Metadata-Version: 2.4
Name: termux-vision
Version: 1.4.4
Summary: Lightweight on-device computer vision and VLM framework utilizing device resources for Android Termux
Author: termux-vision contributors
License: Apache-2.0
Project-URL: Homepage, https://uno-km.vercel.app/lib/vision/
Project-URL: Documentation, https://uno-km.vercel.app/lib/vision/
Project-URL: Repository, https://github.com/uno-km/termux-vision
Project-URL: Issues, https://github.com/uno-km/termux-vision/issues
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Image Processing
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Android
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
License-File: NOTICE
Requires-Dist: numpy>=1.20.0
Requires-Dist: Pillow>=8.0.0
Requires-Dist: termux-llamacpp>=1.3.0
Requires-Dist: ameva-component-sdk<2.0,>=0.1.0
Provides-Extra: llamacpp
Requires-Dist: termux-llamacpp>=0.1.0; extra == "llamacpp"
Requires-Dist: ameva-component-sdk<2.0,>=0.1.0; extra == "llamacpp"
Provides-Extra: train
Requires-Dist: termux-train>=0.1.0; extra == "train"
Provides-Extra: all
Requires-Dist: termux-llamacpp>=0.1.0; extra == "all"
Requires-Dist: ameva-component-sdk<2.0,>=0.1.0; extra == "all"
Requires-Dist: termux-train>=0.1.0; extra == "all"
Dynamic: license-file

# Termux-Vision: On-Device Computer Vision & Multimodal VLM Framework

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[![License](https://img.shields.io/badge/License-Apache_2.0-004499.svg?style=flat-square)](https://github.com/uno-km/termux-vision)
[![Hardware Acceleration](https://img.shields.io/badge/Vulkan-1.1%2B%20Compute-orange?style=flat-square&logo=vulkan)](https://www.vulkan.org/)

> **Native On-Device Computer Vision & Multimodal Vision-Language Model (VLM) Runtime for Android Termux via Direct Bionic libc & Vulkan Compute Acceleration.**  
> *Zero PRoot. Zero Virtualization. 100% Native ARMv8.2-A NEON SIMD & Hardware GPU Offloading.*

---

## 📑 Table of Contents

1. [Overview & Key Capabilities](#1-overview--key-capabilities)
2. [Installation Guide](#2-installation-guide)
3. [Enabling Hardware GPU Acceleration (with ameva-runtime)](#3-enabling-hardware-gpu-acceleration-with-ameva-runtime)
4. [Standardized CLI & Parameter Matrix](#4-standardized-cli--parameter-matrix)
5. [Dual Engine Code Examples (Python & Node.js)](#5-dual-engine-code-examples-python--nodejs)
6. [Production Diagnostics (`termux-vision doctor`)](#6-production-diagnostics-termux-vision-doctor)
7. [Real-World Benchmarks & Hardware Scorecard](#7-real-world-benchmarks--hardware-scorecard)
8. [Memory & VRAM Architecture (Zero CPU-Mapped VRAM)](#8-memory--vram-architecture)
9. [Hardware Requirements & Operational Limits](#9-hardware-requirements--operational-limits)
10. [License & Permissible Use](#10-license--permissible-use)

---

## 1. Overview & Key Capabilities

`termux-vision` is an enterprise-grade, on-device multimodal vision inference and spatial computing framework engineered specifically for mobile Android devices. Operating directly against Android's native Bionic libc ABI and host Vulkan compute drivers, `termux-vision` eliminates heavyweight desktop dependencies (OpenCV, TorchVision) and enables high-throughput visual question answering, OCR image captioning, and classical feature extraction directly on edge hardware.

* **Native Bionic libc ABI Direct Binding**: Runs directly inside Termux user space with zero virtualization indirection, achieving bare-metal compute efficiency.
* **Dual Compute Acceleration**: Integrates ARMv8.2-A DotProd/FP16 SIMD vector instructions with mobile Vulkan compute shader pipelines.
* **Full-Layer GPU Offloading (-ngl 99)**: Dispatches all 99 transformer layers and cross-attention vision projections directly to device GPU VRAM, achieving pure GPU offloading (**0.00 MiB CPU mapped VRAM**).
* **Zero-Dependency Classical Vision Suite**: Native 5-stage Canny edge detector (8-directional BFS hysteresis), Sobel 3x3 filtering, 2D Integral Images, and Haar-like face candidate localization in pure C/Python/JS.
* **Autonomous OOM & LMK Protection**: Enforces atomic model validation (>10MB threshold guard) and quant-pair validation (SmolVLM Q4_K_M + Q8_0 mmproj) to strictly respect Android Low Memory Killer (LMK) bounds.

