我试图运行一段代码进行测试，到目前为止我遇到了这样的问题：
OpenCV错误：断言失败（0＆lt; = roi.x＆amp;＆amp; 0＆lt; = roi.width＆amp;＆amp; roi.x + roi.width＆lt; = m.cols＆amp;＆amp; 0＆lt; Mat中的; = roi.y＆amp;＆amp; 0＆lt; = roi.height＆amp;＆amp; roi.y + roi.height＆lt; = m.rows），文件/home/diji/opencv-3.2.0/modules /core/src/matrix.cpp，第522行 在抛出'cv :: Exception'的实例后终止调用 what（）：/ home / diji / opencv-3.2.0 / modules / core / src / matrix.cpp：522：error：（ - 1515）0＆lt; = roi.x＆amp;＆amp; 0＆lt; = roi.width＆amp;＆amp; roi.x + roi.width＆lt; = m.cols＆amp;＆amp; 0＆lt; = roi.y＆amp;＆amp; 0＆lt; = roi.height＆amp;＆amp; roi.y + roi.height＆lt; = m.rows in function Mat
我已粘贴以下代码
int main() {
// Set-Up
int houghVote = 200;
string arg = "";
// Set up windows
bool showOriginal = 1;
bool showCanny = 1;
bool showHough = 1;
bool showHoughP = 1;
// Capture Input
string window_name = "Processed Video";
namedWindow(window_name, CV_WINDOW_KEEPRATIO); //resizable window;
VideoCapture capture("/home/lane-detection-master/test_video/mm.mp4");
if (!capture.isOpened()) // Caputure Camera
{capture.open(atoi(arg.c_str()));}
capture.set(CV_CAP_PROP_POS_MSEC, 100000); //start the video at 100 seconds in
double dWidth = capture.get(CV_CAP_PROP_FRAME_WIDTH); //get the width of frames of the video
double dHeight = capture.get(CV_CAP_PROP_FRAME_HEIGHT); //get the height of frames of the video
std::cout << "Frame Size = " << dWidth << "x" << dHeight << std::endl;
Size frameSize(static_cast<int>(dWidth), static_cast<int>(dHeight));
// Encode resulting video
// VideoWriter oVideoWriter ("LaneDetection.avi", CV_FOURCC('P','I','M','1'), 20, frameSize, true);
// Process Frame
Mat image;
double frameItr = 0;
image = imread(arg);
int crestCount = 0, frameSkip = 0;
while (1)
{
// capture on intervals to make vid smoother
capture >> image;
frameItr += 100;
capture.set(CV_CAP_PROP_POS_MSEC, frameItr);
if (image.empty())
break;
Mat gray;
cvtColor(image,gray,CV_RGB2GRAY);
vector<string> codes;
Mat corners;
//findDataMatrix(gray, codes, corners);
//drawDataMatrixCodes(image, codes, corners);
// ROI
// optimized? -=> yes
int top = 0;
int left = 0;
int width = 800;
int height = 600;
Rect roi(left,top,width,height);
Mat imgROI = image(roi);
Scalar val = Scalar(0, 0, 0);
copyMakeBorder(imgROI, imgROI, 2, 2, 2, 2, BORDER_CONSTANT, val);
// Display the image
if(showOriginal) {
namedWindow("Original Image");
imshow("Original Image",imgROI);
imwrite("original.bmp", imgROI);
}
// Canny algorithm
Mat contours;
Canny(imgROI,contours,100,200);
Mat contoursInv;
threshold(contours,contoursInv,128,255,THRESH_BINARY_INV);
// Display Canny image
if(showCanny) {
namedWindow("Contours");
imshow("Contours1",contours); // use contoursInv for white
imwrite("contours.bmp", contours);
}
/*
Hough tranform for line detection with feedback
Increase by 25 for the next frame if we found some lines.
This is so we don't miss other lines that may crop up in the next frame
but at the same time we don't want to start the feed back loop from scratch.
