble retVal; double conv = toGray(mRgb, mGray); retVal = (jdouble) conv; return retVal;

}
我搜索了很多关于这个错误，我找不到任何关于它的信息。
也许你们可以帮我解决这个问题。
编辑：

double toGray(Mat captured, Mat target) { std::vector< cv::KeyPoint> keypointsCaptured; std::vector< cv::KeyPoint> keypointsTarget; cv::Mat descriptorsCaptured; cv::Mat descriptorsTarget; //cv::Mat captured; std::vector< cv::DMatch> matches; std::vector< cv::DMatch> symMatches; //std::vector< std::vector< cv::DMatch> > matches1; //std::vector< std::vector< cv::DMatch> > matches2; //Mat captured, target; /*captured = imread("/storage/emulated/0/data/img1.jpg", IMREAD_GRAYSCALE); target = imread("/storage/emulated/0/data/img3.jpg", IMREAD_GRAYSCALE); if (!captured.data) { // Print error message and quit __android_log_print(ANDROID_LOG_INFO, "sometag", "I cant do this."); } if (!target.data) { // Print error message and quit __android_log_print(ANDROID_LOG_INFO, "sometag", "cant do nuthin."); }*/ //cvtColor(capturedR, captured, CV_RGBA2GRAY); //cvtColor(targetR, target, CV_RGBA2GRAY); orb = ORB::create(); //Pre-process resize(captured, captured, Size(480, 360)); medianBlur(captured, captured, 5); resize(target, target, Size(480, 360)); medianBlur(target, target, 5); orb-> detectAndCompute(captured, noArray(), keypointsCaptured, descriptorsCaptured); orb-> detectAndCompute(target, noArray(), keypointsTarget, descriptorsTarget); //__android_log_print(ANDROID_LOG_INFO, "sometag", "keypoints2 size = %d", keypointsTarget.size()); //__android_log_print(ANDROID_LOG_INFO, "sometag", "keypoints size = %d", keypointsCaptured.size()); //Match images based on k nearest neighbour std::vector< std::vector< cv::DMatch> > matches1; matcher.knnMatch(descriptorsCaptured, descriptorsTarget, matches1, 2); //__android_log_print(ANDROID_LOG_INFO, "sometag", "Matches1 = %d",matches1.size()); std::vector< std::vector< cv::DMatch> > matches2; matcher.knnMatch(descriptorsTarget, descriptorsCaptured, matches2, 2); //Ratio filter ratioTest(matches1); ratioTest(matches2); symmetryTest(matches1, matches2, symMatches); ransacTest(symMatches, keypointsCaptured, keypointsTarget, matches); const int symMatchCount = matches.size(); Point2f point1; Point2f point2; float median; float meanBoy = 0; float greatest = 0; float lowest = 0; int count = 0; vector< float> angleList; vector< Point2f> point1List; vector< Point2f> point2List; for (int i = 0; i < matches.size(); i++) { point1 = keypointsCaptured[matches[i].queryIdx].pt; point2 = keypointsTarget[matches[i].trainIdx].pt; point1List.push_back(point1); point2List.push_back(point2); deltaY = ((360 - point2.y) - (360 - point1.y)); deltaX = (point2.x + 480 - point1.x); angle = atan2(deltaY, deltaX) * 180 / PI; cout < < "ORB Matching Results" < < angle < < endl; //if (angle > greatest) greatest = angle; //if (angle < lowest) lowest = angle; meanBoy += angle; angleList.push_back(angle); //std::cout < < "points " < < "(" < < point1.x < < "," < < 360-point1.y< < ") (" < < point2.x < < ","< < 360-point2.y< < ") angle:" < < angle < < std::endl; //std::cout < < angle < < std::endl; } // do something with the best points...//std::cout < < "Mean" < < meanBoy/symMatchCount < < std::endl; vector< float> angleLCopy(angleList); std::sort(angleLCopy.begin(), angleLCopy.end()); /*if(angleList.size() % 2 == 0) median = (angleList[angleList.size()/2 - 1] + angleList[angleList.size()/2]) / 2; else median = angleList[angleList.size()/2]; */ size_t medianIndex = angleLCopy.size() / 2; nth_element(angleLCopy.begin(), angleLCopy.begin() + medianIndex, angleLCopy.end()); median = angleLCopy[medianIndex]; std::cout < < "new Median method " < < angleLCopy[medianIndex] < < std::endl; //std::cout < < "greatest " < < greatest < < "|| lowest "< < lowest < < std::endl; //std::cout < < "No of matches by shehel: " < < angleList[35] < < " size " < < symMatchCount < < std::endl; //std::cout < < "Median" < < median < < std::endl; //std::cout < < matches.size()< < std::endl; count = 0; for (auto i = matches.begin(); i != matches.end(); ) {//std::cout < < angleList.at(count)< < std::endl; //if (angle > greatest) greatest = angle; //if (angle < lowest) lowest = angle; point1 = point1List.at(count); point2 = point2List.at(count); deltaY = ((360 - point2.y) - (360 - point1.y)); deltaX = ((point2.x + 480) - point1.x); angle = atan2(deltaY, deltaX) * 180 / PI; //angleList.push_back (angle); cout < < "Is it sorted? " < < angleList.at(count) < < endl; if (angleList.at(count) > (median + 5) | angleList.at(count) < (median - 5)) { //cout < < "bitch is gone" < < angleList.at(count) < < endl; matches.erase(i); count++; } //{i++; count++; } else { cout < < "Points A (" < < point1.x < < ", " < < point1.y < < ") B (" < < point2.x + 480 < < ", " < < point2.y < < ") Deltas of X" < < deltaX < < " Y " < < deltaY < < "Angle " < < angle < < endl; cout < < "aint going no where" < < angleList.at(count) < < endl; ++i; count++; //if (angle> 0.5 | angle < -0.7) //matches.erase(matches.begin()+i); // do something with the best points... } }return (static_cast< double> (matches.size()) / static_cast< double> (matches1.size())); }

