OpenCV: Using cvGoodFeaturesToTrack with C++ mat variable - c++

I am trying to use the cvGoodFeatureToTrack function in Visual Studio 2010 with the image type as Mat. Most of the examples I have seen use the IplImage pointer.
Right now I have this:
int w, h; // video frame size
Mat grayFrame;
Mat eigImage;
Mat tempImage;
const int MAX_CORNERS = 10;
CvPoint2D32f corners[MAX_CORNERS] = {0};
int corner_count = MAX_CORNERS;
double quality_level = 0.1;
double min_distance = 10;
int eig_block_size = 3;
int use_harris = false;
w = CurrFrame.size().width;
h = CurrFrame.size().height;
cvtColor(CurrFrame, grayFrame, CV_BGR2GRAY);
cvGoodFeaturesToTrack(&grayFrame,
&eigImage,
&tempImage,
corners,
&corner_count,
quality_level,
min_distance,
NULL,
eig_block_size,
use_harris);
It compiles but gives me a memory access violation. Help!

As a starting point, if using C++ anyway (like your use of cv::Mat and cv::cvtColor suggests), then why not use C++ interface for the rest, too?
This would mean the use of cv::goodFeaturesToTrack or a cv::GoodFeaturesToTrackDetector, which are made to work on cv::Mat and friends instead of making unneccessary casts from cv::Mat to IplImage*.
cv::Mat grayFrame;
std::vector<cv::Point2f> corners;
double quality_level = 0.1;
double min_distance = 10;
int eig_block_size = 3;
int use_harris = false;
const int MAX_CORNERS = 10;
cv::cvtColor(CurrFrame, grayFrame, CV_BGR2GRAY);
cv::goodFeaturesToTrack(grayFrame,
corners,
MAX_CORNERS,
quality_level,
min_distance,
cv::noArray(),
eig_block_size,
use_harris);

Related

Correct threshold for HoughCircles() circle detection opencv c++

I want to detect circles in an image with opencv in c++. I have tried various thresholds for the dp, parm1, and parm2 but I could not find the correct ones.
The radius of the circles is around 40 pixels.
I have added a link to sample images. Which values do I need to use in the HoughCircles() for correct results?
Link to sample images.
You can use following parameter values. These settings are able to detect the circles in your images.
cv::Mat img = cv::imread("75.bmp");
cv::Mat img_gray;
cv::cvtColor(img, img_gray, cv::COLOR_BGR2GRAY);
img_gray.convertTo(img_gray, CV_8UC1);
std::vector<cv::Vec3f> circles;
double minDist = 20;
double dp = 1;
double param1 = 200;
double param2 = 10;
int minRadius = 15;
int maxRadius = 25;
cv::HoughCircles(img_gray, circles, cv::HOUGH_GRADIENT, dp, minDist, param1, param2, minRadius, maxRadius);
if (circles.size() > 0) {
for (size_t current_circle = 0; current_circle < circles.size(); ++current_circle) {
cv::Point center(std::round(circles[current_circle][0]), std::round(circles[current_circle][1]));
int radius = std::round(circles[current_circle][2]);
cv::circle(img, center, radius, cv::Scalar(0, 255, 0), 1);
}
}

OPENCV - Impossible to save or show a SurfDescriptor Mat

I'm trying to save my Surf descriptors (of different images) Mat into a file, for later use using the code below:
int offline(int nb) { //creates descriptors image;
Mat img;
int nfeatures = 25;
int nOctaveLayers = 2;
double contrastThreshold = 0.04;
double edgeThreshold = 10;
double sigma = 1.6;
SurfFeatureDetector surfDetector = SURF(edgeThreshold, 2, 2, true, false);
vector< KeyPoint> keypoints;
SurfDescriptorExtractor surfExtractor;
Mat imgDescriptors;
for (int i = 2;i <= nb;i++) {
img = imread("images/" + to_string(i) + ".jpg", CV_LOAD_IMAGE_GRAYSCALE);
if (!img.data)
{
return -1;
}
surfDetector.detect(img, keypoints);
surfExtractor.compute(img, keypoints, imgDescriptors);
imwrite(to_string(i)+".jgp",imgDescriptors); //impossible to save
//imshow("lol", imgDescriptors); //impossible to show
}
return 0;
}
i'm getting this exception : OpenCV Error: Unspecified error (could not find a writer for the specified extension) in cv::imwrite_, file C:\builds\2_4_PackSlave-win64-vc12-shared\opencv\modules\highgui\src\loadsave.cpp, line 275 [RESOLVED BY adding the extension +".jpg".
So i thought i should try to show the image, so i'm having another error:
Any clue about this ? (SECOND EXCEPTION)

