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        "description": "|build-status| |coverage-status| |pypi-reference| |pypi-downloads|\n\nMelon\n=====\n\n| Melon is a lightweight package meant to simplify data processing for Deep Learning.\n\n| It removes the need for boilerplate code to pre-process the data prior to (model) training, testing and inference.\n| It aims at standardizing data serialization and manipulation approaches.\n|\n| The default formats align with the requirements by frameworks such as **Tensorflow** / **PyTorch** / **Keras**.\n| The tool also provides various level of customizations depending on the use-case.\n\nInstallation\n------------\n\nInstall and update using `pip`_:\n\n.. code-block:: text\n\n    $ pip install melon\n\nSupported in Python >= 3.4.0\n\n.. _pip: https://pip.pypa.io/en/stable/quickstart/\n\nExamples\n----------------\n\n**Images**\n\nWith default options_:\n\n.. code-block:: python\n\n    from melon import ImageReader\n\n    def train():\n        source_dir = \"resources/images\"\n        reader = ImageReader(source_dir)\n        X, Y = reader.read()\n        ...\n        with tf.Session() as s:\n            s.run(..., feed_dict = {X_placeholder: X, Y_placeholder: Y})\n\n| ``source_dir`` directory should contain images that need to be read. See `sample directory`_ for reference.\n| In the sample directory there is an optional ``labels.txt`` file that is described in Labeling_.\n\n-------\n\nSince number of images may be too large to fit into memory the tool supports batch-processing.\n\n.. code-block:: python\n\n    from melon import ImageReader\n\n    def train():\n        source_dir = \"resources/images\"\n        options = { \"batch_size\": 32 }\n        reader = ImageReader(source_dir, options)\n        while reader.has_next():\n            X, Y = reader.read()\n            ...\n\n| This reads images in the batches of 32 until all images are read. If ``batch_size`` is not specified then ``reader.read()`` will read all images.\n\n---------------\n\n.. _Custom options:\n\nWith custom options_:\n\n.. code-block:: python\n\n    from melon import ImageReader\n\n    def train():\n        source_dir = \"resources/images\"\n        options = { \"data_format\": \"channels_last\", \"normalize\": False }\n        reader = ImageReader(source_dir, options)\n        ...\n\n| This changes format of data to ``channels-last`` (each sample will be ``Height x Width x Channel``) and doesn't normalize the data. See options_ for available options.\n\n.. _options:\n\nOptions\n------------------\n\n**Images**\n\n    width\n        Width of the output (pixels). default: ``255``\n\n    height\n        Height of the output (pixels). default: ``255``\n\n    batch_size\n        Batch size of each read. default: All images in a directory\n\n    data_format\n        Format of the images data\n\n            | ``channels_first`` - `Channel x Height x Width` (default)\n            | ``channels_last`` - `Height x Width x Channel`\n\n    label_format\n        Format of the labels data\n\n            | ``one_hot`` - as a matrix, with one-hot vector per image (default)\n            | ``label`` -  as a vector, with a single label per image\n\n\n    normalize\n        Normalize data. default: ``True``\n\n    num_threads - number of threads for parallel processing\n        default: Number of cores of the machine\n\n.. _Labeling:\n\nLabeling\n-----------------\n\n| In supervised learning each image needs to be mapped to a label.\n| While the tool supports reading images without labels (e.g. for inference) it also provides a way to label them.\n\n-----\n\n**Generating labels file**\n\n| To generate ``labels`` file use the following command:\n\n.. code-block:: text\n\n    $ melon generate\n    > Source dir:\n\n| After providing source directory the tool will generate ``labels`` file in that directory with blank labels.\n| Final step is to add a label to each row in the generated file.\n|\n| For reference see `sample labels`_:\n\n.. code-block:: text\n\n    #legend\n    pedestrian:0\n    cat:1\n    parrot:2\n    car:3\n    apple tree:4\n\n    #map\n    img275.jpg:1\n    img324.jpg:2\n    img551.jpg:3\n    img928.jpg:1\n    img999.png:0\n    img736.png:4\n\n| ``#legend`` section is optional but ``#map`` section is required to map a label to an image.\n\n-----\n\n**Format of the labels**\n\nLabel's output format can be specified in `Custom options`_. It defaults to ``one-hot`` format.\n\nRoadmap\n-------\n\n- Support for video data (Q1 2019)\n\n- Support for reading from AWS S3 (Q2 2019)\n\n\n\n.. |build-status| image:: https://travis-ci.com/romanjoffee/melon.svg?branch=master\n    :target: https://travis-ci.com/romanjoffee/melon\n\n.. |coverage-status| image:: https://codecov.io/gh/romanjoffee/melon/branch/master/graphs/badge.svg\n   :target: https://codecov.io/gh/romanjoffee/melon/branch/master\n\n.. |pypi-reference| image:: https://badge.fury.io/py/melon.svg\n   :target: https://badge.fury.io/py/melon\n\n.. |pypi-downloads| image:: https://pepy.tech/badge/melon\n   :target: https://pepy.tech/project/melon\n\n.. _`sample directory`: https://github.com/romanjoffee/melon/tree/master/tests/resources/images/sample\n\n.. _`sample labels`: https://github.com/romanjoffee/melon/tree/master/tests/resources/images/sample/labels.txt\n\n\n",
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