libfacedetection

深圳大学计算机科学与软件工程学院的于仕琪教授开源了的人脸检测库，人脸检测速度最快可达1500+ FPS！这是一个用于图像中基于CNN的人脸检测的开源库。CNN模型已转换为C源文件中的静态变量。源代码不依赖于任何其他库。你需要的只是一个C ++编译器。您可以使用C ++编译器在Windows，Linux，ARM和任何平台下编译源代码。 SIMD指令用于加速检测。如果使用Intel CPU或NEON for ARM，则可以启用AVX2。模型文件也已在./models/目录中提供。 examples / libfacedetectcnn-example.cpp显示了如何使用该库。如何编译：在使用g++编译源代码时，请添加-O3以启用优化。使用Microsoft Visual Studio编译源代码时，请选择“最大化速度/ -O2”。

This is an open source library for CNN-based face detection in images. The CNN model has been converted to static variables in C source files. The source code does not depend on any other libraries. What you need is just a C++ compiler. You can compile the source code under Windows, Linux, ARM and any platform with a C++ compiler.

SIMD instructions are used to speed up the detection. You can enable AVX2 if you use Intel CPU or NEON for ARM.

The model file has also been provided in directory ./models/.

examples/libfacedetectcnn-example.cpp shows how to use the library.

How to use the code

You can copy the files in directory src/ into your project, and compile them as the other files in your project. The source code is written in standard C/C++. It should be compiled at any platform which support C/C++.

Some tips:

Please add -O3 to turn on optimizations when you compile the source code using g++.
Please choose 'Maximize Speed/-O2' when you compile the source code using Microsoft Visual Studio.
ENABLE_INT8=ON is recommended for ARM, but it is not recommended for Intel CPU since it cannot gain better speed sometime even worse.
The source code can only run in single thread. If you want to run parally, you can call the face detection function in multiple threads. Yes, multiple-thread is complex in programming.
If you want to achieve best performance, you can run the model (not the source code) using OpenVINO on Intel CPU or Tengine on ARM CPU.

If you want to compile and run the example, you can create a build folder first, then run the command:

mkdir build; cd build; rm -rf *

Cross build for aarch64

Set cross compiler for aarch64 (please refer to aarch64-toolchain.cmake)
Set opencv path since the example code depends on opencv

cmake \
    -DENABLE_INT8=ON \
    -DENABLE_NEON=ON \
    -DCMAKE_BUILD_TYPE=RELEASE \
    -DCMAKE_TOOLCHAIN_FILE=../aarch64-toolchain.cmake \
     ..

make

Native build for avx2

cmake \
    -DENABLE_AVX2=ON \
    -DCMAKE_BUILD_TYPE=RELEASE \
    -DDEMO=ON \
     ..

make

CNN-based Face Detection on Windows

Method	Time	FPS	Time	FPS
	X64	X64	X64	X64
	Single-thread	Single-thread	Multi-thread	Multi-thread
OpenCV Haar+AdaBoost (640x480)	--	--	12.33ms	81.1
cnn (CPU, 640x480)	64.21ms	15.57	15.59ms	64.16
cnn (CPU, 320x240)	15.23ms	65.68	3.99ms	250.40
cnn (CPU, 160x120)	3.47ms	288.08	0.95ms	1052.20
cnn (CPU, 128x96)	2.35ms	425.95	0.64ms	1562.10

OpenCV Haar+AdaBoost runs with minimal face size 48x48
Face detection only, and no landmark detection included
Minimal face size ~12x12
Intel(R) Core(TM) i7-7700 CPU @ 3.6GHz

CNN-based Face Detection on ARM Linux (Raspberry Pi 3 B+)

Method	Time	FPS	Time	FPS
	Single-thread	Single-thread	Multi-thread	Multi-thread
cnn (CPU, 640x480)	512.04ms	1.95	174.89ms	5.72
cnn (CPU, 320x240)	123.47ms	8.10	42.13ms	23.74
cnn (CPU, 160x120)	27.42ms	36.47	9.75ms	102.58
cnn (CPU, 128x96)	17.78ms	56.24	6.12ms	163.50

Face detection only, and no landmark detection included.
Minimal face size ~12x12
Raspberry Pi 3 B+, Broadcom BCM2837B0, Cortex-A53 (ARMv8) 64-bit SoC @ 1.4GHz

Author

Shiqi Yu, [email protected]

Contributors

Jia Wu
Shengyin Wu
Dong Xu

Acknowledgment

The work is partly supported by the Science Foundation of Shenzhen (Grant No. JCYJ20150324141711699 and 20170504160426188).

dmdaguan / libfacedetection Goto Github PK