Mat Image creation from raw buffer data

I have float x, float y, float z values of an image. I want to construct a 16 bit png depth image by copying the z values. The image I am getting as a result has some invalid points. Below is my code.
uint16_t* depthValues = new uint16_t[size];
auto sampleVector(DepthPoints);
for (unsigned int i = 0; i < sampleVector.size(); i++)
{
depthValues[i] = (sampleVector.at(i).z) * 65536;
}
Mat newDepthImage = cv::Mat(var.height, var.width, CV_16UC1,depthValues);
imwrite(Location, CImage);
Can someone tell me, if I can copy the float values into an unsigned char array to create the image?
Is that why my image has invalid points?
auto sampleVector(DepthPoints);
const int size = sampleVector.size();
float* depthValues = new float[size];
for (unsigned int i = 0; i < sampleVector.size(); i++)
{
depthValues[i] = (sampleVector.at(i).z);
}
Mat depthImageOne, depthImageTwo;
Mat depthImageNew = cv::Mat(var.height, var.width, CV_32FC1,depthValues);
normalize(newDepthImageNew, depthImageOne, 1, 0, NORM_MINMAX, CV_32FC1);
depthImageOne.convertTo(depthImageTwo, CV_16UC1, 65536.0,0.0);
imwrite("path", depthImageTwo);
Normalization might cause lose of depth information. I have used normalization for visualization of the images. To preserve the depth information, I used the below code.
Mat depthImageNew = cv::Mat(var.height, var.width, CV_32FC1,depthValues);
depthImageOne.convertTo(depthImageTwo, CV_16UC1, 1000.0,0.0);

