Termux-Vision
디바이스 리소스를 활용한 안드로이드 Termux 제로 디펜던시 온디바이스 컴퓨터 비전 & VLM 멀티모달 추론 엔진
Zero-Dependency On-Device Computer Vision & Multimodal VLM Inference Engine Utilizing Device Resources for Android Termux
📌 Architecture & Overview
순수 ARM64 NEON 커널과 4단계 화질 프리셋(fast, optimal, high, original), 디바이스 리소스 최적화 연산을 결합하여 메모리 격리 환경에서 엣지 검출과 VLM 멀티모달 시각 추론을 실현합니다.
Eliminates heavy C++ dependencies by integrating SIMD NEON spatial image transforms with on-device VLM (Qwen2-VL, SmolVLM) multi-tier resolution presets (fast, optimal, high, original) utilizing device resources under strict memory isolation.
🚀 Installation & Quickstart
Python (PyPI)
pip install termux-vision
import termux_vision as tv
# 1. Zero-Dependency Spatial Filtering (0.01s ultra-fast)
img = tv.io.load_image("photo.jpg")
edges = tv.cv.canny(tv.transforms.to_grayscale(img), 40, 120)
# 2. On-Device VLM Multimodal Inference with 4-Tier Quality Presets
with tv.vlm.load("qwen2-vl-2b-q4", quality="optimal") as engine:
res = engine.describe("photo.jpg", prompt="Describe this scene in detail.", quality="optimal")
print(f"Generated ({res.metrics.tokens_per_second:.1f} t/s): {res.text}")
Node.js / TypeScript (npm)
npm install termux-vision
import tv from 'termux-vision';
// 1. Diagnostics & Hardware Probe
const doc = tv.doctor(true);
console.log(`Vulkan GPU: ${doc.vulkan.status} | RAM: ${doc.hardware.availableRamMb} MB`);
// 2. Multimodal VLM Inference with Quality Preset
const engine = await tv.load({ modelId: 'qwen2-vl-2b-q4', contextLimit: 4096 });
const result = await engine.describe('photo.jpg', { quality: 'optimal', maxTokens: 300 });
console.log(`[${result.metrics.backend.toUpperCase()}] ${result.text}`);
engine.close();
📖 Official Documentation & Benchmarks
- Official Architecture & API Reference
- Ecosystem Metrics & Registry Stats
- AMEVA Open-Source Foundation Portal
📄 License
Licensed under the Apache-2.0 License. Copyright (c) 2026 Eunho Kim (@uno-km).
1 maintainer
Metadata
1 maintainer
Release files for termux-vision 1.2.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| termux_vision-1.2.0.tar.gz | 84.6 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| termux_vision-1.2.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 173.1 kB
Release files / termux_vision-1.2.0.tar.gz
| Download URL | termux_vision-1.2.0.tar.gz |
|---|---|
| Size | 84.6 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
4d632bfcb6264a711f5bb51db8682bf88cfa388355265c18de2f539ce5111569
|
|
BLAKE2b-256 checksum How to use checksums |
17a2196df2f3caa3014112ffdd0d9e51aa3da6da5c7c11eadfb868d7a7176033
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.12.0
|
Release files / termux_vision-1.2.0-py3-none-any.whl
| Download URL | termux_vision-1.2.0-py3-none-any.whl |
|---|---|
| Size | 88.4 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
03980dfcb1bb6ecd28fad7ba0157673fc78bf624f1c889df51b80e09d5b6eef6
|
|
BLAKE2b-256 checksum How to use checksums |
98caadc63eb293c919a5e2b7a081b225a442dd3349b6b8b077644f7a30060f1c
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.12.0
|
