Code and model for the paper "ScanGAN360: A Generative Model of Realistic Scanpaths for 360º Images".
This work was developed using:
* python 3.7.4
* pytorch 1.2.0
* cudatoolkit 10.0.30
* opencv 4.1.2
You can install an environment with all required dependencies using scangan360.yml
file in Anaconda.
The current version of the repository includes a basic, yet functional version to generate scanpaths from a 360º image using the ScanGAN360 model.
python main.py --mode inference
This will read an image image_path = "data/test.jpg"
and generate a set of scanpaths that will be saved in path_to_save = "test/"
. You can modify both those paths, and the number of generated scanpaths n_generated
. Each of the images will contain 25 different scanpaths.
Training is now available. [Updated June 15th]
python main.py --mode train
Make sure you have correctly updated utils.py
, including all the directories required. Also, check the data
folder to download the required images and processed gaze data.
Checkpoints and models are saved periodically in the assigned folder.