Calculating skew of text OpenCV

I am trying to calculate the skew of text in an image so I can correct it for the best OCR results.
Currently this is the function I am using:
double compute_skew(Mat &img)
{
// Binarize
cv::threshold(img, img, 225, 255, cv::THRESH_BINARY);
// Invert colors
cv::bitwise_not(img, img);
cv::Mat element = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(5, 3));
cv::erode(img, img, element);
std::vector<cv::Point> points;
cv::Mat_<uchar>::iterator it = img.begin<uchar>();
cv::Mat_<uchar>::iterator end = img.end<uchar>();
for (; it != end; ++it)
if (*it)
points.push_back(it.pos());
cv::RotatedRect box = cv::minAreaRect(cv::Mat(points));
double angle = box.angle;
if (angle < -45.)
angle += 90.;
cv::Point2f vertices[4];
box.points(vertices);
for(int i = 0; i < 4; ++i)
cv::line(img, vertices[i], vertices[(i + 1) % 4], cv::Scalar(255, 0, 0), 1, CV_AA);
return angle;
}
When I look at then angle in debug I get 0.000000
However when I give it this image I get proper results of a skew of about 16 degrees:
How can I properly detect the skew in the first image?
there are a few other ways to get the skew degree, 1) by hough transform 2) by horizontal projection profile. rotate the image in different angle bins and calculate horizontal projection. the angle with the greatest horizontal histogram value is the deskewed angle.
i have provided below implementation of 1). i believe this to be superior to the boxing method you are using because it requires that you completely clean the image of any noise,which just isnt possible in most of the time.
you should know that the method doesnt work well if there's too much noise. you can reduce noise in different ways depending on what type of "line" you want to treat as the most dominant in the image. i have provided two methods for this. be sure to play with parameters and threshold etc.
results (all run using preprocess2, all run using same parameter set)
code
#include <opencv2/opencv.hpp>
using namespace cv;
using namespace std;
void hough_transform(Mat& im,Mat& orig,double* skew)
{
double max_r=sqrt(pow(.5*im.cols,2)+pow(.5*im.rows,2));
int angleBins = 180;
Mat acc = Mat::zeros(Size(2*max_r,angleBins),CV_32SC1);
int cenx = im.cols/2;
int ceny = im.rows/2;
for(int x=1;x<im.cols-1;x++)
{
for(int y=1;y<im.rows-1;y++)
{
if(im.at<uchar>(y,x)==255)
{
for(int t=0;t<angleBins;t++)
{
double r =(x-cenx)*cos((double)t/angleBins*CV_PI)+(y-ceny)*sin((double)t /angleBins*CV_PI);
r+=max_r;
acc.at<int>(t,int(r))++;
}
}
}
}
Mat thresh;
normalize(acc,acc,255,0,NORM_MINMAX);
convertScaleAbs(acc,acc);
/*debug
Mat cmap;
applyColorMap(acc,cmap,COLORMAP_JET);
imshow("cmap",cmap);
imshow("acc",acc);*/
Point maxLoc;
minMaxLoc(acc,0,0,0,&maxLoc);
double theta = (double)maxLoc.y/angleBins*CV_PI;
double rho = maxLoc.x-max_r;
if(abs(sin(theta))<0.000001)//check vertical
{
//when vertical, line equation becomes
//x = rho
double m = -cos(theta)/sin(theta);
Point2d p1 = Point2d(rho+im.cols/2,0);
Point2d p2 = Point2d(rho+im.cols/2,im.rows);
line(orig,p1,p2,Scalar(0,0,255),1);
*skew=90;
cout<<"skew angle "<<" 90"<<endl;
}else
{
//convert normal form back to slope intercept form
//y = mx + b
double m = -cos(theta)/sin(theta);
double b = rho/sin(theta)+im.rows/2.-m*im.cols/2.;
Point2d p1 = Point2d(0,b);
Point2d p2 = Point2d(im.cols,im.cols*m+b);
