I have an image with four squares of points in it, each with four corner points and other, interior points...
What is the best way to identify each as a separate square so I can process it individually as a Mat, or ROI?
They may be tilted, so the sides in 2d might not look equal, but each will have the same number of points, and each can be contained in a 4-sided polygon.
I have this:
http://i58.tinypic.com/wwdw0l.jpg
...and I want to get to this:
http://i59.tinypic.com/2dm9gtl.jpg
many thanks.
c++, visual studio, opencv
First of all, the small blobs should be detected, i.e. through cv::SimpleBlobDetector class,
Using cv::kmeans() to find centers of blob clusters and to group blobs around the clusters,
Finally, cv::minAreaRect() will find the rotated rectangle of the minimum area enclosing the clustered 2D point set.
Thanks Kornel! Find contours, find moments, use kmeans on the centre points as detailed here:
http://answers.opencv.org/question/36751/kmeans-clustering-for-vectorpoint2f-data-structure/
and use a rotatedrect to get the edges.
Related
I have tried to use edge detection to find the contour of images and try to compare the similarity of the contours by matchshape function. However, results are not as good as expected. I think it may be because of the images are not aligned before calculating the similarity. Therefore, I am asking for a way of aligning two contours in opencv. I am thinking of aligning by first finding the smallest bounding box or circle and then find out translation, rotation or resize needed to align those boxes. Then apply those transformation on the contour and test the similarity of them. Does this method work? Is there any method to align images? Thanks for your help. For your reference, attached are two contours going to be tested. They should be very similar but the distance found is quite large. The first two images have larger distance than that between the first and the last one, which seems contradicts with what it looks like (the last one should be the worst). Thanks.
These kinds of problems are known as registration problems. CPD, BCPD, and ICP would be your best shot.
[https://github.com/neka-nat/probreg][1]
I am trying to detect a ball in an filtered image.
In this image I've already removed the stuff that can't be part of the object.
Of course I tried the HoughCircle function, but I did not get the expected output.
Either it didn't find the ball or there were too many circles detected.
The problem is that the ball isn't completly round.
Screenshots:
I had the idea that it could work, if I identify single objects, calculate their center and check whether the radius is about the same in different directions.
But it would be nice if it detect the ball also if he isn't completely visible.
And with that method I can't detect semi-circles or something like that.
EDIT: These images are from a video stream (real time).
What other method could I try?
Looks like you've used difference imaging or something similar to obtain the images you have..? Instead of looking for circles, look for a more generic loop. Suggestions:
Separate all connected components.
For every connected component -
Walk around the contour and collect all contour pixels in a list
Suggestion 1: Use least squares to fit an ellipse to the contour points
Suggestion 2: Study the curvature of every contour pixel and check if it fits a circle or ellipse. This check may be done by computing a histogram of edge orientations for the contour pixels, or by checking the gradients of orienations from contour pixel to contour pixel. In the second case, for a circle or ellipse, the gradients should be almost uniform (ask me if this isn't very clear).
Apply constraints on perimeter, area, lengths of major and minor axes, etc. of the ellipse or loop. Collect these properties as features.
You can either use hard-coded heuristics/thresholds to classify a set of features as ball/non-ball, or use a machine learning algorithm. I would first keep it simple and simply use thresholds obtained after studying some images.
Hope this helps.
I am trying to develop box sorting application in qt and using opencv. I want to measure width and length of box.
As shown in image above i want to detect only outermost lines (ie. box edges), which will give me width and length of box, regardless of whatever printed inside the box.
What i tried:
First i tried using Findcontours() and selected contour with max area, but the contour of outer edge is not enclosed(broken somewhere in canny output) many times and hence not get detected as a contour.
Hough line transform gives me too many lines, i dont know how to get only four lines am interested in out of that.
I tried my algorithm as,
Convert image to gray scale.
Take one column of image, compare every pixel with next successive pixel of that column, if difference in there value is greater than some threshold(say 100) that pixel belongs to edge, so store it in array. Do this for all columns and it will give upper line of box parallel to x axis.
