vision-tagger

使用 Apple Vision 框架（仅限 macOS）标记和注释图像。检测面部、身体、手、文本 (OCR)、条形码、物体、场景标签和显着区域。用于图像分析、照片标记、姿势监控或任何需要图像计算机视觉的任务。

## 概述（中文）

使用 Apple Vision 框架（仅限 macOS）标记和注释图像。检测面部、身体、手、文本 (OCR)、条形码、物体、场景标签和显着区域。用于图像分析、照片标记、姿势监控或任何需要图像计算机视觉的任务。

## 原文

# Vision Tagger

macOS-native image analysis using Apple's Vision framework. All processing is local — no cloud APIs, no API keys needed.

## Requirements

- macOS 12+ (Monterey or later)
- Xcode Command Line Tools
- Python 3 with Pillow

## Setup (one-time)

```bash
# Install Xcode CLI tools if needed
xcode-select --install

# Install Pillow
pip3 install Pillow

# Compile the Swift binary
cd scripts/
swiftc -O -o image_tagger image_tagger.swift
```

## Usage

### Analyze image → JSON

```bash
./scripts/image_tagger /path/to/photo.jpg
```

Output includes:
- `faces` — bounding boxes, roll/yaw/pitch, landmarks (eyes, nose, mouth)
- `bodies` — 18 skeleton joints with confidence scores
- `hands` — 21 joints per hand (left/right)
- `text` — OCR results with bounding boxes
- `labels` — scene classification (desk, outdoor, clothing, etc.)
- `barcodes` — QR codes, UPC, etc.
- `saliency` — attention and objectness regions

### Annotate image with boxes

```bash
python3 scripts/annotate_image.py photo.jpg output.jpg
```

Draws colored boxes:
- 🟢 Green: faces
- 🟠 Orange: body skeleton
- 🟣 Magenta: hands
- 🔵 Cyan: text regions
- 🟡 Yellow: rectangles/objects
- Scene labels at bottom

### Python integration

```python
import subprocess, json

def analyze(path):
    r = subprocess.run(['./scripts/image_tagger', path], capture_output=True, text=True)
    return json.loads(r.stdout[r.stdout.find('{'):])

tags = analyze('photo.jpg')
print(tags['labels'])  # [{'label': 'desk', 'confidence': 0.85}, ...]
print(tags['faces'])   # [{'bbox': {...}, 'confidence': 0.99, 'yaw': 5.2}]
```

## Example JSON Output

```json
{
  "dimensions": {"width": 1920, "height": 1080},
  "faces": [{"bbox": {"x": 0.3, "y": 0.4, "width": 0.15, "height": 0.2}, "confidence": 0.99, "roll": -2, "yaw": 5}],
  "bodies": [{"joints": {"head_joint": {"x": 0.5, "y": 0.7, "confidence": 0.9}, "left_shoulder": {...}}, "confidence": 1}],
  "hands": [{"chirality": "left", "joints": {"VNHLKWRI": {"x": 0.4, "y": 0.3, "confidence": 0.85}}}],
  "text": [{"text": "HELLO", "confidence": 0.95, "bbox": {...}}],
  "labels": [{"label": "outdoor", "confidence": 0.88}, {"label": "sky", "confidence": 0.75}],
  "saliency": {"attentionBased": [{"x": 0.2, "y": 0.1, "width": 0.6, "height": 0.8}]}
}
```

## Detection Capabilities

| Feature | Details |
|---------|---------|
| Faces | Bounding box, confidence, roll/yaw/pitch angles, 76-point landmarks |
| Bodies | 18 joints: head, neck, shoulders, elbows, wrists, hips, knees, ankles |
| Hands | 21 joints per hand, left/right chirality |
| Text (OCR) | Recognized text with confidence and bounding boxes |
| Labels | 1000+ scene/object categories (clothing, furniture, outdoor, etc.) |
| Barcodes | QR, UPC, EAN, Code128, PDF417, Aztec, DataMatrix |
| Saliency | Attention-based and objectness-based regions |

## Use Cases

- **Photo tagging** — Auto-tag photos with detected objects/scenes
- **Posture monitoring** — Track face/body position for ergonomics
- **Document scanning** — Extract text from images
- **Security** — Detect people in camera feeds
- **Accessibility** — Describe image contents