.. _tensorflowlitemicro_persondetection:

TensorFlowLiteMicro-PersonDetection
======================================================================

Overview
--------

The TFLM example demos the use of a camera to capture an image, perform human detection via the integrated TFLM machine learning platform, and display the results on screen

Configurations
--------------

- Connect the camera device to the CAM interface on the board

- Click   :ref:`LCD-800480W070TC <lcd_800480w070tc>`   section and connect

Toolchain Requirements
----------------------

This example requires a DSP-enabled toolchain to utilize hardware DSP instructions for neural network acceleration:

- **IDE**: Segger Embedded Studio (or other IDEs with DSP toolchain support)
- **GCC Toolchain**: Andes RISC-V toolchain with DSP extension support or ZCC toolchain with DSP extension support

.. note::
   Standard GCC toolchains without DSP extensions will not be able to compile and run this demo correctly. The DSP instructions are essential for efficient neural network inference.

Code Options
------------

- Add code before `find_package` to the project's CMakeLists.txt to get a higher performance


.. code-block:: cmake

           set(RV_ABI "ilp32d")
           set(RV_ARCH "rv32gc")
           set(CONFIG_HPM_MATH_NN_RVP32 1)



Running the example
-------------------

When the example runs successfully, The LCD will display the live screen, the processed image and the recognition result, while the serial terminal will output the score of whether someone is present or not


.. code-block:: text

   person score:-23 no person score 23