I am trying to use opencv via visual c++ to extract contours of an image. I was able to do that using the opencv tutorial for findcontours.
findcontours works in two steps
Detect edges using canny edge detector.
Feed the output of canny to findcontours.
I want to try out the same with 'Structured Forest Edge Detection' (Zitnick et al). I am able to extract the edges and display them, but when I try to feed the output to findcontours. I am getting a 'cv::Exception at memory location 0x0020EE9C' error. (see code below). What am I doing wrong?
Mat src = imread("image.jpg");
src.convertTo(src, CV_32F, 1.0 / 255.0);
Mat edges(src.size(), src.type());
Ptr<StructuredEdgeDetection> pDollar = createStructuredEdgeDetection("model.yml.gz");
pDollar->detectEdges(src, edges);
findContours(edges, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
pDollar->detectEdges(src, edges);
edges type is CV_32F. you must convert it to 8-bit single-channel image
Related
I am new to image processing and trying to get the contours of the apples in these images. To do so, i use openCV. But i do not get a propper contour detection. I want the algorithm also be able to get contours of other objects. So not limmited to apples (= circles).
Original picture
If i follow the instructions there are 4 steps to be taken.
Open the image file
Convert the file to grayscale
Do some processing (blur, errode, dillitate, you name it)
Get the contours
The first point that confuses me is the grayscale conversion.
I did:
Mat image;
Mat HSVimage;
Mat Grayimage;
image = imread(imageName, IMREAD_COLOR); // Read the file
cvtColor(image, HSVimage, COLOR_BGR2HSV);
Mat chan[3];
split(HSVimage, chan);
Grayimage = chan[2];
First question:
Is this correct choice, or shoud i just read the file in Grayscale or use YUV ?
I only use 1 channel of the HSV, is this correct ?
I tried alot of processing methodes, but there are so many i lost track. The best result i got was when i used a treshold and an adaptiveTreshold.
threshold(Grayimage, Grayimage,49, 0, THRESH_TOZERO);
adaptiveThreshold(Grayimage, Tresholdimage, 256, ADAPTIVE_THRESH_GAUSSIAN_C, THRESH_BINARY, 23, 4);
The result i get is:
result after processing
But a find contours does not find a closed object. So i get:
contours
vector<vector<Point> > contours;
vector<Vec4i> hierarchy;
findContours(Tresholdimage, contours, hierarchy, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE, Point(0, 0));
I tried hough cicles,
vector<Vec3f> circles;
HoughCircles(Tresholdimage, circles, HOUGH_GRADIENT, 2.0, 70);
and i got:
hough circles ok
So i was a happy man, but as soon is i tried the code on an other picture, i got:
second picture original
hough circles wrong
I can experiment with the HoughCircles function i see there are alot of posibilities, but i can only detect circles with it, so it is not my first choice.
I am a newbie at this. My questions are:
Is this a correct way or is it better to use technics like blob detection or ML to find the objects in the picture ?
If it is a correct way, what functions should i use to get the better results ?
Regards,
Peter
I am currently trying to determine if a rectangle is well formed such as having perfect corners and straight lines.
This is what I am currently doing now to detect a rectangle
Mat image;
image = imread(argv[1], CV_LOAD_IMAGE_COLOR);
cvtColor( image,image1, CV_BGR2GRAY );
Canny( image1, canny_output,130, 200 );
vector<Point> approx;
vector<vector<Point> > contours;
vector<Vec4i> hierarchy;
findContours(canny_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_NONE, Point(0,0) );
for (unsigned int i=0; i<contours.size(); i++){
if((approx.size() == 4)){
cout<<"It is a rectangle"<<endl;
}
}
I can detect if it is a rectangle but I am not sure how to detect that is a not a well formed rectangle like the images below.
If the test images are like the ones that you have posted here, you could try to use Hough Transform, firstly to detect the lines and the check if the lines are parallel.
Try to have a look how to use OpenCV Hough Transform implementation on on OpenCV here.
However I need more informations to clarify better the answer.
I also checked there are some others threads about this problem you could check the following:
Hough transformation vs Contour detection for Rectangle recognition with perspective projection
Rectangle detection with Hough transform.
Moreover if you are familiar with research, have a look to this paper.
I'm using OpenCV's findContours() for blob-finding, by floodfilling at an arbitrary seed point in the contour and taking the bounding rectangle of the floodfill. However, when two blobs touch at a corner, e.g.
they share a contour, so only one of the two blobs will be floodfilled, depending on which seed point was chosen.
I could change the floodfill connectivity setting from 4 to 8, so that the blobs are fused in the floodfill. What I'd really like to do instead is ignore the small defect and count only the big blob. Can this be done without substantially changing the algorithm?
Unlike for floodfill, there's no way to use findContours with 4-connectivity natively in OpenCV.
