I am currently looking for a proper solution to the following problem, which is not directly programming oriented, but I am guessing that the users of opencv might have an idea:
My stereo camera has a sensor of 1/3.2" 752x480 resolution. I am using the two stereo images of this very camera in order to create a point cloud, thanks to the point cloud library (PCL).
The problem is that I would like to reduce the number of points contained by the point cloud, by directly lowering the resolution of the input images (passing from 752x480 to 376x240).
As it is indicated in the title, I have to adapt the focal of the camera in pixels to this need:
I calculate this very parameter thanks to the following formula:
float focal_pixel = (FOCAL_METERS / SENSOR_WIDTH_METERS)*InputImg.cols;
However the SENSOR_WIDTH_METERS is currently constant and corresponds to the 1/3.2" data converted to meters AND I would like to adapt this to the resolution that I would like to have: 376x240.
I am absolutly not sure if I turned my problem clearly enough to be answered, which would mean that I am going in the wrong direction.
Thank you in advance
edit: the function used to process the stereo image (after computing):
getPointCloud(hori_c_pp, vert_c_pp, focal_pixel, BASELINE_METERS, out_stereo_cloud, ref_texture);
where the two first parameters are just the coordinates of the center of the image, BASELINE_METERS the baseline of my camera out_stereo_cloud my output cloud and eventually ref_texture the color information. This function is taken from the sub library stereo_matching.
For some reason, if I just resize the stereo images, it seems to enter in conflict with the focal_pixel parameters, since the dimension are not the same anymore.
Im very lost on this issue.
As I don't really follow the formulas and method calls you're posting I advise you to use another approach.
OpenCV already gives you the possibility to create voxels using stereo images with the method cv::reprojectImageTo3D. Another question also already discusses the conversion to the according PCL datatype.
If you only want to reproject a certain ROI of your image you should opt for cv::perspectiveTransform as is explained in the documentation I pointed out in the first link.
Related
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
First of all I'm a total newbie in image processing, so please don't be too harsh on me.
That being said, I'm developing an application to analyse changes in blood flow in extremities using thermal images obtained by a camera. The user is able to define a region of interest by placing a shape (circle,rectangle,etc.) on the current image. The user should then be able to see how the average temperature changes from frame to frame inside the specified ROI.
The problem is that some of the images are not steady, due to (small) movement by the test subject. My question is how can I determine the movement between the frames, so that I can relocate the ROI accordingly?
I'm using the Emgu OpenCV .Net wrapper for image processing.
What I've tried so far is calculating the center of gravity using GetMoments() on the biggest contour found and calculating the direction vector between this and the previous center of gravity. The ROI is then translated using this vector but the results are not that promising yet.
Is this the right way to do it or am I totally barking up the wrong tree?
------Edit------
Here are two sample images showing slight movement downwards to the right:
http://postimg.org/image/wznf2r27n/
Comparison between the contours:
http://postimg.org/image/4ldez2di1/
As you can see the shape of the contour is pretty much the same, although there are some small differences near the toes.
Seems like I was finally able to find a solution for my problem using optical flow based on the Lukas-Kanade method.
Just in case anyone else is wondering how to implement it in Emgu/C#, here's the link to a Emgu examples project, where they use Lukas-Kanade and Farneback's algorithms:
http://sourceforge.net/projects/emguexample/files/Image/BuildBackgroundImage.zip/download
You may need to adapt a few things, e.g. the parameters for the corner detection (the frame.GoodFeaturesToTrack(..) method) , but it's definetly something to start with.
Thanks for all the ideas!
I have a very specific application in which I would like to try structure from motion to get a 3D representation. For now, all the software/code samples I have found for structure from motion are like this: "A fixed object that is photographed from all angle to create the 3D". This is not my case.
In my case, the camera is moving in the middle of a corridor and looking forward. Sometimes, the camera can look on other direction (Left, right, top, down). The camera will never go back or look back, it always move forward. Since the corridor is small, almost everything is visible (no hidden spot). The corridor can be very long sometimes.