*/
std::vector<Vec2f> lines;
if (houghVote < 1 or lines.size() > 2) { // we lost all lines. reset
houghVote = 300;
}
else{ houghVote += 25;}
while(lines.size() < 4 && houghVote > 0){
HoughLines(contours,lines,1,PI/180, houghVote);
houghVote -= 5;
}
std::cout << houghVote << "\n";
Mat result(imgROI.size(),CV_8U,Scalar(255));
imgROI.copyTo(result);
// Draw the lines
std::vector<Vec2f>::const_iterator it= lines.begin();
Mat hough(imgROI.size(),CV_8U,Scalar(0));
while (it!=lines.end()) {
float rho= (*it)[0]; // first element is distance rho
float theta= (*it)[1]; // second element is angle theta
if ( (theta > 0.09 && theta < 1.48) || (theta < 3.14 && theta > 1.66) ) { // filter to remove vertical and horizontal lines
// point of intersection of the line with first row
Point pt1(rho/cos(theta),0);
// point of intersection of the line with last row
Point pt2((rho-result.rows*sin(theta))/cos(theta),result.rows);
// draw a line: Color = Scalar(R, G, B), thickness
line( result, pt1, pt2, Scalar(255,255,255), 1);
line( hough, pt1, pt2, Scalar(255,255,255), 1);
}
//std::cout << "line: (" << rho << "," << theta << ")\n";
++it;
}
// Display the detected line image
if(showHough){
namedWindow("Detected Lines with Hough");
imshow("Detected Lines with Hough",result);
imwrite("hough.bmp", result);
}
// Create LineFinder instance
LineFinder ld;
// Set probabilistic Hough parameters
ld.setLineLengthAndGap(10,60); // min accepted length and gap
ld.setMinVote(15); // sit > 3 to get rid of "spiderweb"
// Detect lines
std::vector<Vec4i> li= ld.findLines(contours);
Mat houghP(imgROI.size(),CV_8U,Scalar(0));
ld.setShift(0,0);
ld.drawDetectedLines(houghP);
std::cout << "First Hough" << "\n";
if(showHoughP){
namedWindow("Detected Lines with HoughP");
imshow("Detected Lines with HoughP", houghP);
imwrite("houghP.bmp", houghP);
}
// bitwise AND of the two hough images
bitwise_and(houghP,hough,houghP);
Mat houghPinv(imgROI.size(),CV_8U,Scalar(0));
Mat dst(imgROI.size(),CV_8U,Scalar(0));
threshold(houghP,houghPinv,150,255,THRESH_BINARY_INV); // threshold and invert to black lines
if(showHoughP){
namedWindow("Detected Lines with Bitwise");
imshow("Detected Lines with Bitwise", houghP);
}
Canny(houghPinv,contours,100,350);
li = ld.findLines(contours);
// Test to draw point
//ld.drawPoint(image, Point(320,130));
// Set probabilistic Hough parameters
// more strict than above HoughP
ld.setLineLengthAndGap(5,2);
ld.setMinVote(1);
ld.setShift(top, left);
// draw point on image where line intersection occurs
int yShift = 25;
int allowableFrameSkip = 5;
ld.drawDetectedLines(image);
cv::Point iPnt = ld.drawIntersectionPunto(image, 2);
// track hill crest
int gap = 20;
cv::Point lptl(0, image.rows / 2 + yShift);
cv::Point lptr(gap, image.rows / 2 + yShift);
line(image, lptl, lptr, Scalar(255, 255, 255), 1);// left mid line
cv::Point rptl(image.cols - gap, image.rows / 2 + yShift);
cv::Point rptr(image.cols, image.rows / 2 + yShift);
line(image, rptl, rptr, Scalar(255, 255, 255), 1);// right mid line
cv::Point ulpt(0, image.rows / 2 - 50 + yShift);
cv::Point urpt(image.cols, image.rows / 2 - 50 + yShift);
bool hillCrestFound = (iPnt.y < (image.rows / 2 + yShift)) && (iPnt.y > (image.rows / 2 - 50 + yShift));
if(hillCrestFound) {
crestCount++;
frameSkip = 0;
} else if(crestCount != 0 && frameSkip < allowableFrameSkip)
frameSkip++;
else {
crestCount = 0;
frameSkip = 0;
}
cv::Point txtPt(image.cols / 2 - 31, image.rows / 2 - 140);
if(crestCount > 3)
putText(image, "tracking", txtPt, FONT_HERSHEY_PLAIN, 1, Scalar(0, 0, 255), 2, 8);
std::stringstream stream;
stream << "Lines Segments: " << lines.size();
putText(image, stream.str(), Point(10,image.rows-10), 1, 0.8, Scalar(0,255,0),0);
imshow(window_name, image);
imwrite("processed.bmp", image);
// oVideoWriter.write(image); //writer the frame into the file
char key = (char) waitKey(10);
lines.clear();
}
}
我认为与matrix.cpp文件存在冲突，但无法确定问题到底是什么。任何建议将不胜感激！谢谢！
答案 0 :(得分：0)
当未加载Mat或您尝试访问错误的高度或宽度值（例如负值）时，会发生这种情况。这些是常见的原因。
在您的代码中
int width = 800;
int height = 600;
Rect roi(left,top,width,height);
在这里，您尝试从捕获的图像中裁剪此矩形。但捕获图像的宽度或高度可能会小于此值。此处的矩形必须包含在图像中。
但是，这是一个很长的源代码。在更多情况下代码可能会失败。因此，请使用一些调试来查找问题发生的位置。
注意 I assume there is a conflict with the matrix.cpp file，这种可能性非常小，所以假设matrix.cpp文件没有冲突。