这是PCN-FaceDetection移植到windows的工程文件
双击打开code下的PCN.sln编译即可, 所需:环境VS2015+opencv3.4+Cuda8.0
如遇到缺少mr开头的头文件错误,请参考配置opencv跨平台多版本一次性配置方法
2018.10.14 Source code is available!!!
Progressive Calibration Networks (PCN) is an accurate rotation-invariant face detector running at real-time speed on CPU. This is an implementation for PCN.
Some detection results can be viewed in the following illustrations:
PCN is designed aiming for low time-cost. We compare PCN's speed with other rotation-invariant face detectors' on standard VGA images(640x480) with 40x40 minimum face size. The detectors run on a desktop computer with 3.4GHz CPU, GTX Titan X. The speed results together with the recall rate at 100 false positives on multi-oriented FDDB are shown in the following table. Detailed experiment settings can be found in our paper.
Set minimum size of faces to detect (size
>= 20)
detector.SetMinFaceSize(size);
Set scaling factor of image pyramid (1.4 <= factor
<= 1.6)
detector.SetImagePyramidScaleFactor(factor);
Set score threshold of detected faces (0 <= thresh1
, thresh2
, thresh3
<= 1)
detector.SetScoreThresh(thresh1, thresh2, thresh3);
Smooth the face boxes or not (smooth = true or false, recommend using it on video to get stabler face boxes)
detector.SetVideoSmooth(smooth);
See picture.cpp and video.cpp for details. If you want to reproduce the results on FDDB, please run fddb.cpp. You can rotate the images in FDDB to get FDDB-left, FDDB-right, and FDDB-down, then test PCN on them respectively.
Compile and run:
cd $PCN_ROOT/code
# You should set "CAFFEROOT" in lib.sh, compile.sh, and run.sh first.
sh lib.sh
sh compile.sh picture/video/fddb
sh run.sh picture/video/fddb
- Linux
- Caffe
- OpenCV (2.4.10, or other compatible version)
This code is distributed under the BSD 2-Clause license.
If you find PCN useful in your research, please consider citing:
@inproceedings{shiCVPR18pcn,
Author = {Xuepeng Shi and Shiguang Shan and Meina Kan and Shuzhe Wu and Xilin Chen},
Title = {Real-Time Rotation-Invariant Face Detection with Progressive Calibration Networks},
Booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
Year = {2018}
}
Xuepeng Shi, xuepeng.shi@vipl.ict.ac.cn