I have tried this software and it doesn't work in my particular case (but it's fantastic with normal use). Does anybody can suggest me a library/software/tools/paper that could target my specific needs? Or did you ever needed to implement something like that? Any help is welcome!
Thanks!
What kind of corridors are you talking about and what kind of precision are you aiming for?
A priori, I don't see why your corridor would not be a fixed object photographed from different angles. The quality of your reconstruction might suffer if you only look forward and you can't get many different views of the scene, but standard methods should still work. Are you sure that the programs you used aren't failing because of your picture quality, arrangement or other reasons?
If you have to do the reconstruction yourself, I would start by
1) Calibrating your camera
2) Undistorting your images
3) Matching feature points in subsequent image pairs
4) Extracting a 3D point cloud for each image pair
You can then orient the point clouds with respect to one another, for example via ICP between two subsequent clouds. More sophisticated methods might not yield much difference if you don't have any closed loops in your dataset (as your camera is only moving forward).
OpenCV and the Point Cloud Library should be everything you need for these steps. Visualization might be more of a hassle, but the pretty pictures are what you pay for in commercial software after all.
Edit (2017/8): I haven't worked on this in the meantime, but I feel like this answer is missing some pieces. If I had to answer it today, I would definitely suggest looking into the keyword monocular SLAM, which has recently seen a lot of activity, not least because of drones with cameras. Notably, LSD-SLAM is open source and may not be as vulnerable to feature-deprived views, as it operates directly on the intensity. There even seem to be approaches combining inertial/odometry sensors with the image matching algorithms.
Good luck!
FvD is right in the sense that your corridor is a static object. Your scenario is the same and moving around and object and taking images from multiple views. Your views are just not arranged to provide a 360 degree view of the object.
I see you mentioned in your previous comment that the data is coming from a video? In that case, the problem could very well be the camera calibration. A camera calibration tells the SfM algorithm about the internal parameters of the camera (focal length, principal point, lens distortion etc.) In the absence of knowledge about these, the bundler in VSfM uses information from the EXIF data of the image. However, I don't think video stores any EXIF information (not a 100% sure). As a result, I think the entire algorithm is running with bad focal length information and cannot solve for the orientation.
Can you extract a few frames from the video and see if there is any EXIF information?
I have opencv installed and working on my iphone (big thanks to this community). I'm doing template matching with it. It does find the object in the captured image. However, the exact location seems to be hard to tell.
Please take a look at the following video (18 seconds):
http://www.youtube.com/watch?v=PQnXNZMqpsU
As you can see in the video, it does find the template in the image. But when i move the camera a bit further away, then the found template is positioned somewhere inside that square. That way it's hard to tell the exact location of the found object.
The square that you see is basically the found x,y location of the template plus the width,height of the actual template image.
So basically my question is, is there a way to find the exact location of the found template image? Because currently it can be at any locastion inside that square. No real way to tell the exact location...?
It seems that you're not well-pleased with your template matching algorithm :)
Shortly, there are some ways to improve it, but I would recommend you to try something else. If your images are always as simple as in the video, you can use thresholding, contour finding, blob detection, etc. They are simple and fast.
For a more demanding environment, you may try feature matching. Look for SIFT, SURF, ORB, or other ways to describe your objects with features. Actually, ORB was specifically designed to be fast enough for the limited power of mobile phones.
Try this sample in the OCV samples/cpp/ folder
matching_to_many_images.cpp
And check this detailed answer on how to use feature detectors;
Detecting if an object from one image is in another image with OpenCV
Template matching (cvMatchTemplate()) is not invariant to scale and rotation. When you move the phone back, the image appears smaller, and the template "match" is just the place with the best match score, though it is not really a true match.
If you want scale and/or rotation invariance you will have to try non-template matching methods such as those using 2D-feature descriptors.
Check out the OpenCV samples for examples of how to do this.
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