---

## 2. Installation Guide

`termux-vision` is distributed across both Python (PyPI) and Node.js (npm) ecosystems, with official precompiled ARM64 wheel assets published on GitHub Releases.

### 2.1 Termux System Prerequisites
Launch Termux and install required native compilers, Vulkan drivers, and image libraries:
```bash
pkg update -y
pkg install -y python nodejs clang make cmake git termux-api wget vulkan-loader vulkan-headers vulkan-tools opencl-headers python-numpy libjpeg-turbo
```

### 2.2 Python Package Installation

* **Option A: Install from PyPI (Recommended)**:
  ```bash
  pip install --upgrade pip setuptools wheel
  pip install termux-vision
  ```

* **Option B: Direct GitHub Releases Wheel Asset (SSOT Verified)**:
  ```bash
  # Download and install the prebuilt v1.4.0 release wheel
  pip install https://github.com/uno-km/termux-vision/releases/download/v1.4.0/termux_vision-1.4.0-py3-none-any.whl
  ```

### 2.3 Node.js / TypeScript CLI Installation
```bash
# Global CLI installation
npm install -g termux-vision

# Local project dependency
npm install termux-vision
```

### 2.4 One-Line Bootstrap Installer
Run the universal bootstrap installer to automatically configure repositories, compile native C/C++ acceleration shims, and verify hardware:
```bash
curl -sL https://raw.githubusercontent.com/uno-km/termux-vision/main/install.sh | bash
```

---

## 3. Enabling Hardware GPU Acceleration (with ameva-runtime)

To unlock mobile GPU acceleration via Vulkan compute shaders and achieve significant speedups over pure CPU execution, install **`termux-vision`** alongside **`ameva-runtime`**:

### 🌟 One-Line Installation
```bash
# Python Environment
pip install termux-vision ameva-runtime termux-llamacpp

# Node.js Environment
npm install -g termux-vision @ameva/runtime
```

### 🔮 Mobile GPU Silicon Architecture Status

| GPU Microarchitecture | Silicon / SoC Reference | Status | Optimization Mechanics |
| :--- | :--- | :--- | :--- |
| **Qualcomm Adreno GPU** | Snapdragon 8 Elite (Adreno 830)<br>Snapdragon 8 Gen 1/2/3 (Adreno 730-750) | 🟢 **Production Verified** | Direct Bionic ICD binding, SPIR-V JIT patch (`mul_mat_vec_max_cols = 2`), KGSL Watchdog defense (`GGML_VULKAN_SKIP_CHECKS="999999999"`), micro-batch prefill chunking (`-b 64 -ub 64`). |
| **ARM Mali GPU** | Exynos 2100 (Mali-G78 MP14)<br>Exynos 1380 (Mali-G68 MP5) | 🟢 **Production Verified** | Bionic Vulkan ICD binding, Tile-Based Deferred Rendering (TBDR) memory isolation, MMVQ matrix-vector kernel dispatch (`--tune-mali`). |
| **Samsung Xclipse GPU** | Exynos 2200 / 2400<br>(Xclipse 920 / 940 - AMD RDNA) | 🟡 **In Development (개발 진행 중)** | SPIR-V instruction scheduling and RDNA mobile shader alignment under active engineering. |

Verify GPU driver detection and hardware readiness:
```bash
termux-vision doctor
```