Follow the same procedure, but from last column and last row (ie. from bottom to top), it will give lower line parallel to x axis.
Likewise find lines parallel to y axis as well. Now i have four arrays of points, one for each side.
Now this gives me good results if box is placed in such a way that its sides are exactly parallel to X and Y axis. If box is placed even slightly oriented in some direction, it gives me diagonal lines which is obvious as shown in below image.
As shown in image below i removed first 10 and last 10 points from all four arrays of points (which are responsible for drawing diagonal lines) and drew the lines, which is not going to work when box is tilted more and also measurements will go wrong.
Now my question is,
Is there any simpler way in opencv to get only outer edges(rectangle) of box and get there dimensions, ignoring anything printed on the box and oriented in whatever direction?
I am not necessarily asking to correct/improve my algorithm, but any suggestions on that also welcome. Sorry for such a big post.
I would suggest the following steps:
1: Make a mask image by using cv::inRange() (documentation) to select the background color. Then use cv::not() to invert this mask. This will give you only the box.
2: If you're not concerned about shadow, depth effects making your measurment inaccurate you can proceed right away with trying to use cv::findContours() again. You select the biggest contour and store it's cv::rotatedRect.
3: This cv::rotatedRect will give you a rotatedRect.size that defines the width en the height of your box in pixels
Since the box is placed in a contrasting background, you should be able to use Otsu thresholding.
threshold the image (use Otsu method)
filter out any stray pixels that are outside the box region (let's hope you don't get many such pixels and can easily remove them with a median or a morphological filter)
find contours
combine all contour points and get their convex hull (idea here is to find the convex region that bounds all these contours in the box region regardless of their connectivity)
apply a polygon approximation (approxPolyDP) to this convex hull and check if you get a quadrangle
if there are no perspective distortions, you should get a rectangle, otherwise you will have to correct it
if you get a rectangle, you have its dimensions. You can also find the minimum area rectangle (minAreaRect) of the convexhull, which should directly give you a RotatedRect
I'm trying to extract the cube from the image (looks like a square...). I've used canny and dilate to get the edges and remove the noise.
I'm not even sure if it is possible to get the square out in a robust way.
Advice appreciated!
Thanks.
It's not excessively hard.
Sort all edges by direction. Look for a pair of edges in one direction with another pair 90 degrees rotated. Check for rough proximity. If so, they probably form a rectangle. Check the edge distances to pick the squares from the rectangles, and to discard small squares. Check if you have sufficiently large parts of the edge to be convinced the entire edge must exist. An edge might even be broken in 2. Check if the 4 edges now found delimit an area that is sufficiently uniform.
The last bit is a bit tricky. That's domain knowlegde. Could there be other objects inside the square, and could they touch or overlap the edges of the square?
You can utilize color information and kmeans clustering as explained in the link.
As long as target object color differs from the background, the pixels of the square object can be detected accurately.
I have an Image on which i have extracted several contours with 1st) cvCanny and 2nd) findContours. I'm only interested in the external Points, so I got several closed contours that i do analyse further. I'm looking for ellipses or circles and due to some overlap in the image i got some contours that are actually interesting for me but my algorithm discards them because they do not look elliptic.
Is there a way to dividide those contours, e.g. based on the small connecting "bridges" between two overlapping contours detected as one?
In this example i would want to just cut the rod on the lower right corner.
Due to performance issues, Hough circle detection is not an option.
Thanks!
Never worked with these sorts of algorithms before, but here's an idea: Define a minimum length L between points less than which you'd want to create a bridge. Then for each point on the contour, construct the tangent line segment of length L with its origin at that point. Wherever that tangent line segment intersects two points you will have a place where the contour is effectively getting 'pinched' as with the rod/ellipse junction in your figure. When this happens draw the bridge, which will be the tangent segment itself.
It might be easier to imagine or do if you take a single segment at a single point (say at the top of your curve, oriented to the left) and you move the segment around the contour, moving it along the bridges created online when the above condition is met.