I am working on an object detection code and I chose the edge orientation histogram as a descriptor for the matching.
I am facing a problem in the back projected histogram as i don't seem to have a good matching , the back projected image is mostly white, which means that i cannot use meanshift or so for detection of the object.
Please help me regarding this matter:
here is what i've done so far:
Take an initial ROI (the target needed to be detected in the video stream).
convert ROI to grayscale
apply sobel operator for both x, y derivatives.
calculate orientation using opencv phase function (from derivative x and derivative y)
make a histogram of the generated orientation. with the following specs:
(range : 0 to 2 PI) , (single channel) , (256 bins)
normalize the histogram
the code for doing these steps is the following:
Mat ROI_grad_x, ROI_grad_y , ROI_grad , ROI_gray;
Mat ROI_abs_grad_x, ROI_abs_grad_y;
cvtColor(ROI, ROI_gray, CV_BGR2GRAY);
/// Gradient X
Sobel( ROI_gray, ROI_grad_x, CV_16S, 1, 0, 3 );
/// Gradient Y
Sobel( ROI_gray, ROI_grad_y, CV_16S, 0, 1, 3 );
convertScaleAbs( ROI_grad_x, ROI_abs_grad_x );
convertScaleAbs( ROI_grad_y, ROI_abs_grad_y );
addWeighted( ROI_abs_grad_x, 0.5, ROI_abs_grad_y, 0.5, 0, ROI_grad );
Mat ROI_orientation = Mat::zeros(ROI_abs_grad_x.rows, ROI_abs_grad_y.cols, CV_32F); //to store the gradients
Mat ROI_orientation_norm ;
ROI_grad_x.convertTo(ROI_grad_x,CV_32F);
ROI_grad_y.convertTo(ROI_grad_y,CV_32F);
phase(ROI_grad_x, ROI_grad_y, ROI_orientation , false);
Mat ROI_orientation_hist ;
float ROI_orientation_range[] = {0 , CV_PI};
const float *ROI_orientation_histRange[] = {ROI_orientation_range};
int ROI_orientation_histSize =256;
//calcHist( &ROI_orientation, 1, 0, Mat(), ROI_orientation_hist, 1, &ROI_orientation_histSize, &ROI_orientation_histRange , true, false);
calcHist( &ROI_orientation, 1, 0, Mat(), ROI_orientation_hist, 1, &ROI_orientation_histSize, ROI_orientation_histRange , true, false);
normalize( ROI_orientation_hist, ROI_orientation_hist, 0, 255, NORM_MINMAX, -1, Mat() );
then , and for each camera frame captured , I do the following steps:
convert to grayscale
apply sobel operator for both x derivative and y derivative.
compute orientation using phase opencv function (using the 2 derivatives mentioned above)
back project the histogram onto the orientation frame matrix to get the matches.
the code used for this part is the following :
Mat grad_x, grad_y , grad;
Mat abs_grad_x, abs_grad_y;
/// Gradient X
Sobel( frame_gray, grad_x, CV_16S, 1, 0, 3 );
/// Gradient Y
Sobel( frame_gray, grad_y, CV_16S, 0, 1, 3 );
convertScaleAbs( grad_x, abs_grad_x );
convertScaleAbs( grad_y, abs_grad_y );
addWeighted( abs_grad_x, 0.5, abs_grad_y, 0.5, 0, grad );
///======================
Mat orientation = Mat::zeros(abs_grad_x.rows, abs_grad_y.cols, CV_32F); //to store the gradients
Mat orientation_norm ;
grad_x.convertTo(grad_x,CV_32F);
grad_y.convertTo(grad_y,CV_32F);
phase(grad_x, grad_y, orientation , false);
Mat EOH_backProj ;
calcBackProject( &orientation, 1, 0, ROI_orientation_hist, EOH_backProj, ROI_orientation_histRange, 1, true );
So , what seems to be the problem in my approach ?!
Thanks alot.
Related
I know that to implement the following
I would use this code:
Mat o_k;
Mat Lapl;
double lambda;
Laplacian(o_k, Lapl, o_k.depth(), 1, 1, 0, BORDER_REFLECT);
Lapl = 1.0 - 2.0*lambda*Lapl;
However, I am trying to implement in OpenCV the following equation:
I know the div, or divergence, term would be like this, right?
int ksize = parser.get<int>("ksize");
int scale = parser.get<int>("scale");
int delta = parser.get<int>("delta");
Sobel(res, sobelx, CV_64F, 1, 0, ksize, scale, delta, BORDER_DEFAULT);
Sobel(res, sobely, CV_64F, 0, 1, ksize, scale, delta, BORDER_DEFAULT);
div = sobelx + sobely;
Where res is the result of the term in parenthesis. But how I get the term in parenthesis?
Or am I doing this wrong? Would div above actually be equal to the gradient of res? If so, then how do I get the divergence?
EDIT:
According to this link, the magnitude can also be computed as mag = abs(x) + abs(y): https://docs.opencv.org/2.4/doc/tutorials/imgproc/imgtrans/sobel_derivatives/sobel_derivatives.html#sobel-derivatives
And since the div of a gradient is the Laplacian, would the below code be equivalent to the 2nd equation?
