meross-local-mqtt
AI-basketball-analysis
| meross-local-mqtt | AI-basketball-analysis | |
|---|---|---|
| 1 | 12 | |
| 0 | 923 | |
| - | - | |
| 0.0 | 0.0 | |
| over 3 years ago | about 1 year ago | |
| Python | Python | |
| - | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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meross-local-mqtt
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Ask HN: Show me your Half Baked project
My first real public project: a way to handle some smart plugs on a LAN (https://github.com/wsw70/meross-local-mqtt).
The project itself is not that interesting for the majority of pepole. What was nice for me was this kind of thrill to push something for everyone to share.
I put more attention into the details, description, etc. A very fulfilling learning experience.
This is going to help me release the 10 or so other projects I was keeping private but was ashamed to publish. I just need to find some time to polish them.
AI-basketball-analysis
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[P] Basketball Shots Detection and Shooting Pose Analysis (Open Source)
Source code: https://github.com/chonyy/AI-basketball-analysis
- Show HN: Visualizing Basketball Trajectory and Analyzing Shooting Pose
- Automatically Overlaying Baseball Pitch Motion and Trajectory in Realtime (Open Source)
- Show HN: AI Basketball Analysis Web App and API
- Show HN: Visualize and Analyze Basketball Shots and Shooting Pose with ML
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Ask HN: Show me your Half Baked project
I built an app to visualize and analyze basketball shots and shooting pose with machine learning.
https://github.com/chonyy/AI-basketball-analysis
The result is pretty nice. However, the only problem is the slow inference speed. I'm now refactoring the project structure and changing the model to a much faster YOLO model.
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Show HN: Automatic Baseball Pitching Motion and Trajectory Overlay in Realtime
Thanks for asking! This is not a noob question.
I would say that the similar workflow could be applied to any ball-related sports. The object detection and the tracking algorithm is basically the same. Then, you could add any sport-specific feature!
For example, I have used a similar method to build AI Basketball Analysis.
https://github.com/chonyy/AI-basketball-analysis
- Show HN: AI Basketball Analysis in Realtime
- Show HN: AI Basketball Visualization
What are some alternatives?
meross-homeassistant - Custom component that leverages the Meross IoT library to integrate with Homeassistant
Deep-SORT-YOLOv4 - People detection and optional tracking with Tensorflow backend.
amcrest2mqtt - Expose all events from an Amcrest device to an MQTT broker
openpifpaf - Official implementation of "OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association" in PyTorch.
go-live - 🗂️ go-live is an ultra-light server utility that serves files, HTML or anything else, over HTTP.
ws-monitoring - A simple & lightweight realtime monitoring web UI + server in Node.js
veems - An open-source platform for online video.
pyodide - Pyodide is a Python distribution for the browser and Node.js based on WebAssembly
FastMOT - High-performance multiple object tracking based on YOLO, Deep SORT, and KLT 🚀
pcopy - pcopy is a temporary file host, nopaste and clipboard across machines. It can be used from the Web UI, via a CLI or without a client by using curl.
SynthDet - SynthDet - An end-to-end object detection pipeline using synthetic data