I have an image on the wall. I'd like to create its 3D perspective views by myself. Suppose the points on the images, camera location, orientation of the camera are given, how can I do to obtain the 3d perspective matrix to play with the original image?
I understand I can use the orientation of the camera to calculate the 3d rotation matrix, but I've no idea how to calculate the subsequent projection matrix...
I've come across this link (see Section Perspective Projection), but I don't understand what's going on after projection.. And what is the difference between the camera position and the viewer's position?
Thanks a lot.
use openGl and its open example to solve your problem.
in bellow link there are good samples to undestand 3d reconstruction:
http://www.songho.ca/opengl/gl_transform.html
wish helpful
Related
I'm building a 3D app that uses SceneKit. My scene will have various 3D objects and a moveable perspective camera.
The user can load a 2D image into the scene, which I will display on a 3D plane using the image as the material.
What I need to be able to do is to initially show the image as if it were actually 2D, where the pixel width and height are the same as the image and it is not distorted by the camera perspective. So basically I need to know how to position that plane in relation to the camera to make it look 2D.
Thanks in advance for any tips :)
It's not clear where you are getting stuck.
If you're just looking for where to start, look at SCNPlane and SCNBillboardConstraint.
I do not know SceneKit, so what I suggest might not be doable in the specific program, but could you perhaps use an orthographic camera perspective? It removes a lot of the visual depth from a scene, combining that with some flat lighting might accomplish the look you are going for.
I'm working on a project with ARKit and I'm trying to do a perspective correction of the ARFrame.capturedImage to orient a piece of paper sitting on a detected plane so I can feed that into a CoreML model which expects images to be taken from directly overhead.
ARKit gives me the device orientation relative to the plane (ARCamera.transform, ARCamera.eulerAngles, and ARCamera.projectionMatrix all look promising).
So I have the orientation of the camera (and I know the plane is horizontal since that's all ARKit detects right now).. but I can't quite figure out how to create a GLKMatrix4 that will perform the correct perspective correction.
Originally I thought it would be as easy as transforming by the inverse of ARCamera.projectionMatrix but that doesn't appear to work at all; I'm not entirely sure what that matrix is describing.. it doesn't seem to change much based on the device orientation.
I've tried creating my own matrix using GLKMatrix4Rotate and the roll/pitch/yaw but that didn't work.. I couldn't even get it working with a single axis of rotation.
I found GLKMatrix4MakePerspective, GLKMatrix4MakeOrtho, and GLKMatrix4MakeFrustum which seem to do perspective transforms but I can't figure out how to take the information I have and translate it to the inputs of those functions to make the proper perspective transformation.
Edit:
As an example to better explain what I'm trying to do, I used the Perspective Warp tool in Photoshop to transform an example image; what I want to know is how to come up with a matrix that will perform a similar transform given the info I have about the scene.
I ended up using iOS11 Vision's Rectangle Detection and then feeding it into Core Image's CIPerspectiveCorrection filter.
I solved using OpenCV perspective transformation. (https://docs.opencv.org/trunk/da/d6e/tutorial_py_geometric_transformations.html,https://docs.opencv.org/2.4/modules/imgproc/doc/geometric_transformations.html#getperspectivetransform)
If you're able to get the corners of your paper in the scene (for example with an ARReferenceImage and project them in 2D), take them. Otherwise you can try to detect the corners through OpenCV directly (see https://stackoverflow.com/a/12636153/9298773) from the UIImage taken from sceneView.snapshot() with sceneView of type ARSceneView. In this last case I'd suggest you to binarize first and to change the MAX_CORNERS variable in the snippet at the link above to 4 (the 4 corners of your paper).
Then create a new cv::Mat with width and height of your choice respecting the proportion width and height of your paper and do perspective transform. For a guideline of this last paragraph, take a look at the section "Perspective Correction using Homography" at this link: https://www.learnopencv.com/homography-examples-using-opencv-python-c/#download. Succintly: you ask opencv to find an appropriate transform to project your prospected paper points into a perfectly rectangular plane (your new cv::Mat)
How do I recover correct image from a radially distorted image using OpenCV? for example:
Please provide me useful links.
Edit
The biggest problem is I neither have the camera used for taking the pic nor the chessboard image.
Is that even possible?
Well, there is not much to do if you don't have the camera, or at least the model of it. As you may know a usual camera model is pin-hole, this basically consist in the 3D world coordinates are transformed (mapped) to the camera image plane 2D coordinates.
Camera Resectioning
If you don't have access to the camera or at least two chessboard images, you can't estimate the focal, principal point, and distortion coefficients. At least not in a traditional way, if you have more images than the one that you showed or a video from that camera you could try auto or self calibration.
Camera auto-calibration
Another auto-calibration
yet another
Opencv auto-calibration
I've 2 pictures of the same scene from an uncalibrated camera. The pics are from a slightly different angle and scale(zoom) and I'd like to superpose them, rejecting any kind of shake. In other words, I should transform them so the shake becomes imperceptible, do a Motion Compensation.
I've already tried using a simple SURF (feature) detector along with Homography but sometimes the result isn't satisfactory. So I am thinking about trying Image Rectification to compensate the motion.
- Would it work with slight changes, such as user shake?
- Would it really work to reject shake for these 2 frames? And for a bigger buffer of pictures (10 maybe)?
- Anyone knows if it would fix scale disparity (different zoom in the images)?
- What the algorithm really do? Will it transform both pictures into a third orientation?
If there is a better solution, I would be glad to know =)
EDIT
I don't aim to compensate blur motion but the displacement itself. For example, in this file the author compensates the angle difference between two cameras by Image Rectification. How does it actually work? Does it always create an intermediate picture orientation or can I specify that one of the pictures shall remains still??
Also, would I be able to apply this to many frames or it would always find an intermediate orientation for each two frames I put in?
Cheers,
I'm not sure how well superimposing the images would work. Another way to remove blur (including motion blur which should dominate in handheld camera devices) from an image is by blind deconvolution. It is basically a method of finding the inverse of the blur filter that was physically applied (camera shaken) to the real image. There's plenty of techniques out on the web. I've specifically had good results using a modified version of the algorithm in this paper: http://www.cse.cuhk.edu.hk/~leojia/all_final_papers/motion_deblur_cvpr07.pdf
It also comes with an executable file somewhere around the web so you can see if it's fit for your purpose.
Good luck out there!
Suppose I've got two images taken by the same camera. I know the 3d position of the camera and the 3d angle of the camera when each picture was taken. I want to extract some 3d data from the images on the portion of them that overlaps. It seems that OpenCV could help me solve this problem, but I can't seem to find where my camera position and angle would be used in their method stack. Help? Is there some other C library that would be more helpful? I don't even know what keywords to search for on the web. What's the technical term for overlapping image content?
You need to learn a little more about camera geometry, and stereo rig geometry. Unless your camera was mounted on a special rig, it's rather doubtful that its pose at each image can be specified with just an angle and a point. Rather, you'd need three angles (e.g. roll, pitch, yaw). Plus, if you want your reconstruction to be metrical accurate, you need to calibrate accurately the focal length of the camera (at a minimum).