Metadata-Version: 2.1
Name: handshape-datasets
Version: 0.1.3
Summary: A single library to (down)load all existing sign language handshape datasets. This library allows you to automatically download and load various sign language handshape datasets. Currently supporting 13 different datasets.
Home-page: https://github.com/midusi/handshape_datasets
Author: Facundo Quiroga - Brian Rey - Tomás Cortizas - Jeremias Dominguez Vega
Author-email: fquiroga@lidi.info.unlp.edu.ar
License: GNU Affero General Public License v3 or later (AGPLv3+)
Project-URL: Bug Tracker, https://github.com/midusi/handshape_datasets/issues
Project-URL: Documentation, https://github.com/midusi/handshape_datasets
Project-URL: Source Code, https://github.com/midusi/handshape_datasets
Description: 
        
        
        A single library to (down)load all existing sign language handshape datasets.
        
        ![handshape](http://facundoq.github.io/unlp/lsa16/files/dataset/1_1_1.png "sample handshape") 
        ![handshape](http://facundoq.github.io/unlp/lsa16/files/dataset/2_1_1.png "sample handshape")
        
        There are [various handshape datasets](http://facundoq.github.io/unlp/sign_language_datasets/) for Sign Language. However:
        * Each dataset has its own format and many are hard to find. 
        * Each dataset has its own mapping of handshapes to classes. While Signs depend on the specific Sign Language for a country/region, handshapes are universal. Hence, they could be shared between datasets/tasks. 
        
        This library aims to provide two main features:
        * A simplified API to download and load handshape datasets
        * A mapping between datasets so that datasets can be merged for training/testing models.
        
        We hope it will help Sign Language Recognition develop further, both for research and application development.
        
        If you wish to add a dataset you can make a push request, file an issue, or write to handshape.datasets@at@gmail.
        
        This library is a *work in progress*. Contributions are welcome.
        
        ## 						Working with images
        
        - **<u>Identifiying Hand Classes</u>**
        
        | Letter | Class ID |
        | :----: | :------: |
        |   a    |    0     |
        |   b    |    1     |
        |   c    |    2     |
        |   d    |    3     |
        |   e    |    4     |
        |   f    |    5     |
        |   g    |    6     |
        |   h    |    7     |
        |   i    |    8     |
        |   j    |    9     |
        
        ### 						How to use?
        
            Import handshape_datasets
        
            handshape_datasets.load("dataset_id")
        Download, extract and preprocess the dataset. The function will return "x" 
        that contain an array with the images and metadata, this one contain an array with classes and if it have, an array with 
        subjects or differents other values. For example, in lsa16 "x" will return a shape of (800,32,32,3). Also you could give
        a version value if its available to the selected dataset and you could give a boolean value to delete temporary files if 
        its possible
         
        Example:
         
            handshape_datasets.load("lsa16",version="color",delete=True) --> download, extract and preprocess the lsa16 dataset in
            version "color" and delete the temporary files if its have a .npz file.
        
            handshape_datasets.clear("dataset_id") --> Delete all the local files for the dataset, if its exist.
        
            handshape_datasets.list_datasets() --> Returns a table with the information for the availables datasets
        
            handshape_datasets.delete_temporary_files("dataset_id") --> Delete the local files if its exist a .npz file
        
        ## 						How to use the dataset in keras?
        
        First, you must to load the dataset
        
            dataset = handshape_datasets.load(dataset_id, version=ver, delete=supr)
        
        You could have the input_shape and the number of classes
        
            input_shape = self.dataset[0][0].shape
            classes = self.dataset[1]['y'].max() + 1
        
        Then you must to build a model
        
            base_model = keras.applications.mobilenet.MobileNet(input_shape=(input_shape[0],self.input_shape[1],3), 
                                                                        weights='imagenet', include_top=False)
            output = keras.layers.GlobalAveragePooling2D()(base_model.output)
            output = keras.layers.Dense(32, activation='relu')(output)
            output = keras.layers.Dense(self.classes, activation='softmax')(output)
            model = Model(inputs=base_model.input, outputs=output)
            model.compile(optimizer='Adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
        
        Its optional to split the dataset for the validation data when fit the model
        
            X_train, X_test, Y_train, Y_test = sklearn.model_selection.train_test_split(self.dataset[0], self.dataset[1]['y'],
                                                                                            test_size=test_size,
                                                                                            stratify=self.dataset[1]['y'])
        
        At last you must to fit the model
        
            history = model.fit(X_train, Y_train, batch_size=self.batch_size, epochs=self.epochs,
                                         validation_data=(X_test, Y_test))
        
        ## 						How to use Handshape-datasets with Google Colab:
        
           https://colab.research.google.com/drive/1kY-YrbegGFVT7NqVaeA4RjXYRVlZiISR?usp=sharing
        
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: License :: OSI Approved :: Python Software Foundation License
Classifier: License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)
Requires-Python: >=3.6.0
Description-Content-Type: text/markdown
