# Person detection example This example shows how you can use Tensorflow Lite to run a 250 kilobyte neural network to recognize people in images captured by a camera. It is designed to run on systems with small amounts of memory such as microcontrollers and DSPs. This uses the experimental int8 quantized version of the person detection model. ## Deploy to ESP32 The following instructions will help you build and deploy this sample to [ESP32](https://www.espressif.com/en/products/hardware/esp32/overview) devices using the [ESP IDF](https://github.com/espressif/esp-idf). The sample has been tested on ESP-IDF version `release/v4.2` and `release/v4.4` with the following devices: - [ESP32-DevKitC](http://esp-idf.readthedocs.io/en/latest/get-started/get-started-devkitc.html) - [ESP32-S3-DevKitC](https://docs.espressif.com/projects/esp-idf/en/latest/esp32s3/hw-reference/esp32s3/user-guide-devkitc-1.html) - [ESP-EYE](https://github.com/espressif/esp-who/blob/master/docs/en/get-started/ESP-EYE_Getting_Started_Guide.md) - [ESP32-S3-EYE](https://github.com/espressif/esp-bsp/tree/master/bsp/esp32_s3_eye) - [ESP32-S3-Korvo-2](https://github.com/espressif/esp-bsp/tree/master/bsp/esp32_s3_korvo_2) - [ESP32-S2-Kaluga](https://github.com/espressif/esp-bsp/tree/master/bsp/esp32_s2_kaluga_kit) (limited performance on ESP32-S2: ~1-2 FPS) ### Install the ESP IDF Follow the instructions of the [ESP-IDF get started guide](https://docs.espressif.com/projects/esp-idf/en/latest/get-started/index.html) to setup the toolchain and the ESP-IDF itself. The next steps assume that the [IDF environment variables are set](https://docs.espressif.com/projects/esp-idf/en/latest/get-started/index.html#step-4-set-up-the-environment-variables) : * The `IDF_PATH` environment variable is set * `idf.py` and Xtensa-esp32 tools (e.g. `xtensa-esp32-elf-gcc`) are in `$PATH` ### Dependencies This example requires an external component [esp32-camera](https://components.espressif.com/components/espressif/esp32-camera) and optionally on selected Board Support Package. All these components are distributed via [IDF Component Manager](https://docs.espressif.com/projects/esp-idf/en/latest/esp32/api-guides/tools/idf-component-manager.html). ### Building the example Set the chip target (For esp32s3 target, IDF version `release/v4.4` is needed): ``` idf.py set-target esp32s3 ``` Then build with `idf.py` ``` idf.py build ``` ### Load and run the example To flash and monitor (replace `/dev/ttyUSB0` with the device serial port): ``` idf.py --port /dev/ttyUSB0 flash monitor ``` Use `Ctrl+]` to exit. ### Using Display If your development board has a display, input from the camera can be shown on it. This feature is enabled by specific [Board Support Package](https://github.com/espressif/esp-bsp). Select your development board BSP in menuconfig: `Application Configuration -> Select BSP`. ### Using CLI for inferencing Not all dev boards come with camera and you may wish to do inferencing on static images. There are 10 [images](static_images/sample_images/README.md) embedded into the application. * To switch to CLI mode just define the following line in [esp_main.h](main/esp_main.h): ``` #define CLI_ONLY_INFERENCE 1 ``` * To run an inferencing you need to type following on `idf.py monitor` window: ``` detect_image <image_number> ``` where `<image_number>` is in [0, 9]. The output is person and no_person score printed on the log screen.
To create a project from this example, run:
idf.py create-project-from-example "espressif/esp-tflite-micro^1.3.0:person_detection"