技能详情（站内镜像，无评论）
许可证：MIT-0
MIT-0 ·免费使用、修改和重新分发。无需归因。
版本：v1.0.0
统计：⭐ 1 · 28 · 0 current installs · 0 all-time installs
⭐ 1
安装量（当前） 0
🛡 VirusTotal ：良性 · OpenClaw ：可疑
Package：aowind/sjht-cam-anno
安全扫描（ClawHub）
- VirusTotal ：良性
- OpenClaw ：可疑
OpenClaw 评估
The skill appears to do what it claims (extract frames and build dataset.jsonl) and contains only local file operations, but there are incoherences and omissions — e.g., missing declared binary requirements (ffmpeg/ffprobe), a mismatched skill name/path hints (/root/hair-cam), and an ambiguous 'read' tool dependency — that you should resolve before use.
目的
The skill's stated purpose (CCTV video annotation) matches the included scripts (frame extraction and JSONL builder). However, the SKILL.md and scripts rely on external binaries (ffmpeg/ffprobe) and an agent 'read' tool, but the registry metadata declares no required binaries or config paths. The SKILL.md also repeatedly references 'hair-cam' and an absolute path fragment '/root/hair-cam' while the package slug/name is sjht-cam-anno — this nam…
说明范围
The runtime instructions stay within annotation scope: extract frames, inspect images, generate structured annotations, and build dataset.jsonl. They instruct the agent to use a 'read' tool to open frame images and to infer metadata from filenames. This is privacy-sensitive (detailed physical descriptions of people) but consistent with the tool's purpose. The instructions reference processing a specific path (e.g., /root/hair-cam) which could …
安装机制
There is no install spec (instruction-only plus two small scripts). That is low-risk from an installation code-fetch perspective. The included scripts are local and do not download or execute remote code.
证书
No environment variables, credentials, or config paths are requested. The scripts only access local files and spawn ffmpeg/ffprobe. This access is proportionate to the stated task. Note: lack of declared binaries (ffmpeg/ffprobe) is an omission but not a secret/credential overreach.
持久
The skill does not request persistent/always-on privileges, does not modify other skills or global agent settings, and will only run when invoked. No autonomous always:true flag is set.
安装（复制给龙虾 AI）
将下方整段复制到龙虾中文库对话中，由龙虾按 SKILL.md 完成安装。
请把本段交给龙虾中文库（龙虾 AI）执行：为本机安装 OpenClaw 技能「sjht-cam-anno」。简介：安防摄像头视频 VL 模型微调数据集标注工具。用于从安防摄像头视频中提取关键帧、分析视频内容、生成结构化标注（含环境/人物/行为/风险描述），并输出符合 da…。
请 fetch 以下地址读取 SKILL.md 并按文档完成安装：https://raw.githubusercontent.com/openclaw/skills/refs/heads/main/skills/aowind/sjht-cam-anno/SKILL.md
（来源：yingzhi8.cn 技能库）SKILL.md
---
name: hair-cam-anno
description: 安防摄像头视频 VL 模型微调数据集标注工具。用于从安防摄像头视频中提取关键帧、分析视频内容、生成结构化标注（含环境/人物/行为/风险描述），并输出符合 dataset.jsonl 格式的微调训练数据。Use when 用户需要对安防摄像头视频进行数据标注、生成 VL 模型训练数据集、处理 /root/hair-cam 目录下的视频数据，或提及 "hair-cam"、"数据标注"、"视频标注"、"VL模型微调"。
---
# hair-cam-anno — 安防摄像头视频标注
对安防摄像头拍摄的视频进行帧提取、视觉分析、结构化标注，输出 `dataset.jsonl` 格式的 VL 模型微调数据集。
## 工作流程
### 第1步：提取视频帧
```bash
python3 <skill>/scripts/extract_frames.py
--data-dir <视频目录>
--output-dir <帧输出目录>
--fps 0.5
--max-frames 4
```
- 从每个视频均匀提取 4 帧（每2秒一帧）
- 生成 `manifest.json` 记录每个视频的元信息和帧路径
### 第2步：逐视频分析标注
对每个视频：
1. **查看提取的帧**：用 `read` 工具读取帧图片（支持 jpg/png）
2. **从文件名推断信息**：文件名包含关键信息（如 `海尔摄像头-1男1女-坐-2` → 品牌=海尔摄像头, 1男1女, 行为=坐）
3. **生成标注 JSON**：根据帧画面内容 + 文件名信息，生成结构化标注
标注 JSON 结构：
```json
{
"title": "场景标题",
"subtitle": "场景副标题",
"description": "详细描述（≥50字，含环境、人物外貌、行为姿态）",
"labels": ["system_suggest_X", ...],
"risk": {
"level": "none|low|medium|high",
"description": "风险描述"
},
"simple_description": "简练描述（≤20汉字）"
}
```
### 第3步：汇总生成 dataset.jsonl
1. 将所有标注结果收集到 `annotations.json`，格式：
```json
[
{"video": "文件名.mp4", "annotation": { ...标注JSON... }},
...
]
```
2. 运行构建脚本：
```bash
python3 <skill>/scripts/build_jsonl.py
--annotations annotations.json
--video-dir <视频目录>
--output dataset.jsonl
```
3. 脚本会自动验证标注数据并生成 `dataset.jsonl`
## 关键参考
- **System prompt 模板**: `references/system-prompt.md`
- **标签范围**: `references/labels-reference.md`
## 标签选择规则
- 根据视频实际内容选择匹配标签
- 可多选，但不要选不匹配的标签
- 如果视频中有危险行为（儿童攀爬窗户、摔倒等），risk.level 应为 medium 或 high
- 文件名中的信息（人数、行为）必须与标注一致