I have a specific question with regards head pose, I am looking into
creating a cylindrical model that maps 2D points to this 3D model,
and then compare points from successive frames using optical flow,
so that later on I can perform pose estimation.
I discovered that opencv has a warp function
http://docs.opencv.org/modules/stitching/doc/warpers.html#detail::CylindricalWarper : public detail::RotationWarperBase
I also have read that OpenGl might be used for this.
I think what I am looking for is a texture map, but here is an example of what I am looking for....
https://www.youtube.com/watch?v=Etj_aktbnwM
Any guidance would be much appreciated,
Thanks.
Here is a good tutorial to get you started:
http://www.morethantechnical.com/2010/03/19/quick-and-easy-head-pose-estimation-with-opencv-w-code/
I have modified this code in one project to create a realtime head pose tracker. The initial feature points were detected autmatically, and tracked along the frame sequence.
Related
I am trying to do image alignment like posted on adrian blog like this image or in this link.
I want to do image alignment on this kind of image. The problem is I want to automatically detect the 4 point edges which are hard to detect in this kind of images with contour detection like in the tutorial.
Now I can do alignment just fine with manually input edge coordinates. Some of my friends suggest me to detect the edges with dlib landmark detection, but as far as I can see it mostly uses shape in which dlib automatically marking the landmark.
Do I miss something here? Or is there any tutorial or even basic guide about how to do that?
Maybe you can try to detect edges on a Gaussian pyramid. You can find an explanation here https://en.wikipedia.org/wiki/Pyramid_(image_processing). The basic idea is that by filtering with Gaussian filters of increasing size, the small objects are blurred. Thus at some scale, we get only edges of the showcase (maybe need further processing).
Here is the tutorial of opencv on image pyramid: https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_pyramids/py_pyramids.html.
I think wavelet pyramid (do wavelet transform several times) may work for your problem, since wavelet can reduce the details in image.
We have modified sample code for the C API so Tango pose data (position (x,y,z) and quaternion (x,y,z,w)) is published as PoseStamped ROS messages.
We are attempting to visualize the pose using Rviz. The pose data appears to need some transformation as the rotation of the Rviz arrow does not match the behavior of the Tango when we move it around.
We realize that in the sample code, before visualization on the Tango screen, the pose data is transformed into a 4x4 Pose matrix (function PoseData::GetExtrinsicsAppliedOpenGLWorldFrame), which is then multiplied left and right by various matrices representing changes of coordinate frames (for instance, Tango to OpenGL).
Ideally, we would be able to apply a similar transformation to the pose data before publishing it for visualization. However we must keep the pose data in the position (x,y,z) and quaternion (x,y,z,w) format in order to publish it in a PoseStamped message, and we do not see what transform to apply.
We have looked at the Tango coordinate systems conventions but the transformations the Tango developers suggest we apply are only suited for pose data in a Pose matrix format. We have also attempted to apply transformations applied by Ologic in their code to no avail.
Does anyone have any suggestions on how to transform Tango pose data, without changing its format, for correct visualization on the Rviz OpenGL interface?
If it's OpenGL convention, you will basically need to do a transformation on the left hand side of the pose data. The c++ motion tracking example has a line doing this operation here. You could ignore the rotation part, but just apply following code:
glm::mat4 opengl_world_T_opengl_camera = tango_gl::conversions::opengl_world_T_tango_world() * start_service_T_deivce;
I know that is a late answer but it can maybe help others people.
If you want to visualize any data with Rviz, I assume that you want to use ros. Then maybe the best way to do it is to use the rasjava library to do your Tango android app. It works well for me. I you just have to use poseStamp, odometry and tf publisher on your tango device and then display the topic with rviz. Morever it is one of the best way to keep the real-time aspect.
Moreover here there is 2 good way to learn how to use rosjava :
https://github.com/ologic/Tango
https://github.com/rosjava/android_core/tree/master
I am attempting to implement a visual odometry solution in opencv, and running into a few problems. This is quite a broad question, so I apologise in advance, however I have a number of questions.
