Metadata-Version: 1.1
Name: wai.tfimageclass
Version: 0.0.5
Summary: Image classification using tensorflow.
Home-page: https://github.com/waikato-datamining/tensorflow/tree/master/image_classification
Author: Peter Reutemann and TensorFlow Team
Author-email: fracpete@waikato.ac.nz
License: Apache 2.0 License
Description: Image classification (not object detection) using `tensorflow <https://www.tensorflow.org/>`__.
        
        Based on example code located here:
        
        `https://www.tensorflow.org/hub/tutorials/image_retraining <https://www.tensorflow.org/hub/tutorials/image_retraining>`__
        
        
        Installation
        ============
        
        - install virtual environment::
        
            virtualenv -p /usr/bin/python3.7 venv
        
        - install tensorflow (1.x or 2.x works)
        
          - with GPU (1.x)::
        
              ./venv/bin/pip install "tensorflow-gpu<2.0.0"
        
          - with GPU (2.x)::
        
              ./venv/bin/pip install "tensorflow-gpu>=2.0.0"
        
          - CPU only (1.x)::
        
              ./venv/bin/pip install "tensorflow<2.0.0"
        
          - CPU only (2.x)::
        
              ./venv/bin/pip install "tensorflow>=2.0.0"
        
        - install library
        
          - via pip::
        
              ./venv/bin/pip install wai.tfimageclass
        
          - from source (from within the directory containing the `setup.py` script)::
        
              ./venv/bin/pip install .
        
        
        Usage
        =====
        
        All scripts support `--help` option to list all available options.
        
        
        Train
        -----
        
        - For training, use module `wai.tfimageclass.train.retrain` or console script `tfic-retrain`
        - For evaluating a built model, use module `wai.tfimageclass.train.stats` or console script `tfic-stats`
        
        
        Training data
        -------------
        
        All the data for building the model must be located in a single directory, with each sub-directory representing
        a *label*. For instance for building a model for distinguishing flowers (daisy, dandelion, roses, sunflowers, tulip),
        the data directory looks like this::
        
           |
           +- flowers
              |
              +- daisy
              |
              +- dandelion
              |
              +- roses
              |
              +- sunflowers
              |
              +- tulip
        
        
        Predict
        -------
        
        Once you have built a model, you can use it as follows:
        
        - For making predictions for a single image, use module `wai.tfimageclass.predict.label_image` or console
          script `tfic-labelimage`
        - For polling images in a directory and making continous predictions with CSV companion files, use
          module `wai.tfimageclass.predict.poll` or console script `tfic-poll`
        
        Changelog
        =========
        
        0.0.5 (2020-08-06)
        ------------------
        
        - `label_image.py`, `poll.py` and `stats.py` can now re-use the info JSON file
          generated by `retrain.py` to simplify command-line parameters (input_height,
          input_width, input_layer, output_layer, labels)
        - improved help output of argument parsers: outputting description, command-line
          and default values now
        
        
        0.0.4 (2020-08-04)
        ------------------
        
        - `poll.py` now has new `--continuous` flag to allow for continuous or single batch predictions
        
        
        0.0.3 (2020-07-28)
        ------------------
        
        - `poll.py`: added ability to split images into grid of equal sized images, obtaining
          a classification for each sub-image.
        - fixed license: now uses Apache 2.0 instead of MIT
        
        
        0.0.2 (2019-11-14)
        ------------------
        
        - added missing `MANIFEST.in`
        
        
        0.0.1 (2019-11-01)
        ------------------
        
        - initial release
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Programming Language :: Python :: 3