Sobel(res, sobelx, CV_64F, 1, 0, ksize, scale, delta, BORDER_DEFAULT);
Sobel(res, sobely, CV_64F, 0, 1, ksize, scale, delta, BORDER_DEFAULT);
convertScaleAbs( sobelx, abs_grad_x );
convertScaleAbs( sobely, abs_grad_y );
/// Total Gradient (approximate)
Mat mag;
addWeighted( abs_grad_x, 1, abs_grad_y, 1, 0, mag);
Laplacian(o_k, Lapl, o_k.depth(), 1, 1, 0, BORDER_REFLECT);
Mat top;
top = lambda * Lapl;
Mat result;
divide(top, mag, result);
Why is my code snippet giving me weird results for projected points?
//Generate the one 3D Point which i want to project onto 2D plane
vector<Point3d> points_3d;
points_3d.push_back(Point3d(10, 10, 100));
Mat points3d = Mat(points_3d);
//Generate the identity matrix and zero vector for rotation matrix and translation vector
Mat rvec = (Mat_<double>(3, 3) << (1, 0, 0, 0, 1, 0, 0, 0, 1));
Mat tvec = (Mat_<double>(3, 1) << (0, 0, 0));
//Generate a camera intrinsic matrix
Mat K = (Mat_<double>(3,3)
<< (1000, 0, 50,
0, 1000, 50,
0, 0, 1));
//Project the 3D Point onto 2D plane
Mat points_2d;
projectPoints(points_3d, rvec, tvec, K, Mat(), points_2d);
//Output
cout << points_2d;
I get as projected 2D Point
points_2d = (-1.708699427820658e+024, -9.673395654445999e-026)
If i calculate it on a paper on my own, i'm expecting a point points_2d = (150, 150) with that formula:
Add cv::Rodrigues(InputArray src, OutputArray dst, OutputArray jacobian=noArray()). OpenCv uses rotation vector inside calculation instead of rotation matrix. Rodrigues transformation allows you to convert rotation vector to matrix and matrix to vector. Below i attached part of your code with one line added.
//Generate the identity matrix and zero vector for rotation matrix and translation vector
Mat rvec,rMat = (Mat_<double>(3, 3) << (1, 0, 0, 0, 1, 0, 0, 0, 1));
Rodrigues(rMat,rvec); //here
Mat tvec = (Mat_<double>(3, 1) << (0, 0, 0));
And it should work properly. It also will be better to define distortion coefficents as
Mat dist = Mat::zeros(8,1,CV_32f);
EDIT:
One more remark, you have little syntax error in matrix initialization:
cv::Mat rvec,rMat = (cv::Mat_<double>(3, 3) << /* ( */1, 0, 0, 0, 1, 0, 0, 0, 1); //you had error here
cv::Rodrigues(rMat, rvec);
cv::Mat tvec = (cv::Mat_<double>(3, 1) <</* ( */ 0, 0, 0); //and here
It works on my computer after changes.
I want to apply the Tenengrad algorithm to a central rectangular region inside the image. Assuming that I have the coordinates of the vertices of the rectangular region or maybe one corner and the dimensions, how can I modify the following code to apply the sharpness measure over the selected region ?
double tenengrad(const cv::Mat& src, int ksize)
{
cv::Mat Gx, Gy;
cv::Sobel(src, Gx, CV_64F, 1, 0, ksize);
cv::Sobel(src, Gy, CV_64F, 0, 1, ksize);
cv::Mat FM = Gx.mul(Gx) + Gy.mul(Gy);
double focusMeasure = cv::mean(FM).val[0];
return focusMeasure;
}
cv::Mat imageRegion;
imageRegion = src(cv::Rect(x, y, width, height));
creates a matrix that points to the region of the original image specified by the rectangle (x, y, width, height). Modifying imageRegion will modify the original image src. So you can use imageRegion instead of src
cv::Mat Gx, Gy;
cv::Sobel(imageRegion, Gx, CV_64F, 1, 0, ksize);
cv::Sobel(imageRegion, Gy, CV_64F, 0, 1, ksize);
cv::Mat FM = Gx.mul(Gx) + Gy.mul(Gy);
double focusMeasure = cv::mean(FM).val[0];
I need to detect the iris of the eye picture I have using HoughCircle function thats available in opencv2. So ,
// Read the image
src = imread("D:/001R_3.png");
if( !src.data )
{ return -1; }
/// Convert it to gray
cvtColor( src, src_gray, CV_BGR2GRAY );
/// Reduce the noise so we avoid false circle detection
GaussianBlur( src_gray, src_gray, Size(9, 9), 2, 2 );
/////////////
/// Generate grad_x and grad_y
Mat grad_x, grad_y;
Mat abs_grad_x, abs_grad_y;
Sobel( src_gray, grad_x, ddepth, 1, 0, 3, scale, delta, BORDER_DEFAULT );
convertScaleAbs( grad_x, abs_grad_x );
/// Gradient Y
//Scharr( src_gray, grad_y, ddepth, 0, 1, scale, delta, BORDER_DEFAULT );
Sobel( src_gray, grad_y, ddepth, 0, 1, 3, scale, delta, BORDER_DEFAULT );
