Metadata-Version: 2.1
Name: hcai-datasets
Version: 0.0.1
Summary: !Alpha Version! - This repository contains code to make datasets stored on th corpora network drive of the chair compatible with the [tensorflow dataset api](https://www.tensorflow.org/api_docs/python/tf/data/Dataset)
Home-page: https://github.com/pypa/sampleproject
Author: Dominik Schiller
Author-email: dominik.schiller@uni-a.de
License: UNKNOWN
Project-URL: Bug Tracker, https://hcai.eu/git/project/hcai_datasets
Description: ### Description
        This repository contains code to make datasets stored on th corpora network drive of the chair compatible with the [tensorflow dataset api](https://www.tensorflow.org/api_docs/python/tf/data/Dataset) .
        
        ### Currently available Datasets
        
        | Dataset       | Status        | Url  |
        | :------------- |:-------------:| :-----|
        | audioset      | ❌              | https://research.google.com/audioset/ |
        | ckplus        | ✅             | http://www.iainm.com/publications/Lucey2010-The-Extended/paper.pdf |
        | faces         | ✅             |    https://faces.mpdl.mpg.de/imeji/ |
        | is2021_ess    | ❌             |    -|
        | librispeech   | ❌              |    https://www.openslr.org/12 |
        
        
        ### Example Usage
        
        ```python
        import os
        import tensorflow as tf
        import tensorflow_datasets as tfds
        import hcai_datasets
        from matplotlib import pyplot as plt
        
        # Preprocessing function
        def preprocess(x, y):
          img = x.numpy()
          return img, y
        
        # Creating a dataset
        ds, ds_info = tfds.load(
          'hcai_example_dataset',
          split='train',
          with_info=True,
          as_supervised=True,
          builder_kwargs={'dataset_dir': os.path.join('path', 'to', 'directory')}
        )
        
        # Input output mapping
        ds = ds.map(lambda x, y: (tf.py_function(func=preprocess, inp=[x, y], Tout=[tf.float32, tf.int64])))
        
        # Manually iterate over dataset
        img, label = next(ds.as_numpy_iterator())
        
        # Visualize
        plt.imshow(img / 255.)
        plt.show()
        ```
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
