I am a noob in Computer Vision, so please pardon me if I make any mistakes. I have two images, taken from a stereo camera. The images are from slightly different viewpoints, as intended. I also have the camera matrices and the rotation and translation matrices of both the cameras. What I want to do is to warp the images in such a way that their epipolar lines are horizontal and have the same vertical height. Something like this Wikipedia image. So being a noob in computer vision and OpenCV, I am finding it difficult to do this, therefore looking for some guidance here.
Hey, y'all! new CV practitioner here. Just doing a project at the moment and am quite stuck with how to approach a problem. I am trying to predict xy coordinates of a new image, given training images and their respective cords. I have turned it into a classification problem with CNN's by grouping each of the images into their own "rooms" which tend to have similar features. This works quite well. However, to fine-tune this so that instead of taking the average of the room, there is a more sophisticated process to pinpoint the location of the new image, I am unsure how to proceed.
After reading into the subject, there seem to many quite a few approaches. A lot of them involve epipolar geometry and/or camera matrices. Feature matching, epipole lines, SIFT/Flann, Ransac.. it's a whole new world out there! I'm excited but slightly overwhelmed... As someone that isn't familiar with linear algebra, I want to be sure of an algorithm/approach so that I spend my time learning the right material.
Does anyone have any suggestions? Are there any particular techniques that I should use? If so please help a brother out! IN love with CV but would like a bit of a kick in the right direction :P Thanks in advance y'all!.
A lot of readers have been asking us to write a post on stereo vision.
So, here is our first post on epipolar geometry and stereo vision. It is geared toward beginners.
You will learn the basics of stereo vision, epipolar geometry, and fundamental matrices.
https://www.learnopencv.com/introduction-to-epipolar-geometry-and-stereo-vision/
We are also sharing code in both C++ and Python - https://github.com/spmallick/learnopencv/tree/master/EpipolarGeometryAndStereoVision
https://preview.redd.it/igf6h3wcjd861.jpg?width=600&format=pjpg&auto=webp&s=53e04b955fc765590bd36492ff8079f19d474351
Hi, I'm building a pipeline to automate 360 virtual tours in Real Estate so, mostly in indoor scenarios. From 360 panoramic views I decompose each panoramic in 6 local views and compare one of panoramic A vs all local views in panoramic B to find similar objects and build hotspots (with epipolar geometry) to move between different panoramics
The pipeline is:
https://preview.redd.it/i6g84zsrfc261.png?width=750&format=png&auto=webp&s=7a5e0048bcd0b66f954132a06aea106c0a16d667
And the article we were inspired is (from 2016 ) I already build the pipeline with openCV with SIFT + Knn for matching and it's working correctly in most clear cases like this one:
https://preview.redd.it/ui6rjsqfjc261.png?width=878&format=png&auto=webp&s=71749d7ad58e20e1e9e238102f4e0f628586cc8b
As indoor scenes have lots of repetitive patterns and structures (edges) it is needed SIFT parametrization and some feature filtering but I'm having too many false positives. Apart from lots of false positives, it is failing in more difficult cases like this one (entering into the WC). I also tried to improve SIFT detection to work with binary masks to avoid points in walls, curtains, or ceiling but it doesn't improve significantly
https://preview.redd.it/szwgcwn3kc261.png?width=854&format=png&auto=webp&s=b72323ef9b4ec824a5411bea347762142e13d232
I thinking I nearly squeeze out at maximum the openCV sift pipeline, so I move to deeplearning and I found some incredible projects related to robotics and how to move in indoor scenarios. I'm testing Superglue matcher and results are quite similar to SIFT in keypoints generation but I see great improvements in confidence results at matching.
https://preview.redd.it/djx1fy1wvc261.png?width=607&format=png&auto=webp&s=8bfe7f928e934cae19bf4ec5cbcfbf12e3cc0a63
I need to explore more parametrization but I have no improvement in entering into my WC
https://preview.redd.it/e96gdmmjxc261.png?width=586&format=png&auto=webp&s=5f915d89d5b8158bfbe870e9440b473a18999627
Thank you for reading me until here, do you people have some recommendations for this kind of project?
Once again, thank you!
Juan Luis
https://preview.redd.it/ok4a1cui3cg51.png?width=960&format=png&auto=webp&s=448f66b17c61d5fe4dbf026e12ccf42f6dc5a64d
Today are releasing a new version for Kornia that includes different functionalities to work with 3D augmentations and volumetric data, local features matching, homographies and epipolar geometry.
In short, we list the following new features:
- Support to PyTorch v1.6.0.
