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human_face_detect

README.md

Human Face Detection [中文]

This project is an example of human face detection interface. The input to this interface is a static image. The detection results are confidence scores and coordinate values shown in Terminal, which can be converted by a tool into an image shown on your PC screen.

Below is the structure of this project:

human_face_detect/
├── CMakeLists.txt
├── image.jpg
├── main
│   ├── app_main.cpp
│   ├── CMakeLists.txt
│   └── image.hpp
├── partitions.csv
├── README.md
├── README_cn.md
└── result.png

Run the Example

  1. Open Terminal and go to esp-dl/examples/human_face_detect, the directory where this project is stored:

    cd ~/esp-dl/examples/human_face_detect

  2. Set SoC target:

    idf.py set-target [SoC]

    Replace [SoC] with your target, such as esp32, esp32s2, and esp32s3.

  3. Flash the program and launch IDF monitor to obtain the fractional and coordinate values of detection results:

    idf.py flash monitor

... ...

[0] score: 0.987580, box: [137, 75, 246, 215]
    left eye: (157, 131), right eye: (158, 177)
    nose: (170, 163)
    mouth left: (199, 133), mouth right: (193, 180)

  4. The tool display_image.py stored in examples/tool/ allows you to directly view the image of detection results. According to instructions on Tools, run the following command:

    python display_image.py -i ../human_face_detect/image.jpg -b "(137, 75, 246, 215)" -k "(157, 131, 158, 177, 170, 163, 199, 133, 193, 180)"

    The image of detection results will show on your PC screen as follows:

Other Configuration

At the beginning of ./main/app_main.cpp, there is a macro definition called TWO_STAGE that defines target detection algorithms. As annotations suggest:

  • TWO_STAGE = 1: two-stage detectors with higher accuracy (support for facial landmarks) but lower speed.
  • TWO_STAGE = 0: one-stage detectors with relatively lower accuracy (no support for facial landmarks) but higher speed.

You can experience the differences of the two detectors.

Customize Input Image

In this example project, ./main/image.hpp is the default input image. You can use the script convert_to_u8.py following instructions on Tools, to convert your own image into C/C++ code in replace of the default input image.

  1. Save your image to directory ./examples/human_face_detect , and use examples/tool/convert_to_u8.py to convert the image into an hpp file:

    # Assume you are in human_face_detect 

python ../tool/convert_to_u8.py -i ./image.jpg -o ./main/image.hpp

  2. According to steps in Section Run the Example, flash the firmware, print the confidence scores and coordinate values of detection results, and view the image of detection results.

Latency

SoC TWO_STAGE = 1 TWO_STAGE = 0
ESP32 415,246 us 154,687 us
ESP32-S2 1,052,363 us 309,159 us
ESP32-S3 56,303 us 16,614 us

Results above are based on the default configuration of this example.