line(orig,p1,p2,Scalar(0,0,255),1);
double skewangle;
skewangle= p1.x-p2.x>0? (atan2(p1.y-p2.y,p1.x-p2.x)*180./CV_PI):(atan2(p2.y-p1.y,p2. x-p1.x)*180./CV_PI);
*skew=skewangle;
cout<<"skew angle "<<skewangle<<endl;
}
imshow("orig",orig);
}
Mat preprocess1(Mat& im)
{
Mat ret = Mat::zeros(im.size(),CV_32SC1);
for(int x=1;x<im.cols-1;x++)
{
for(int y=1;y<im.rows-1;y++)
{
int gy = (im.at<uchar>(y-1,x+1)-im.at<uchar>(y-1,x-1))
+2*(im.at<uchar>(y,x+1)-im.at<uchar>(y,x-1))
+(im.at<uchar>(y+1,x+1)-im.at<uchar>(y+1,x-1));
int gx = (im.at<uchar>(y+1,x-1) -im.at<uchar>(y-1,x-1))
+2*(im.at<uchar>(y+1,x)-im.at<uchar>(y-1,x))
+(im.at<uchar>(y+1,x+1)-im.at<uchar>(y-1,x+1));
int g2 = (gy*gy + gx*gx);
ret.at<int>(y,x)=g2;
}
}
normalize(ret,ret,255,0,NORM_MINMAX);
ret.convertTo(ret,CV_8UC1);
threshold(ret,ret,50,255,THRESH_BINARY);
return ret;
}
Mat preprocess2(Mat& im)
{
// 1) assume white on black and does local thresholding
// 2) only allow voting top is white and buttom is black(buttom text line)
Mat thresh;
//thresh=255-im;
thresh=im.clone();
adaptiveThreshold(thresh,thresh,255,CV_ADAPTIVE_THRESH_GAUSSIAN_C,THRESH_BINARY,15,-2);
Mat ret = Mat::zeros(im.size(),CV_8UC1);
for(int x=1;x<thresh.cols-1;x++)
{
for(int y=1;y<thresh.rows-1;y++)
{
bool toprowblack = thresh.at<uchar>(y-1,x)==0 || thresh.at<uchar>(y-1,x-1)==0 || thresh.at<uchar>(y-1,x+1)==0;
bool belowrowblack = thresh.at<uchar>(y+1,x)==0 || thresh.at<uchar>(y+1, x-1)==0 || thresh.at<uchar>(y+1,x+1)==0;
uchar pix=thresh.at<uchar>(y,x);
if((!toprowblack && pix==255 && belowrowblack))
{
ret.at<uchar>(y,x) = 255;
}
}
}
return ret;
}
Mat rot(Mat& im,double thetaRad)
{
cv::Mat rotated;
double rskew = thetaRad* CV_PI/180;
double nw = abs(sin(thetaRad))*im.rows+abs(cos(thetaRad))*im.cols;
double nh = abs(cos(thetaRad))*im.rows+abs(sin(thetaRad))*im.cols;
cv::Mat rot_mat = cv::getRotationMatrix2D(Point2d(nw*.5,nh*.5), thetaRad*180/CV_PI, 1);
Mat pos = Mat::zeros(Size(1,3),CV_64FC1);
pos.at<double>(0)=(nw-im.cols)*.5;
pos.at<double>(1)=(nh-im.rows)*.5;
Mat res = rot_mat*pos;
rot_mat.at<double>(0,2) += res.at<double>(0);
rot_mat.at<double>(1,2) += res.at<double>(1);
cv::warpAffine(im, rotated, rot_mat,Size(nw,nh), cv::INTER_LANCZOS4);
return rotated;
}
int main(int argc, char** argv)
{
string src="C:/data/skew.png";
Mat im= imread(src);
Mat gray;
cvtColor(im,gray,CV_BGR2GRAY);
Mat preprocessed = preprocess2(gray);
imshow("preprocessed2",preprocessed);
double skew;
hough_transform(preprocessed,im,&skew);
Mat rotated = rot(im,skew* CV_PI/180);
imshow("corrected",rotated);
waitKey(0);
return 0;
}
the approach you posted has its own "ideal binarization" assumption. the threshold value directly affects the process. utilize otsu threshold, or think about DFT for a generic solution.
otsu trial:
int main()
{
Mat input = imread("your text");
cvtColor(input, input, CV_BGR2GRAY);
Mat img;
cv::threshold(input, img, 100, 255, cv::THRESH_OTSU);
cv::bitwise_not(img, img);
imshow("img ", img);
waitKey(0);
vector<Point> points;
findNonZero(img, points);
cv::RotatedRect box = cv::minAreaRect(points);
double angle = box.angle;
if (angle < -45.)
angle += 90.;
cv::Point2f vertices[4];
box.points(vertices);
for(int i = 0; i < 4; ++i)
cv::line(img, vertices[i], vertices[(i + 1) % 4], cv::Scalar(255, 0, 0));
imshow("img ", img);
waitKey(0);
return 0;
}