You should take a look at findContours() documentation.
findContours can return multiple contours if they appear in the image, in your case, if you choose 4-connectivity, you should get 2 contours, and then you can compare their bounding box size to decide which one to keep.
cv::Mat img = cv::imread('test.png', 0);
std::vector<std::vector<cv::Point> > contours;
std::vector<cv::Vec4i> hierarchy;
cv::findContours(img, contours, hierarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
for (size_t i = 0;i < contours.size(); ++i) {
cv::Rect bbox = cv::boundingRect(contours[i]);
std::cout<<"Contour"<<i<<" Area"<<bbox.area()<<std::endl;
}
Hope this helps.
I have an OpenCV application fed from a webcam stream of an office interior (lot's of details) where I have to find an artificial marker. The marker is a black square on white background. I use Canny to find edges and cvFindContours for contouring, then approxPolyDP and co. for filtering and finding candidates, then use local histogram to filter further, bla bla bla...
This works more or less, but not exactly how I want. FindContours always returns a closed loop, even if Canny creates a non-closed line. I get a contour walking on both sides of the line forming a loop. For closed edges on the Canny image (my marker), I get 2 contours, one on the inside, and an other on the outside.
I have to problems with this operation:
I get 2 contours for each marker (not that serious)
the most trivial filtering is not usable (reject non-closed contours)
So my question: is it possible to get non-closed contours for non-closed Canny edges?
Or what is the standard way to solve the above 2 issues?
Canny is a very good tool, but I need a way convert the 2D b/w image, into something easily process-able. Something like connected components listing all pixels in walking order of the component. So I can filter for loops, and feed it into approxPolyDP.
Update: I missed some important detail: the marker can be in any orientation (it's not front facing the camera, no right angles), in fact what I'm doing is 3D orientation estimation, based on the 2D projection of the marker.
I found a clean and easy solution for the 2 issues in the question. The trick is enable 2 level hierarchy generation (in findCountours) and look for contours which have a parent. This will return the inner contour of closed Canny edges and nothing more. Non-closed edges are discarded automatically, and each marker will have a single contour.
vector<vector<Point> > contours;
vector<Vec4i> hierarchy;
findContours(CannyImage, contours, hierarchy, CV_RETR_CCOMP, CV_CHAIN_APPROX_NONE, Point(0,0) );
for (unsigned int i=0; i<contours.size(); i++)
if (hierarchy[i][3] >= 0) //has parent, inner (hole) contour of a closed edge (looks good)
drawContours(contourImage, contours, i, Scalar(255, 0, 0), 1, 8);
It also works the other way around, that is: look for contours which have a child (hierarchy[i][2] >= 0), but in my case the parent check yields better results.
I had the same problem with duplicate contours and even dilate and erode could not solve it:
Mat src=imread("E:\\test.bmp"),gry,bin,nor,dil,erd;
GaussianBlur( src, nor, Size(5,5),0 );
cvtColor(nor,gry,CV_BGR2GRAY);
Canny(gry,bin,100,150,5,true);
dilate(bin,dil,Mat());
erode(dil,erd,Mat());
Mat tmp=bin.clone();
vector<vector<Point>> conts;
vector<Vec4i> hier;
findContours(tmp,conts,hier,CV_RETR_TREE,CV_CHAIN_APPROX_SIMPLE);
This image (test.bmp) contains 3 contours but findContours returned 6!
I used threshold and problem solved:
Mat src=imread("E:\\test.bmp"),gry,bin,nor,dil,erd;
GaussianBlur( src, nor, Size(5,5),0 );
cvtColor(nor,gry,CV_BGR2GRAY);
threshold(gry,bin,0,255,THRESH_BINARY+THRESH_OTSU);
vector<vector<Point>> conts;
vector<Vec4i> hier;
findContours(bin,conts,hier,CV_RETR_TREE,CV_CHAIN_APPROX_SIMPLE);
Now it returns 4 contours which the 1st one is the image boundary(contour with index 0) and can be easily skipped.
This how I would do it
1. Canny for edge detection
2. Use houghtransform to detect the edges.
3. Detect the two edges that do an angle of 90.
I got the no of contours of this image using function of cvFindcontour its fine and also i got the each contour using to increment contour->h_next.
cvFindContours( src, storage, &contour, sizeof(CvContour), CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE );
but sequence of the contour was random,is there any way to find the contour as such given format in the image? my ultimate aim was to find the area of outer squares alone(i.e)(1,2,3,4,9,10,15,16,17,18 alone).please can you suggest some algorithm or function to find the contour with this sequence.
Thanks in advance.
I would suggest :
find the center of masses of each square
find the orientation of the checkboard by a fit or a hough transform
perform rotation of the center of mass coordinates
sort squares according to new coordinates