Visual Motif Removal

Source code for the paper Blind Visual Motif Removal from a Single Image.

Prerequisites

• Pytorch ≥ 0.4
• Coco pythonApi: for python 2.7 or for python 3 (optional for images datasets).

A pre-trained semi-transparent emojis removal model is available by running the script: demo / run_demo.py.

Training

Start a training session, by run the file train / train_main.py.
Different training configurations are placed at the top.

Paths configurations

• root_path – the main data path.
• train_tag – your network name. The checkpoint folder will be named after this tag.
• cache_root - list of directories with prepared training and test images. See Create new Datasets section for more information.

Network configurations

• num_blocks – number of residual blocks between each layer.
• shared_depth – shared layers between the decoders.
• use_vm_decoder – If True, the network will contain a motif decoder branch.

Testing

The utils / visualize_utils.py script may assist in order to run a trained network on different images. The root_path and train_tag from above should be defined on top.

Datasets

Images
The data_prep / coco_download.py script might be helpful to download a collection of images from Microsoft COCO dataset.

Text Motifs
The visual Motifs may be generated from a text file. examples of the text format are found at data / text folder or use the split_text.py script on a row text file.

Create new Dataset
To create a training data use the file utils / cache_utils.py. In there you will define the dataset configurations:

• dataset_tag- name for the dataset
• images_root – path to a background images folder.
• cache_root- where should the data be saved.
• vm_root – path to the motifs dataset. May lead to:
  
  • Motif image/s file or folder.
  • Text file (.txt), as described at the previous item.