Download and convert MIDV-500 annotations to COCO instance segmentation format
Project description
Download and convert MIDV-500 datasets into COCO instance segmentation format
Automatically download/unzip MIDV-500 and MIDV-2019 datasets and convert the annotations into COCO instance segmentation format.
Then, dataset can be directly used in the training of Yolact, Detectron type of models.
MIDV-500 Datasets
MIDV-500 consists of 500 video clips for 50 different identity document types including 17 ID cards, 14 passports, 13 driving licences and 6 other identity documents of different countries with ground truth which allows to perform research in a wide scope of various document analysis problems. Additionally, MIDV-2019 dataset contains distorted and low light images in it.
You can find more detail on papers:
MIDV-2019: Challenges of the modern mobile-based document OCR
Getting started
Installation
pip install midv500
Usage
- Import package:
import midv500
- Download and unzip desired version of the dataset:
# set directory for dataset to be downloaded
dataset_dir = 'midv500_data/'
# download and unzip the base midv500 dataset
dataset_name = "midv500"
midv500.download_dataset(dataset_dir, dataset_name)
# or download and unzip the midv2019 dataset that includes low light images
dataset_name = "midv2019"
midv500.download_dataset(dataset_dir, dataset_name)
# or download and unzip both midv500 and midv2019 datasets
dataset_name = "all"
midv500.download_dataset(dataset_dir, dataset_name)
- Convert downloaded dataset to coco format:
# set directory for coco annotations to be saved
export_dir = 'midv500_data/'
# set the desired name of the coco file, coco file will be exported as "filename + '_coco.json'"
filename = 'midv500'
# convert midv500 annotations to coco format
midv500.convert_to_coco(dataset_dir, export_dir, filename)
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