.. tab:: Windows

    - Go to `<https://www.anaconda.com/download/>`_
    - Download `Anaconda Python 3.7 version for Windows <https://repo.anaconda.com/archive/Anaconda3-2018.12-Windows-x86_64.exe>`_
    - Run the downloaded executable (``.exe``) file to begin the installation. See `here <https://docs.anaconda.com/anaconda/install/windows/>`_ for more details.
    - (Optional) In the next step, check the box "Add Anaconda to my PATH environment variable". This will make Anaconda your default Python distribution, which should ensure that you have the same default Python distribution across all editors.

.. tab:: Linux

    - Go to `<https://www.anaconda.com/download/>`_
    - Download `Anaconda Python 3.7 version for Linux <https://repo.anaconda.com/archive/Anaconda3-2018.12-Linux-x86_64.sh>`_
    - Run the downloaded bash script (``.sh``) file to begin the installation. See `here <https://docs.anaconda.com/anaconda/install/linux/>`_ for more details.
    - When prompted with the question "Do you wish the installer to prepend the Anaconda<2 or 3> install location to PATH in your /home/<user>/.bashrc ?", answer "Yes". If you enter "No", you must manually add the path to Anaconda or conda will not work.

1. The simplest to install, but also in most cases the slowest in terms of performance, is :ref:`tensorflow_cpu`, which runs directly on the CPU of your machine. 
2. Alternatively, if you own a (compatible) Nvidia graphics card, you can take advantage of the available CUDA cores to speed up the computations performed by TesnsorFlow, in which case you should follow the guidelines for installing :ref:`tensorflow_gpu`.  

Getting setup with an installation of TensorFlow CPU can be done in 3 simple steps.

Create a new Conda virtual environment (Optional)
*************************************************
* Open a new `Anaconda/Command Prompt` window 
* Type the following command:

    .. code-block:: posh

        conda create -n tensorflow_cpu pip python=3.6

* The above will create a new virtual environment with name ``tensorflow_cpu``
* Now lets activate the newly created virtual environment by running the following in the `Anaconda Promt` window:

    .. code-block:: posh

        activate tensorflow_cpu

Once you have activated your virtual environment, the name of the environment should be displayed within brackets at the beggining of your cmd path specifier, e.g.:

.. code-block:: ps1con

    (tensorflow_cpu) C:\Users\sglvladi>

Install TensorFlow CPU for Python
*********************************
- Open a new `Anaconda/Command Prompt` window and activate the `tensorflow_cpu` environment (if you have not done so already)
- Once open, type the following on the command line:

    .. code-block:: posh

        pip install --ignore-installed --upgrade tensorflow==1.9

- Wait for the installation to finish

Test your Installation
**********************
- Open a new `Anaconda/Command Prompt` window and activate the `tensorflow_cpu` environment (if you have not done so already)
- Start a new Python interpreter session by running:

    .. code-block:: posh

        python

- Once the interpreter opens up, type:

    .. code-block:: python

        >>> import tensorflow as tf

- If the above code shows an error, then check to make sure you have activated the `tensorflow_cpu` environment and that tensorflow_cpu was successfully installed within it in the previous step.
- Then run the following:

    .. code-block:: python

        >>> hello = tf.constant('Hello, TensorFlow!')
        >>> sess = tf.Session()

- Once the above is run, if you see a print-out similar (or identical) to the one below, it means that you could benefit from installing TensorFlow by building the sources that correspond to you specific CPU. Everything should still run as normal, just slower than if you had built TensorFlow from source.

    .. code-block:: python

        2019-02-28 11:59:25.810663: I T:\src\github\tensorflow\tensorflow\core\platform\cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2

- Finally, for the sake of completing the test as described by TensorFlow themselves (see `here <https://www.tensorflow.org/install/install_windows#validate_your_installation>`_), let's run the following:

    .. code-block:: python

        >>> print(sess.run(hello))
        b'Hello, TensorFlow!'

.. _tensorflow_gpu:

TensorFlow GPU

.. tab:: Windows

    Follow this `link <https://developer.nvidia.com/cuda-90-download-archive?target_os=Windows&target_arch=x86_64&target_version=10&target_type=exenetwork>`_ to download and install CUDA Toolkit v9.0.

.. tab:: Linux

    Follow this `link <https://developer.nvidia.com/cuda-90-download-archive?target_os=Linux&target_arch=x86_64>`_ to download and install CUDA Toolkit v9.0 for your Linux distribution.

