I want to use OpenCV to detect an imperfect elliptic contour in binary images. Unfortunately, the elliptic contour consists of separate large dots with even larger gaps (of up to 25 pixels) between them.
I have tried OpenCV contour detection, but it doesn't work. It only marks the locations of individual dots instead of generating one enclosure for the contour.
How can I detect the contour using OpenCV? Please help. Here's a sample image
My final goal is to fit the dotted loop with an ellipse. Other dots nearby are noise data points. I have tried to get the contour centers for each dot or cluster of dots and put the xy coordinates of those centers into an array. I hope that FitEllipse will capture only the contour centers forming that loop. But when I do the FitEllipse, I got an exception "Emgu.CV.Util.CvException: 'OpenCV: n >= 0 && (depth == CV_32F || depth == CV_32S)' "
private void btnGO_Click(object sender, EventArgs e)
{
Mat pic = new Mat();
pic = CvInvoke.Imread("test image.png",
Emgu.CV.CvEnum.ImreadModes.Grayscale);
VectorOfVectorOfPoint contours= new VectorOfVectorOfPoint();
Mat hierarchy = new Mat();
CvInvoke.FindContours(pic, contours, hierarchy,
Emgu.CV.CvEnum.RetrType.Tree,
Emgu.CV.CvEnum.ChainApproxMethod.ChainApproxSimple);
Moments moments = new Moments();
List<Point> contourCentersList = new List<Point>();
for (int i = 0; i < contours.Size; i++)
{
int x;
int y;
moments = CvInvoke.Moments(contours[i]);
if (moments.M00 == 0)
continue;
x = Convert.ToInt32(moments.M10 / moments.M00);
y = Convert.ToInt32(moments.M01 / moments.M00);
ContourCentersList.Add(new Point(x, y));
}
Mat contourCenters = new Mat();
contourCenters.SetTo(contourCentersList.ToArray());
RotatedRect ellipse = new RotatedRect();
ellipse = CvInvoke.FitEllipse(contourCenters);
}
In order to obtain a contour you can pre-process the image to get the not connected areas connected.
For this purpose you can for example apply blur and then threshold the image.
Below your image and the result of applying blur and threshold:
Related
After some simple preprocessing I am receiving boolean mask of segmented images.
I want to "enhance" borders of the mask and make them more smooth. For that I am using OPEN morphology filter with a rather big circle kernel , it works very well until the distance between segmented objects is enough. But In alot of samples objects stick together. Is there exists some more or less simple method to smooth such kind of images without changing its morphology ?
Without applying a morphological filter first, you can try to detect the external contours of the image. Now you can draw these external contours as filled contours and then apply your morphological filter. This works because now you don't have any holes to fill. This is fairly simple.
Another approach:
find external contours
take the x, y of coordinates of the contour points. you can consider these as 1-D signals and apply a smoothing filter to these signals
In the code below, I've applied the second approach to a sample image.
Input image
External contours without any smoothing
After applying a Gaussian filter to x and y 1-D signals
C++ code
Mat im = imread("4.png", 0);
Mat cont = im.clone();
Mat original = Mat::zeros(im.rows, im.cols, CV_8UC3);
Mat smoothed = Mat::zeros(im.rows, im.cols, CV_8UC3);
// contour smoothing parameters for gaussian filter
int filterRadius = 5;
int filterSize = 2 * filterRadius + 1;
double sigma = 10;
vector<vector<Point> > contours;
vector<Vec4i> hierarchy;
// find external contours and store all contour points
findContours(cont, contours, hierarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE, Point(0, 0));
for(size_t j = 0; j < contours.size(); j++)
{
// draw the initial contour shape
drawContours(original, contours, j, Scalar(0, 255, 0), 1);
// extract x and y coordinates of points. we'll consider these as 1-D signals
// add circular padding to 1-D signals
size_t len = contours[j].size() + 2 * filterRadius;
size_t idx = (contours[j].size() - filterRadius);
vector<float> x, y;
for (size_t i = 0; i < len; i++)
{
x.push_back(contours[j][(idx + i) % contours[j].size()].x);
y.push_back(contours[j][(idx + i) % contours[j].size()].y);
}
// filter 1-D signals
vector<float> xFilt, yFilt;
GaussianBlur(x, xFilt, Size(filterSize, filterSize), sigma, sigma);
GaussianBlur(y, yFilt, Size(filterSize, filterSize), sigma, sigma);
// build smoothed contour
vector<vector<Point> > smoothContours;
vector<Point> smooth;
for (size_t i = filterRadius; i < contours[j].size() + filterRadius; i++)
{
smooth.push_back(Point(xFilt[i], yFilt[i]));
}
smoothContours.push_back(smooth);
drawContours(smoothed, smoothContours, 0, Scalar(255, 0, 0), 1);
cout << "debug contour " << j << " : " << contours[j].size() << ", " << smooth.size() << endl;
}
Not 100% sure what you are trying to achieve, but this may be an avenue to explore... the tool potrace takes images and converts them to vectorised images which involves smoothing. It prefers PGM format input files so I use ImageMagick to prepare them. Anyway, here is an example of the command and the result so see what you think:
convert disks.png pgm:- | potrace - -s -o out.svg
I have converted the resulting SVG file to a PNG so I can upload it to SO.
