CLI for post-processing functions

Author: schilling40

flamingo_tools/postprocessing/cli.py

@@ -0,0 +1,149 @@
+"""private
+"""
+import argparse
+
+from .label_components import label_components_single
+from .cochlea_mapping import tonotopic_mapping_single
+from flamingo_tools.measurements import object_measures_single
+
+
+def label_components():
+    parser = argparse.ArgumentParser(
+        description="Script to label segmentation using a segmentation table and graph connected components.")
+
+    parser.add_argument("-i", "--input", type=str, required=True, help="Input path to segmentation table.")
+    parser.add_argument("-o", "--output", type=str, required=True,
+                        help="Output path. Either directory (for --json) or specific file otherwise.")
+    parser.add_argument("--force", action="store_true", help="Forcefully overwrite output.")
+
+    # options for post-processing
+    parser.add_argument("--cell_type", type=str, default="sgn",
+                        help="Cell type of segmentation. Either 'sgn' or 'ihc'.")
+    parser.add_argument("--min_size", type=int, default=1000,
+                        help="Minimal number of pixels for filtering small instances.")
+    parser.add_argument("--min_component_length", type=int, default=50,
+                        help="Minimal length for filtering out connected components.")
+    parser.add_argument("--max_edge_distance", type=float, default=30,
+                        help="Maximal distance in micrometer between points to create edges for connected components.")
+    parser.add_argument("-c", "--components", type=str, nargs="+", default=[1], help="List of connected components.")
+
+    # options for S3 bucket
+    parser.add_argument("--s3", action="store_true", help="Flag for using S3 bucket.")
+    parser.add_argument("--s3_credentials", type=str, default=None,
+                        help="Input file containing S3 credentials. "
+                        "Optional if AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY were exported.")
+    parser.add_argument("--s3_bucket_name", type=str, default=None,
+                        help="S3 bucket name. Optional if BUCKET_NAME was exported.")
+    parser.add_argument("--s3_service_endpoint", type=str, default=None,
+                        help="S3 service endpoint. Optional if SERVICE_ENDPOINT was exported.")
+
+    args = parser.parse_args()
+
+    label_components_single(
+        table_path=args.input,
+        output_path=args.output,
+        cell_type=args.cell_type,
+        component_list=args.components,
+        max_edge_distance=args.max_edge_distance,
+        min_component_length=args.min_component_length,
+        min_size=args.min_size,
+        force_overwrite=args.force,
+        s3=args.s3,
+        s3_credentials=args.s3_credentials,
+        s3_bucket_name=args.s3_bucket_name,
+        s3_service_endpoint=args.s3_service_endpoint,
+    )
+
+
+def tonotopic_mapping():
+    parser = argparse.ArgumentParser(
+        description="Script to extract region of interest (ROI) block around center coordinate.")
+
+    parser.add_argument("-i", "--input", type=str, required=True, help="Input path to segmentation table.")
+    parser.add_argument("-o", "--output", type=str, required=True,
+                        help="Output path. Either directory or specific file.")
+    parser.add_argument("--force", action="store_true", help="Forcefully overwrite output.")
+
+    # options for tonotopic mapping
+    parser.add_argument("--animal", type=str, default="mouse",
+                        help="Animyl type to be used for frequency mapping. Either 'mouse' or 'gerbil'.")
+    parser.add_argument("--otof", action="store_true", help="Use frequency mapping for OTOF cochleae.")
+    parser.add_argument("--apex_position", type=str, default="apex_higher",
+                        help="Use frequency mapping for OTOF cochleae.")
+
+    # options for post-processing
+    parser.add_argument("--cell_type", type=str, default="sgn",
+                        help="Cell type of segmentation. Either 'sgn' or 'ihc'.")
+    parser.add_argument("--max_edge_distance", type=float, default=30,
+                        help="Maximal distance in micrometer between points to create edges for connected components.")
