I want to find the depth map for stereo images.At present i am working on the internet image,I want to take stereo images so that i can work on it by my own.How to take best stereo images without much noise.I have single camera.IS it necessary to do rectification?How much distance must be kept between the cameras?
Not sure I've understood your problem correclty - will try anyway
I guess your currently working with images from middlebury or something similar. If you want to use similar algorithms you have to rectify your images because they are based on the assumption that corresponding pixels are on the same line in all images. If you actually want depth images (!= disparity images) you also need to get the camera extrinsics.
Your setup should have two cameras and you have to make sure that they don't change there relative position/orientation - otherwise your rectification will break apart. In the first step you have to calibrate your system to get intrinsic and extrinsic camera parameters. For that you can either use some tool or roll your own with (for example) OpenCV (calib-module). Print out a calibration board to calibrate your system. Afterwards you can take images and use the calibration to rectify the images.
Regarding color-noise:
You could make your aperture very small and use high exposure times. In my own opinion this is useless because real world situations have to deal with such things anyway.
In short, there are plenty of stereo images on the internet that are already rectified. If you want to take your own stereo images you have to follow these three steps:
The relationship between distance to the object z (mm) and disparity in pixels D is inverse: z=fb/D, where f is focal length in pixels and b is camera separation in mm. Select b such that you have at least several pixels of disparity;
If you know camera intrinsic matrix and compensated for radial distortions you still have to rectify your images in order to ensure that matches are located in the same row. For this you need to find a fundamental matrix, recover essential matrix, apply rectifying homographies and update your intrinsic camera parameters... or use stereo pairs from the Internet.
The low level of noise in the camera image is helped by brightly illuminated scenes, large aperture, large pixel size, etc.; however, depending on your set up you still can end up with a very noisy disparity map. The way to reduce this noise is to trade-off with accuracy and use larger correlation windows. Another way to clean up a disparity map is to use various validation techniques such as
error validation;
uniqueness validation or back-and-force validation
blob-noise supression, etc.
In my experience:
-I did the rectification, so I had to obtain the fundamental matrix, and this may not be correct with some image pairs.
-Better resolution of your camera is better for the matching, I use OpenCV and it has an implementation of BRISK descriptor, it was useful for me.
-Try to cover the same area and try not to do unnecessary rotations.
-Once you understand the Theory, OpenCV is a good friend. Here is some result, but I am still working on it:
Depth map:
Rectified images:
Related
i used the opencv sample code for stereo camera calibration to get the intrinsics and extrinsics of my stereo camera. I used 149 image pairs and the program detected 114 image pairs
Result of my Calibration:
..... 114 pairs have been successfully detected.
Running stereo calibration ...
done with RMS error = 1.60208
average epipolar error = 1.15512
i know the error should be below 1 but i only get below 1 of error in small number of image pairs. so im not sure if my result is good or bad.
You should be able to get an error below 1, but it's not so bad. I also do the calibration with around 100 of images. I often got a few images to discard in which the detection was not reliable.
If you decreased the number of images down to 10 images, then the calibration might overfit for these cases. The error would then not be reliable.
In the calibration process, the problems I faced came from the calibration setup. My recommendations are the following:
Check that your calibration pattern is perfectly flat. In my case I printed on adhesive paper and glued it on a piece of glass.
Check that your calibration pattern is not symmetrical in rotation, otherwise the pose estimation could be wrong.
Check the intermediate pattern points detection. There are some examples in opencv to show the corners or circles centers detected points.
The error can be also displayed for each frame. This can help you to understand for which images you have a problem. If you see that these images actually have a detection problem, you can discard them.
If you acquire videos and not images, both cameras should be synchronized with a hardware connection. In my case I cannot have such a link, therefore I built some kind of holder for the calibration target to keep it still, and I acquired only images, not videos.
This won't reduce your calibration error, but use very different pattern positions to cover the maximum of the field of view.
If your depth of field is small and you have blurry images before/after the focus because of that, change from the chessboard pattern to a circles pattern (functions also available in opencv).