I have to calculate white pixels and compare two contours in opencv that is one contour in first and the fifth frame then fifth and the tenth frame and so on.
I searched a lot about how to find next contour in a video. but all in vain. I am in doubt whether opencv has the function to find next contour. I am completely confused by reading the tutorials and other things.
I have done this. I doubt my logic.
cvFindContours(bgModel->foreground, memory, &contour, sizeof(CvContour),CV_RETR_CCOMP,CV_CHAIN_APPROX_SIMPLE, cvPoint(0,0));
if(FrameNumber%5==0)
{
for( ; contour != 0; contour = contour->h_next )
{
double area = fabs(cvContourArea(contour,CV_WHOLE_SEQ, 0));
I dont know what to do after this. how to get next contour????
Here is a code fragment iterating contours your counour is in 'c' variable.
//Contour Stuffz
CvMemStorage* g_storage = NULL;
g_storage = cvCreateMemStorage(0);
CvSeq* contours = 0;
cvFindContours(r2, g_storage, &contours);
for (CvSeq* c = contours; c != NULL; c=c->h_next) {
//*** Contour Stuff
int nContourPoints = c->total; // Get total number of points in the chain
if (nContourPoints > 46) { ...
This is regarding a project that concerns detection of text in an image using OpenCV in C. The process is to detect the colors inside and outside the corresponding contours and the way to do that is to draw normals on the contours in equal spaced positions and extract the pixel colors in the corresponding positions of the normals end-points.
I am trying to implement this using the following code but it's not working. I mean, its drawing the normals but not in and equi-spaced fashion.
for( ; contours!=0 ; contours = contours->h_next )
{
CvScalar color = CV_RGB( rand()&255, rand()&255, rand()&255 );
cvDrawContours( cc_color, contours, color, CV_RGB(0,0,0), -1, 1, 8, cvPoint(0,0) );
ptr = contours;
for( i=1; i<ptr->total; i++)
{
p1 = CV_GET_SEQ_ELEM( CvPoint, ptr, i );
p2 = CV_GET_SEQ_ELEM( CvPoint, ptr, i+1 );
x1 = p1->x;
y1 = p1->y;
x2 = p2->x;
y2 = p2->y;
printf("%d %d %d %d\n",x1,y1,x2,y2);
draw_normals(x1,y1,x2,y2);
}
}
So is there a way to find the length of a contour so that I can divide it by the number of normals I want to draw. Thanks in advance.
EDIT: The draw_normal function draws the normals between two points passed to it as parameters.
So is there a way to find the length of a contour?
Yes, you can find length of a contour using OpenCV standard function , cvarcLength().
Check Documentation here.
I have a problem with filling white holes inside a black coin so that I can have only 0-255 binary images with filled black coins. I have used a Median filter to accomplish it but in that case connection bridge between coins grows and it goes impossible to recognize them after several times of erosion... So I need a simple floodFill like method in opencv
Here is my image with holes:
EDIT: floodfill like function must fill holes in big components without prompting X, Y coordinates as a seed...
EDIT: I tried to use the cvDrawContours function but it doesn't fill contours inside bigger ones.
