Start implementation of CLI functionality

Author: constantinpape

flamingo_tools/segmentation/cli.py

@@ -0,0 +1,56 @@
+"""private
+"""
+import argparse
+
+from .unet_prediction import run_unet_prediction
+from .synapse_detection import marker_detection
+from ..model_utils import get_model_path
+
+
+def _get_model_path(model_type, checkpoint_path=None):
+    if checkpoint_path is None:
+        model_path = get_model_path(model_type)
+    else:
+        model_path = ...   # TODO
+    return model_path
+
+
+def run_segmentation():
+    """private
+    """
+    parser = argparse.ArgumentParser(description="")
+    parser.add_argument("-i", "--input_path", required=True, help="The path to the input data.")
+    parser.add_argument("-k", "--input_key", required=True, help="The key to the input data.")
+    parser.add_argument("-o", "--output_folder", required=True)
+    parser.add_argument("-m", "--model_type", required=True)
+    parser.add_argument("-c", "--checkpoint_path")
+    parser.add_argument("--min_size", type=int, default=250)
+    # TODO other stuff
+    args = parser.parse_args()
+
+    segmentation_models = ["SGN", "IHC", "SGN-lowres", "IHC-lowres"]
+    if args.model_type not in segmentation_models:
+        raise ValueError
+    model_path = _get_model_path(args.model_type, args.checkpoint_path)
+    run_unet_prediction(
+        input_path=args.input_path, input_key=args.input_key,
+        output_folder=args.output_folder, model_path=model_path,
+        min_size=args.min_size,
+    )
+
+
+def run_detection():
+    """private
+    """
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-m", "--model_type", default="Synapses")
+    args = parser.parse_args()
+    detection_models = ["Synapses"]
+    if args.model_type not in detection_models:
+        raise ValueError
+    model_path = _get_model_path(args.model_type, args.checkpoint_path)
+    # TODO
+    marker_detection(
+        input_path=args.input_path, input_key=args.input_key,
+        output_folder=args.output_folder, model_path=model_path,
+    )

setup.py

@@ -10,7 +10,10 @@
     license="MIT",
     entry_points={
         "console_scripts": [
-            "convert_flamingo = flamingo_tools.data_conversion:convert_lightsheet_to_bdv_cli"
+            "flamingo_tools.convert_data = flamingo_tools.data_conversion:convert_lightsheet_to_bdv_cli",
+            "flamingo_tools.run_segmentation = flamingo_tools.segmentation.cli:run_segmentation",
+            "flamingo_tools.run_detection = flamingo_tools.segmentation.cli:run_detection",
+            # TODO: MoBIE conversion, tonotopic mapping
         ],
         "napari.manifest": [
             "cochlea_net = flamingo_tools:napari.yaml",