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Trimap Synthesis with a Conditional Adversarial Network

Trimap generation through a cGAN net, based on the architecture pix2pixHD, given an input image containing a person. This trimap could be use as an input for the Information Flow Matting algorithm, thus obtaining an alpha channel for the person.

Results obtained for the synthetic composite test set

Test dataset based on the Adobe Dataset for background-matting. The results showed were obtained for the 200th epoch.

Input Output Groundtruth

Results obtained for the natural composite test set

This dataset was tailored for this test. The background of each image is not a product of a computational composition. Images copyright goes to their respective owners. The results showed were obtained for the 200th epoch.

Input Output Groundtruth

Getting Started

Prerequisites

  • Linux or macOS
  • Python 3.X
  • NVIDIA GPU * (At least 4Gb of VRAM) + CUDA cuDNN

*Note: Even thought we tested and trained the network with a GPU of this characteristics, the authors of the model recommend a more powerful option for improved results, at least a GPU with 11 Gb of VRAM.

Installation

  • Follow the Installation instructions from the NVIDIA repository

  • Search inside the cloned repository folder pix2pixHD, for the train.py file and replace it with the one found in this repository here. This fixes some broken and old dependencies.

  • Download the weights for the trained model (G and D nets) from our Drive folder. After download, create the following folder structure pix2pixHD/checkpoints/w and place the files inside w. The result structure should look like this:

     	pix2pixHD/
 		checkpoints/
 			w/
 				200_net_D.pth
 				200_net_G.pth

  • At this point the network should be installed!

Testing and Training

In the folder scripts we provided useful scripts for performing this tasks. Copy the contents of this folder in the root directory pix2pixHD/.

Testing

We included a test dataset test_dataset.zip, download this file and extract it inside the pix2pixHD/datasets/ folder. To initiate the test, from the root folder, call the testing routine:

$ ./test.sh

The results will be stored in the pix2pixHD/results folder

Training

Due to being licensed, we cannot share the foreground dataset for our training. Please contact Brian Price [email protected] if you are interested in getting the dataset.

To train the net with your own dataset, create two folders inside pix2pixHD/datasets/w/ called train_A and train_B. Inside the first folder the input images are placed and in the second folder, the expected result for the input images. Files names should correspond between folders i.e.

train_A/
	0.png # A photo to extract a trimap from

train_B/
	0.png # The expected trimap

To start the training, call the following routine:

$ ./train.sh

Note: We provide a useful python script for expanding the dataset: moreData.py

Continue training

The steps are basically the same that for training, place the dataset in the corresponding folders. To continue training called the following routine:

$ ./continue_train.sh

Note: For default this routine will continue training from the last epoch that is provided in the Drive Folder (200th epoch).

Evolution of the net

In this link it's possible to see the evolution of the net during the training epochs. An html page was generated to aid in the visualization.

Utils: Upscaling

As the network works with a rather small resolution, we provide a python script: upscaling.py, that takes the original input image and scales the resulting trimap extracted from the net, to needed dimensions:

I/O Image
Input
Net trimap
Upscaled

Authors

  • Mauricio Aravena Cifuentes (Wauro21)
  • Diego Badillo San Juan (Zimrahin)