Here is my code:
CvMemStorage mem = cvCreateMemStorage(0);
CvSeq contours = new CvSeq();
CvSeq ptr = new CvSeq();
int sizeofCvContour = Loader.sizeof(CvContour.class);
cvThreshold(gray, gray, 150, 255, CV_THRESH_BINARY_INV);
int numOfContours = cvFindContours(gray, mem, contours, sizeofCvContour, CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE);
System.out.println("The num of contours: "+numOfContours); //prints 87, ok
Random rand = new Random();
for (ptr = contours; ptr != null; ptr = ptr.h_next()) {
Color randomColor = new Color(rand.nextFloat(), rand.nextFloat(), rand.nextFloat());
CvScalar color = CV_RGB( randomColor.getRed(), randomColor.getGreen(), randomColor.getBlue());
cvDrawContours(gray, ptr, color, color, -1, CV_FILLED, 8);
}
CanvasFrame canvas6 = new CanvasFrame("drawContours");
canvas6.showImage(gray);
Result: (you can see black holes inside each coin)
There are two methods to do this:
1) Contour Filling:
First, invert the image, find contours in the image, fill it with black and invert back.
des = cv2.bitwise_not(gray)
contour,hier = cv2.findContours(des,cv2.RETR_CCOMP,cv2.CHAIN_APPROX_SIMPLE)
for cnt in contour:
cv2.drawContours(des,[cnt],0,255,-1)
gray = cv2.bitwise_not(des)
Resulting image:
2) Image Opening:
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))
res = cv2.morphologyEx(gray,cv2.MORPH_OPEN,kernel)
The resulting image is as follows:
You can see, there is not much difference in both cases.
NB: gray - grayscale image, All codes are in OpenCV-Python
Reference. OpenCV Morphological Transformations
A simple dilate and erode would close the gaps fairly well, I imagine. I think maybe this is what you're looking for.
A more robust solution would be to do an edge detect on the whole image, and then a hough transform for circles. A quick google shows there are code samples available in various languages for size invariant detection of circles using a hough transform, so hopefully that will give you something to go on.
The benefit of using the hough transform is that the algorithm will actually give you an estimate of the size and location of every circle, so you can rebuild an ideal image based on that model. It should also be very robust to overlap, especially considering the quality of the input image here (i.e. less worry about false positives, so can lower the threshold for results).
You might be looking for the Fillhole transformation, an application of morphological image reconstruction.
This transformation will fill the holes in your coins, even though at the cost of also filling all holes between groups of adjacent coins. The Hough space or opening-based solutions suggested by the other posters will probably give you better high-level recognition results.
In case someone is looking for the cpp implementation -
std::vector<std::vector<cv::Point> > contours_vector;
cv::findContours(input_image, contours_vector, CV_RETR_LIST, CV_CHAIN_APPROX_NONE);
cv::Mat contourImage(input_image.size(), CV_8UC1, cv::Scalar(0));
for ( ushort contour_index = 0; contour_index < contours_vector.size(); contour_index++) {
cv::drawContours(contourImage, contours_vector, contour_index, cv::Scalar(255), -1);
}
cv::imshow("con", contourImage);
cv::waitKey(0);
Try using cvFindContours() function. You can use it to find connected components. With the right parameters this function returns a list with the contours of each connected components.
Find the contours which represent a hole. Then use cvDrawContours() to fill up the selected contour by the foreground color thereby closing the holes.
I think if the objects are touched or crowded, there will be some problems using the contours and the math morophology opening.
Instead, the following simple solution is found and tested. It is working very well, and not only for this images, but also for any other images.
here is the steps (optimized) as seen in http://blogs.mathworks.com/steve/2008/08/05/filling-small-holes/
let I: the input image
1. filled_I = floodfill(I). // fill every hole in the image.
2. inverted_I = invert(I)`.
3. holes_I = filled_I AND inverted_I. // finds all holes
4. cc_list = connectedcomponent(holes_I) // list of all connected component in holes_I.
5. holes_I = remove(cc_list,holes_I, smallholes_threshold_size) // remove all holes from holes_I having size > smallholes_threshold_size.
6. out_I = I OR holes_I. // fill only the small holes.
In short, the algorithm is just to find all holes, remove the big ones then write the small ones only on the original image.
