domm-f/cameragesturehotkeys — explained in plain English
Analysis updated 2026-05-18
Trigger a keyboard shortcut hands-free by making a specific body pose in front of a webcam.
Set up custom hotkeys for accessibility or hands-busy workflows like streaming or presenting.
Tune pose detection sensitivity to reduce accidental hotkey activations.
Install a packaged Windows app without needing to set up Python yourself.
| domm-f/cameragesturehotkeys | 0xallam/my-recipe | 0xhassaan/nn-from-scratch | |
|---|---|---|---|
| Stars | 0 | — | 0 |
| Language | Python | Python | Python |
| Last pushed | — | 2022-11-22 | — |
| Maintenance | — | Dormant | — |
| Setup difficulty | easy | moderate | moderate |
| Complexity | 2/5 | 2/5 | 4/5 |
| Audience | general | general | developer |
Figures from each repo's GitHub metadata at analysis time.
Windows-only, most users just run the packaged installer instead of installing Python.
CameraGestureHotkeys is a Windows application that watches your webcam for upper body poses you have saved ahead of time, and presses a keyboard shortcut whenever it recognizes one of them. The README states that camera frames are processed locally on your own computer and are never uploaded or saved anywhere. To create a gesture, you give it a name, type in the keyboard shortcut you want it to trigger, such as ctrl plus shift plus alt plus s, then click a button to capture the pose and hold still while it collects samples of you making that pose. Several settings let you tune how it behaves: match strictness controls how closely you need to match the saved pose before it counts, cooldown sets a minimum time between activations, and hold frames controls how many camera frames in a row need to match before the hotkey fires, trading speed for fewer accidental triggers. There is also a setting to pick which camera to use if the wrong one opens by default. Most people are expected to use a packaged Windows installer that the project can build, which installs the app with a Start menu entry, an optional desktop shortcut, and a normal uninstaller. Developers can instead run it directly from source using Python 3.11, installing the listed dependencies and starting the app with a Python command, since the pose recognition model needed is already included in the repository. Saved gesture profiles are stored in a JSON file in the user's AppData folder, and uninstalling the packaged app removes both the program and these saved gestures. The project is licensed under the MIT License.
A Windows app that watches your webcam for saved body poses and triggers a keyboard shortcut when it recognizes one, with all processing done locally.
Mainly Python. The stack also includes Python.
Use freely for any purpose, including commercial use, as long as you keep the copyright notice.
Setup difficulty is rated easy, with roughly 5min to a first successful run.
Mainly general.
This repo across BitVibe Labs
double-check against the repo, no cap.