I am trying to use OpenCV to detect an Aruco marker in the image below, specifically the one on clearly visible on the headphones. It has been working earlier, but it seems to have a hard time detecting it for most locations. It is clearly in view. Is the image too blurry? Are there things I can change?
Related
I need to detect where objects (mostly people) are in relation to a wall. I can have a fixed position camera in the ceiling so I thought to get an image of the space with nothing in it. Then use the difference of that and the current camera image to get an image with just the things. Then I can do blob detection I think to get the positions (only need x).
Does this seem sound? I'm not very accomplished in OpenCV so am looking for some advice.
That would be one way of going about it, but not very robust as the video feed won't produce consistent precise images so the background will never be nicely subtracted out, and people walking through the scene will occlude light and could also possibly match parts of your background.
This process of removing the background from a video is simply dubbed "background subtraction" and there are built-in OpenCV methods for it.
OpenCV has tutorials on their site showing the basics, for both python and C++.
I've been trying for a while to detect an image that looks like this:
Unfortunately i haven't been very lucky.
This image has to be detected so that I can "crop" a scene to whatever is below it.
I've been trying different feature detectors, like ORB, FAST and BRISK. And although I'm getting some key-points that look promising, when I try to find the Homography and apply the perspective transform to calculate my "scene corners" the results make absolutely no sense.
I suspect that the issue might be in how "simple" the marker is? since the corner points of the image are like technically the same, and within the small triangle in the middle, they are also very alike.
I'm looking for an advice or suggestion in how to approach this problem.
Edit:
My object to detect is an image. But the scene to be detected in is the video feed from an iOS camera device.
Edit 2:
I've replaced the top image with something more complex hoping the increase in keypoints would finally allow me to detect the object, but still no luck.
Here is the new top image:
And this is a sample of a frame: (taken as a screenshot of the iphone screen)
Keypoints detected:
I'm working on computer screen detection using emgucv (a c# opencv wrapper ).
I want to detect my computer screnn and draw a rectangle on it.
To help in this process, I used 3 Infrared Leds on the screen of the computer which I detect firtsly and after the detection, I could find the screen areas below those 3 leds.
Here is the results after the detection of the 3 leds.
The 3 red boxes are the detected leds.
.
And in general I have something like this
Does anyone have an idea about how I can proceed to detect the whole screan area ?
This is just a suggestion but, if you know for a fact that your computer screen is below your LEDs, you could try using OpenCV GrabCut algorithm. Draw a rectangle below the LEDs, large enough to contain the screen (maybe you could guess the size from the space between the LEDs) and use it to initialize the GrabCut.
Let me know what kind of results you get.
You can try to use a camera with no IR filter(This is mostly all night vision cameras) so that you can get a more intense light from the LEDs hence making it stand out than what your display would have then its a simple blob detection to get there position.
Another solution would be using ARUCO markers on the display if the view angle you are tending to use are not very large then its should be a compelling option and even the relative position of the camera with the display can be predicted also if that is what you want. With the detection of ARUCO you can get the angles that the plane of the display is placed at hence making the estimation of the display area with them.
I would like some hints, maybe more, on detecting a custom image marker in a real-time video feed. I'm using OpenCV, iPhone and the camera feed.
By custom image marker I'm referring to a predefined image, but it can be any kind of image (not a specific designed marker). For example, it can be a picture of some skyscrapers.
I've already worked with ARTags and understand how they are detected, but how would I detect this custom image and especially find out its position & orientation?
What makes a good custom image to be detected successfully?
Thanks
The most popular markers used in AR are
AR markers (a simple form of QR codes) - those detected by AR tookit & others
QR codes. There are plenty of examples on how to create/detect/read QR.
Dot grids. Similar with the chess grids used in calibration. It seems their detection can be more robust than the classical chess grid. OpenCV has codes related to dot grid detection in the calibration part. Also, the OpenCV codebase offers a good starting point to extract 3D position and orientation.
Chess grids. Similar to dot grids. They were the standard calibration pattern, and some people used them for marker detection of a long time. But they lost their position to dot grids recently, when some people discovered that dots can be detected with better accuracy.
Note:
Grids are symmetrical. I bet you already know that. But that means you will not be able to
recover full orientation data from them. You will get the plane where the grid lies, but nothing more.
Final note:
Code and examples for the first two are easily found on the Internet. They are considered the best by many people. If you decide to use the grid patterns, you have to enjoy some math and image processing work :) And it will take more.
This answer is valid no more since Vuforia is now a paid engine.
I think you should give Vuforia a try. It's a AR engine that can use any image you want as a marker. What makes a good marker for Vuforia is high frequency images.
http://www.qualcomm.com/solutions/augmented-reality
Vuforia is a free to use engine.
I'm trying to track the position of a robot from an overhead webcam. However, as I don't have much access to the robot or the environment, so I have been working with snapshots from the webcam.
The robot has 5 bright LEDs positioned strategically which are a different enough color from the robot and the environment so as to easily isolate.
I have been able to do just that using EmguCV, resulting in a binary image like the one below. My question is now, how to I get the positions of the five blobs and use those positions to determine the position and orientation of the robot?
I have been experimenting with the Emgu.CV.VideoSurveillance.BlobTrackerAuto class, but it stubbornly refuses to detect the blobs in the above image. Being a bit of a newbie when it comes to any of this, I'm not sure what I could be doing wrong.
So what would be the best method of obtaining the positions of the blobs in the above image?
I can't tell you how to do it with emgucv in particular, you'd need to translate the calls from opencv to emgucv. You'd use cv::findContours to get the blobs and cv::moments to get the position of the blobs (the formula to get the middle points of the blobs is in the documentation of cv::moments). Then you'd use cv::estimateRigidTransform to get the position and orientation of the robot.
I use cvBlob library to work blobs. Yesterday i worked with it to detect small blobs and works fine.
I wrote a python module to do this very thing.
http://letsmakerobots.com/node/38883#comments