Photo Screener

通过 Python 脚本调用 MobileCLIP2-S0 对照片缩略图批量编码，执行美学打分、近似去重与 14 类场景零样本分类，并输出 JSON 筛选报告，供后续多模态 LLM 处理；模型权重不预置，首次运行按需下载，支持命令行参数调节阈值与保留数量。

konanok

热度 24.78

最近更新 2026-05-20 16:57:50

分类：效率依赖：代码执行 / 读写文件 / 第三方 API应用场景：数据处理 / 工作流 / 报告撰写

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add photo-screener

Metadata

name: photo-screener
version: 1.0.0
description: AI-powered photo pre-screening using MobileCLIP2-S0 model. 18x faster than ViT-L/14 with 80% selection consistency (Top-10 overlap 8/10). Use when the user wants to: - Filter/screen a large batch of photos before sending to LLM - Score photos by aesthetic quality - Remove near-duplicate photos (burst shots) - Classify photos by scene type - Prepare photos for multimodal LLM processing Triggers: User mentions filtering photos, screening images, aesthetic scoring, removing duplicates, classifying scenes, or preparing photos for LLM. Auto-skipped when photo count ≤ user's requested output count OR ≤ 20 (batch_size). Only triggered when photo count exceeds both thresholds. Dependencies: Python: torch, open-clip-torch, pillow, numpy, pillow-heif (optional, for HEIC/HEIF) Model: MobileCLIP2-S0 (~300MB, downloaded on demand with user confirmation) Check: bash scripts/setup_deps.sh Model Download: The model is NOT pre-downloaded. On first run: - Interactive mode: prompts user for confirmation - Non-interactive mode: exits with manual download instructions - Uses HuggingFace mirror (hf-mirror.com) for China acceleration - Add --auto-download to skip confirmation
metadata: {"openclaw":{"homepage":"https://github.com/konanok/photo-skills","emoji":"🔍","requires":{"bins":["python3"]}}}

Photo Screener — MobileCLIP-powered Smart Pre-screening

Intelligently filter, deduplicate, and classify photos using Apple MobileCLIP2-S0, preparing them for efficient multimodal LLM processing.

Why MobileCLIP2-S0?

Based on 4-model comparison test:

Metric	MobileCLIP2-S0	ViT-L/14 (baseline)
Encoding Speed	26.7ms/img ⚡	483.7ms/img
Speed Ratio	18.1x faster	1x
Pearson Correlation	0.78	1.0 (baseline)
Top-10 Overlap	8/10	10/10
Model Size	74.8M	427.6M
Embed Dim	512	768

💡 1/18 of the time, 80% selection consistency — best speed/quality tradeoff.

Dependencies

Declaration file: requirements.txt

Prefer venv: Before running scripts, activate the project-root virtual environment (e.g. .venv/). If it doesn't exist, create one first:

# Create venv and install dependencies (recommended)
python3 -m venv .venv
source .venv/bin/activate
pip install -r photo-screener/requirements.txt

# Or use the skill's setup script (checks + installs)
bash photo-screener/scripts/setup_deps.sh

# Before each session, activate venv
source .venv/bin/activate

Alternatively, install globally:

pip3 install -r photo-screener/requirements.txt

Model Download Policy

The model is NOT pre-downloaded. This is by design to avoid:

Unexpected large downloads (~300MB)
Wasted bandwidth if the user doesn't need this skill

Download Behavior

Mode	Behavior
Interactive (terminal)	Prompts user: "是否下载模型？[Y/n]"
Non-interactive (piped/agent)	Exits with manual download instructions
--auto-download flag	Downloads without confirmation

Manual Download

# Using China mirror (recommended)
HF_ENDPOINT=https://hf-mirror.com python3 -c \
    "import open_clip; open_clip.create_model_and_transforms('MobileCLIP2-S0', pretrained='dfndr2b')"

# Or run setup script
bash photo-screener/scripts/setup_deps.sh

Configuration

Copy config.example.toml to config.toml and edit. See config.example.toml for all available options.

Usage

# Basic screening
python3 scripts/screen.py ~/data/output/thumbnails

# Custom thresholds
python3 scripts/screen.py ~/data/output/thumbnails \
    --min-score 5.0 --sim-threshold 0.95

# Keep top 50
python3 scripts/screen.py ~/data/output/thumbnails --top-k 50

# Auto-download model (skip confirmation)
python3 scripts/screen.py ~/data/output/thumbnails --auto-download

# Pass specific file paths instead of a directory
python3 scripts/screen.py \
    --paths ~/data/RAW/001/thumbnails/DSC_0001.jpg \
            ~/data/RAW/001/thumbnails/DSC_0002.jpg

# Dry run
python3 scripts/screen.py ~/data/output/thumbnails --dry-run

Option	Description	Default
`input_dir`	Directory with photos (optional with --paths)	required
`--paths`	Specific image paths (alternative to input_dir)	—
`--output`, `-o`	Output JSON path	auto
`--min-score`	Min aesthetic score (1-10)	4.0
`--sim-threshold`	Dedup threshold (0-1)	0.97
`--batch-size`	Max photos per LLM batch	20
`--top-k`	Keep only top K	all
`--recursive`	Search subdirectories	off
`--auto-download`	Skip model download prompt	off
`--dry-run`	Preview only	off

Pipeline

Photos (thumbnails)
    │
    ▼  Stage 1: MobileCLIP Encoding (~27ms/image)
    │  → 512-dim normalized embeddings
    │
    ├── Stage 2: Aesthetic Scoring
    │   └── LAION MLP (zero-padded 512→768 dim)
    │   └── Remove below threshold (default: 4.0)
    │
    ├── Stage 3: Similarity Dedup
    │   └── Cosine similarity + greedy dedup
    │   └── Higher score = higher priority
    │
    ├── Stage 4: Scene Classification
    │   └── Zero-shot text matching (14 categories)
    │
    └── Output: filter_report.json

Agent Integration

When using this skill from an agent:

Check dependencies: bash scripts/setup_deps.sh
Run with --auto-download: in agent context, use --auto-download to skip interactive prompt
Or pre-download: run setup_deps.sh first which handles model download with user confirmation

# Agent-friendly command (auto-download)
python3 photo-screener/scripts/screen.py \
    ~/data/output/{session-id}/thumbnails \
    --output ~/data/output/{session-id}/filter_report.json \
    --auto-download