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A Python package for manipulating PNG files exported or imported using the Theia web application

Project description

Theia PNG

CI Google Colab

A Python package for manipulating PNG files exported or imported using the Theia web application. These files have data embedded in the PNG in a COCO JSON format compatible form. This package unpacks and makes the data readily usable.

Quick Start

  1. Create a virtual environment.

    python3 -m venv .venv
    
  2. Activate the virtual environment.

    source .venv/bin/activate
    
  3. Install tspng.

    python3 -m pip install tspng
    
  4. Create a png_dump.py script to extract inference results from a PNG file,

    import json
    
    from tspng.extraction import extract_from_file
    
    print(json.dumps(extract_from_file(PATH_TO_FILE), indent=2))
    

    where PATH_TO_FILE is replaced with the path to a .ts.png file on disk.

  5. Run the png_dump.py script.

    $ python3 ./png_dump.py
    {
    "info": {
       "description": "Theiascope image",
       "url": "http://www.theiascientific.com",
       "version": "1.0",
       "year": 2023,
       "contributor": "Theia Scientific, LLC",
       "date_created": "2023-05-10 19:22:47.722802+00:00"
    },
    "licenses": {
       "url": "http://www.theiascientific.com",
       "id": 1,
       "name": "Proprietary"
    },
    "images": [
       {
          "license": 1,
          "file_name": "20230510T192247Z.722_crimson-notebook (PML).ts.png",
          "height": 512,
          "width": 512,
          "date_captured": "2023-05-10 19:22:47.722802+00:00",
          "id": 3783,
          "field_of_view": [
          0,
          0,
          512,
          512
          ],
          "scale_bar": {
          "dimensions": [
             25,
             501,
             128,
             1
          ],
          "length": 100.0,
          "units_abbr": "nm",
          "units_name": "nanometers"
          }
       }
    ],
    "annotations": [...],  // Omitted for clarity
    "models": [
       {
          "id": 17,
          "configuration": {
          "image_processing": {
             "brightness": 0,
             "clahe": false,
             "contrast": 1.0,
             "gamma": 1.0,
             "gray": false,
             "invert": false
          },
          "max_concurrency": 2,
          "num_cpus": 0,
          "num_gpus": 1.0,
          "box_nms_thresh": 0.7,
          "crop_n_layers": 0,
          "crop_nms_thresh": 0.7,
          "crop_overlap_ratio": 0.3413333333333333,
          "crop_n_points_downscale_factor": 1,
          "min_mask_region_area": 0,
          "points_per_side": 32,
          "points_per_batch": 64,
          "pred_iou_thresh": 0.88,
          "stability_score_thresh": 0.95,
          "stability_score_offset": 1.0,
          "weights_file": {
             "filename": "sam_vit_b_01ec64.pth",
             "version": "default",
             "path": "/sam/vit-b"
          }
          },
          "created": "2023-05-09 19:46:18.309323+00:00",
          "family": "SAM",
          "name": "vit-b",
          "pid": 1
       }
    ],
    "categories": [
       {
          "supercategory": "defect",
          "id": 1,
          "name": ""
       }
    ]
    }
    

Contributing

  1. Create a virtual environment.

    python3 -m venv .venv
    
  2. Activate the virtual environment.

    source .venv/bin/activate
    
  3. Clone this repository.

    git clone https://github.com/Theia-Scientific/theia-png.git && cd theia-png
    
  4. Install the dependencies.

    python3 -m pip install .[dev]
    
  5. Build the package.

    python3 -m build
    

Testing

Testing is divided into unit and integration tests. Unit tests are located in the package source code tree and are defined on a per-module basis with a test_<module>.py format, while the integration tests are defined in the tests directory.

License

Acknowledgments

This material is based upon work supported by the U.S. Department of Energy, Office of Nuclear Energy under Award Number DE-SC0021529.

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