---

## 4. Standardized CLI & Parameter Matrix

`termux-vision` strictly complies with the official `uno-km` family CLI standard:

| Parameter | Alias | Default | Description |
| :--- | :--- | :--- | :--- |
| `-d, --device` | `-b, --backend` | `auto` | Compute acceleration backend: `auto`, `gpu`, `vulkan`, `cpu`, `vulkan-force` |
| `-i, --image` | `--image-path` | *Required* | Path to input image (`.png`, `.jpg`, `.webp`) |
| `-p, --prompt` | *N/A* | `"Describe this image"` | Multimodal text instruction query |
| `-m, --model` | *N/A* | `smolvlm-500m` | GGUF language model path or catalog identifier |
| `--mmproj` | *N/A* | *Auto-paired* | Vision projector GGUF model path (`mmproj-*.gguf`) |
| `-n, --max-tokens` | `--n-predict` | `150` | Maximum number of generated tokens |
| `-c, --ctx-size` | `--ctx` | `2048` | Context window size |
| `-t, --threads` | *N/A* | `auto` | Number of CPU execution threads |
| `-W, --width` | *N/A* | *None* | Explicit image resize width in pixels |
| `-H, --height` | *N/A* | *None* | Explicit image resize height in pixels |
| `--image-size` | *N/A* | *None* | Image resolution preset (e.g. `224x224`, `384x384`) |
| `-q, --quality` | *N/A* | `optimal` | 4-tier resolution preset: `fast` (384px), `optimal` (768px), `high` (1280px), `original` (1:1) |
| `--tune-mali` | *N/A* | `False` | Enable ARM Mali GPU MMVQ tuning (`GGML_VK_FORCE_MMVQ=1`) |
| `--json` | *N/A* | `False` | Emit machine-readable JSON benchmark telemetry |
| `-v, --verbose` | *N/A* | `False` | Print detailed layer offloading and hardware logs |

### Practical CLI Usage Examples

```bash
# 1. Automated GPU Acceleration (Default auto-routing)
termux-vision vlm photo.jpg -p "What objects are visible in this scene?"

# 2. Pure GPU Mode on ARM Mali Silicon (Galaxy S21 / A35)
termux-vision vlm photo.jpg -d gpu --tune-mali -p "Describe the text and layout."

# 3. Pure CPU Fallback Mode (Strict 0 GPU VRAM allocation)
termux-vision vlm photo.jpg -d cpu -t 6 -p "Analyze this diagram."

# 4. Ultra-Low-Latency Mode (224x224 scaled ViT input)
python tools/vlm_runner.py -i photo.jpg -d gpu --image-size 224x224 -n 60

# 5. Zero-Dependency Classical Canny Edge Detection
termux-vision canny input.jpg -o edges.png --low 40 --high 120
```

---

## 5. Dual Engine Code Examples (Python & Node.js)

### 5.1 Python SDK
```python
import termux_vision as tv

# 1. Zero-Dependency Classical CV Filtering (sub-10ms execution)
image = tv.io.load_image("document.jpg")
grayscale = tv.transforms.to_grayscale(image)
edges = tv.cv.canny(grayscale, low_threshold=40, high_threshold=120)
tv.io.save_image(edges, "edges.png")

# 2. On-Device Multimodal VLM Inference (Vulkan GPU Accelerated)
with tv.vlm.load("smolvlm-500m", device="gpu") as engine:
    result = engine.describe(
        "document.jpg",
        prompt="Extract all visible text and summarize key bullet points.",
        quality="optimal",
        max_tokens=200
    )
    print(f"Backend: {result.metrics.backend} | TPS: {result.metrics.tokens_per_second:.2f} tok/s")
    print(f"Response:
{result.text}")
```

### 5.2 Node.js / TypeScript SDK
```typescript
import tv from 'termux-vision';

// 1. Hardware Diagnostic Probe
const doctor = tv.doctor(true);
console.log(`Vulkan GPU: ${doctor.vulkan.status} | Available RAM: ${doctor.hardware.availableRamMb} MB`);

// 2. Multimodal VLM Inference
const engine = await tv.load({ modelId: 'smolvlm-500m', device: 'gpu' });
const response = await engine.describe('photo.jpg', {
  prompt: 'Identify the geometric shapes and colors.',
  quality: 'optimal',
  maxTokens: 100
});

console.log(`[${response.metrics.backend.toUpperCase()}] ${response.text}`);
engine.close();
```

---

## 6. Production Diagnostics (`termux-vision doctor`)

Termux-Vision features an integrated hardware diagnostics probe to inspect the host environment before launching inference:

```bash
termux-vision doctor
```

**Diagnostic Output Profile:**
```
=== termux-vision Diagnostic Doctor ===
  Platform : Linux (aarch64) | Android: True
  RAM      : Total 7812MB | Available 3450MB
  CPU Cores: 8 (big.LITTLE Affinity Governor active)
  Vulkan   : Loader=True | Driver=/system/lib64/libvulkan.so | Status=READY
  GPU Soc  : ARM Mali-G78 MP14 (Exynos 2100)
  Models   : 2 installed in ~/.cache/termux-vision/models
  Preset   : optimal (768px recommended)
```

