Run foreign

CP5/active_plugins/cpforeign/__init__.py

@@ -0,0 +1,3 @@
+# -*- coding: utf-8 -*-
+
+__all__ = ["server"]

CP5/active_plugins/cpforeign/bimz.py

@@ -0,0 +1,109 @@
+import numpy as np
+import logging
+
+from bioimageio.spec import InvalidDescr, load_description
+from bioimageio.spec.model.v0_5 import ModelDescr
+import bioimageio.core.prediction as bi_pred
+
+from skimage.filters import threshold_otsu
+from skimage.measure import label
+from skimage.morphology import closing, square
+from skimage.segmentation import clear_border
+
+from server import ForeignToolClient
+
+logger = logging.getLogger(__name__)
+
+# https://bioimage.io/#/?tags=affable-shark&id=10.5281%2Fzenodo.5764892
+MODEL_ID = "affable-shark"
+MODEL_DOI = "10.5281/zenodo.11092561"
+
+def load_model():
+    loaded_description = load_description(MODEL_ID)
+    if isinstance(loaded_description, InvalidDescr):
+        raise ValueError(f"Failed to load {MODEL_ID}")
+    elif not isinstance(loaded_description, ModelDescr):
+        raise ValueError("This notebook expects a model 0.5 description")
+
+    model = loaded_description
+    example_model_id = model.id
+    assert example_model_id is not None
+
+    try:
+        descr = load_description(MODEL_ID)
+    except InvalidDescr as e:
+        logger.error(f"Invalid description: {e}")
+        return None
+
+    return descr
+
+def predict(input_image, model):
+    out = bi_pred.predict(model=model, inputs={'input0': input_image}, skip_postprocessing=True, skip_preprocessing=True)
+    return np.array(out.members['output0'].data[0])
+
+def run(image_data, image_header):
+    model = load_model()
+
+    logger.debug("loaded model")
+
+    # scaled image
+    im = image_data.copy()
+    logger.debug(f"provided image of shape {im.shape}, type {im.dtype}")
+    # im = (image_data / np.iinfo(image_data.dtype).max).astype(np.float32)
+
+    pad_y = (64 - image_data.shape[0] % 64) % 64
+    pad_x = (64 - image_data.shape[1] % 64) % 64
+    # padded image
+    im = np.pad(im, ((0, pad_y), (0, pad_x)), mode='constant', constant_values=0)
+    logger.debug(f"padded image of shape {im.shape}, type {im.dtype}")
+
+    # input image
+    im = im.reshape([1,1,im.shape[0],im.shape[1]])
+    logger.debug(f"input image of shape {im.shape}, type {im.dtype}")
+
+    # output image
+    logger.debug("running prediction")
+    res = predict(im, model)
+    del im
+    logger.debug(f"output image of shape {res.shape}, dtype {res.dtype}")
+
+    # unpadded result
+    res = res[:, :image_data.shape[0], :image_data.shape[1]]
+    logger.debug(f"de-padded output image of shape {res.shape}, dtype {res.dtype}")
+
+    # just the foreground probabilities, ignore boundaries
+    res = res[0]
+    logger.debug(f"using only fg prob of shape {res.shape}, dtype {res.dtype}")
+
+    # threshold above certain prob
+    thresh = threshold_otsu(res)
+    logger.debug(f"threshold image shape {thresh.shape}, dtype {thresh.dtype}")
+    # make binary, with closing (remove small holes in fg with dilate then erode)
+    bw = closing(res > thresh, square(3))
+    logger.debug(f"binary image of shape {bw.shape}, type {bw.dtype}")
+
+    # remove border cells
+    # cleared = clear_border(bw)
+    # labels = label(cleared)
+
+    # convert to labels
+    labels = label(bw)
+    logger.debug(f"labels of shape {labels.shape}, dtype {labels.dtype}")
+
+    return labels
+
+
+def main():
+    client = ForeignToolClient(7878, cb=run)
+    client.receive_images()
+
+if __name__ == "__main__":
+    # init logging
+    logging.root.setLevel(logging.DEBUG)
+    stream_handler = logging.StreamHandler()
+    fmt = logging.Formatter("  [%(process)d|%(levelno)s] %(name)s::%(funcName)s: %(message)s")
+    stream_handler.setFormatter(fmt)
+    logging.root.addHandler(stream_handler)
+
+    logger.debug("Starting bimz.py")
+    main()