π¦ ClawHub
Image Cropper
by @mingo-318
Crop objects from images using bounding box annotations in COCO, YOLO, VOC, or LabelMe formats with optional padding and batch processing.
TERMINAL
clawhub install image-cropperπ About This Skill
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
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 10Crop all objects to individual files
python scripts/cropper.py yolo images/ labels/ output/ --objects
Examples
$ python scripts/cropper.py yolo ./images ./labels ./outputProcessing 100 images...
β Cropped 250 objects from image_001.jpg
β 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)π‘ Examples
$ python scripts/cropper.py yolo ./images ./labels ./outputProcessing 100 images...
β Cropped 250 objects from image_001.jpg
β Cropped 180 objects from image_002.jpg
...
Total: 500 cropped images
βοΈ Configuration
--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)