Exception thrown for cvCalcOpticalFlowHS() opencv

Heey, I'm trying to sort out the function of Optical Flow of openCV, but for some reason I'm getting an exception in visual studio:
Unhandled exception at 0x772615de in Optical_flow.exe: Microsoft C++ exception: cv::Exception at memory location 0x0036f334..
With breakpoints I found out that the error lies within the cvCalcOpticalFlowHS function.
I'm using openCV 2.1
#include <cv.h>
#include <highgui.h>
using namespace cv;
int init() {
return 0;
}
int main(int argc, char **args) {
CvCapture* capture = cvCaptureFromFile("Video/Wildlife.wmv");
double fps = cvGetCaptureProperty(capture, CV_CAP_PROP_FPS);
CvSize size;
size.width = (int)cvGetCaptureProperty(capture, CV_CAP_PROP_FRAME_WIDTH);
size.height = (int)cvGetCaptureProperty(capture, CV_CAP_PROP_FRAME_HEIGHT);
CvVideoWriter* writer = cvCreateVideoWriter("result.avi", 0, fps,size, 1);
IplImage* curFrame = cvQueryFrame(capture);
Mat u = Mat(size, CV_32FC2);
Mat v = Mat(size, CV_32FC2);
CvTermCriteria IterCriteria;
IterCriteria.type = CV_TERMCRIT_ITER | CV_TERMCRIT_EPS;
IterCriteria.max_iter = 500;
IterCriteria.epsilon = 0.01;
while(1) {
IplImage* nextFrame = cvQueryFrame(capture);
if(!nextFrame) break;
u = Mat::zeros(size, CV_32FC2);
v = Mat::zeros(size, CV_32FC2);
/* Do optical flow computation */
cvCalcOpticalFlowHS(&curFrame, &nextFrame, 0, &u, &v, 0.01, IterCriteria);
cvWriteFrame(writer, curFrame);
curFrame = nextFrame;
}
cvReleaseVideoWriter(&writer);
cvReleaseCapture(&capture);
return 0;
}
Anyone has seen this problem before or sees the mistake I made?
Best Regards
Remco
From the documentation, curFrame and nextFrame should be 8-bit single channel. You are currently just pulling these from the loaded file without checking/converting them as necessary. Can you confirm that the input is of the right type?
Also you have a nasty mix of C++ style cv::Mat with C style IplImage*. I'd suggest you upgrade to a more recent version of OpenCV (2.4 has recently been released), and try to stick with the one or other of the C++ or C style methods.
Note also that this optical flow method is classed as obsolete with a recommendation to use either calcOpticalFlowPyrLK() for sparse features or calcOpticalFlowFarneback() for dense features.
Below is some example code demonstrating calcOpticalFlowFarneback(), which is what I believe you are trying to achieve. It takes data from the webcam rather than a file.
#include <opencv2/opencv.hpp>
using namespace cv;
void drawOptFlowMap(const cv::Mat& flow,
cv::Mat& cflowmap,
int step,
const cv::Scalar& color
)
{
for(int y = 0; y < cflowmap.rows; y += step)
for(int x = 0; x < cflowmap.cols; x += step)
{
const cv::Point2f& fxy = flow.at<cv::Point2f>(y, x);
cv::line(cflowmap,
cv::Point(x,y),
cv::Point(cvRound(x+fxy.x),cvRound(y+fxy.y)),
color);
cv::circle(cflowmap, cv::Point(x,y), 2, color, -1);
}
}
int main(int argc, char **args) {
VideoCapture cap(0); // open the default camera
if(!cap.isOpened()) // check if we succeeded
return -1;
Mat newFrame, newGray, prevGray;
cap >> newFrame; // get a new frame from camera
cvtColor(newFrame, newGray, CV_BGR2GRAY);
prevGray = newGray.clone();
double pyr_scale = 0.5;
int levels = 3;
int winsize = 5;
int iterations = 5;
int poly_n = 5;
double poly_sigma = 1.1;
int flags = 0;
while(1) {
cap >> newFrame;
if(newFrame.empty()) break;
cvtColor(newFrame, newGray, CV_BGR2GRAY);
Mat flow = Mat(newGray.size(), CV_32FC2);
/* Do optical flow computation */
calcOpticalFlowFarneback(
prevGray,
newGray,
flow,
pyr_scale,
levels,
winsize,
iterations,
poly_n,
poly_sigma,
flags
);
drawOptFlowMap(flow, newFrame, 20, CV_RGB(0,255,0));
namedWindow("Output",1);
imshow("Output", newFrame);
waitKey(1);
prevGray = newGray.clone();
}
return 0;
}
The above code is pretty similar to the fback.cpp sample code which comes with OpenCV.