.. tab:: Windows

    - Go to `<https://developer.nvidia.com/rdp/cudnn-download>`_
    - Create a user profile if needed and log in
    - Select `cuDNN v7.0.5 (Feb 28, 2018), for CUDA 9.0 <https://developer.nvidia.com/rdp/cudnn-download#a-collapse705-9>`_
    - Download `cuDNN v7.0.5 Library for Windows 10 <https://developer.nvidia.com/compute/machine-learning/cudnn/secure/v7.0.5/prod/9.0_20171129/cudnn-9.0-windows10-x64-v7>`_
    - Extract the contents of the zip file (i.e. the folder named ``cuda``) inside ``<INSTALL_PATH>\NVIDIA GPU Computing Toolkit\CUDA\v9.0\``, where ``<INSTALL_PATH>`` points to the installation directory specified during the installation of the CUDA Toolkit. By default ``<INSTALL_PATH>`` = ``C:\Program Files``.

.. tab:: Linux

    - Go to `<https://developer.nvidia.com/rdp/cudnn-download>`_
    - Create a user profile if needed and log in
    - Select `cuDNN v7.0.5 (Feb 28, 2018), for CUDA 9.0 <https://developer.nvidia.com/rdp/cudnn-download#a-collapse705-9>`_
    - Download `cuDNN v7.0.5 Library for Linux <https://developer.nvidia.com/compute/machine-learning/cudnn/secure/v7.0.5/prod/9.0_20171129/cudnn-9.0-linux-x64-v7>`_
    - Follow the instructions under Section 2.3.1 of the `CuDNN Installation Guide <https://developer.download.nvidia.com/compute/machine-learning/cudnn/secure/v7.0.5/prod/Doc/cuDNN-Installation-Guide.pdf?KuMH0SWQKOxRm-iCAdfWlxEMK7eWjI528XHuZvaXjt73sOFgHT0dczMVRMRx8NqSNxabcGwzsgBgdTeshiZqQ7QmMQ3DwdTQHbjJGu04-Dw1F4Eyvd8B9u_U5YkSthOTFCASAp-MWj6Ki9RIK209dftWXhk7Df33u2__kbsKa5L9a0BXvRfTjZ-LZzH3zQpydg>`_ to install CuDNN.

.. tab:: Windows

    - Go to `Start` and Search "environment variables"
    - Click the Environment Variables button
    - Click on the ``Path`` system variable and select edit
    - Add the following paths:
        
        - ``<INSTALL_PATH>\NVIDIA GPU Computing Toolkit\CUDA\v9.0\bin``
        - ``<INSTALL_PATH>\NVIDIA GPU Computing Toolkit\CUDA\v9.0\libnvvp``
        - ``<INSTALL_PATH>\NVIDIA GPU Computing Toolkit\CUDA\v9.0\extras\CUPTI\libx64``
        - ``<INSTALL_PATH>\NVIDIA GPU Computing Toolkit\CUDA\v9.0\cuda\bin``

.. tab:: Linux 

    As per Section 7.1.1 of the `CUDA Installation Guide for Linux <http://developer.download.nvidia.com/compute/cuda/9.0/Prod/docs/sidebar/CUDA_Installation_Guide_Linux.pdf>`_, append the following lines to ``~/.bashrc``:

    .. code-block:: bash

        # CUDA related exports
        export PATH=/usr/local/cuda-9.0/bin${PATH:+:${PATH}}
        export LD_LIBRARY_PATH=/usr/local/cuda-9.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

(tensorflow_gpu) C:\Users\sglvladi>

Building on the assumption that you have just created your new virtual environment (whether that's `tensorflow_cpu`,`tensorflow_gpu` or whatever other name you might have used), there are some packages which need to be installed before installing the models. 

+---------------------------------------------+
| Prerequisite packages                       |
+--------------+------------------------------+
| Name         | Tutorial version-build       |
+==============+==============================+
| pillow       | 5.4.1-py36hdc69c19_0         |
+--------------+------------------------------+
| lxml         | 4.3.1-py36h1350720_0         |
+--------------+------------------------------+
| jupyter      | 1.0.0-py36_7                 |
+--------------+------------------------------+
| matplotlib   | 3.0.2-py36hc8f65d3_0         |
+--------------+------------------------------+
| opencv       | 3.4.2-py36h40b0b35_0         |
+--------------+------------------------------+

The packages can be installed using ``conda`` by running:

