I am trying to understand how to apply cv2.undistort function only on a subset of the image.
Camera calibration was done through cv2.findChessboardCorner and it seems to be working fine. I find that undistortion, however, is very slow, averaging around 9 fps on a 1080x1920 image. For the purpose of the project, I am interested only in the fixed subset of image, usually something like img[100:400].
What is the good way to approach this problem? It seems to be wasteful to undistort entire image only when stripe of 100 pixels is needed.
From the docs:
The function is simply a combination of cv::initUndistortRectifyMap (with unity R ) and cv::remap (with bilinear interpolation). See the former function for details of the transformation being performed.
So by calling undistort in a loop you are recomputing the un-distortion maps over and over - there is no caching, and their computation is expensive, since it involves solving a polynomial equation for every pixel. IIUC your calibration is fixed, so you should compute them only once using initUndistortRectifyMap(), and then pass the maps to remap() in your main loop.
The kind of "cropping" you describe is doable, but it may require some work and little experimentation because the undistortion maps used by OpenCV are in 1:1 correspondence with the un-distorted image, and store pixel (vector) offsets. This means that you need to crop out the maps portions corresponding to the rectangle of the image you care about, and then edit their values, which are x and y offsets from the un-distorted to the distorted image. Specifically, you need to apply to them the inverse of the 2D translation that brings that rectangle you care about to the top-left corner of the image canvas. Then you should be able to call remap() passing as destination image just an empty image the size of the undistorted cropped rectangle.
All in all, I'd first try the first recommendation (don't call undistort, separate map generation from remapping), and only try the second step if really can't keep up with the frame rate.
Related
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 am trying to determine the movement and rotation of an object (can be plain-colored, but does not have to be) on a not completely constant background. Here is an example:
Using keypoints to find the transformation as in the tutorials does not work because the objects I am dealing with do not necessarily provide enough edges for this.
Building the difference image and doing a segmentation there also often fails, because of the changed background. In this example it is not that bad, but there could be changed reflections or slight deformations.
Any ideas on how I to find the transformation matrix (affine, with only four degrees of freedom) that maps the object (in this example the blue thing) from one image to the other?
Use binary threshold on result of images subtraction(as described here - Foreground Extraction) - it should delete small changes(which are results of changes in lightning). Before that you may try to use some filter to blur edges, median filter may be a good option(but try different filters too) - try using this technique on both input images and result of images subtraction.
//edit:
For determine the transformation you may try to use SURF - http://docs.opencv.org/doc/tutorials/features2d/feature_homography/feature_homography.html
If you don't need to calculate rotation try to use optical flow technique - http://robots.stanford.edu/cs223b05/notes/CS%20223-B%20T1%20stavens_opencv_optical_flow.pdf or much simpler method:
1. Calculate geometrical center(just add positions of all points and divide the result by number of points) of marker contour on first and on second image(name them contour1 and contour2). Alternatively you may calculate center of mass of filled contour - it's up to you.
2. You transformation is: movementVector = centerOfContour2 - centerOfContour1
If the results will be not accurate enough, try to find biggest contour on image and draw this contour on empty image(so you won't have any noises, artifacts etc). Perform all operations on the new image.
I have a processed binary image of dimension 300x300. This processed image contains few object(person or vehicle).
I also have another RGB image of the same scene of dimensiion 640x480. It is taken from a different position
note : both cameras are not the same
I can detect objects to some extent in the first image using background subtraction. I want to detect corresponding objects in the 2nd image. I went through opencv functions
getAffineTransform
getPerspectiveTransform
findHomography
estimateRigidTransform
All these functions require corresponding points(coordinates) in two images
In the 1st binary image, I have only the information that an object is present,it does not have features exactly similar to second image(RGB).
I thought conventional feature matching to determine corresponding control points which could be used to estimate the transformation parameters is not feasible because I think I cannot determine and match features from binary and RGB image(am I right??).
If I am wrong, what features could I take, how should I proceed with Feature matching, find corresponding points, estimate the transformation parameters.
The solution which I tried more of Manual marking to estimate transformation parameters(please correct me if I am wrong)
Note : There is no movement of both cameras.
Manually marked rectangles around objects in processed image(binary)
Noted down the coordinates of the rectangles
Manually marked rectangles around objects in 2nd RGB image
Noted down the coordinates of the rectangles
Repeated above steps for different samples of 1st binary and 2nd RGB images
Now that I have some 20 corresponding points, I used them in the function as :
findHomography(src_pts, dst_pts, 0) ;
So once I detect an object in 1st image,
I drew a bounding box around it,
Transform the coordinates of the vertices using the above found transformation,
finally draw a box in 2nd RGB image with transformed coordinates as vertices.