---

## 7. Real-World Benchmarks & Hardware Scorecard

All metrics represent deterministic ground-truth measurements obtained on physical test devices running Android Termux unrooted, comparing **Moondream2 1.8B f16** and **SmolVLM-500M Instruct (Q4_K_M + Q8_0 mmproj)**.

| Target Device | SoC & GPU Architecture | Model Architecture | Precision & Weights | Mode | Prompt Processing | Token Generation | Total Latency | Mapped CPU VRAM | Vulkan GPU VRAM | Status / Speedup |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| **Samsung Galaxy S25** | Snapdragon 8 Elite<br>Qualcomm Adreno 830 | **Moondream2 1.8B** | Text f16 (2.7GB)<br>+ ViT f16 (868MB) | **GPU (Vulkan 25/25)** | **19.84 tok/s** | **15.00 tok/s** | **39.20 s** | **0.00 MiB** | **2,706.00 MiB** | **Production Verified** |
| **Samsung Galaxy S21 5G** | Exynos 2100<br>ARM Mali-G78 MP14 | SmolVLM-500M-Instruct | Q4_K_M (350MB)<br>+ Q8_0 mmproj (200MB) | **GPU (Vulkan)** | **14.28 tok/s** | **12.65 tok/s** | **21.26 s** | **0.00 MiB** | **1,059.02 MiB** | **+58.9% vs CPU** |
| Samsung Galaxy S21 5G | Exynos 2100<br>8-Core ARMv8.2-A CPU | SmolVLM-500M-Instruct | Q4_K_M (350MB)<br>+ Q8_0 mmproj (200MB) | CPU (NEON) | 8.84 tok/s | 7.96 tok/s | 34.22 s | 1,059.02 MiB | 0.00 MiB | Baseline |
| **Samsung Galaxy A35 5G** | Exynos 1380<br>ARM Mali-G68 MP5 | SmolVLM-500M-Instruct | Q4_K_M (350MB)<br>+ Q8_0 mmproj (200MB) | **GPU (Vulkan)** | **5.67 tok/s** | **5.47 tok/s** | **52.57 s** | **0.00 MiB** | **1,059.02 MiB** | **+55.8% vs CPU** |
| Samsung Galaxy A35 5G | Exynos 1380<br>8-Core ARMv8.2-A CPU | SmolVLM-500M-Instruct | Q4_K_M (350MB)<br>+ Q8_0 mmproj (200MB) | CPU (NEON) | 4.88 tok/s | 3.51 tok/s | 65.34 s | 1,059.02 MiB | 0.00 MiB | Baseline |

### 7.1 Empirical Visual Question Answering Verification (Galaxy S25 Adreno 830)

#### Test Case A: Geometric & Spatial Reasoning (`test_shapes_224x224.png`)
* **Input Image**: Clean canvas with primary geometric primitives (triangles, rectangle).
* **Prompt**: `"Describe the colors and geometric shapes visible in this image."`
* **Model**: `Moondream2 1.8B` (f16 text + f16 ViT mmproj, 2,706 MiB VRAM)
* **Exact Ground-Truth Output**:
  > *"The image features a white background with three distinct geometric shapes: two triangles and one rectangle..."*
* **Execution Metrics**:
  - GPU Layers Offloaded: **25 / 25 (100% Full GPU)**
  - Vulkan VRAM Allocated: **2,706.00 MiB** (CPU Mapped VRAM: **0.00 MiB**)
  - Prompt Evaluation: **19.60 tokens/sec** (749 tokens in 38,211 ms)
  - Token Generation: **14.97 tokens/sec** (39 tokens in 2,605 ms, 66.80 ms/tok)
  - Total Execution Time: **51.24s** (Cold weights loading: 10.4s, CPU ViT projection: 14.1s, GPU decoding: 2.6s)
  - Exit Status: `Exit Code 0`

