A python framework for unstructured data processing
Unstructured data is information that is not arranged according to a predefined schema or data model. Image, text, video, and nested JSON are the most common types of unstructured data we collected in real-world applications.

We have just released [Towhee 0.6](https://github.com/towhee-io/towhee), a framework for doing ML jobs over unstructured data. Our latest release includes [`DataCollection`](https://towhee.readthedocs.io/en/branch0.6/data_collection/get_started.html), a new user-centric method-chaining API that enables rapid development and prototyping of unstructured data applications.

`DataCollection` is designed to behave as a python list or generator, with some enhancement features such as `method-chaining coding style`, `parallel execution`, and `exception handling`. Here is a short example of image animation:

```python
import towhee

towhee.glob['path']('./test.png') \
      .image_decode['path', 'origin']() \
      .img2img_translation.animegan['origin', 'facepaintv2'](model_name = 'facepaintv2') \
      .img2img_translation.animegan['origin', 'hayao'](model_name = 'hayao') \
      .img2img_translation.animegan['origin', 'paprika'](model_name = 'paprika') \
      .img2img_translation.animegan['origin', 'shinkai'](model_name = 'shinkai') \
      .select['origin', 'facepaintv2', 'hayao', 'paprika', 'shinkai']() \
      .show()
```

[`image_decode`](https://towhee.io/image-decode/cv2) and [`img2img_translation.animegan`](https://towhee.io/img2img-translation/animegan) are predefined `operator`s from [towhee hub](https://towhee.io). We already have nearly a hundred operators, officially maintained or contributed by our users.

You can check the result from [the tutorial](https://github.com/towhee-io/towhee/blob/main/tutorials/anime_style_transformer.ipynb). 

Documentation for DataCollection is available [here](https://towhee.readthedocs.io/en/branch0.6/data_collection/get_started.html). We will be releasing code examples and tutorials using the new API in the upcoming weeks.

Would appreciate some feedback and contribution :)