But this doesnt mark the box in 2nd RGB image exactly over the person/object. Instead it is drawn somewhere else. Though I take several sample images of binary and RGB and use several corresponding points to estimate the transformation parameters, it seems that they are not accurate enough..
What are the meaning of CV_RANSAC and CV_LMEDS option, ransacReprojecThreshold and how to use them?
Is my approach good...what should I modify/do to make the registration accurate?
Any alternative approach to be used?
I'm fairly new to OpenCV myself, but my suggestions would be:
Seeing as you have the objects identified in the first image, I shouldn't think it would be hard to get keypoints and extract features? (or maybe you have this already?)
Identify features in the 2nd image
Match the features using OpenCV FlannBasedMatcher or similar
Highlight matching features in 2nd image or whatever you want to do.
I'd hope that because all your features in the first image should be positives (you know they are the features you want), then it'll be relatively straight forward to get accurate matches.
Like I said, I'm new to this so the ideas may need some elaboration.
It might be a little late to answer this and the asker might not see this, but if the 1st image is originally a grayscale then this could be done:
1.) 2nd image ----> grayscale ------> gray2ndimg
2.) Point to Point correspondences b/w gray1stimg and gray2ndimg by matching features.
I am trying to track motion of a toy car. I have recorded few videos and now trying to calculate rotation.
My problem is extracting features from object surface is quit challenging due to motion blur. Below image shows a cropped image from a video frame. The distortion happen in horizontal lines. The distortion seen in this image happens when object is moving. When the object is not moving there is no distortion.
Image shows distorted image of the car when its moving forward in a diagonal path cross the image frame.
I tried a wiener filter, based on median and variance but it didn't do much improvement. It only gave me a smoothed image as if Gaussian blur was applied on it.
What type of enhancements should I do to get a better image?
video - 720 x 576 frames - 25fps
from the picture provided it looks like you need to de-interlace the video rather than just trying to filter what's there; i remember doing this by just taking every other scan line and then doing a resize to put it back in perspective.
i found a pretty cool site that talks about deinterlacing in case you'd like to see if you might have other possibilities:
http://www.100fps.com/
(oh, and i have not inspected the image very closely so it's possible that there is some other interlacing scheme going on than just every other line; in which case my first answer wouldn't work properly. and it does imply that you will lose some resolution but that's just the nature of interlaced video...)
Given that your camera outputs interlaced video, you are better off using one field of the video. Either only use the even lines of the image or only the odd lines. The image will be squashed but you won't be mixing two images together.
Yep, that image needs to be de-interlaced. Correcting "distortion" due to linear movement is a different thing, you need to do a linear directional filtering depending on the speed of the vehicle, the distance to the camera and the obturation speed.
You have to first calculate the impulse response for a given set of conditions (those above, which represent the deviation or the distance between the same point taken at the beggining of the capture and the end of it), and then apply the inverse filtering. You may need to use some filtering or image processing toolkit, if using Matlab it's going to be easy.
Did you try:
deconvblind
Follow the example on deconvblind mathworks. It might work well on your example image.
Another example - Image Restoration
The following algorithm is a very simple de-interlaceing method:
cv::Mat input = cv::imread("img.jpg");
cv::Mat tmp(input.rows/2, input.cols*2, input.type(), input.data);
tmp = tmp.colRange(0, input.cols);
cv::Mat output;
cv::resize(tmp, output, Size(), 1, 2);
I intend to make a program which will take stereo pair images, taken by a single camera, and then correct and crop them so that when the images are viewed side by side with the parallel or cross eye method, the best 3D effect will be achieved. The left image will be the reference image, the right image will be modified for corrections. I believe OpenCV will be the best software for these purposes. So far I believe the processing will occur something like this:
Correct for rotation between images.
Correct for y axis shift.
Doing so will I imagine result in irregular black borders above and below the right image so:
Crop both images to the same height to remove borders.
Compute stereo-correspondence/disparity
Compute optimal disparity
Correct images for optimal disparity
Okay, so that's my take on what needs doing and the order it occurs in, what I'm asking is, does that seem right, is there anything I've missed, anything in the wrong order etc. Also, which specific functions of OpenCV would I need to use for all the necessary steps to complete this project? Or is OpenCV not the way to go? Much thanks.
OpenCV is great for this.
There is a whole chapter in:
And all the sample code for this in the book ships with the opencv distribution
edit: Roughly the steps are:
Remap each image to remove lens distortions and rotate/translate views to image center.
Crop pixels that don't appear in both views (optional)
Find matching objects in each view (stereoblock matching) create disparity map
Reproject disparity map into 3D model