jnkl314/vbge

Video Background Eraser

Video Background Eraser

Result Preview

This C++ pipeline combines two DCNNs and some computer vision preprocessing in order to remove the background of images from a video.
The output is a directory of RGBA images. The alpha component of background pixels are set to 0.
The contour of the foreground objects have progressive alpha values to blend them with new backgrounds.

Requirements

Tested on Ubuntu 18.04.

Direct dependencies:

• TCLAP
  • sudo apt install libtclap-dev
• libtorch
  • (Cuda enabled) https://download.pytorch.org/libtorch/cu101/libtorch-cxx11-abi-shared-with-deps-1.4.0.zip
  • (without Cuda) https://download.pytorch.org/libtorch/cpu/libtorch-cxx11-abi-shared-with-deps-1.4.0%2Bcpu.zip
    
• Opencv 4.2.0
  • built from sources (https://github.com/opencv/opencv/tree/4.2.0 and contribs https://github.com/opencv/opencv_contrib/tree/4.2.0).

Components

The pipeline is composed by :

• Semantic Segmentation using DeepLabV3
  • see my other repository https://github.com/jnkl314/DeepLabV3FineTuning
  • weights were fine-tuned for skydiver segmentation, and frozen with PyTorch JIT Tracing
  • Dropbox link : https://www.dropbox.com/s/8p05l9qamo5n5wc/best_deeplabv3_skydiver.pt?dl=0
• Optical flow to track pixels of foreground objects over time
• Trimap generation using morpho math
• Deep Image Matting to improve alpha on objects contour
  • pretrained weights come from https://github.com/foamliu/Deep-Image-Matting-PyTorch
  • also frozen using PyTorch JIT Tracing
  • Dropbox link : https://www.dropbox.com/s/u059b4wfwqwwb47/best_DeepImageMatting.pt?dl=0

Build

cd ./samples/
mkdir build
cd build
cmake ../VideoBackgroundEraser/
make -j8

Launch Example

#!/bin/bash
BIN=build/VideoBackgroundEraser

INPUT=../data/YourVideo.mp4
OUTPUT=../data/results/
OUTPUT2=../data/results_grid/
MODEL1=../data/best_deeplabv3_skydiver.pt
MODEL2=../data/best_DeepImageMatting.pt
BG="-b 0"
CUSTOM="--useCuda --hideDisplay -t"

OPTIONS="-m ${MODEL1} -n ${MODEL2} -i ${INPUT} -o ${OUTPUT} -p ${OUTPUT2} ${BG} ${CUSTOM}"

echo $BIN $OPTIONS
$BIN $OPTIONS

Usage

USAGE: 

 VideoBackgroundEraser  [-r <float>]
                        [-t]
                        [-b <list<int>>] ... 
                        -n <string> -m <string>
                        [-p <string>]
                        [-o <string>]
                        [--hideDisplay]
                        [-c]
                        -i <string>
                        [--] [--version] [-h]
  Where: 

   -r <float>,  --imageMatting_scale <float>
     Rescale for Deep Image Matting

   -t,  --enable_temporalManagement
     Enable temporal management of scene to improve accuracy between
     frames. Might not work well for video where the background is moving

   -b <list<int>>,  --background_classId_list <list<int>>  (accepted
      multiple times)
     IDs of the background in the model

   -n <string>,  --DeepImageMattingModelPath <string>
     (required)  Path to a PyTorch JIT binary .pb containing the trained
     model DeepImageMatting

   -m <string>,  --DeepLabV3ModelPath <string>
     (required)  Path to a PyTorch JIT binary .pb containing the trained
     model DeepLabV3

   -p <string>,  --outputPathGrid <string>
     Path to a directory to save rgb result with grid

   -o <string>,  --outputPath <string>
     Path to a directory to save rgba result

   --hideDisplay
     Hide display of source image and result

   -c,  --useCuda
     Use Cuda for inference

   -i <string>,  --inputPath <string>
     (required)  Path to video or a directory+pattern

FFMPEG Utility

Once the background is replaced with the tool/code of your choice, ffmpeg can be used to compress the images in a video file :

ffmpeg -i your_result/%08d.png -c:v libx264 -crf 20 -pix_fmt yuv420p your_result.mp4