osrf/rocker is a lovely docker project which make building ROS environment rapidly. It supports Ubuntu 20 and the latest Nvidia driver. ROS GUI applications and tools work properly with hardware acceleration.
However, when I tried to run Gazebo 7 under Ubuntu 20, there is no title bar on the top and I cannot resize this window.
In macOS, windows created by Tkinter do not automatically get focus. This is annoying when using PyCharm. If you do not take any actions, the created window will be hidden behind PyCharm. An alternative is to set the window to the top and then cancel it (code show as below). This will make the window visible after creation.
Although YCB Benchmarks has provided a series of mesh files, they still cannot be used directly in Gazebo because of the missing some of the physical properties.
I made SDF models for most YCB objects that can be used directly in Gazebo, and provided them with the following optimizations:
Using OpenMP can improve the performance of YOLO, but turning on this option on macOS High Sierra 10.13 will cause the compilation process to fail. This article was written on 2020-12-02.
This tutorial provides step-by-step instructions to compile OpenCV 4 with Qt 5 for Python 3 and C++ on macOS High Sierra 10.13. For the latest macOS version, I think you can install OpenCV from Homebrew directly. I wrote this tutorial on Dec. 2020 and the High Sierra is a previous macOS system and installing OpenCV directly from Homebrew will fail so I have to compile it manually.
The YCB-Video dataset contributed by PoseCNN is based on the YCB dataset. 21 objects are selected, and photographed with an RGBD camera to make 92 videos. The entire data set contains 133827 frames.
21 Objects
The YCB-Video dataset is hard to download from Google Drive. Because it only consists of a super large compressed file (256GB) and does not support resumable transmission. Once the download fails, you will face a download limit (usually 24 hours) and download again from the first byte!
So I used sub-volume compression, each file is round 10GB in size with a verifiable hash. Although multi-threaded downloading is still not allowed, at least you can re-download from a sub-volume instead of the whole. Furthermore, I added text labels for YOLO training to each image under the data folder.
Surprisingly, one process prints the query results, while another process prints an empty collection (array). The code that returned an empty collection did not trigger a deadlock exception as expected.
Thanks for the great work of neverleave0916 who created the docker image of yolov4!
Without the need of a local superpower graphics card. You can use Vast.ai to try yolov4 rapidly. Vasi.ai is a online platform that provides GPU rental services at a good value.
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