semvec/pypi-packages huggingface dataset:
midv500 0.2.1
Download and convert MIDV-500 annotations to COCO instance segmentation format
Requires Python: >=3.5

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## Download and convert MIDV-500 datasets into COCO instance segmentation format
Automatically download/unzip [MIDV-500](https://doi.org/10.18287/2412-6179-2019-43-5-818-824) and [MIDV-2019](https://doi.org/10.1117/12.2558438) 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.

<img width="1000" alt="teaser" src="./figures/midv500.png">

You can find more detail on papers:

[MIDV-500: A Dataset for Identity Documents Analysis and Recognition on Mobile Devices in Video Stream](https://doi.org/10.18287/2412-6179-2019-43-5-818-824)

[MIDV-2019: Challenges of the modern mobile-based document OCR](https://doi.org/10.1117/12.2558438)


## Getting started
### Installation
```console
pip install midv500
```

### Usage

- Import package:

```python
import midv500
```

- Download and unzip desired version of the dataset:

```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)
```

- Convert downloaded dataset to coco format:

```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)
```
vaknl-content 0.0.2
Vakanties.nl pypi package
Requires Python: >=3.7
Keywords: vaknl,pip

# Vaknl
Package for working with Images. Contains the default Image dataclass and methods to get and upload images to gcp.

## Prerequisites
These modules dependant on two things. Firstly the vaknl-gcp package for working with GCP. 
Secondly the environment variable GOOGLE_CLOUD_PROJECT a.k.a. gcp project id. 

## Images

#### Class Acco with Images:
```python
@dataclass
class Acco:
    @dataclass
    class Image:
        url: str  # original url
        bucket_url: str = None  # bucket url
        width: int = None  # pixel with
        height: int = None  # pixel height
        bytes_size: int = None  # bytes size of image
        labels: list = []  # labels of the image
        descriptions: list = []  # descriptions of the image

    giataid: str
    source: str  # provider of the data
    images: list
    timestamp: str = None  # timestamp updated
```

#### get_image(image_url, auth=None):
Gets an images from the internet with a retry fall back of 3 times. 
```
returns width, heigth, size, data
```

#### upload_image_to_storage(id, bucket_name, image_url):
Gets images from the internet and uploads it to storage.
```
returns width, heigth, byte_size, blob_url
```
kubeflow-utils 0.0.1
Kubeflow utils package helping to speed up building ML model.
Requires Python: >=3.6

# Kubeflow Utils

This is a simple Kubeflow utils package helping to speed up building ML model.

Features included:
- generate .docker/config.json for Kubeflow Fairing
- generate .kaggle/kaggle.json for Kaggle API
- download dataset through Kaggle API