My understanding of the problem currently is:
Obtain some model to represent the correspondence between two successive images, be that optical flow or feature matching.
Obtain the fundamental (and then essential if needed) matrix from these point correspondences.
Calculate [R|t] from that.
I am aware of the findFundamentalMat function in openCV, but I think that only takes 2D point matches? In Scaramuzza and Fraundorfers paper 'Visual Odometry - pt1' they suggest that 3-D to 2-D correspondences will be most accurate.
I guess then my question is can I use the depth data retrieved from a kinect, giving me 3-D feature points, be used in opencv to give me an egomotion estimation?
I've also taken a look at solvePnP, but as far as I'm aware this only solves for a single frame (for when you know the real model space coordinates of features, like with a fiducial marker)
Although I did consider if I track 3D points between two frames, solving the perspective in the first frame, then in the second frame with the same points should give me a transformation between the two?
I apologize for this badly formulated question, I am still new to computer vision. Rather than attempting to answer this question if it is too much of a minefield, I would be appreciative of a point to any related literature or opencv tutorials for odometry. Thanks.
There is an example rgbdodometry.cpp in opencv\samples\cpp folder.
Have you seen it?
Some background:
Hi all! I have a project which involves cloud imaging. I take pictures of the sky using a camera mounted on a rotating platform. I then need to compute the amount of cloud present based on some color threshold. I am able to this individually for each picture. To completely achieve my goal, I need to do the computation on the whole image of the sky. So my problem lies with stitching several images (about 44-56 images). I've tried using the stitch function on all and some subsets of image set but it returns an incomplete image (some images were not stitched). This could be because of a lack of overlap of something, I dunno. Also the output image has been distorted weirdly (I am actually expecting the output to be something similar to a picture taken by a fish-eye lense).
The actual problem:
So now I'm trying to figure out the opencv stitching pipeline. Here is a link:
http://docs.opencv.org/modules/stitching/doc/introduction.html
Based on what I have researched I think this is what I want to do. I want to map all the images to a circular shape, mainly because of the way how my camera rotates, or something else that has uses a fairly simple coordinate transformation. So I think I need get some sort of fixed coordinate transform thing for the images. Is this what they call the homography? If so, does anyone have any idea how I can go about my problem? After this, I believe I need to get a mask for blending the images. Will I need to get a fixed mask like the one I want for my homography?
Am I going through a possible path? I have some background in programming but almost none in image processing. I'm basically lost. T.T
"So I think I need get some sort of fixed coordinate transform thing for the images. Is this what they call the homography?"
Yes, the homography matrix is the transformation matrix between an original image and the ideal result. It warps an image in perspective so it can fit in stitching to the other image.
"If so, does anyone have any idea how I can go about my problem?"
Not with the limited information you provided. It would ease the problem a lot if you know the order of pictures (which borders which.. row, column position)
If you have no experience in image processing, I would recommend you use a tutorial covering stitching using more basic functions in detail. There is some important work behind the scenes, and it's not THAT harder to actually do it yourself.
Start with this example. It stitches two pictures.
http://ramsrigoutham.com/2012/11/22/panorama-image-stitching-in-opencv/
I am working on a project to stitch together around 400 high resolution aerial images around 36000x2600 to create a map. I am currently using OpenCV and so far I have obtained the match points between the images. Now I am at a lost in figuring out how to get the matrix transformation of the images so I can begin the stitching process. I have absolutely no background in working with images nor graphics so this is a first time for me. Can I get some advice on how I would approach this?
The images that I received also came with a data sheet showing longitude, latitude, airplane wing angle, altitude, etc. of each image. I am unsure how accurate these data are, but I am wondering if I can use these information to perform the proper matrix transformation that I need.
Thanks
Do you want to understand the math behind the process or just have an superficial idea of whats going on and just use it?
The regular term for "image snitching" is image alignment. Feed google with it and you'll find tons of sources.
For example, here.
Best regards,
zhengtonic
In recent opencv 2.3 release...they implemented a whole process of image stitching. Maybe it is worth looking at.