Affordance segmentation of hand-occluded containers from exocentric images

[arXiv] [webpage] [trained model]

Table of Contents

1. Installation
   1. Setup specifics
   2. Requirements
   3. Instructions
2. Running demo
3. Training and testing data
4. Enquiries, Question and Comments
5. License

Installation

Setup specifics

The models testing were performed using the following setup:

• OS: Ubuntu 20.04.6 LTS
• Kernel version: 5.15.0-69-generic
• CPU: Intel® Core™ i9-9900 CPU @ 3.10GHz
• Cores: 16
• RAM: 32 GB
• GPU: NVIDIA GeForce RTX 2080 Ti
• Driver version: 470.199.02
• CUDA version: 11.4

Requirements

• Python 3.8
• PyTorch 1.8.0
• Torchvision 0.9.0
• OpenCV 4.8.0.76
• Numpy 1.22.1
• Tqdm 4.66.1

Instructions

# Create and activate conda environment
conda create -n occluded_affordance_segmentation python=3.8
conda activate occluded_affordance_segmentation
    
# Install libraries
pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html
pip install onnx-tool==0.5.4 opencv-python numpy==1.22.1 tqdm

Running demo

Download model checkpoint ACANet.zip, and unzip it.

Use the images in the folder test_dir or try with your own images. The folder structure is DATA_DIR/rgb.

To run the model and visualise the output:

python3 demo.py --model_name=MODEL_NAME --data_dir=DATA_DIR  --checkpoint_path=CHECKPOINT_PATH --visualise_overlay=True --dest_dir=DEST_DIR

• Replace MODEL_NAME with acanet
• DATA_DIR: directory where data are stored
• CHECKPOINT_PATH: path to the .pth file
• DEST_DIR: path to the destination directory. This flag is considered only if you save the predictions --save_res=True or the overlay visualisation --save_overlay=True. Results are automatically saved in DEST_DIR/pred, overlays in DEST_DIR/vis.

You can test if the model has the same performance by running inference on the images provided in test_dir/rgb and checking if the output is the same of test_dir/pred .

Training and testing data

To recreate the training and testing splits of the mixed-reality dataset:

1. Download the dataset folders rgb, mask, annotations, affordance and unzip them in the preferred folder SRC_DIR.
2. Run utils/split_dataset.py --src_dir=SRC_DIR --dst_dir=DST_DIR to split into training, validation and testing sets. DST_DIR is the directory where splits are saved.
3. Run utils/create_dataset_crops.py --data_dir=DATA_DIR --save=True --dest_dir=DEST_DIR to perform the cropping window procedure described in the paper. This script performs also the union between the arm mask and the affordance masks. DATA_DIR is the directory containing the rgb and affordance folders e.g. DST_DIR/training following the naming used for the previous script. DEST_DIR is the destination directory, where to save cropped rgb images, and segmentation masks.

Enquiries, Question and Comments

If you have any further enquiries, question, or comments, or you would like to file a bug report or a feature request, use the Github issue tracker.

License

This work is licensed under the MIT License. To view a copy of this license, see LICENSE.