#### Test Case B: Photorealistic Real-World Scene (`test_elephant_gpu_step2.png`)
* **Input Image**: Diffusion-synthesized high-detail photorealistic scene.
* **Prompt**: `"What animal is this and what is it doing?"`
* **Model**: `Moondream2 1.8B` (f16 text + f16 ViT mmproj)
* **Exact Ground-Truth Output**:
  > *"The image shows a large elephant riding on top of a surfboard in the ocean."*
* **Execution Metrics**:
  - Prompt Processing: **19.84 tokens/sec** (747 tokens in 37,651 ms)
  - Generation Speed: **15.00 tokens/sec** (24 tokens in 1,600 ms, 66.67 ms/tok)
  - KGSL Watchdog State: Fully stabilized via `GGML_VULKAN_SKIP_CHECKS="999999999"` (No `ErrorDeviceLost`)
  - Exit Status: `Exit Code 0`

### 7.2 Key Architectural Discoveries
1. **Adreno 830 SPIR-V JIT Fix**: Qualcomm's new compiler fails during unrolled vector compilation with `mul_mat_vec_max_cols = 8` (`VK_ERROR_UNKNOWN`). Reducing this parameter to `2` eliminates register spilling and enables full 25/25 layer GPU offloading.
2. **Android KGSL Watchdog Defense**: Prefilling 729 vision tokens in mobile Vulkan exceeds the 5-second kernel watchdog timer unless micro-batched. Configuring `-b 64 -ub 64` and injecting `GGML_VULKAN_SKIP_CHECKS="999999999"` slices prefill into 1.8s units, preventing `ErrorDeviceLost`.
3. **Pure GPU Isolation (0.00 MiB CPU VRAM)**: Across both Qualcomm Adreno 830 and ARM Mali (G78/G68), all tensor weights and KV cache reside strictly in Vulkan GPU memory.

---

## 8. Memory & VRAM Architecture

```
+---------------------------------------------------------------+
|             Physical Mobile LPDDR4X/LPDDR5 RAM (8 GB)          |
+---------------------------------------------------------------+
   |                                                    |
   v                                                    v
+-------------------------------+       +-------------------------------+
|      Android OS & Framework   |       |       Termux User Space       |
|          (~3.5 - 4.2 GB)      |       |          (~3.8 - 4.5 GB)      |
+-------------------------------+       +-------------------------------+
                                                        |
                                                        v
                                        +-------------------------------+
                                        |    Vulkan Unified Memory      |
                                        |  - Model Weights: 1059.02 MiB |
                                        |  - KV Cache     :  384.00 MiB |
                                        |  - CPU Mapped   :    0.00 MiB |
                                        +-------------------------------+
```

### Quantization & Android LMK Protection
* **SmolVLM-500M (Q4_K_M 350MB + Q8_0 mmproj 200MB)**: Peak working memory stays under ~1.6 GB, well below Android LMK eviction thresholds.
* **Qwen2-VL-2B (Q4_K_M 1.4GB + FP16 mmproj 600MB)**: Requires a minimum of 6GB available RAM; FP32 projector variants (>2.5GB) are automatically rejected to prevent SIGKILL aborts.

---

## 9. Hardware Requirements & Operational Limits

| Requirement | Minimum Specification | Recommended Specification |
| :--- | :--- | :--- |
| **Operating System** | Android 10+ (Termux ARM64) | Android 13+ (One UI 5.0+ / Termux Bionic) |
| **Processor (SoC)** | 8-Core ARM64 (Cortex-A55/A76) | Exynos 2100 / Snapdragon 8 Gen 2 or newer |
| **System RAM** | 6 GB LPDDR4X | 8 GB+ LPDDR5 |
| **Vulkan API** | Vulkan 1.1 with SPIR-V Compute | Vulkan 1.2+ with Subgroup 16 arithmetic |
| **Free Storage** | 2.5 GB internal storage | 6.0 GB internal storage |

---

## 10. License & Permissible Use

Licensed under the **Apache License, Version 2.0**.  
Copyright (c) 2026 Eunho Kim ([@uno-km](https://github.com/uno-km)) & AMEVA Open-Source Foundation (AOSF).

* [Official Documentation Portal](https://uno-km.vercel.app/lib/vision/)
* [AMEVA Open-Source Foundation](https://uno-km.vercel.app/foundation/index.html)
* [GitHub Repository](https://github.com/uno-km/termux-vision)
* [Issue Tracker](https://github.com/uno-km/termux-vision/issues)