编辑2：

cv::Mat ransacTest( const std::vector< cv::DMatch> & matches, const std::vector< cv::KeyPoint> & keypoints1, const std::vector< cv::KeyPoint> & keypoints2, std::vector< cv::DMatch> & outMatches) { // Convert keypoints into Point2f std::vector< cv::Point2f> points1, points2; cv::Mat fundemental; for (std::vector< cv::DMatch> :: const_iterator it= matches.begin(); it!= matches.end(); ++it) { // Get the position of left keypoints float x= keypoints1[it-> queryIdx].pt.x; float y= keypoints1[it-> queryIdx].pt.y; points1.push_back(cv::Point2f(x,y)); // Get the position of right keypoints x= keypoints2[it-> trainIdx].pt.x; y= keypoints2[it-> trainIdx].pt.y; points2.push_back(cv::Point2f(x,y)); } // Compute F matrix using RANSAC std::vector< uchar> inliers(points1.size(),0); if (points1.size()> 0& & points2.size()> 0){ cv::Mat fundemental= cv::findFundamentalMat( cv::Mat(points1),cv::Mat(points2), // matching points inliers,// match status (inlier or outlier) CV_FM_RANSAC, // RANSAC method distance,// distance to epipolar line confidence); // confidence probability // extract the surviving (inliers) matches std::vector< uchar> ::const_iterator itIn= inliers.begin(); std::vector< cv::DMatch> ::const_iterator itM= matches.begin(); // for all matches for ( ; itIn!= inliers.end(); ++itIn, ++itM) { if (*itIn) { // it is a valid match outMatches.push_back(*itM); } } if (refineF) { // The F matrix will be recomputed with // all accepted matches // Convert keypoints into Point2f // for final F computation points1.clear(); points2.clear(); for (std::vector< cv::DMatch> :: const_iterator it= outMatches.begin(); it!= outMatches.end(); ++it) { // Get the position of left keypoints float x= keypoints1[it-> queryIdx].pt.x; float y= keypoints1[it-> queryIdx].pt.y; points1.push_back(cv::Point2f(x,y)); // Get the position of right keypoints x= keypoints2[it-> trainIdx].pt.x; y= keypoints2[it-> trainIdx].pt.y; points2.push_back(cv::Point2f(x,y)); } // Compute 8-point F from all accepted matches if (points1.size()> 0& & points2.size()> 0){ fundemental= cv::findFundamentalMat( cv::Mat(points1),cv::Mat(points2), // matches CV_FM_8POINT); // 8-point method } } } return fundemental;

}
答案当你在toGray函数中循环匹配时，我看到可能发生崩溃：