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
Related
I am trying to create a panorama and I am stuck on the part where I have two separate warped images in two cv::Mat's and now I need to align them and create one single cv::Mat. I also need to average the pixel color value where the pixels in the images overlap to do elementary blending. Is there a built in function in opencv that can do this for me? I have been following the Basic Stitching Pipeline. I'm not sure how I can align and blend the images. I looked up a solution that does feature matching between the images and then we get the homography and just use the translation vector to align the images. Is this what I should be using?
Here are the warped images:
Image 1:
Image 1:
Generating a panaroma from a set of images is usually done using homographies. The reason for this is explained very well here.
You can refer to the code given by Eduardo here. It is also based on feature matching though.
You are right, you need to start with finding descriptors for features in the image (Brief descriptor might be a good idea) and then do feature matching. Once you have the correspondences, you will use those correspondences to estimate the homography. The homography will help you warp one of the image with respect to the other. Post this, you can simply blend them together (by simply add the two images, or taking the maximum value of the at each pixel between the two images)
I have a large set of images which are cellphone photos taken of driver’s licenses (but this could apply to any type of document). They come in all shapes and sizes, meaning, different angles of the camera, different distances from the camera to the driver’s license, different lighting, etc.
Is there any way in OpenCV to identify in each image a known landmark, then crop, resize, rotate each image so that for the final result, I have a set of images that are completely uniform (e.g. driver’s license fills the whole image, they all look the same, aligned the same, etc)?
Thanks!
One approach (using the functionality available in OpenCV) that I can suggest is to:
Train a Haar Cascade Classifier to recognize the landmark (works quite well for logos)
Take a few rotations of the image and look for the logo in each of them. You should find one or more matches, depending on the number of rotations that you use
Perform edge detection and project the edge pixels to the vertical axis. The projection with largest gaps will represent the spacing between text, and lead you to select one image that is closest to the correct orientation
Now you can crop, zoom or un-zoom this image using the logo position using the known properties of a driving license (logo is so many inches from the top left etc.).
Instead of a Haar cascade classifier, you can also match SIFT features of a logo with that of the photo.
I did not post any code or examples because the question is very broad. But you can easily find OpenCV documentation and examples for each of these steps.
Is there any way in OpenCV to
identify in each image a known landmark: there are several ways to do it, see here: https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_feature2d/py_table_of_contents_feature2d/py_table_of_contents_feature2d.html
crop: yes, subset it and don't forget to copyTo Select a subset of a Mat and copy them to create a new mat in C++/Opencv
resize, rotate each image so that for the final result, I have a set
of images that are completely uniform: you should use transformations like shown here: https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_imgproc/py_geometric_transformations/py_geometric_transformations.html
this tutorial should be useful for you: https://www.learnopencv.com/homography-examples-using-opencv-python-c/
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 am looking for an efficient way to detect the small boxes around the numbers (see images)?
I already tried to use hough transformation with no success. Any ideas? I need some hints! I am using opencv...
For inspiration, you can have a look at the
Matlab video sudoku solver demo and explanation
Sudoku Grab, an Iphone App, whose author explains the computer vision part on his blog
Alternatively, if you are always hunting for the same grid you could deploy something like this:
Make a perfect artificial template of the grid and detect or save all coordinates from all corners.
In the target image, do the same thing, for example with Harris points. Be creative, you might also be able to use the distinct triangles that can be found in your images.
Using the coordinates from the template and the found harris points, determine the affine transformation x = Ax' between the template and the target image. That transformation can then be used to map the template grid onto the target image. At the very least this will give you some prior information to help guide further segmentation.
The gist of the idea and examples of the estimation of affine matrix A can be found on the site of Zissermans book Multiple View Geometry in Computer Vision and Peter Kovesi
I'd start by trying to detect the rectangular boundary of the overall sheet, then applying a perspective transform to make it truly rectangular. Crop that portion of the image out. If possible, then try to make the alternating white and grey sub-rectangles have an equal background brightness - maybe try adaptive histogram equalization.
Then the Hough transform might perform better. Alternatively, you could then take an approach that's broadly similar to this demonstration by Robert Bemis on MATLAB Central (it's analysing a DNA microarray image rather than Lotto cards, but it's essentially finding bounding boxes of items arranged in a grid). At a high level, the approach is to calculate the autocorrelation along columns and rows of pixels to detect the periodicity of the items in the grid, and use that to impose a bounding box on each item.
Sorry the above advice is mostly MATLAB-based; I'm afraid I'm not an opencv user, but hopefully it will give you some ideas at least.
I have an image of the target logo that I am trying to use to find target logos in other images. I am currently running two different detection algorithms to help me detect any logos on the image. The first detection I use is Histogram based in which I search the image for a general area on screen where the colors are very similar. From there I run SIFT to further get the object that I am looking for. This works on most logos however the Target logo that I have isn't even picking up and keypoints in the logo.
I was wondering if there was anything I could do to help locate some keypoints in the image. Any advice is greatly appreciated.
Below is the image that isn't being picked up by SIFT:
Thanks in advance.
EDIT
I tired using Julien's idea for template matching based and different scales and rotations of the model, but still got little results. I have included an image that I am trying to test against.
There is no keypoint in your image...
Why ?
Because there is no keypoint in a uniform color plane (why would there be ? as it is uniform nothing is an highlight)
Because everything is symmetric in your image, it wouldn't really help to have keypoints, according to certain feature extractor they would have the same feature vectors
Because there's no corner or high gradient in cross directions which would result in keypoints fro many feature detectors
What you could try is a template matching method if you are searching for this logo without big changes (rotation, translation, noise etc) a simple correlation is the easiiiiest.
If you want to go further, one of my idea, that I have never implemented but which could be funny : would be to have sets of this image that you scale, rotate, warp, desaturate, increase noise with functions and then apply template matching with this set of images you got from your former template...
Well this idea comes from SIFT and Wavelet transform, where we use sort of functions that we change in some ways (rotation, noise, frequency etc...) in order to give robustness to our transform against these basic changes that occur in any image that you want to "inspect".
That could be an idea for you !
Here is an image summarizing my idea, you rotate and scale your template, actually it creates a new rotated/scaled template that you can try to match, it will increase robustness (even if it can be very long if you choose a lot of parameters to change). Well i'm not saying that's an algorithm, but it could be a funny and very basic idea to try...
Julien,
There is another reason that this logo is problematic for feature matching. Most features work pretty bad with artificial images that doesn't have any smoothness. All the derivatives are exactly 1 pixel size and features detector rely on derivatives. You have to smooth the image a bit. Ofcorse for this specific logo it will not help due to high symmetry. You can use hough transform to detect circles inside circles. It would give you better results in comparison with template matching.
I think you can try using MSER features- https://en.wikipedia.org/wiki/Maximally_stable_extremal_regions
See an example:
https://www.mathworks.com/examples/matlab-computer-vision/mw/vision_product-TextDetectionExample-automatically-detect-and-recognize-text-in-natural-images