.. code-block:: posh

    conda install <package_name>(=<version>), <package_name>(=<version>), ..., <package_name>(=<version>)

where ``<package_name>`` can be replaced with the name of the package, and optionally the package version can be specified by adding the optional specifier ``=<version>`` after ``<package_name>``. For example, to simply install all packages at their latest versions you can run:

.. code-block:: posh

    conda install pillow, lxml, jupyter, matplotlib, opencv

Alternatively, if you don't want to use Anaconda you can install the packages using ``pip``:

.. code-block:: posh

    pip install <package_name>(==<version>) <package_name>(==<version>) ... <package_name>(==<version>)

but you will need to install ``opencv-python`` instead of ``opencv``.

Downloading the TensorFlow Models

TensorFlow
└─ models
    ├── official
    ├── research
    ├── samples
    └── tutorials

The Tensorflow Object Detection API uses Protobufs to configure model and
training parameters. Before the framework can be used, the Protobuf libraries
must be downloaded and compiled. 

This should be done as follows:

- Head to the `protoc releases page <https://github.com/google/protobuf/releases>`_
- Download the latest ``*-win32.zip`` release (e.g. ``protoc-3.5.1-win32.zip``)
- Create a folder in ``C:\Program Files`` and name it ``Google Protobuf``.
- Extract the contents of the downloaded ``*-win32.zip``, inside ``C:\Program Files\Google Protobuf``
- Add ``C:\Program Files\Google Protobuf\bin`` to your ``Path`` environment variable (see :ref:`set_env`)
- In a new `Anaconda/Command Prompt` [#]_, ``cd`` into ``TensorFlow/models/research/`` directory and run the following command:

    .. code-block:: python

        # From within TensorFlow/models/research/
        protoc object_detection/protos/*.proto --python_out=.

    .. important::

        If you are on Windows and using Protobuf 3.5 or later, the multi-file selection wildcard (i.e ``*.proto``) will not work but you can do one of the following:

        .. tabs:: 

            .. tab:: Windows Powershell

                .. code-block:: python

                    # From within TensorFlow/models/research/
                    Get-ChildItem object_detection/protos/*.proto | foreach {protoc "object_detection/protos/$($_.Name)" --python_out=.}


            .. tab:: Command Prompt

                .. code-block:: python

                        # From within TensorFlow/models/research/
                        for /f %i in ('dir /b object_detection\protos\*.proto') do protoc object_detection\protos\%i --python_out=.


.. [#] NOTE: You MUST open a new `Anaconda/Command Prompt` for the changes in the environment variables to take effect.


Adding necessary Environment Variables

.. tab:: Linux
    
    This can be done by either adding to ``~/.bashrc`` or running the following:

    .. code-block:: bash

        export PYTHONPATH=$PYTHONPATH:<PATH_TO_TF>/TensorFlow/models/research/object_detection

.. tab:: Windows

    The following folder must be added to your ``PYTHONPATH`` environment variable (See :ref:`set_env`):

        - ``<PATH_TO_TF>\TensorFlow\models\research\object_detection``

.. code-block:: python

    # From within TensorFlow/models/research/
    python setup.py build
    python setup.py install

The above commands essentially build and install the ``object_detection`` Python package. 

**DRAWBACK**: The above commands need to be run everytime there is a change/update of the ``object_detection`` package.

.. tab:: Linux

    The `Installation docs <https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md>`_ suggest that you either run, or add to ``~/.bashrc`` file, the following command, which adds these packages to your PYTHONPATH:

    .. code-block:: bash

        # From within tensorflow/models/research/
        export PYTHONPATH=$PYTHONPATH:<PATH_TO_TF>/TensorFlow/models/research:<PATH_TO_TF>/TensorFlow/models/research/slim

.. tab:: Windows
    
    The only way that I found works best, is to simply add the following folders to your ``PYTHONPATH`` environment variable (See also :ref:`set_env`):

    - ``<PATH_TO_TF>\TensorFlow\models\research``
    - ``<PATH_TO_TF>\TensorFlow\models\research\slim``

where, in both cases, ``<PATH_TO_TF>`` replaces the absolute path to your ``TesnorFlow`` folder. (e.g. ``<PATH_TO_TF>`` = ``C:\Users\sglvladi\Documents`` if ``TensorFlow`` resides within your ``Documents`` folder)

The ``pycocotools`` package should be installed if you are interested in using COCO evaluation metrics.

