autotable
AI-basketball-analysis
| autotable | AI-basketball-analysis | |
|---|---|---|
| 1 | 12 | |
| - | 923 | |
| - | - | |
| - | 0.0 | |
| - | about 1 year ago | |
| Python | ||
| - | GNU General Public License v3.0 or later |
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autotable
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Ask HN: Show me your Half Baked project
https://gitlab.com/docmenthol/autotable
It's a datatable written in Elm. I wrote it early on while I was learning, so I'm certain there is a lot I could update about it. Even still, it does its job pretty well. Sorting, filtering, editing, and reordering columns are all there. The way it's constructed allows new features to be built on top of it without any need to learn a table (or component) API. Just interact with the table state directly, the types make it pretty easy.
One major problem is that columns are obnoxious to define. It's just a giant record type. And in general I'm just not happy with the code. I'll likely revisit this project again soon and rewrite some key parts.
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?
observable-state-tree - An observable state tree is a normal object except that listeners can be bound to any subtree of the state tree.
Deep-SORT-YOLOv4 - People detection and optional tracking with Tensorflow backend.
openpifpaf - Official implementation of "OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association" in PyTorch.
veems - An open-source platform for online video.
go-live - 🗂️ go-live is an ultra-light server utility that serves files, HTML or anything else, over HTTP.
FastMOT - High-performance multiple object tracking based on YOLO, Deep SORT, and KLT 🚀
SynthDet - SynthDet - An end-to-end object detection pipeline using synthetic data
hof - Framework that joins data models, schemas, code generation, and a task engine. Language and technology agnostic.
live_data
human-detection - yolov3 tensorflow object detection and report human movements in persian
fiftyone - The open-source tool for building high-quality datasets and computer vision models
espn-api - ESPN Fantasy API! (Football, Basketball)