I've started using OpenCV few days back, My aim is to detect a circle and its centre, I've used hough transform, I'm using a webcam of resolution 640x480, It is working but the circle keeps on changing its position, to better explain it I posted a screen grab on youtube https://www.youtube.com/watch?v=6EGePHkGrok
Here is the code http://pastebin.com/zRG4Yfzy ,yes I know its a bit messy.
First the full video is shown, when the camera stabilizes I press ESC, then the processing begins on the ROI 250x250.
I've added few trackbars to change to parameters of hough transform and amount of blur, changing the blur amount doesn't solve the problem
How to stabilize the circle? Also the camera will not move so no tracking is needed.
Or should I adopt a completely new method of doing this?
According to my understanding I need to apply some sort of filter.
The object has many circular contours, but all have the same centre, so any of the circular contour is detected its fine.
PS:I'm no Image Processing expert, I patched up the code from various sites and books
Hough transforms are known to be error prone.
For your case, you may find contours in your image and filter them by their circularity.
1- grayscale
2- low pass filter (gaussian blur)
3- canny edge detection
4- find contours and list their areas.
5- draw min enclosing circles to your contours.
6- select the contour which has min enclosing circle area closest to contour area.
7- find center of mass of the contour using moments F3 type "mass centers"
Related
I try the Canny edge detection algorithm to find the edge of a simple circle.
cv:: Canny()
The function returns a
cv::Mat edge
But I do not know how to use these "edge".
My goal is the draw the "edge" back to the original image and read the information within that edge (in this case, the edge is a circle)
I look through the cv:: read function and only find the drawContour function which is not an edge.
And I also do not how to find the coordinate of the edge so that I can do something about the inner part of the circle edge.
I am new to openCV, any suggestion is appreciated.
edges is array (Mat) of the same size as source picture, and it contains zero pixels and max value (255) pixels at edges that Canny function found
You can emphasize edges at source image (by white color), making bitwise_or operation with edges (in my). Or transform edges to color image to draw color edges. Here is example of using edges as mask.
Edges is raster result. To get set of segments, use findContours function on edges, then you can use drawContours
Note that this information is well-googlable.
You need learn how to traverse a cv::Mat object.
http://docs.opencv.org/2.4/doc/tutorials/core/how_to_scan_images/how_to_scan_images.html#the-efficient-way
Besides, I suggest u read first few chapters of the book learning opencv to master the basic usage of this library, now there is a third edition. You can also find many examples in "InstallPath\opencv\sources\samples" and the official tutorial: http://docs.opencv.org/2.4/doc/tutorials/tutorials.html
I want to detect multiple (similar) rectangular objects in an image that have a lot of substructure within them. So, my current plan is to use gaussian blur, morphology and edge detection (Canny). After using edge detection I get this (with very low threshold parameters):
What I want in the end is the outline of the greater rectangles. See:
Currently I try to get this by using HoughLines and findContours afterwards. For this to work, I need to fiddle a lot with the threshold parameters for Canny and HoughLines.
When I get it right for one image the parameters most likely will not work for the next one (e.g. the edges in the previous image were less dominant leading to too many lines detected by the hough transformation). Another problem is that sometimes inner structures are equally or less dominant than one side of the outer edges.
I tried to use a stronger blur or morphology but at some point this blurred away the small gap between the rectangles.
Can I extract the bigger rectangles somehow else given the edge image (preferably with opencv)?
Getting the 4 corner points would be enough.
i have several contours that consist of several black regions in my image. Directly adjacent to these black regions are some brighter regions that do not belong to my contours. I want to add these brighter regions to my black region and therefor extend my contour in OpenCv.
Is there a convenient way to extend a contour? I thought about looking at intensity change from my gradient-image created with cv::Sobel and extend until the gradient changes again, meaning the intensity of pixel is going back to the neither black nor bright regions of the image.
Thanks!
Here are example images. The first picture shows the raw Image, the second the extracted Contour using Canny & findContours, the last one the Sobel-Gradient intensity Image of the same area.
I want to include the bright boundaries in the first image to the Contour.
Update: Now i've used some morphological operations on the Sobelgradients and added a contour around them (see Image below). Next step could be to find the adjacent pair of purple & red contours, but it seems very much like a waste of procession time to actually have to search for directly adjacent contours. Any better ideas?
Update 2: My solution for now is to search for morphed gradient (red) contours in a bounding box around my (purple) contours and pick the one with correct orientation & size. This works for gradient contours where the morphological operation closes the "rise" and "fall" gradient areas like in Figure 3. But it is still a bad solution for cases in which the lighted area is wider then in the image above. Any idea is still very much appreciated, thanks!
What you're trying to do is find two different features and merge them. It's not terribly difficult but you have to use multiple copies of the image to make it happen.
Make one copy, and threshold it for the dark portion
Make another copy and threshold it for the light portion
Merge both thresholded images into a new image
Apply a morphological operation like opening or closing (depending on how you threshold) This will connect nearby components
Find contours in the resultant image
Use those contours on your original image. This will work since all the images are the same size and all based off of the original.
I would like to apply OCR to some pictures of 7 segment displays on a wall. My strategy is the following:
Covert Img to Grayscale
Blur img to reduce false edges
Threshold the img to a binary img
Apply Canny Edge detection
Set Region of Interest (ROI) base on a pattern given by the silhouette of the number
Scale ROI and Template match the region
How to set a ROI so that my program doesn't have to look for the template through the whole image? I would like to set my ROI base on the number of edges found or something more useful if someone can help me.
I was looking into Cascade Classification and Haar but I don't know how to apply it to my problem.
Here is an image after being pre-processed and edge detected:
original Image
If this is representative of the number of edges you'll have to deal with you could try a nice naive strategy like sliding a ROI-finder window across the binary image which just sums the pixel values, and doesn't fire unless that value is above a threshold. That should optimise out all the blank surfaces.
Edit:
Ok some less naive approaches. If you have some a-priori knowledge, like you know the photo is well aligned (and not badly rotated or skewed), you could do some passes with a low-high-low-high grate tuned to capture the edges either side of a segment, using different scales in both x and y dimensions. A good hit in both directions will give clues not only about ROI but what scale of template to begin with (too large and too small grates won't hit both edges at once).
You could do blob detection, and then apply your templates to blobs in turn (falling back on merging blobs if the template matching score is below a threshold, in case your number segment is accidentally partitioned). The size of the blob might again give you some hint as to the scale of template to apply.
First of all, given that the original image has a LED display and so the illuminated region is has a higher intensity than the trest, I'd perform say a Yuv colour transformation on the original image and then work with the intensity plane (Y).
Next, if you know that the image is well aligned (i.e. not rotated) I would suggest applying separate horizontal and vertical edge detectors rather than a generic edge detector (you are not interested in diagonal lines). E.g.
sobelx = cv2.Sobel( img, cv2.CV_64F, 1, 0, ksize=5 )
sobely = cv2.Sobel( img, cv2.CV_64F, 0, 1, ksize=5 )
Otherwise you might use contour detection to find the bounds of the digits (though you may need to perform a dilate to close the gaps between LED segments.
Next I would construct horizontal and vertical histograms of the output from these edge or contour detections. These would help you to identify 'busy' regions of the image which contain many edges.
Finally, I'd threshold the Y plane and explore each of the ROIs with my template.
I am using openCv with C++ and I am trying to find a moving ball under different lighting conditions. So far I am able to filter an image by thresholding it using HSV color space. The problem with this is that it will pick up other object that have a similar color. It is very tedious to figure out the exact hsv range everytime there is a ball with different color/background.
Is there a way for me to apply any filter on the thresholded binary image to detect only the objects that are moving? This way I will only find the ball and not other objects since they are usually stationary.
Thank you,
Varun
Simplest approach would be frame differencing / background learning in an image sequence.
frame differencing: substract two successive frames, the result is the moving part (you will probably only get the edges of moving objects)
background learning: e.g. build an average over 50 frames, this would be your learned background, then substract the current frame, again the difference is the moving part