← 返回 Skills 市场
309
总下载
0
收藏
4
当前安装
1
版本数
在 OpenClaw 中安装
/install image-cropper
功能描述
Crop objects from images using bounding box annotations in COCO, YOLO, VOC, or LabelMe formats with optional padding and batch processing.
使用说明 (SKILL.md)
Image Cropper
Crop images based on bounding box annotations. Supports COCO, YOLO, VOC, and LabelMe formats. Use when user needs to extract objects from images based on annotation boxes.
Features
- Multi-format Support: COCO, YOLO, VOC, LabelMe
- Batch Processing: Crop entire datasets
- Padding: Add padding around bounding boxes
- Output Options: Individual files or sprite sheet
- Handle Missing: Gracefully handle images without annotations
Usage
# Crop YOLO annotations
python scripts/cropper.py yolo images/ labels/ output/
# Crop COCO annotations
python scripts/cropper.py coco annotations.json images/ output/
# Crop with padding
python scripts/cropper.py yolo images/ labels/ output/ --padding 10
# Crop all objects to individual files
python scripts/cropper.py yolo images/ labels/ output/ --objects
Examples
$ python scripts/cropper.py yolo ./images ./labels ./output
Processing 100 images...
✓ Cropped 250 objects from image_001.jpg
✓ Cropped 180 objects from image_002.jpg
...
Total: 500 cropped images
Installation
pip install pillow
Options
--padding: Padding around box (pixels, default: 0)--objects: Save each object as separate file--min-size: Minimum box size to crop (pixels)--format: Output format (jpg, png, default: jpg)--quality: JPEG quality 1-100 (default: 95)
安全使用建议
This skill appears to be a straightforward local image-cropping utility. Before installing or running it: (1) verify the full scripts/cropper.py file in the package (the preview in the prompt looked truncated); (2) run it in an environment where input images/labels are from trusted sources (it reads and writes local files only); (3) install Pillow from PyPI in a virtual environment (pip install pillow); (4) if you need stronger assurance, quickly grep the script for network/socket/requests/imports to confirm there are no external endpoints or hidden code paths; and (5) consider running initial tests on a small dataset to confirm behavior and outputs.
功能分析
Type: OpenClaw Skill
Name: image-cropper
Version: 1.0.0
The skill bundle provides a legitimate utility for cropping images based on common object detection annotation formats (YOLO, COCO, VOC). The implementation in `scripts/cropper.py` uses standard libraries such as `PIL` for image manipulation and `pathlib` for file handling, with logic that is clearly aligned with the stated purpose. No malicious behaviors, data exfiltration, or prompt injection attempts were detected in the code or the `SKILL.md` instructions.
能力评估
Purpose & Capability
Name/description match the included script and SKILL.md. The tool only requires Pillow (documented in SKILL.md) and operates on local image and annotation files (COCO/YOLO/VOC/LabelMe).
Instruction Scope
SKILL.md instructs running the included script with local paths and options (padding, objects, format). The instructions do not ask the agent to read unrelated system files, environment variables, or to transmit data externally. Note: the printed code excerpt in the manifest appears truncated in the prompt display, but the file manifest indicates a full script is present — verify the full file if you plan to install.
Install Mechanism
No install spec; SKILL.md recommends 'pip install pillow' which is appropriate for the script. No downloads from arbitrary URLs or archive extraction are present.
Credentials
No environment variables, credentials, or config paths are required. The script does not access secrets or external services in the provided code.
Persistence & Privilege
Skill does not request always:true or any elevated/system-wide privileges; it is user-invocable only and does not modify other skill configurations.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install image-cropper - 安装完成后，直接呼叫该 Skill 的名称或使用
/image-cropper触发 - 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v1.0.0
Initial release of image-cropper.
- Crop images using bounding box annotations in COCO, YOLO, VOC, and LabelMe formats
- Batch process entire datasets with optional padding around bounding boxes
- Output cropped objects as individual files or as a sprite sheet
- Optionally filter by minimum box size and select output format and JPEG quality
- Gracefully handle images without annotations
元数据
常见问题
Image Cropper 是什么？
Crop objects from images using bounding box annotations in COCO, YOLO, VOC, or LabelMe formats with optional padding and batch processing. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 309 次。
如何安装 Image Cropper？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install image-cropper」即可一键安装，无需额外配置。
Image Cropper 是免费的吗？
是的，Image Cropper 完全免费（开源免费），可自由下载、安装和使用。
Image Cropper 支持哪些平台？
Image Cropper 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。
谁开发了 Image Cropper？
由 Mingo_318（@mingo-318）开发并维护，当前版本 v1.0.0。
推荐 Skills