convertScaleAbs( grad_y, abs_grad_y );
/// Total Gradient (approximate)
addWeighted( abs_grad_x, 0.5, abs_grad_y, 0.5, 0, grad );
///////////////
vector<Vec3f> circles;
/// Apply the Hough Transform to find the circles
HoughCircles( grad, circles, CV_HOUGH_GRADIENT, 1, grad.rows/8, 200, 100,0,0 );
/// Draw the circles detected
for( size_t i = 0; i < circles.size(); i++ )
{
Point center(cvRound(circles[i][0]), cvRound(circles[i][1]));
cout<<center;
int radius = cvRound(circles[i][2]);
// circle center
cout<<radius;
circle(src, center, 3, Scalar(0,255,0), -1, 8, 0 );
// circle outline
circle(src, center, radius, Scalar(0,0,255), 3, 8, 0 );
}
/// Show your results
namedWindow( "Hough Circle Transform Demo", CV_WINDOW_AUTOSIZE );
imshow( "Hough Circle Transform Demo",src );
}
So here is my code, only the outer portion of the eye is detected where as i want the pupil and the iris boundary to be detected and thats not happening , I referred the link OpenCV: Using Hough Circle Transformation to detect iris
but it doesn't work that way. Instead of canny edge detector have used sobel. Suggestions please.
The fifth parameter of the Hough transform is the minDist, or minimum distance (in pixels) between circles. You have this parameter set to the number of rows in the image divided by 8, which means that any overlapping circles (such as the pupil and iris of your eye) will not be returned because they are too close together.
I'd set this number as a variable instead of hard-coding it, and experiment with a set of much smaller numbers until you find something that works.
I'm trying to achieve background subtraction in openCV 2.2 using the cv namespace (Qt4.7). I have the following code which compiles fine but when running the program breaks because one mat doesn't equal the other but I can't find out where it is and I'm currently going through the API reference to try and find it.
cvtColor( mcolImage, mcolImage, CV_BGR2RGB);
cvtColor( mcolImage, gscaleImage, CV_RGB2GRAY);
acc = Mat(Size(440,320), CV_32FC3);
accSQ = Mat(Size(440,320), CV_32FC3);
//we accumulate into a Mat to get an frames average
Mat avg;
accumulateWeighted(gscaleImage, acc, 3.0, Mat());
accumulateSquare(gscaleImage, accSQ, Mat());
multiply(acc, acc, avg, 1);
Mat sigma, sigmaSQRT;
subtract(accSQ, avg, sigmaSQRT, Mat());
sqrt(sigmaSQRT, sigma); //Holds the standard deviation
Mat fgImage; //hold the foreground image
cv::absdiff(avg,gscaleImage, fgImage);
//GaussianBlur(gscaleImage, gscaleImage, Size(7,7), 2, 2 );
Mat buff ;
//convert to black and white
threshold(fgImage, buff, 75, THRESH_BINARY, 100);
dilate(buff, buff, Mat(3, 3, CV_8UC1), Point(-1, -1), 1, BORDER_CONSTANT, Scalar(1.0, 1.0, 1.0, 0));
erode(buff, buff, Mat(3, 3, CV_8UC1), Point(-1, -1), 1, BORDER_CONSTANT, Scalar(1.0, 1.0, 1.0, 0));
//rectangle(gscaleImage, cvPoint(100, 300), cvPoint(200, 100), cvScalar(255, 255, 255, 0), 1);
QImage colImagetmp((uchar*)mcolImage.data, mcolImage.cols, mcolImage.rows, mcolImage.step,
QImage::Format_RGB888 ); //Colour
QImage gscaleImagetmp ((uchar*)gscaleImage.data, gscaleImage.cols, gscaleImage.rows, gscaleImage.step,
QImage::Format_Indexed8); //Greyscale. I hope
QImage bwImagetmp((uchar*)buff.data, buff.cols, buff.rows, buff.step,
QImage::Format_Indexed8);
//Setup a colour table for the greyscale image
QVector<QRgb> colorTable;
for (int i = 0; i < 256; i++) colorTable.push_back(qRgb(i, i, i));
bwImagetmp.setColorTable(colorTable);
gscaleImagetmp.setColorTable(colorTable);
ui.intDisplay->setPixmap(QPixmap::fromImage(bwImagetmp));
ui.bwDisplay->setPixmap(QPixmap::fromImage(gscaleImagetmp));
ui.colDisplay->setPixmap( QPixmap::fromImage(colImagetmp ));
Thanks for the help in advanced.
Edit:
After going through the code I found that the absdiff(avg, gscaleImage, fgImage); is where the program is crashing. I think it maybe crashing on the second parameter but not sure.
I solved it (I think) by declaring a new temporary Mat and converting that specifically (using avg.convert() ) to match the gscaleImage type and size.