A face dataset generator with out-of-focus blur detection and dynamic interval adjustment.

Overview

README

1 Getting Started

1.1 face_recognition

download from dilb :whms

pip install dlib-19.17.99-cp37-cp37m-win_amd64.whl
pip install face_recognition

1.2 skimage

conda install scikit-image

1.3 tqdm

pip install tqdm

1.4 scipy

download numpy+mkl and scipy from [here](Python Extension Packages for Windows - Christoph Gohlke (uci.edu)).

pip install numpy-1.21.5+mkl-cp37-cp37m-win_amd64.whl
pip install scipy-1.7.3-cp37-cp37m-win_amd64.whl

1.5 pytorch

Just follow the steps on the official website to install.

2 Usage

Place the video files in the video folder and write the names of the videos to be processed in the List_of_testing_videos.txt file.

python face_cut.py -r 512 -t 500 -m 0 -i 5 -s 60 # -b 10

-r :Resolution of the captured face

-t :The range of tolerable original face resolution(faces with resolution greater than r-t will be captured and resize to r)

-m :Crop and alignment,0-VGGface,1-FFHQ

-i :Initial face capture interval

-s :Number of skipped frames when no face is detected

-b :Threshold value for out-of-focus blur detection

The two parameters Max_int = 40 and Min_int = 5 in the file refer to the maximum and minimum values of the dynamic acquisition interval. Increasing Min_int and Max_int decreases the number of face acquisitions and increases the difference between face images.

2 ACK

void_zxh

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