Follow Along!
==============


.. raw:: html

    <div style="background: #C3F8FF" class="admonition note custom">
        <p style="background: light-blue" class="admonition-title">
            Follow along: Segmentation Examples
        </p>
        <div class="line-block">
            <div class="line">The program launching process along with parameter settings are all simplified and set up on the Jupyter Notebook Environment.</div>
            <div class="line">The segmentation task is able to operate with various input pictures. Here are the examples:</div>
        </div>
        <ul>
            <li>City Scapes</li>
            <li>Outdoor (off-road)</li>
            <li>Segmenting Human Images</li>
            <li>Variaty Objects and People</li>
            <li>In-Doors</li>
        </ul>
        <div class="line">(The Jetson Board used for these examples are => Jetson Nano)</div>
    </div>

.. raw:: html

    <hr>

-   02_5-1. segmentation.ipynb
-   | Running the cell code
    | `Ctrl + Enter`

.. thumbnail:: /_images/ai_segmentation_depth/segmentation.png


-   Initialize your output stream, and your path, and import in the Image library

.. code-block:: python

    from IPython.display import Image

    %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'


CityScapes
-----------

-   Check all the available pictures within the system

    .. code-block:: python

        !ls $INPUT_PATH/city_*


-   Pick one of the images with urban city scene and initialize the image/ output name. 

    .. code-block:: python

        image_name = 'ChangeMe'
        output_name = 'city_result.jpg'
        %env IMAGE_NAME = $image_name
        %env OUTPUT_NAME = $output_name

        Image(filename=input_path+'/'+image_name) 


    .. thumbnail:: /_images/ai_segmentation_depth/city.jpg

|

-   Segment the picture

    .. code-block:: python

        %%capture
        !python3 $PROGRAM_PATH/segnet.py --network=fcn-resnet18-cityscapes $INPUT_PATH/$IMAGE_NAME $OUTPUT_PATH/$OUTPUT_NAME



|

-   Show the resulting image


    .. code-block:: python

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


    .. thumbnail:: /_images/ai_segmentation_depth/city_seg.jpg

Outdoor (off-road)
-------------------

-   Check all the available pictures within the system

    .. code-block:: python

        !ls $INPUT_PATH/trail_*


-   Pick one of the images with outdoor scenes and initialize the image/ output name. 

    .. code-block:: python

        image_name = 'ChangeMe'
        output_name = 'trail_result.jpg'
        %env IMAGE_NAME = $image_name
        %env OUTPUT_NAME = $output_name

        Image(filename=input_path+'/'+image_name) 


    .. thumbnail:: /_images/ai_segmentation_depth/outdoor.jpg

|

-   Segment the picture

    .. code-block:: python

        %%capture
        !python3 $PROGRAM_PATH/segnet.py --network=fcn-resnet18-deepscene $INPUT_PATH/$IMAGE_NAME $OUTPUT_PATH/$OUTPUT_NAME


|

-   Show the resulting image


    .. code-block:: python

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


    .. thumbnail:: /_images/ai_segmentation_depth/outdoor_seg.jpg


Segmenting Human Images
-------------------------

-   Check all the available human pictures within the system

    .. code-block:: python

        !ls $INPUT_PATH/humans_*



-   Pick one of the images with people and initialize the image/ output name. 

    .. code-block:: python

        image_name = 'ChangeMe'
        output_name = 'humans_seg_result.jpg'
        %env IMAGE_NAME = $image_name
        %env OUTPUT_NAME = $output_name

        Image(filename=input_path+'/'+image_name) 

    .. thumbnail:: /_images/ai_segmentation_depth/people.jpg

|

-   Segment the picture

    .. code-block:: python

        %%capture
        !python3 $PROGRAM_PATH/segnet.py --network=fcn-resnet18-mhp $INPUT_PATH/$IMAGE_NAME $OUTPUT_PATH/$OUTPUT_NAME



|

-   Show the resulting image


    .. code-block:: python

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


    .. thumbnail:: /_images/ai_segmentation_depth/people_seg.jpg


Variaty Objects and People
----------------------------

-   Check all the available pictures within the system

    .. code-block:: python

        !ls $INPUT_PATH/object_*


-   Pick one of the images with variaty of objects and people and initialize the image/ output name. 

    .. code-block:: python

        image_name = 'ChangeMe'
        output_name = 'object_result.jpg'
        %env IMAGE_NAME = $image_name
        %env OUTPUT_NAME = $output_name

        Image(filename=input_path+'/'+image_name) 


    .. thumbnail:: /_images/ai_segmentation_depth/object.jpg

|

-   Segment the picture

    .. code-block:: python

        %%capture
        !python3 $PROGRAM_PATH/segnet.py --network=fcn-resnet18-voc $INPUT_PATH/$IMAGE_NAME $OUTPUT_PATH/$OUTPUT_NAME



|

-   Show the resulting image


    .. code-block:: python

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


    .. thumbnail:: /_images/ai_segmentation_depth/object_seg.jpg


In-Doors
-----------

-   Check all the available pictures within the system

    .. code-block:: python

        !ls $INPUT_PATH/room_*


-   Pick one of the images with urban city scene and initialize the image/ output name. 

    .. code-block:: python

        image_name = 'ChangeMe'
        output_name = 'room_result.jpg'
        %env IMAGE_NAME = $image_name
        %env OUTPUT_NAME = $output_name

        Image(filename=input_path+'/'+image_name) 
 
    .. thumbnail:: /_images/ai_segmentation_depth/indoor.jpg

|

-   Segment the picture

    .. code-block:: python

        %%capture
        !python3 $PROGRAM_PATH/segnet.py --network=fcn-resnet18-sun $INPUT_PATH/$IMAGE_NAME $OUTPUT_PATH/$OUTPUT_NAME


|

-   Show the resulting image


    .. code-block:: python

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


    .. thumbnail:: /_images/ai_segmentation_depth/indoor_seg.jpg