homography | Homography Matrix Estimation using SVD | Math library

Need Help, I am trying to align 2 id cards using OpenCV. If I do it for 2 id cards from the same person then the result works like the picture below

Before Alignment :

The alignment work perfectly if i tried with the same person id as below picture :

But if I do for 2 id cards that come from two different person then the result is messy, need help on how to do the alignment in this case

I try to create Structured-light 3D scanner.

Camera calibration is copy of OpenCV official tutorial. As resutlt I have camera intrinsic parameters(camera matrix).

Projector calibration maybe is not correct but process was: Projector show chessboard pattern and camera take some photos from different angles. Images are cv.undistored with camera parameters and then result images are used for calibration with OpenCV official tutorial. As result I have projector intrinsic parameters(projector matrix).

From cv.calibrate I have rotarion and transition vectors as results but vectors count are equal to images count and I thing it is not corect ones because I move camera and projector in calibration. My new idea is to project chessboard on scanning background, perform calibration and in this way I will have Rotation vector and Transition vector. I don't know is that correct way.

Process of scanning is:

Generate patterns -> undistor patterns with projector matrix -> Project pattern and take photos with camera -> undistort taken photos with camera matrix

I use GrayCode pattern and with cv.graycode.getProjPixel and have pixels mapping between camera and projector. My projector is not very high resolution and last patterns are not very readable. I will create custom function that generate mapping without the last patterns.

I don't know how to get depth map(Z) from all this information. My confution is because there are 3 coordinate systems - camera, projector and world coordinate system.

How to find 'Z' with code? Can I just get Z from pixels mapping between image and pattern?

Information that have:

• p(x,y,1) = R*q(x,y,z) + T - where p is image point, q is real world point(maybe), R and T are rotation vector and transition vector. How to find R and T?
• Z = B.f/(x-x') - where Z is coordinate(depth), B-baseline(distanse between camera and projector) I can measure it by hand but maybe this is not the way, (x-x') - distance between camera pixel and projector pixel. I don't know how to get baseline. Maybe this is Transition vector?
• I tried to get 4 meaning point, use them in cv.getPerspectiveTransform and this result to be used in cv.reprojectImageTo3D. But cv.getPerspectiveTransform return 3x3 matrix and cv.reprojectImageTo3D use Q-4×4 perspective transformation matrix that can be obtained with stereoRectify.

Similar Questions:

• How is point cloud data acquired from the structured light 3D scanning? - Answer is you need to define a vector that goes from the camera perspective center to the pixel in the image and then rotate this vector by the camera rotation. But I don't know how to define/find thid vercor and Rotation vector is needed.
• How to compute the rotation and translation between 2 cameras? - Question is about R and T between two cameras but almost everywhere writes that projector is inverse camera. One good answer is The only thing you have to do is to make sure that the calibration chessboard is seen by both of the cameras. But I think if I project chessboard pattern it will be additional distored by wall(Projective transormation)

There are many other resources and I will update list with comment. I missed something and I can't figure out how to implement it.

I am looking to speed up some bundle adjustment code by specifying which images are overlapping. I created a function that places a grid of points in each image and then tests the pairwise overlap. Basically, it approximates the intersection area of the photos from the initial homography. Here is an example these 4 overlapping photos:

Ive been working off of this example https://github.com/amdecker/ir/blob/master/Stitcher.py

I am reading some projective geometry image warping code from Google

i wrote this code by python and opencv i have 2 images (first is an image from football match 36.jpg) :

36.jpg

and (second is pitch.png an image (Lines of football field (Red Color)) with png format = without white background) :

pitch.png

with this code , i selected 4 coordinates points in both of 2 images (4 corners of right penalty area) and then with ( cv2.warpPerspective ) and show it , we can show that first image from (Top View) as below:

top view

my question is this: what changes need in my code that (red color Lines of second image) show on first image same below images (that i draw in paint app):

desired

thanks in advance , for your help

this is my code :

I use python OpenCV to register images, and once I've found the homography matrix H, I use cv2.warpPerspective to compute final the transformation.

However, it seems that cv2.warpPerspective is limited to short encoding for performance purposes, see here. I didn't some test, and indeed the limit of image dimension is 32767 pixels so 2^15, which makes sense with the explanation given in the other discussion.

Is there an alternative to cv2.warpPerspective? I already have the homography matrix, I just need to do the transformation.

My aim is to stitch 1-2 thousand images together. I find the key points in all the images, then I find the matches between them. Next, I find the homography between the two images. I also take into account the current homography and all the previous homographies. Finally, I warp the images based on combined homography. (My code is written in python 2.7)

The issue I am facing is that when I overlay the warped images, they become extremely bright. The reason is that most of the area between two consecutive images is common/overalapping. So, when I overlay them, the intensities of the common areas increase by a factor of 2 and as more and more images are overalid the moew bright the values become and eventually I get a matrix where all the pixels have the value of 255.

Can I do something to adjust the brightness after every image I overlay?

I am combining/overlaying the images via open cv function named cv.addWeighted()
dst = cv.addWeighted( src1, alpha, src2, beta, gamma)

here, I am taking alpha and beta = 1

dst = cv.addWeighted( image1, 1, image2, 1, 0)

I also tried decreasing the value of alpha and beta but here a problem comes that, when around 100 images have been overlaid, the first ones start to vanish probably because the intensity of those images became zero after being multiplied by 0.5 at every iteration. The function looked as follows. Here, I also set the gamma as 5:
dst = cv.addWeighted( image1, 0.5, image2, 0.5, 5)

Can someone please help how can I solve the problem of images getting extremely bright (when aplha = beta = 1) or images vanishing after a certain point (when alpha and beta are both around 0.5).

This is the code where I am overlaying the images:

i need to make a panoramic view from serie of pictures (3 pictures). After that i created orb, detected and computed keypoints and descriptors for the three picture, i matched the most likely similars keypoints between:

1. image 1 and image 2

2. image 2 and image 3

Then i know to compute and find the panoramic view between only 2 images , and i do it between img1 and img2, and between img2 and img3. But Then for the last step , i want to find the panoramic view of theses 3 pictures using affine tranformation with Ransac algorithm from opencv. And i don't know to do that ( panoramic view for 3 pictures. So, I have to of course choose image 2 as the center of the panorama

I didn't find a good explanation or an enough good explanation for me in order to compute and fine the panoramic view of theses 3 pictures. Someone can help me please to implement what i need please ?

here is my code where i print panorama between img1 and img2, and between img2 and img3:

I have to do the following work: