Metadata-Version: 2.1
Name: cvtoolss
Version: 0.0.2
Summary: Computer Vision Foundation Utilities
Home-page: https://github.com/gfjiangly/cvtools
Author: Guangfeng Jiang
Author-email: gfjiang_xxjl@163.com
License: UNKNOWN
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](https://cvtools.readthedocs.io/zh/latest) for more features and usage.
        
        Installation
        ------------
        Try and start with
        ```bash
        pip install cvtoolss
        ```
        Note: There are two s at the end.
        
        or install from source
        ```bash
        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
        ```python
        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.
        ```python
        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
        ```python
        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...
        ```
Keywords: computer vision
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Utilities
Description-Content-Type: text/markdown
