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        "author": "Marcelo Lerendegui",
        "author_email": "marcelo@gradienthealth.io",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "Intended Audience :: Education",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: Apache Software License",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering :: Mathematics",
            "Topic :: Software Development :: Libraries",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "# TensorFlow Decode DICOM\n\n## Operations\n\nDecode DICOM contains two Tensorflow Operations that allow reading information from DICOM files.\n\n### Decode DICOM Image\n    loads a dicom image file and returns its pixel information in the specified output format.\n\n### Decode DICOM Data\n    loads a dicom image file and returns a sting tensor with the values of each of the supplied tags.\n\n\n#### Inputs\n\n##### contents:\n    * Description: byte string with the file bytes. Use tf.io.read_file to read the file contents into a string.\n    * Type: string\n\n#### Attributes\n\n##### dtype:\n    * Description: Type for the output tensor\n    * Type: Dtype\n    * Possible Values: uint8, uint16, uint32, uint64, float, float16, double\n    * Default Value: uint16\n\n##### color_dim:\n    * Description: Whether or not to include the color_dimension. If decoded image is monochrome and this attribute is True the output tensor will have an extra singleton dimension for the color.\n    * Type: Bool\n    * Default Value: True\n    \n##### on_error:\n    * Description: This attribute establishes the behavior in case an error occurs on opening the image or if the output type cannot accomodate all the possible input values. *'strict'* throws an error, *'skip'* returns 0 and *'lossy'* continues with the operation as if no error occurred.\n    * Possible Values: 'strict', 'skip', 'lossy'\n    * Default Value: 'skip'\n\n##### scale:\n    * Description: This attribute establishes what to do with the scale of the input values. *'auto'* will autoscale the input values, if the output type is integer, *'auto'* will use the maximum output scale, if the output is float, *'auto'* will scale to [0,1]. *'preserve'* keeps the values as they are, an input value greater than the maximum possible output will be clipped.  \n    * Possible Values: 'auto', 'preserve'\n    * Default Value: 'preserve'\n\n#### Outputs\n\n##### output:\n    * Description: Pixel data of the DICOM image\n    * Type: Tensor of selected type (see attribute dtype)\n    * Dimensions: [F, I, J, C]\n        * F: Number of frames\n        * I: Number of rows\n        * J: Number of columns\n        * C: Color dimensions\n\n\n## Installation\n\n```bash\n    pip install gradient-decode-dicom\n```",
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        "keywords": "tensorflow dicom custom op machine learning",
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        "summary": "Gradient Decode DICOM is a dicom image and tag reader op for TensorFlow",
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