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

.. raw:: html

    <div style="background: #C3F8FF" class="admonition note custom">
        <p style="background: light-blue" class="admonition-title">
            Follow along: Object Detection Example
        </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>
        <ul>
            <li>Open the object_detection.ipynb jupyter notebook</li>
            <li>Initialize your output stream, and your path, and import in the Image library</li>
            <li>Check all the available pictures with vairous objects and people</li>
            <li>Pick one of the image and initialize the image/ output name. </li>
            <li>Execute object detection on the chosen picture </li>
            <li>Display the result</li>
        </ul>
        <div class="line">(The Jetson Board used for these examples are => Jetson Nano)</div>
    </div>

.. raw:: html

    <hr>


-   object_detection.ipynb
-   | Running the cell code
    | `Ctrl + Enter`

.. thumbnail:: /_images/ai_pose_detect/object_det.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'



-   Check all the pictures, you may wish to pick images with ``cat_*.jpg``, ``dog_*.jpg``, etc. 

    .. code-block:: python

        !ls $INPUT_PATH/cat_*
        !ls $INPUT_PATH/dog_*


    .. thumbnail:: /_images/ai_pose_detect/object_det1.png

|

    .. code-block:: python

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

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


-   Detecting objects or people within the picture!

    .. code-block:: python

        %%capture
        !python3 $PROGRAM_PATH/detectnet.py --network=ssd-mobilenet-v2 $INPUT_PATH/$IMAGE_NAME $OUTPUT_PATH/$OUTPUT_NAME




|

-   Show the resulting image


    .. code-block:: python

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


    .. thumbnail:: /_images/ai_pose_detect/object_det2.jpg