If you don't have a strong distortion in your images (e.g. a photo with an iphone doesn't really show a strong fisheye-like distortion), consider forcing K3=0.
In my case, I fixed the "principal point" in the middle of the image, because the algorithm always found crazy values for these parameters, like for K3.
Hope this helps a bit. Good luck!
I am trying to reconstruct the real-world coordinates of 3D points from two images taken from the same camera. The camera is not calibrated, but the movement (translation and rotation) is known. In short:
Requirement:
No calibration
Extra constraints other than image point correspondences:
Known camera translation and rotation
Same camera used in all views
I understand that, from image point correspondences alone, a scene can be reconstructed only up to a projective transformation. With more constraints, an affine or similarity reconstruction may be done. In my case, I need a similarity reconstruction.
Given the above constraints, is a similarity reconstruction possible? If possible, how should I go about doing it?
I have tried to attack the problem from a few angles. Since I am not mathematically fluent, I try to use opencv as much as possible.
findFundamentalMat() from the two images, hopefully extract the two camera matrices somehow, then triangulatePoints(). As you could have guessed, I got stuck in the middle, unable to obtain camera matrices from fundamental matrix.
The textbook "Multiple View Geometry in Computer Vision" (by Hartley and Zisserman) gives an expression (p.256, Result 9.14) that expresses the camera matrices in terms of fundamental matrix and one of the epipoles. However, without knowing the camera's intrinsic parameters (requirement: no calibration), I don't see how I can get the epipole.
I also try to treat my problem as a stereo system and use opencv's stereo*** functions. But they all seem to require human intervention to calibrate, which violates my requirement.
So, that's why I ask the question here today. The key is still, given those extra constraints, is a similarity reconstruction possible? I am not smart enough to understand the wealth of knowledge out there, and not able to come up with my own solution. Any help is appreciated.
I have two set of corresponding matches that I want to compute Homography Matrix between them. However, I found that the transformation between this points can not be modeled using just the Homography Matrix. I figured this by observing some lines in the original set of points have not represented as lines in the second set.
For example:
The previous state is very extreme in real the distortion is much less than that. It is usually a distortion because of the first set of points were extracted from image that was taken by scanner where the other set of points were extracted from a photo taken by mobile phone.
The Question:
How can I expand or Generalize the Homography matrix to make it includes this case? Or in other words, I want a non-line-preserve transformation model to use it instead of the Homography Matrix, Any Suggestion?
P.S OpenCV library is prefered if there is something ready to use.
EDIT:
Eliminating the distortion may not be an option for me because the photos are somewhat complex and I do not have the same Camera always plus I supposed to deal with images from unknown source (back-end separated from front-end). However, I have a reference which is planner and a query which has perspective + distoration effect which I want to correct it after I could found the corresponding pair matches.
It would be better if you had provided some examples of your images, so that we can understand your case better. From the description it seems that you are dealing with camera distortion.
Typical approach is to perform camera calibration once, then undistort each frame and finally work with images where straight lines look straight. All of these tasks are possible with OpenCV, consider the link above.
In case you cannot perform camera calibration to estimate distortion - there isn't much you can do. Try to calculate and apply homography on unrectified images - if the cameras don't have wide angle lens this should look ok (consider this case for example)
I'm currently implementing the stereovision with OpenCV. Now I'm using the Stereo_Calib sample to remove the distortion en rectify the image. Removing the distortion works fine.
But when I apply rectification, the image is very warped.
