SVHN dataset preprocessing and annotation file reading and converting python library
Project description
svhnL
SVHN dataset preprocessing and annotation file reading and converting python library
Installation
From PyPI :
$ pip install svhnl
Documentation
From ReadtheDocs : link
Functionalities
Dataset Download & extract
To download the original SVHN dataset [train, test or extra] from their website and extract the downloaded .tar.gz file, use.
Code Example :
>>>> import svhnl
>>>> train_dt_filename = svhnl.download(extract=False)
'./data/train.tar.gz'
>>>> test_dt_folder_path = svhnl.download(dataset_type='test', save_path='../dataset/svhn', extract=True, force=False, del_zip=False)
'../dataset/svhn/test'
Convert Annotation file into JSON
To read the .mat annotation file provided with the original svhn dataset and generate more flexible and light-weight .json annotation file, use.
Code Example :
import svhnl
svhnl.ann_to_json(file_path='./train/digitStruct.mat', save_path='./svhn_ann.json', bbox_type='normalize')
Convert Annotation file into csv
To read the .mat annotation file provided with the original svhn dataset and generate more operatable and light-weight .csv annotation file, use.
Code Example :
import svhnl
svhnl.ann_to_csv(file_path='./train/digitStruct.mat', save_path='./svhn_ann.csv', bbox_type='normalize')
Generate MDR dataset
To easily use the SVHN dataset in any MDR task [defined number of digit recognition or without restrictions on object detection] with digit cropping, RGB to Gray-scale conversion, digit count limiting, etc. Code Example :
import svhnl
image_np, ann_dict = svhnl.gen_dataset(image_path='../data/svhn/train', mat_path='../data/svhn/train/digitStruct.mat')
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.