如何调查随机安卓原生函数调用错误?

首先，我很抱歉问题的标题，我遇到了一个问题，我找不到有关我得到的日志错误的任何信息。

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代码是使用c++完成的，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相机并开始将每一帧与参考图像匹配。

在每一帧上，我调用一个名为detectFeatures的本机方法，该方法返回一个双精度值，表示两个图像匹配的百分比。

有时应用程序可以工作，有时它会崩溃，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)

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的信息，但我找不到任何关于它的信息.

也许你们可以帮助我解决这个问题。

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;

}