Amodal Panoptic Segmentation Evaluation

This repository contains scripts for evaluating amodal panoptic segmentation task results. It provides functions to compute amodal panoptic quality metrics: Amodal Panoptic Quality (APQ) and Amodal Parsing Coverage (APC) as presented in the paper.

Prerequisites

• Python 3.x
• Dependencies listed in requirements.txt

Installation

1. Clone the repository:

   git clone https://github.com/robot-learning-freiburg/amodal-panoptic.git

2. Install

   cd amodal_panoptic_eval
   pip install .

Dataset Format

This section describes the expected data format for the evaluation script.

PNG Encoding

The dataset employs 1-channel uint16 PNG files (*_ampano.png) to encode visible regions of objects and stuff in the scene. The encoding scheme is detailed below:

• Stuff Classes: Pixels corresponding to "stuff" classes are encoded directly with their semantic_class_id, uniquely identifying the stuff class of each pixel.

• Thing Classes: Pixels associated with "thing" classes are encoded with semantic_class_id*1000 + instance_id.

JSON Metadata

Each PNG file is accompanied by a corresponding JSON file (*_ampano.json). The JSON file is structured as a dictionary, where each key represents a unique object identified by semantic_class_id*1000 + instance_id, and contains the following information:

• occlusion_mask: An optional occlusion mask for the object, encoded in Run-Length Encoding (RLE). This mask indicates parts of the object occluded by others.

• amodal_mask: The amodal mask for the object, also in RLE. It represents the complete shape of the object, including both visible and occluded regions.

Note: Semantic class ids refer to 'id' and not 'trainId'.

Usage

To evaluate amodal panoptic segmentation results, follow these steps:

1. Prepare the ground truth and prediction folders:
   • Ground Truth: Place it in a folder named amodal_panoptic_seg, located within any base directory. While subfolders under amodal_panoptic_seg can vary, each must include *_ampano.png and *_ampano.json files for amodal panoptic segmentation ground truth.
   • Prediction: The prediction folder must also contain a subfolder named amodal_panoptic_seg, mirroring the corresponding folder structure and including prediction files.

For example:

    /gt_directory
        /amodal_panoptic_seg
            /subfolder1
                image11_ampano.png
                image11_ampano.json
                image12_ampano.png
                image12_ampano.json
                ...

            /subfolder2
                image21_ampano.png
                image21_ampano.json
                image22_ampano.png
                image22_ampano.json
                ...

    /prediction_directory
        /amodal_panoptic_seg
            /subfolder1
                image11_ampano.png
                image11_ampano.json
                image12_ampano.png
                image12_ampano.json
                ...

            /subfolder2
                image21_ampano.png
                image21_ampano.json
                image22_ampano.png
                image22_ampano.json
                ...

2. Run the evaluation script:

   python -m amodal_panoptic_eval.eval --gt-folder /path/to/ground_truth/amodal_panoptic_seg --prediction-folder /path/to/predictions/amodal_panoptic_seg

   Optional arguments:

   • --results-file: File to store computed panoptic quality. Default: resultAmodalPanoptic.json
   • --dataset-name: Name of the dataset. Default: amodalSynthDrive
   • --cpu-num: Number of CPU cores to use for evaluation. Default: -1 (use all available cores)

3. The evaluation results will be printed to the console and saved in the specified results file.

License

This project is licensed under the GNU General Public License v3 (GPLv3) - see the LICENSE file for details.

Acknowledgments

We have used utility functions from other open-source projects. We especially thank the authors of Cityscapes.