I've been looking around the internet to find a proper imfill function (as the one in Matlab) but working in C with OpenCV. After some reaserches, I finally came up with a solution :
IplImage* imfill(IplImage* src)
{
CvScalar white = CV_RGB( 255, 255, 255 );
IplImage* dst = cvCreateImage( cvGetSize(src), 8, 3);
CvMemStorage* storage = cvCreateMemStorage(0);
CvSeq* contour = 0;
cvFindContours(src, storage, &contour, sizeof(CvContour), CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE );
cvZero( dst );
for( ; contour != 0; contour = contour->h_next )
{
cvDrawContours( dst, contour, white, white, 0, CV_FILLED);
}
IplImage* bin_imgFilled = cvCreateImage(cvGetSize(src), 8, 1);
cvInRangeS(dst, white, white, bin_imgFilled);
return bin_imgFilled;
}
For this: Original Binary Image
Result is: Final Binary Image
The trick is in the parameters setting of the cvDrawContours function:
cvDrawContours( dst, contour, white, white, 0, CV_FILLED);
dst = destination image
contour = pointer to the first contour
white = color used to fill the contour
0 = Maximal level for drawn contours. If 0, only contour is drawn
CV_FILLED = Thickness of lines the contours are drawn with. If it is negative (For example, =CV_FILLED), the contour interiors are drawn.
More info in the openCV documentation.
There is probably a way to get "dst" directly as a binary image but I couldn't find how to use the cvDrawContours function with binary values.
I'm trying to draw contours around blobs in a binary image, however, sometimes, openCV draws a single contour around two distinct blobs. below is an example. How can i solve this issue?
Here it should draw two bounding boxes for the blob on the right and separately for the one of the left. I agree they are close but enough distance in between them. I'm only drawing External contours instead of the tree or list. I'm also using cvFindNextContour(contourscanner) as this is a easier implementation for my case.
Thanks
EDIT:
Image displayed in the "output" window is from a different function which does just image subtraction. Image displayed in the "contours" window is in the function pplfind(). "output" image is passed to img_con().
IplImage* img_con(IplImage* image){
int ppl;
CvMemStorage* memstr = cvCreateMemStorage();
IplImage* edges = cvCreateImage(cvGetSize(image),8,1);
cvCanny(image,edges,130,255);
CvContourScanner cscan = cvStartFindContours(image,memstr,sizeof(CvContour),CV_RETR_EXTERNAL,CV_CHAIN_APPROX_NONE,cvPoint(0,0));
ppl = pplfind(cscan,cvGetSize(image));
if (ppl !=0 )
printf("Estimated number of people: %d\n",ppl);
cvEndFindContours(&cscan);
cvClearMemStorage(memstr);
return edges;
}
int pplfind(CvContourScanner cscan, CvSize frSize){
ofstream file; char buff[50];
file.open("box.txt",ofstream::app);
int ppl =0;
CvSeq* c;
IplImage *out = cvCreateImage(frSize,8,3);
while (c = cvFindNextContour(cscan)){
CvRect box = cvBoundingRect(c,1);
if ((box.height > int(box.width*1.2))&&(box.height>20)){//&&(box.width<20)){//
ppl++;
cvRectangle(out,cvPoint(box.x,box.y),cvPoint(box.x+box.width,box.y+box.height),CV_RGB(255,0,50),1);
cvShowImage("contours",out);
//cvWaitKey();
}
//printf("Box Height: %d , Box Width: %d ,People: %d\n",box.height,box.width,ppl);
//cvWaitKey(0);
int coord = sprintf_s(buff,"%d,%d,%d\n",box.width,box.height,ppl);
file.write(buff,coord);
}
file.close();
cvReleaseImage(&out);
return ppl;
}
I've never used cvFindNextContour, but running cvFindContours with CV_RETR_EXTERNAL on your image seems to work fine:
I use OpenCV + Python, so this code might not be useful for you, but for the sake of completeness here it goes:
contours = cv.findContours(img, cv.CreateMemStorage(0), mode=cv.CV_RETR_EXTERNAL)
while contours:
(x,y,w,h) = cv.BoundingRect(contours)
cv.Rectangle(colorImg, (x,y), (x+w,y+h), cv.Scalar(0,255,255,255))
contours = contours.h_next()
Edit: you asked how to draw only those contours with certain properties; it would be something like this:
contours = cv.findContours(img, cv.CreateMemStorage(0), mode=cv.CV_RETR_EXTERNAL)
while contours:
(x,y,w,h) = cv.BoundingRect(contours)
if h > w*1.2 and h > 20:
cv.Rectangle(colorImg, (x,y), (x+w,y+h), cv.Scalar(0,255,255,255))
contours = contours.h_next()