+    parser.add_argument("-c", "--components", type=str, nargs="+", default=[1], help="List of connected components.")
+
+    # options for S3 bucket
+    parser.add_argument("--s3", action="store_true", help="Flag for using S3 bucket.")
+    parser.add_argument("--s3_credentials", type=str, default=None,
+                        help="Input file containing S3 credentials. "
+                        "Optional if AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY were exported.")
+    parser.add_argument("--s3_bucket_name", type=str, default=None,
+                        help="S3 bucket name. Optional if BUCKET_NAME was exported.")
+    parser.add_argument("--s3_service_endpoint", type=str, default=None,
+                        help="S3 service endpoint. Optional if SERVICE_ENDPOINT was exported.")
+
+    args = parser.parse_args()
+
+    tonotopic_mapping_single(
+        table_path=args.input,
+        output_path=args.output,
+        force_overwrite=args.force,
+        animal=args.animal,
+        otof=args.otof,
+        apex_position=args.apex_position,
+        cell_type=args.cell_type,
+        max_edge_distance=args.max_edge_distance,
+        component_list=args.components,
+        s3=args.s3,
+        s3_credentials=args.s3_credentials,
+        s3_bucket_name=args.s3_bucket_name,
+        s3_service_endpoint=args.s3_service_endpoint,
+    )
+
+
+def object_measures():
+    parser = argparse.ArgumentParser(
+        description="Script to compute object measures for different stainings.")
+
+    parser.add_argument("-o", "--output", type=str, nargs="+", required=True,
+                        help="Output path(s). Either directory or specific file(s).")
+    parser.add_argument("-i", "--image_paths", type=str, nargs="+", default=None,
+                        help="Input path to one or multiple image channels in ome.zarr format.")
+    parser.add_argument("-t", "--seg_table", type=str, default=None,
+                        help="Input path to segmentation table.")
+    parser.add_argument("-s", "--seg_path", type=str, default=None,
+                        help="Input path to segmentation channel in ome.zarr format.")
+    parser.add_argument("--force", action="store_true", help="Forcefully overwrite output.")
+
+    # options for object measures
+    parser.add_argument("-c", "--components", type=str, nargs="+", default=[1], help="List of components.")
+    parser.add_argument("-r", "--resolution", type=float, nargs="+", default=[0.38, 0.38, 0.38],
+                        help="Resolution of input in micrometer.")
+    parser.add_argument("--bg_mask", action="store_true", help="Use background mask for calculating object measures.")
+
+    # options for S3 bucket
+    parser.add_argument("--s3", action="store_true", help="Flag for using S3 bucket.")
+    parser.add_argument("--s3_credentials", type=str, default=None,
+                        help="Input file containing S3 credentials. "
+                        "Optional if AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY were exported.")
+    parser.add_argument("--s3_bucket_name", type=str, default=None,
+                        help="S3 bucket name. Optional if BUCKET_NAME was exported.")
+    parser.add_argument("--s3_service_endpoint", type=str, default=None,
+                        help="S3 service endpoint. Optional if SERVICE_ENDPOINT was exported.")
+
+    args = parser.parse_args()
+
+    object_measures_single(
+        out_paths=args.output,
+        image_paths=args.image_paths,
+        table_path=args.seg_table,
+        seg_path=args.seg_path,
+        force_overwrite=args.force,
+        s3=args.s3,
+    )

setup.py

@@ -13,7 +13,10 @@
             "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
+            "flamingo_tools.label_components = flamingo_tools.postprocessing.cli:label_components",
+            "flamingo_tools.tonotopic_mapping = flamingo_tools.postprocessing.cli:tonotopic_mapping",
+            "flamingo_tools.object_measures = flamingo_tools.postprocessing.cli:object_measures",
+            # TODO: MoBIE conversion
         ],
         "napari.manifest": [
             "cochlea_net = flamingo_tools:napari.yaml",