Coding Explanation
====================

Major Functionalities
----------------------

For our Object Detection task, we use NVIDIA built python program called the ``detectnet.py``.
The program does 3 major functions:

1.  It manages input streams whether it is an image, a video or a camera.
2.  It manages network inference on the said input stream.
3.  It manages output stream along with overlaying results from the model. 

With our python program, the user must specify 

1.  The input stream medium. (whether to input an image, a video or a camera)
2.  Which model network to use for the inference. 
3.  The output stream medium. (whether to output an image, a video or a camera)


-   Within our ``object_detection.ipynb``, we can see that we first initialize the *PATH* for our input and output images.
    We set up jupyter environment variable with ``env`` as well as python variable.

    .. code-block:: python

        %env DISPLAY=:0
        %env PROGRAM_PATH=/home/zeta/jetson-inference/build/aarch64/bin
        %env INPUT_PATH=/home/zeta/jetson-inference/build/aarch64/bin/images
        %env OUTPUT_PATH=/home/zeta/jetson-inference/build/aarch64/bin/images/test

        input_path='/home/zeta/jetson-inference/build/aarch64/bin/images'
        output_path='/home/zeta/jetson-inference/build/aarch64/bin/images/test'

    By setting up environment variables, we can call upon these PATH variables, when we wish to run a shell command 
    within our jupyter notebook. 

-   After choosing and initializing the name of our input images we can see that the ``IMAGE_NAME`` variable is also being 
    set to be an environment variable. 

-   When we execute our python program (detectnet.py), we can see the three parameters the user has set up

    .. code-block:: python

        !python3 $PROGRAM_PATH/detectnet.py --network=ssd-mobilenet-v1 $INPUT_PATH/$IMAGE_NAME $OUTPUT_PATH/$OUTPUT_NAME

    With ``!python3 $PROGRAM_PATH/detectnet.py`` as our activation function, we have:
    
    1.  ``--network=ssd-mobilenet-v1``: to specify which network we wished to use. (for this case SSD-Mobilenet-v1 network)
    2.  ``$INPUT_PATH/$IMAGE_NAME``: to specify our input image location.
    3.  ``$OUTPUT_PATH/$OUTPUT_NAME``: to specify our output image location. 


-   Jupyter notebook allows us view images within our browser environment. For this we have used IPython library with display module. 

    .. code-block:: python

        from IPython.display import Image
    
    -   To view the images simply specify the location of the image. For example, to view the result of our inference:

        .. code-block:: python 

            Image(filename=output_path+'/detect_result.jpg')