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2017-05-26 10:29:17 -0600 | commented question | Re-distorting a set of points after camera calibration Thank you! I have not been able to make a difference using this suggestion. |
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2017-05-26 10:18:40 -0600 | asked a question | High RMS calibrateCamera Python I have made a script that calibrates using an assymetric pattern imaged by a thermal camera. For some reason my RMS error is around 15. However, undistorting my images does a decent job. I have made a GUI to adjust the blobdetector values for each image manually and return the ret, and corner values on approval. Am I misunderstanding that the return value for the function should return an error roughly between 0.1 and 1? Or should I use another function to correctly calculate the RMS? |
2017-05-20 06:49:09 -0600 | commented question | Re-distorting a set of points after camera calibration Can you explain more in detail what you mean? This is my current code to get the camera calibration: Which flags would I need to set to turn off the fisheye distortion model / possibly get better calibration? My image turns out pretty bad when I try to undistort it the the next time around |
2017-05-19 16:36:14 -0600 | commented question | Re-distorting a set of points after camera calibration Hi LBerger, The iterative method is ment to enhance the accuracy of the circle positions, since these thermal camera images are usually very low resolution, and thus less accurate. The circles are made by using a laser cutter and the cardboard is heated so that it thermally contrasts with the background. |
2017-05-19 16:26:54 -0600 | commented answer | Re-distorting a set of points after camera calibration This worked perfectly! Thank you so much Here is my code I used in Python. I have no idea why the initial undistortion does not work that. |
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2017-05-15 19:03:40 -0600 | asked a question | Re-distorting a set of points after camera calibration I am working on a project in Python to calibrate a small thermal camera sensor (FLIR Lepton). Because of limited resolution initial distortion removal is not very exact. By using an iterative method I should be able to refine this calibration (for those of you with access to scientific articles, see this link). This requires me to take the following steps:
I am stuck at step four. Below you see the commands I used to remove the camera distortion from the original image using the camera matrices and the distortion matrix. So far I am not able to figure out how to reverse these commands to remap the new point positions to the original image. So far I am able to do (roughly in order of the steps above):
I have looked online and found the same question a bunch of times, with several examples in C++, which I cannot fully comprehend and modify for my purposes. I have tried the solution suggested by this post but this has not yielded the desired results, see last image above. There is my code of that solution Then using this command to call the function I am new to OpenCV and computer vision, so my knowledge about the algebra of these solutions is limited. Any hands on examples or correction to my current code would be greatly appreciated. Kind regards, Bart |