I am using OpenCV Haar Algorithm to track the Head and overlay an image over the Head.
What I am doing is saving frames generated by camera and overlaying image over each frames.
And time is not a constraint as I am not doing it Real-Time.
My code is working fine for say 45 degree of left and right rotation of Head.
But I need something which will track up to 90 degree of rotation.
Even I got many reference of OpenCV functions and link to estimate Head Pose
Please provide me some reference. Code Examples will be cool.
Thanks in advance
You can use an algorithm like SURF (you have samples in OpenCV package) and use it over a picture of the face, the over the image, and then use SURF descriptors to match the points and estimate the 3d position of the face in the image.
You can use the same code on the sample "find_obj" but replace the image by the face picture you want to track.
Hope this helps.
There is a functionc in openCV called POSIT that permit to estimate the pose of 3d object in a single image. It implements POSIT algorithm. Try to have a look there.
You could check the EHCI project at http://code.google.com/p/ehci/ as it gives a nice overview about POSIT and Lukas Kanade.
Related
In OpenCV implementation, instrinsic parameters of the camera is used to correct geometric distortion.
So camera calibration is performed to obtain instrinsic parameters using multiple chessboard images.
Currently I learned that geometric distortion can be corrected using only one chessboard image.
I try to figure out how it is done, but still can't find one way to do it.
http://www.imatest.com/docs/distortion-methods-and-modules/
https://www.edmundoptics.com/resources/application-notes/imaging/distortion/
I find the two above links. It describes the radial distortion. However we can't
guarantee that the camera is parallel to the chessboard when capturing the chessboard.
I can detect the corners of the chessboard, but some corners is distorted, so I can't
fit lines because fitting can only handle noise.
Any help are appreciated.
Please take a look at this paper and this paper. Moreover, this paper proves that you can correct distortion using single image without calibration target based on identifying straight lines on image such as edges of the buildings.
I don't know whether this functionality is implemented in OpenCV but the math in those papers is should be relatively easy to implement it using OpenCV.
When camera is moving forward/backward, I think stereo rectification would not work. I guess I have to search along the epipolar line directly. Currently, my only clue is to use LineIterator to iterate through epipolar line, and use its neighbors as descriptor. This is very unintelligent method. So I want to know if there is other ways, or helpful opencv functions.
Old problem - Mark Pollefeys had a nice solution for it about 18 years ago. The idea is to rectify in polar coordinates.
See his short paper and lecture notes, and look around for tech reports from there.
You could try with Scale Invariant Feature Transform (SIFT) maybe with this descriptor you could get the key-points and stabilize the image with some transformation function per frame.
SIFT in Matlab
What will be the procedure to correct the following distorted images ? It looks like the images are bulging out from center. These are of the same QR code, and so a combination of such images can be used to arrive at a single correct and straight image.
Please advice.
The distortion you are experiencing is called "barrel distortion". A technical name is "combination of radial distortion and tangential distortions"
The solution for your problem is openCV camera calibration module. Just google it and you will find documentations in openCV wiki. More over, openCV already has built in source code examples of how to calibrate the camera.
Basically, You need to print an image of a chess board, take a few pictures of it, run the calibration module (built in method) and get as output transformation matrix. For each video frame you apply this matrix (I think the method called cvUndistort()) and it will straighten the curved lines in the image.
Note: It will not work if you change the zoom or focal length of the camera.
If camera details are not available and uncontrollable - then your problem is very serious. There is a way to solve the distortion, but I don't know if openCV has built in modules for that. I am afraid that you will need to write a lot of code.
Basically - you need to detect as much as possible long lines. Then from those lines (vertical and horizontal) you build a grid of intersection points. Finally you fit the grid of those points to openCV calibration module.
If you have enough intersection points (say 20 or more) you will be able to calculate the distortion matrix and un-distort the image.
You will not be able to fully calibrate the camera. In other words, you will not be able to run a one time process that calculates the expected distortion. Rather - in each and every video frame, you will calculate the distortion matrix directly - reverse it and un-distort the image.
If you are not familiar with image processing techniques or unable to find a reliable open source code which directly solves your problem - then I am afraid that you will not be able to remove the distortion. sorry
i have a stereopair,
photo 1: http://savepic.org/1671682.jpg
photo 2: http://savepic.org/1667586.jpg
there is coordinate system in each image. How can I find coordinates of point A in this system using OpenCV library. It would be nice to see sample code.
I've looked for it at opencv.willowgarage.com/documentation/cpp/camera_calibration_and_3d_reconstruction.html but haven't found (or haven't understood :) )
Your 'stereo' images are fine. What you have already done is solve the correspondence problem: in both images you have indicated points 'A'. This means that you know which pixel corresponds to eachother labeling point 'A'.
What you want to do, is triangulate where your camera is. You can only do this by first calibrating your camera. This is inside of OpenCV already.
http://docs.opencv.org/doc/tutorials/calib3d/camera_calibration/camera_calibration.html
http://docs.opencv.org/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html
This gives you the exact vector/ray of light for each vector, and the optical center of your cameras through which the ray passes. Moreover, you need stereo calibration. This establishes the orientation and position of each camera with respect through each other.
From that point on, your triangulation is simple, knowing the pixel location in both images of point 'A'. You have
Location and orientation of camera 1 and camera 2
Otical Ray Vector (pixel location) from the cameras to label 'A'.
So you have 2 locations in space, and 2 rays from these location. The intersection of these rays is your 3D answer.
Note that in practice there rays will never exactly intersect (2 lines in 3D rarely do), so you need to approximate. Use opencv function triangulatePoints(), using the input of the stereo calibration and the pixel index relating to label A.
Firstly of all this is not truly a stereo pair. A nice stereo pair needs to have 60%-80% overlap usually small rotation differences between images. Even if this pair had the necessary BASE to be a good stereo pair due to the extremely kappa rotation the resulting epipolar image would be useless.
Secondly among others you should take a look at the camera calibration and collinearity equations both supported by OpenCV
http://en.wikipedia.org/wiki/Camera_resectioning
http://en.wikipedia.org/wiki/Collinearity_equation
You need to understand the maths.
If the page isn't enough then you should look at the opencv book - it devotes a couple of chapters to this. Then there are a lot of textbooks that cover it in more detail
i need to find a marker like the ones used in Augmented Reality.
Like this:
I have a solid background on algebra and calculus, but no experience whatsoever on image processing. My thing is Php, sql and stuff.
I just want this to work, i've read the theory behind this and it's extremely hard to see in code for me.
The main idea is to do this as a batch process, so no interactivity is needed. What do you suggest?
Input : The sample image.
Output: Coordinates and normal vector in 3D of the marker.
The use for this will be linking images that have the same marker to spatialize them, a primitive version of photosync we could say. Just a caroussel of pinned images, the marker acting like the pin.
The reps given allowed me to post images, thanks.
You can always look at the open source libraries such as ARToolkit and see how it works but generally in order to get the 3D coordinates of marker you would need to:
Do the camera calibration.
Find marker in image using local features for example.
Using calibrated camera parameters and 2D coordinates of marker do the approximation the 3D coordinates.
I've never implemented sth similar by myself but I think this is a general concept you should apply on your method.
Your problem can be solved by perspective n point camera pose estimation. When you can reasonably assume that all correspondences are correct, a linear algorithm should do.
Since the marker is planar, you can also recover the displacement from the homography between the model plane and the image plane (link). As usual, best results are obtained by iterative algorithms (link).