- Local descriptors matching, homography and epipolar geometry API.
- 3D augmentations and low level API to work with volumetric data.
Local features matching
We include an kornia.feature.matching API to perform local descriptors matching such classical and derived version of the nearest neighbor (NN).
https://preview.redd.it/8muh96dj3cg51.png?width=594&format=png&auto=webp&s=93e00208afb29941e2f8b51aa0c02cbc015e2418
https://preview.redd.it/8zosovmj3cg51.png?width=658&format=png&auto=webp&s=a0c74c179652743a24c2b6c3ba588c4caa038592
Homography and epipolar geometry
We also introduce kornia.geometry.homography including different functionalities to work with homographies and differentiable estimators based on the DLT formulation and the iteratively-reweighted least squares (IRWLS).
https://preview.redd.it/0exbmwok3cg51.png?width=1200&format=png&auto=webp&s=10d9b8b2f4d3b9a2e2bc340ece2722521f3936f3
https://preview.redd.it/wx1of1yk3cg51.png?width=657&format=png&auto=webp&s=51f7d676b6a735fb9cc628fac5ead484087e73d5
In addition, we have ported some of the existing algorithms from opencv.sfm to PyTorch under kornia.geometry.epipolar that includes different functionalities to work with Fundamental, Essential or Projection matrices, and Triangulation methods useful for Structure from Motion problems.
3D augmentations and volumetric
We expand the kornia.augmentaion with a series of operators to perform 3D augmentations for volumetric data.
https://i.redd.it/sy3lviwl3cg51.gif
In this release, we include the following first set of geometric 3D augmentations methods:
- RandomDepthicalFlip3D (along depth axis)
- RandomVerticalFlip3D (alo
https://reddit.com/link/r9icue/video/kb8b8q59vq381/player
Hi everyone,
I have released a series of interactive computer vision notebooks. If you are interested in learning about any of the following subjects, give them a try!
- Camera calibration- Perspective projection
- 3D point triangulation- Quaternions as 3D pose representation
- Perspective-n-point (PnP) algorithm
- Levenberg–Marquardt optimization
- Epipolar geometry
- Relative poses from stereo views
- Bundle adjustment
- Structure from motion
The notebooks do not require installation and can be run in a browser using Binder (startup may take a few seconds): https://mybinder.org/v2/gh/maxcrous/multiview_notebooks/main
The video in this post is a preview of some of the visualizations in the notebooks.
I am also open-sourcing a documented implementation of SIFT in Python meant as an aid for studying the algorithm.
The source code of these projects can be found at
https://github.com/maxcrous/multiview_notebooks
https://github.com/maxcrous/SIFT
I am working through the OpenCV tutorials in python and I’m currently working on calculating the Epipolar lines from two images. However, even though I’m using the exact code from the OpenCV tutorial and the same test images I’m getting very strange results.
The F matrix looks completely ruined. These values are definitely not correct.
[[-1.60622769e-05 1.36431322e-04 -1.08742243e-04] [ 4.08336438e-05 -3.65632851e-06 -6.03366278e-02] [-1.82580135e-03 9.77636192e-03 1.00000000e+00]]
There are nearly 5000 keypoints per image, but only 182 “inliers”, which also sounds broken.
I would expect those match points to have the same color for each match. that’s what the code says… so something up to and including the matching must have gone wrong.
I tried this code using both SIFT and ORB feature detection algorithms and both result in the same problem.
The epiploar geometry tutorial https://docs.opencv.org/4.5.0/da/de9/tutorial_py_epipolar_geometry.html
Broken Result: Broken epipolar line result
Tutorial Result: Tutorial result
Code to recreate:
>import cv2 as cv
>
>import numpy as np
>
>from matplotlib import pyplot as plt
>
>def drawlines(img1, img2, lines, pts1, pts2):
>
>"""img1 - image on witch we draw the epilines for the points in img2
>
>lines - corresponding epilines"""
>
>r, c = img1.shape
>
>img1 = cv.cvtColor(img1, cv.COLOR_GRAY2BGR)
>
>img2 = cv.cvtColor(img2, cv.COLOR_GRAY2BGR)
>
>for r,pt1,pt2 in zip(lines, pts1, pts2):
>
>color = tuple(np.random.randint(0, 255, 3).tolist())
>
>x0, y0 = map(int, [0, -r[0]/r[1]])
>
>x1, y1 = map(int, [c, -(r[2] + r[0]*c/r[1])])
>
>img1 = cv.line(img1, (x0, y0), (x1, y1), color, 1)
>
>img1 = cv.circle(img1, tuple(pt1), 5, color, -1)
>
>img2 = cv.circle(img2, tuple(pt2), 5, color, -1)
>
>return img1, img2
>
>if __name__ == '__main__':
>
>img1 = cv.imread('left.jpg', 0) # queryingImage # left image
>
>img2 = cv.imread('right.jpg', 0) # trainImage # right image
>
>orb = cv.ORB_create(nfeatures=5000)
>
># find the keypoints and descriptors with SIFT
>
>
Do your worst!