This is the code to rectify the images. The parameters rmap are calculated in the same way as in the Stereo_calib example (see here)
void StereoCalibration::StereoRectify(Mat &imageLeft, Mat &imageRight)
{
Mat imLeft, imRight;
remap(imageLeft, imLeft,DistLeft.rmap[0], DistLeft.rmap[1], CV_INTER_CUBIC);
remap(imageRight,imRight, DistRight.rmap[0], DistRight.rmap[1], CV_INTER_CUBIC);
imageLeft = imLeft;
imageRight = imRight;
}
I realise this question is a few years old however, I have recently had a similar issue. Building on morynicz answer about "bad chessboard" patterns to calibrate stereo images, I found that even with a slight deformation in your chessboard pattern, for example that it isn't flat, can produce large warping in the stereo image pair on rectification. The algorithms in OpenCV, for instance, assume a flat chessboard pattern is being presented such that any physical deformation in that pattern will be wrongly attributed to distortions in the camera optics (or in the relative orientations of the two camera sensors). The algorithms will then try really hard to remove this false distortion leading to very warped images.
To avoid this problem, were possible, use a tablet (or other electronic screen) to display the chessboard pattern as it is then guaranteed to be flat.
Additionally, you should check that the images you are using to calibrate the stereo pair are in focus and have no motion blur or image tearing.
If using OpenCV to do the rectification do some experimentation with the flags used in the stereoCalibrate function as this may lead to a more "optimised" rectification for your particular application.
For anyone looking for help on this, I was dealing with very large scale resolution images and was getting very low reprojection error rate with good calibration images. I was getting very warped stereo pairs after rectification and a really bad depth map.
One thing to try is if your images are warped you might need to down-sample them.
Another thing to try is to combine the flags in stereoCalibrate instead of just choosing one.
Something like this worked for me :
cv2.stereoCalibrate(
object_points, image_points_left,image_points_right,
camera_matrix_left,dist_left,
camera_matrix_right, dist_right,
(5472,3648),None,None,None,None,
cv2.CALIB_FIX_ASPECT_RATIO + cv2.CALIB_ZERO_TANGENT_DIST + cv2.CALIB_USE_INTRINSIC_GUESS + cv2.CALIB_SAME_FOCAL_LENGTH + cv2.CALIB_RATIONAL_MODEL,criteria
)
I had the same problem, and I think that the issue was bad chessboard used to calibration or mixing up the maps.
I started working on opencv stereo image calibration and rectification recently and I was getting similar images. Although it is true to make sure the board is straight and it is true that we need to take multiple images on the corners and in the middle of the camera at different x,y,z and skew positions, what did the trick for me was the flags in stereoCalibrate. I used all the flags specified in the opencv docs except for INTRINSIC_GUESS and it started very nice undistorted and rectified images.
I am working with a set of calibrated images that form a ring around a foreground object (1). I used Fusiello's method (1) to rectify adjacent pairs of images, and then I performed disparity estimation.
When I take the matched points from a stereo pair and triangulate them, it forms an accurate point cloud. Unfortunately, when I triangulate the points from another stereo image pair, this point cloud never aligns correctly with the original cloud.
Should calibrated, rectified images' point clouds merge together automatically?
Thanks in advance for any help you can offer.
This might be due to the accuracy of calibration - both intrinsic (i.e. the same camera model - and how it handles distortion) and extrinsic (i.e. the camera pose in real space). Together, of course, these dictate the ultimate accuracy of your re-projection.
Do you have a measure of error for camera calibration - in terms of MSE re-projection?
Cumulative error is often noticeable in my experience if simply iterating over subsequent images. Some form of global optimisation often needs to be performed to first correct positions for all the camera poses.
The accuracy of your disparity estimation is also a factor. Not only in terms of the algorithm you using, but also in relation to the stereo baseline and how it relates to the size/nature of the object in question (how concave/convex), and how many sampling of the images you are taking (and the quality of those images - exposure/depth-of-field/etc).
Fundamentally, just how "off" are your point clouds? Are they close to being aligned (you could do a bit of ICP before triangulation...). Are they closer in the "centre" of the re-projection? Are they worse for projections taken from opposing images on opposite sides of the object?
Remember as well that (due to the discrete sampling) you shouldn't expect points to ever be re-projected exactly "on-top" on one another. Some form of binning operation during the triangulation pipeline usually occurs for handling this (hence most of the research work in visual hull -> voxels -> marching cubes -> triangulated surface around this...)
Have you checked out MeshLab BTW?