Panoptic segmentation project

This small project attempts to perform panoptic segmentation on COCO. The project combines DeepLab for semantic segmentation and MaskRCNN for instance segmentation.

Project structure

• The Inference notebook contains the lines of code to perform both segmentation and instance segmentations.
• The StuffInstanceToPanoptic2chPng notebook shows how semantic and instance segmentation results are merged to make a 2-channel PNG image.
• The stuffInstanceToPanoptic2chPng does the same as the notebook of the same name, but it will process a whole folder rather than just one image. It uses panoptic_2ch_conversion_config.json as a config file.

Notes

Symlinks to the coco and pycocotools folders will be required for the code to work properly. Modifying the related paths should also work. (pycocotools should be built for this whole project to work - it's added to this project as a submodule if needed.)

Current status

At the moment, the whole pipeline is unfortunately incomplete. COCO val set has been converted to 2-channel PNG images but

• some images from the panoptic data set seem to be absent from either the detection or the stuff data sets - they were skipped during inference,
• since the panopticapi assumes all images are properly provided, it isn't possible to run the code unless it is modified,
• some labels seem to be incorrectly converted, which also prevents the panopticapi code to run properly.

Once these problems are solved, I should be able to evaluate this approach.

Some commands

To convert a detection and a segmentation mask into a 2-channel PNG: python stuffIntanceToPanoptic2chPng.py --config_path "panoptic_2ch_conversion_config.json"

To convert from the 2-channel format to the COCO format: python panopticapi/format_converter.py --source_folder data/out/val2017/p2ch/ --images_json_file ../coco/annotations/panoptic_val2017.json --categories_json_file panopticapi/panoptic_coco_categories.json --segmentations_folder data/out/val2017/segm --predictions_json_file data/out/val2017/segm.json

Sources

• DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
  L. C. Chen, G. Papandreou, I. Kokkinos et al.,
  In arXiv, 2016.

• DeepLab with Pytorch
  K. Nakashima,
  On GitHub, 2018.

• Detectron 2018
  R. Girshick, I. Radosavovic, G. Gkioxari, P. Dollár and K. He,
  On GitHub, 2018.

• A Pytorch implementation of Detectron
  R. Tseng,
  On GitHub, 2018.

• Mask RCNN
  A. Kirillov, K. He, R. Girshick, C. Rother, P. Dollár,
  In arXiv, 2018.

• Panoptic segmentation
  K. He, G. Gkioxari, P. Dollár, R. Girshick,
  In qrXiv, 2018.