.. tabs::

    .. tab:: Windows

        Run the following command to install ``pycocotools`` with Windows support:

        .. code-block:: bash

            pip install git+https://github.com/philferriere/cocoapi.git#subdirectory=PythonAPI


        Note that, according to the `package's instructions <https://github.com/philferriere/cocoapi#this-clones-readme>`_, Visual C++ 2015 build tools must be installed and on your path. If they are not, make sure to install them from `here <https://go.microsoft.com/fwlink/?LinkId=691126>`_.

    .. tab:: Linux
    
        Download `cocoapi <https://github.com/cocodataset/cocoapi>`_ to a directory of your choice, then ``make`` and copy the pycocotools subfolder to the ``Tensorflow/models/research`` directory, as such: 

        .. code-block:: bash

            git clone https://github.com/cocodataset/cocoapi.git
            cd cocoapi/PythonAPI
            make
            cp -r pycocotools <PATH_TO_TF>/TensorFlow/models/research/


The default metrics are based on those used in Pascal VOC evaluation. To use the COCO object detection metrics add `metrics_set: "coco_detection_metrics"` to the `eval_config` message in the config file. To use the COCO instance segmentation metrics add `metrics_set: "coco_mask_metrics"` to the `eval_config` message in the config file.


.. _test_tf_models:

Test your Installation
~~~~~~~~~~~~~~~~~~~~~~

- Open a new `Anaconda/Command Prompt` window and activate the `tensorflow_gpu` environment (if you have not done so already) 
- ``cd`` into ``TensorFlow\models\research\object_detection`` and run the following command:

    .. code-block:: posh

        # From within TensorFlow/models/research/object_detection
        jupyter notebook

- This should start a new ``jupyter notebook`` server on your machine and you should be redirected to a new tab of your default browser.
- Once there, simply follow `sentdex's Youtube video <https://youtu.be/COlbP62-B-U?t=7m23s>`_ to ensure that everything is running smoothly.
- If, when you try to run ``In [11]:``, Python crashes, have a look at the `Anaconda/Command Prompt` window you used to run the ``jupyter notebook`` service and check for a line similar (maybe identical) to the one below:

    .. code-block:: python

        2018-03-22 03:07:54.623130: E C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\stream_executor\cuda\cuda_dnn.cc:378] Loaded runtime CuDNN library: 7101 (compatibility version 7100) but source was compiled with 7003 (compatibility version 7000).  If using a binary install, upgrade your CuDNN library to match.  If building from sources, make sure the library loaded at runtime matches a compatible version specified during compile configuration.

- If the above line is present in the printed debugging, it means that you have not installed the correct version of the cuDNN libraries. In this case make sure you re-do the :ref:`cudnn_install` step, making sure you instal cuDNN v7.0.5.

 

.. _labelImg_install:

LabelImg Installation
---------------------

For Windows and Linux you can download the precompiled binary `here <http://tzutalin.github.io/labelImg/>`_ .
The steps for installing from source follow below.

Create a new Conda virtual environment

(labelImg) C:\Users\sglvladi> 

- Inside you ``TensorFlow`` folder, create a new directory, name it ``addons`` and then ``cd`` into it.
- To download the package you can either use `Git <https://git-scm.com/downloads>`_ to clone the `labelImg repo <https://github.com/tzutalin/labelImg>`_ inside the ``TensorFlow\addons`` folder, or you can simply download it as a `ZIP <https://github.com/tzutalin/labelImg/archive/master.zip>`_ and extract it's contents inside the ``TensorFlow\addons`` folder. To keep things consistent, in the latter case you will have to rename the extracted folder ``labelImg-master`` to ``labelImg``. [#]_
- You should now have a single folder named ``addons\labelImg`` under your ``TensorFlow`` folder, which contains another 4 folders as such:

.. code-block:: bash

    TensorFlow
    ├─ addons
    │   └── labelImg
    └─ models
        ├── official
        ├── research
        ├── samples
        └── tutorials

.. [#] The latest repo commit when writing this tutorial is `8d1bd68 <https://github.com/tzutalin/labelImg/commit/8d1bd68ab66e8c311f2f45154729bba301a81f0b>`_.

Installing dependencies and compiling package

- Open a new `Anaconda/Command Prompt` window and activate the `tensorflow_gpu` environment (if you have not done so already) 
- ``cd`` into ``TensorFlow\addons\labelImg`` and run the following command:

    .. code-block:: posh
        
        python labelImg.py
        # or       
        python  labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]