![]() Since then there have been a few changes to core parts of the code in the calibration program, calibrate_stereo.py, which means that you’ll need to update your old calibration. If you were working with the old repository, you might have been producing bad calibrations. If you’ve followed the previous posts, you’ll have built and calibrated your stereo camera and perhaps started producing disparity maps. This is only a problem if you have a special setup – otherwise the default settings should work fine now. Also, you won’t be able to work with the StereoBMTuner for at least a few days to tweak your block matching algorithm. The bottom line: If you’ve already calibrated your camera, you’ll need to modify your calibration or do it again. If your results are bad, try tuning your block matching algorithm and make sure that you’ve told the programs the correct device number for the left and right camera – otherwise you might be trying to work crosseyed, and that won’t work well at all. If you’re looking for an easy way to take pictures, you can use show_webcams to view the stereo webcam views and, if desired, save pictures in specified intervals.Īs with any passive stereo camera setup, you’ll have the best results with a texture-rich background. Two point clouds produced with images_to_pointcloud That means that I need about 0.4 seconds to read two pictures, create a 3D model of what they saw, do some very rudimentary filtering to eliminate the most obviously irrelevant points. 1.89 seconds is for writing the output file – disk access is just about the most expensive thing you can do. On my machine this takes about 2.3 seconds to complete on two images with dimensions 640×480. use_stereobm Use StereoBM rather than StereoSGBM block matcher. h, -help show this help message and exit Read images taken with stereo pair and use them to produce 3D point clouds You can call it like images_to_pointcloud -help If you’ve built your stereo camera and calibrated it, you’re ready to go. You can see an example at the top of the page. One utility that comes with the package, images_to_pointcloud, that takes two images captured with a calibrated stereo camera pair and uses them to produce colored point clouds, exporting them as a PLY file that can be viewed in MeshLab. Afterwards I’ll explain the code behind it and how it’s changed since the last posts, so that you’ll know whether you need to change anything on your end before you can start producing your point clouds. So let’s get straight to the good stuff: How to produce and visualize point clouds. The entire workflow for producing 3D point clouds from stereo images is doable with my StereoVision package, which youcan install from PyPI with: Short and sweet: How to make a point cloud without worrying too much about the details ![]() ![]() Two images taken with a calibrated stereo camera pair, with two perspectives of the resultant point cloud.
0 Comments
Leave a Reply.AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |