Anonymizing videos by lightDSFD

Modified by Jongkuk Lim

History

This repository was forked from lightDSFD by Jian Li

Introduction

Simple implementation of video anonymization. If you are looking for a more accurate version, check Anonymizing videos by DSFD. And, if you are looking for a simpler example, noone video is implemented by only OpenCV examples.

Comparisons

Requirements

CUDA supported enviornment

• Torch >= 0.3.1
• Torchvision >= 0.2.1
• (Tested on torch 1.3.1 and Torchvision 0.4.2)
• Python 3.6

Usage

usage: blur_video.py [-h] [--vertical VERTICAL] [--verbose VERBOSE]
                     [--reduce_scale REDUCE_SCALE] [--rotate ROTATE]
                     [--trained_model TRAINED_MODEL] [--threshold THRESHOLD]
                     [--cuda CUDA] [--widerface_root WIDERFACE_ROOT]
                     file out

Detailed arguments

positional arguments:
  file                  Video file path
  out                   Output video path
  
 optional arguments:
  -h, --help            show this help message and exit
  --vertical VERTICAL   0 : horizontal video(default), 1 : vertical video
  --verbose VERBOSE     Show current progress and remaining time
  --reduce_scale REDUCE_SCALE
                        Reduce scale ratio. ex) 2 = half size of the input.
                        Default : 2
  --rotate ROTATE       Detect faces with rotation. 0 : No rotation, 1 : 90°,
                        2: 90°, 270°, 3 : 90°, 180°, 270°. Default : 0
  --trained_model TRAINED_MODEL
                        Trained state_dict file path to open
  --threshold THRESHOLD
                        Final confidence threshold
  --cuda CUDA           Use cuda to train model
  --widerface_root WIDERFACE_ROOT
                        Location of VOC root directory