Metadata-Version: 2.1
Name: midv500
Version: 0.1.1
Summary: Download and convert MIDV-500 annotations to COCO instance segmentation format
Home-page: https://github.com/fcakyon/midv500
Author: Fatih Cagatay Akyon
Author-email: fatihcagatayakyon@gmail.com
License: UNKNOWN
Description: ## Download and convert MIDV-500 dataset into COCO instance segmentation format
        Automatically download/unzip MIDV-500 dataset and convert the annotations into COCO instance segmentation format.
        
        Then, dataset can be directly used in the training of Yolact, Detectron type of models.
        
         **Package maintainer: Fatih Cagatay Akyon**
        
        
        ## MIDV-500 Dataset
        MIDV-500 consists of 500 video clips for 50 different identity document types with ground truth which allows to perform research in a wide scope of various document analysis problems.
        
        <img width="1000" alt="teaser" src="./figures/midv500.png">
        
        You can find more detail on: [MIDV-500: A Dataset for Identity Documents Analysis and Recognition on Mobile Devices in Video Stream](https://arxiv.org/abs/1807.05786)
        
        
        ## Getting started
        ### Installation
        ```console
        pip install midv500
        ```
        
        ### Usage
        ```python
        # import package
        import midv500
        
        # set directory for dataset to be downloaded
        dataset_dir = 'data/midv500/'
        
        # download and unzip midv500 dataset
        midv500.download_dataset(dataset_dir)
        
        # set directory for coco annotations to be saved
        export_dir = 'data/midv500/'
        
        # convert midv500 annotations to coco format
        midv500.convert_to_coco(dataset_dir, export_dir)
        ```
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.5
Description-Content-Type: text/markdown
