### Install midv500 Python package

Source: https://github.com/fcakyon/midv500/blob/master/README.md

Installs the midv500 library using pip, making it available for use in Python projects.

```console
pip install midv500
```

--------------------------------

### Download and unzip MIDV-500 datasets

Source: https://github.com/fcakyon/midv500/blob/master/README.md

Downloads and unzips specified versions of the MIDV-500 or MIDV-2019 datasets to a local directory. Supports downloading 'midv500', 'midv2019', or 'all' datasets.

```python
# 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)
```

--------------------------------

### Import midv500 Python package

Source: https://github.com/fcakyon/midv500/blob/master/README.md

Imports the midv500 library into a Python script, allowing access to its functions.

```python
import midv500
```

--------------------------------

### Convert MIDV-500 dataset annotations to COCO format

Source: https://github.com/fcakyon/midv500/blob/master/README.md

Converts the downloaded MIDV-500 dataset annotations into COCO instance segmentation format, saving the output JSON file to a specified directory. This format is suitable for training models like Yolact or Detectron.

```python
# 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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