person_detection

# 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.