Convert a labeled image mask to a JSON file compatible with labelme. Useful to export predictions and then fine tune by hand with labelme.

mask_to_labelme_json.py

import pathlib

import numpy as np

def mask_to_labelme(
    labeled_image: np.ndarray,
    filename: pathlib.Path,
    overwrite: bool = False,
    simplify: bool = True,
    simplification_threshold: float = 5.0,
) -> None:
    import io
    import base64
    import json

    import imageio
    from imantics import Mask
    from shapely.geometry import Polygon

    output_file = filename.replace_(".tif", ".json")
    if overwrite or output_file.exists():
        return
    polygons = Mask(labeled_image).polygons()
    shapes = list()
    for point in polygons.points:

        if not simplify:
            poly = np.asarray(point).tolist()
        else:
            poly = np.asarray(
                Polygon(point).simplify(simplification_threshold).exterior.coords.xy
            ).T.tolist()
        shape = {
            "label": "A",
            "points": poly,
            "group_id": None,
            "shape_type": "polygon",
            "flags": {},
        }
        shapes.append(shape)

    f = io.BytesIO()
    imageio.imwrite(f, tifffile.imread(filename), format="PNG")
    f.seek(0)
    encoded = base64.encodebytes(f.read())

    payload = {
        "version": "4.5.6",
        "flags": {},
        "shapes": shapes,
        "imagePath": filename.name,
        "imageData": encoded.decode("ascii"),
        "imageHeight": labeled_image.shape[0],
        "imageWidth": labeled_image.shape[1],
    }
    with open(output_file.as_posix(), "w") as fp:
        json.dump(payload, fp, indent=2)