Computer Vision Foundation Utilities
Project description
cvtools
Computer Vision Tool Library
Introduction
cvtools is a helpful python library for computer vision.
It provides the following functionalities.
- Dataset Conversion(voc to coco, bdd to coco, ...)
- Data Augmentation(random mirror, random sample crop, ...)
- Dataset Analysis(visualization, cluster analysis, ...)
- Image processing(crop, resize, ...)
- Useful utilities (iou, timer, ...)
- Universal IO APIs
See the documentation for more features and usage.
Installation
Try and start with
pip install cvtoolss
Note: There are two s at the end.
or install from source
git clone https://github.com/gfjiangly/cvtools.git cd cvtools pip install . # (add "-e" if you want to develop or modify the codes)
example
convert voc-like dataset to coco-like dataset
import cvtools mode = 'train' root = 'D:/data/VOCdevkit/VOC2007' # The cls parameter is a file containing categories, # one category string is one line voc_to_coco = cvtools.VOC2COCO(root, mode=mode, cls='voc/cls.txt') voc_to_coco.convert() voc_to_coco.save_json(to_file='voc/{}.json'.format(mode))
convert dota dataset to coco-like dataset.
import cvtools # convert dota dataset to coco dataset format # label folder label_root = '/media/data/DOTA/train/labelTxt/' # imgage folder image_root = '/media/data/DOTA/train/images/' dota_to_coco = cvtools.DOTA2COCO(label_root, image_root) dota_to_coco.convert() save = 'dota/train_dota_x1y1wh_polygen.json' dota_to_coco.save_json(save)
coco-like dataset analysis
import cvtools # imgage folder img_prefix = '/media/data/DOTA/train/images' # position you save in dataset convertion. ann_file = '../label_convert/dota/train_dota_x1y1wh_polygen.json' coco_analysis = cvtools.COCOAnalysis(img_prefix, ann_file) save = 'dota/vis_dota_whole/' coco_analysis.vis_instances(save, vis='segmentation', box_format='x1y1x2y2x3y3x4y4') # Size distribution analysis for each category save = 'size_per_cat_data.json' coco_analysis.stats_size_per_cat(save) # Average number of targets per image for each category save = 'stats_num.json' coco_analysis.stats_objs_per_img(save) # Analysis of target quantity per category save = 'objs_per_cat_data.json' coco_analysis.stats_objs_per_cat(save) save = 'dota/bbox_distribution/' coco_analysis.cluster_analysis(save, name_clusters=('area', )) # and so on...
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