It really does, I swear!
For context I'm a Refuse Driver (Garbage man) & today I was on food waste. After I'd tipped I was checking the wagon for any defects when I spotted a lone pea balanced on the lifts.
I said "hey look, an escaPEA"
No one near me but it didn't half make me laugh for a good hour or so!
Edit: I can't believe how much this has blown up. Thank you everyone I've had a blast reading through the replies 😂
They’re on standbi
Pilot on me!!
Buenosdillas
Dad jokes are supposed to be jokes you can tell a kid and they will understand it and find it funny.
This sub is mostly just NSFW puns now.
If it needs a NSFW tag it's not a dad joke. There should just be a NSFW puns subreddit for that.
Edit* I'm not replying any longer and turning off notifications but to all those that say "no one cares", there sure are a lot of you arguing about it. Maybe I'm wrong but you people don't need to be rude about it. If you really don't care, don't comment.
What did 0 say to 8 ?
" Nice Belt "
So What did 3 say to 8 ?
" Hey, you two stop making out "
When I got home, they were still there.
I won't be doing that today!
You take away their little brooms
This morning, my 4 year old daughter.
Daughter: I'm hungry
Me: nerves building, smile widening
Me: Hi hungry, I'm dad.
She had no idea what was going on but I finally did it.
Thank you all for listening.
There hasn't been a post all year!
It’s pronounced “Noel.”
I simply went into Bernhard Riemann's Wikipedia page and it said he was innovative in contributions to analysis, number theory, and differential geometry. I didn't / don't know what the latter is so I clicked on it; ultimately finding myself on the 'branches of Geometry' page. Holy crap. I have recently been interested in learning Mathematics, but geez, I'm feeling completely overwhelmed. For reference, I'm a teenager who is wanting to go into the mathematics field possibly as a mathematician or mechanical engineer. Do mathematicians need to know ALL of these branches? I know, I know: it all depends on what area I'm moving towards. But geez, does the smartest mathematician know this whole list?
"Absolute geometry, Affine geometry, Algebraic geometry, Analytic geometry, Archimedes' use of infinitesimals, Birational geometry, Complex geometry, Combinatorial geometry, Computational geometry, Conformal geometry, Constructive solid geometry, Contact geometry, Convex geometry, Descriptive geometry, Differential geometry, Digital geometry, Discrete geometry, Distance geometry, Elliptic geometry, Enumerative geometry, Epipolar geometry, Finite geometry, Fractal geometry, Geometry of numbers, Hyperbolic geometry, Incidence geometry, Information geometry, Integral geometry, Inversive geometry, Inversive ring geometry, Klein geometry, Lie sphere geometry, Non-Euclidean geometry..."
And it goes on. Do I need to know all of these branches? are these just theories? And that's just for Geometry.
After all his first name is No-vac
What, then, is Chinese rap?
Edit:
Notable mentions from the comments:
-
Spanish/Swedish/Swiss/Serbian hits
-
French/Finnish art
-
Country/Canadian rap
-
Chinese/Country/Canadian rock
-
Turkish/Tunisian/Taiwanese rap
There hasn't been a single post this year!
(Happy 2022 from New Zealand)
Nothing, it just waved
A lot of readers have been asking us to write a post on stereo vision.
So, here is our first post on epipolar geometry and stereo vision. It is geared toward beginners.
You will learn the basics of stereo vision, epipolar geometry, and fundamental matrices.
https://www.learnopencv.com/introduction-to-epipolar-geometry-and-stereo-vision/
We are also sharing code in both C++ and Python - https://github.com/spmallick/learnopencv/tree/master/EpipolarGeometryAndStereoVision
https://preview.redd.it/0my1p0ldjd861.jpg?width=600&format=pjpg&auto=webp&s=a778305ee9d855d27dd32efbf564c068fcaf6da7
A lot of readers have been asking us to write a post on stereo vision.
