I am trying to recognize the numbers on the 7-segment LCD display(like the digital watch), and first of all I want to 'draw' a bounding box to focus on the LCD screen to get rid of most of the background noise, but I am still not sure which method is suitable for this task. Can anyone help me?
I won't tell you the exact answer but as far as I can tell you should take a look at the OpenCV library. The keyword you're looking for in the first place is: Region Of Interest (ROI), ie. the bounding box.
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I've used opencv to detect humanoid blobs from mounted cameras. My code basically finds large contours, puts boxes around them and merges the intersecting boxes.
I'd now like to be able to track those boxes across the screen and hopefully get an estimation of the box speed.
Could anyone tell me how to link the box from the first frame with the one in the second? If I could do that, I'm sure I could figure the rest out.
As Micka said, OpenCV 3 has a TLD (Tracking Learning Detection) tracker.
http://answers.opencv.org/question/52872/how-to-perform-tld-tracking-on-roi-bounding-box-using-opencv-c-builtin-methods/
http://docs.opencv.org/3.0-beta/modules/tracking/doc/tracker_algorithms.html#tld
I'm having the user capture an image of a barcode that they are asked to place inside a bounding box drawn on the camera overlay. I'd like to detect whether that barcode was properly placed in the box. What would be the best way to detect this? OpenCV's houghLines? And how would I figure out if the lines detected from houghLines are the barcode's corner and that they match up with the camera overlay's bounding box?
Thanks in advance for any help
The post How to find corners on a Image using OpenCv has the solution for your problem. I did the same for my project and it worked like a charm :) If need help in understanding or tuning the algorithm, don't hesitate.
Cheers
did somebody tried to find a pizzamarker like this one with "only" OpenCV so far?
I was trying to detect this one but couldn't get good results so far. I do not know where this marker is in picture (no ROI is possible), the marker will be somewhere in the room (different ligthning effects) and not faceing orthoonal towards us. What I want - the corners and later the orientation of this marker extracted with the corners but first of all only the 5Corners. (up, down, left, right, center)
I was trying so far: threshold, noiseclearing, find contours but nothing realy helped for a good result. Chessboards or square markers are normaly found because of their (parallel) lines- i guess this can't help me here...
What is an easy way to find those markers?
How would you start?
Use other colorformat like HSV?
A step-by-step idea or tutorial would be realy helpfull. Cause i couldn't find tuts at the net. Maybe this marker isn't called pizzamarker -> does somebody knows the real name?
thx for help
First - thank you for all of your help.
It seems that several methods are usefull. Some more or less time expansive.
For me it was the easiest with a template matching but not with the same marker.
I used only a small part of it...
this can be found 5 times(4 times negative and one positive) in this new marker:
now I use only the 4 most negatives Points and the most positive and got my 5 points that I finaly wanted. To make this more sure, I check if they are close to each other and will do a cornerSubPix().
If you need something which can operate in real-time I'd go down the edge detection route and look for intersecting lines like these guys did. Seems fast and robust to lighting changes.
Read up on the Hough Line Transform in openCV to get started.
Addendum:
Black to White is the strongest edge you can have. If you create a gradient image and use the strongest edges found in the scene (via histogram or other) you will be able to limit the detection to only the black/white edges. Look for intersections. This should give you a small number of center points to apply Hough ellipse detection (or alternate) to. You could rotate in a template as a further check if you wish.
BTW.. OpenCV has Edge Detection, Hough transform and FitEllipse if you do go down this route.
actually this 'pizza' pattern is one of the building blocks of the haar featured used in the
Viola–Jones object detection framework.
So what I would do is compute the summed area table, or integral image using cv::integral(img) and then run exhaustive search for this pattern, on various scales (size dependant).
In each window you are using only 9 points (top-left, top-center, ..., bottom left).
You can train and use cvHaarDetectObjects to detect the marker using VJ.
Probably not the fastest method but it should work.
You can find more info on object detection methods using OpenCV here: http://opencv.willowgarage.com/documentation/object_detection.html
I am using some functions such as color contour tracking and image matching which are already available in OpenCV .. I am trying to identify a pink duck, more specifically the head of the duck, but these two functions don't give me the outcome I am expecting for some reasons such as :
the color thing don't always work perfect because the change in the lightning , which accordingly would change the color seen by the camera.
when I use the image matching thing, I use one image of the duck which I took from a specific position and it can identify the duck only when he is in that position, but I want to identify it even when I rotate the duck or play around with it.
Does anyone have an ideas about a better way to track a certain object ?
Thank you
Have you tried converting the image into the hsv colourspace? This colourspace tries to remove the effects of lighting so might be able to improve your colour-based segmentation.
To identify the head of the duck, once you have identified the duck as a whole you could perhaps identify the orientation (using template matching with a set of templates from different viewpoints, or haar cascades, or ...) and then use the known orientation and an empirical rule to determine where the head is located. For example, if you detect the duck in an upright position within a certain bounding box, the head is assumed to be located in the top third of that bounding box.
I think it might just take little more than what OpenCV provides straight forward way.
Given your specific question, you might just want to try shape descriptors of some sort.
Basically, try to take Duck's head's pictures shape from various angles and capture the shapes from it.
Now, you can find a likelihood model (forgive me for not a very accurate term) that can validate the hypothesis that a given captured shape indeed belongs to the class of Duck's head or not. Color can just be an additional feature that might help.
If you are a new person in this field - try catch hold of Duda and Hart: Pattern Classification. This doesn't have solution to find-the-duck-problem but will shape your thinking.
I have a problem to detect object in images or video frames.
I have a task that is detect some people or something who enter into the sight of web camera, and then my system will be alarm.
Next step is recognize which kind of thing the object is, in this phase I know use Hough transform to detect line, circle, even rectangle. But when a people come into the sight of camera, people's profile is more complex than line, circle and rectangle. How can i recognize the object is people not a car.
I need help to know that.
thanks in advance
I suggest you look at the paper "Histograms of Oriented Gradients for Human Detection" by Dalal and Triggs. They used Histograms of Oriented Gradients to detect humans in images
I think one method is to use Bayesian analysis on your image and see how that matches with a database of known images. I believe some people run a wavelet transform to emphasize more noticeable features.