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Description:

cvtoolss 0.0.5

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...