So, here is our first post on epipolar geometry and stereo vision. It is geared toward beginners.
https://preview.redd.it/jl6j2rw3kd861.jpg?width=600&format=pjpg&auto=webp&s=39186288cca07a3a1607a9a1089e3e0cebcab048
You will learn the basics of stereo vision, epipolar geometry, and fundamental matrices.
https://www.learnopencv.com/introduction-to-epipolar-geometry-and-stereo-vision/
We are also sharing code in both C++ and Python - https://github.com/spmallick/learnopencv/tree/master/EpipolarGeometryAndStereoVision
https://preview.redd.it/saelb40s0cg51.png?width=960&format=png&auto=webp&s=a7c98c28f8813b3e1b72296b420456fc8e98a56f
Today we are releasing a new version for Kornia that includes different functionalities to work with 3D augmentations and volumetric data, local features matching, homographies and epipolar geometry.
In short, we list the following new features:
- Support to PyTorch v1.6.0.
- Local descriptors matching, homography and epipolar geometry API.
- 3D augmentations and low level API to work with volumetric data.
Local features matching
We include an kornia.feature.matching API to perform local descriptors matching such classical and derived version of the nearest neighbor (NN).
https://preview.redd.it/cu9cg0fp0cg51.png?width=594&format=png&auto=webp&s=dbfc6154698435a9447865abfe171d144477dfba
https://preview.redd.it/x3c5c5q71cg51.png?width=658&format=png&auto=webp&s=cbcd9ae539871f56dea2e489685950fb0ce3ac96
Homography and epipolar geometry
We also introduce kornia.geometry.homography including different functionalities to work with homographies and differentiable estimators based on the DLT formulation and the iteratively-reweighted least squares (IRWLS).
https://preview.redd.it/qc8sfcf51cg51.png?width=1200&format=png&auto=webp&s=446cc11c36e7ac29e65c6f118c978c6d7fe1ecc2
https://preview.redd.it/236bv8881cg51.png?width=657&format=png&auto=webp&s=93ccb4319b0d52e605ba4b16c8ddf73dfd7da1d9
In addition, we have ported some of the existing algorithms from opencv.sfm to PyTorch under kornia.geometry.epipolar that includes different functionalities to work with Fundamental, Essential or Projection matrices, and Triangulation methods useful for Structure from Motion problems.
3D augmentations and volumetric
We expand the kornia.augmentaion with a series of operators to perform 3D augmentations for volumetric data.
https://i.redd.it/48hqrpd91cg51.gif
In this release, we include the following first set of geometric 3D augmentations methods:
- RandomDepthicalFlip3D (along depth axis)
- RandomVerticalFlip3D
https://preview.redd.it/coyyuk8vjcg51.png?width=960&format=png&auto=webp&s=4a4f9cd02434de96e0e130ad8d7b7b3bc48b2e7a
We are releasing a new version for Kornia that includes different functionalities to work with 3D augmentations and volumetric data, local features matching, homographies and epipolar geometry.
In short, we list the following new features:
- Support to PyTorch v1.6.0.
- Local descriptors matching, homography and epipolar geometry API.
- 3D augmentations and low level API to work with volumetric data.
Local features matching
We include an kornia.feature.matching API to perform local descriptors matching such classical and derived version of the nearest neighbor (NN).
https://preview.redd.it/ma7ghuawjcg51.png?width=594&format=png&auto=webp&s=a1a8cd7c473fbf6883c1104781819ccbffe72acf
https://preview.redd.it/mjf5wswwjcg51.png?width=658&format=png&auto=webp&s=966b8827b5844c964e8553fca15abc64eced2331
Homography and epipolar geometry
We also introduce kornia.geometry.homography including different functionalities to work with homographies and differentiable estimators based on the DLT formulation and the iteratively-reweighted least squares (IRWLS).
https://preview.redd.it/18or79xzjcg51.png?width=1200&format=png&auto=webp&s=47024a11c1b6258cd22f3fb384615ee5c63d0d37
https://preview.redd.it/jvicn10zjcg51.png?width=657&format=png&auto=webp&s=941d865cab78ce12e12f3f1f7afca9ad5b0bd961
In addition, we have ported some of the existing algorithms from opencv.sfm to PyTorch under kornia.geometry.epipolar that includes different functionalities to work with Fundamental, Essential or Projection matrices, and Triangulation methods useful for Structure from Motion problems.
3D augmentations and volumetric
We expand the kornia.augmentaion with a series of operators to perform 3D augmentations for volumetric data.
https://i.redd.it/1pe7y6u0kcg51.gif
In this release, we include the following first set of geometric 3D augmentations methods:
- RandomDepthicalFlip3D (along depth axis)
- RandomVerticalFlip3D (along height axis)
- RandomHoriz
