Finalize synapse evaluation

Author: constantinpape

scripts/export_lower_resolution.py

@@ -7,7 +7,7 @@
 import zarr
 
 from flamingo_tools.s3_utils import get_s3_path, BUCKET_NAME, SERVICE_ENDPOINT
-from skimage.segmentation import relabel_sequential
+# from skimage.segmentation import relabel_sequential
 
 
 def filter_component(fs, segmentation, cochlea, seg_name, components):
@@ -44,6 +44,8 @@ def export_lower_resolution(args):
         print(data.shape)
         if args.filter_by_components is not None:
             data = filter_component(fs, data, args.cochlea, channel, args.filter_by_components)
+        if args.binarize:
+            data = (data > 0).astype("uint8")
         tifffile.imwrite(out_path, data, bigtiff=True, compression="zlib")
 
 
@@ -54,6 +56,7 @@ def main():
     parser.add_argument("--output_folder", "-o", required=True)
     parser.add_argument("--channels", nargs="+", default=["PV", "VGlut3", "CTBP2"])
     parser.add_argument("--filter_by_components", nargs="+", type=int, default=None)
+    parser.add_argument("--binarize", action="store_true")
     args = parser.parse_args()
 
     export_lower_resolution(args)

scripts/synapse_marker_detection/extract_training_data.py

@@ -191,10 +191,32 @@ def process_training_data_v3(visualize=True):
             extract_training_data(imaris_file, output_folder, tif_file=None, crop=True)
 
 
+def process_eval_data(visualize=False):
+    input_root = "/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/AnnotatedImageCrops/SynapseValidation"  # noqa
+    test_folders = ["M227R_IHC-synapsecrops", "MLR227L_IHC-synapsecrops"]
+    test_output = "/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/training_data/synapses/test_data/v3"  # noqa
+    exclude_names = []
+
+    for folder in test_folders:
+
+        if visualize:
+            output_folder = None
+        else:
+            output_folder = test_output
+
+        imaris_files = sorted(glob(os.path.join(input_root, folder, "*.ims")))
+        for imaris_file in imaris_files:
+            if os.path.basename(imaris_file) in exclude_names:
+                print("Skipping", imaris_file)
+                continue
+            extract_training_data(imaris_file, output_folder, tif_file=None, crop=True)
+
+
 def main():
     # process_training_data_v1()
     # process_training_data_v2(visualize=True)
-    process_training_data_v3(visualize=False)
+    # process_training_data_v3(visualize=False)
+    process_eval_data(visualize=False)
 
 
 if __name__ == "__main__":

scripts/validation/synapses/prediction.py

@@ -51,8 +51,12 @@ def pred_synapse_impl(input_path, output_folder):
 def predict_synapses():
     files = sorted(glob(os.path.join(INPUT_ROOT, "*.zarr")))
     for ff in files:
-        print("Segmenting", ff)
         output_folder = os.path.join(OUTPUT_ROOT, Path(ff).stem)
+        if os.path.exists(os.path.join(output_folder, "predictions.zarr", "prediction")):
+            print("Synapse prediction in", ff, "already done")
+            continue
+        else:
+            print("Predicting synapses in", ff)
         pred_synapse_impl(ff, output_folder)
 
 
@@ -68,8 +72,12 @@ def pred_ihc_impl(input_path, output_folder):
 def predict_ihcs():
     files = sorted(glob(os.path.join(INPUT_ROOT, "*.zarr")))
     for ff in files:
-        print("Segmenting", ff)
         output_folder = os.path.join(OUTPUT_ROOT, f"{Path(ff).stem}_ihc")
+        if os.path.exists(os.path.join(output_folder, "predictions.zarr", "prediction")):
+            print("IHC segmentation in", ff, "already done")
+            continue
+        else:
+            print("Segmenting IHCs in", ff)
         pred_ihc_impl(ff, output_folder)
 
 
@@ -147,8 +155,8 @@ def process_everything():
 
 
 def main():
-    process_everything()
-    # check_predictions()
+    # process_everything()
+    check_predictions()
 
 
 if __name__ == "__main__":

scripts/validation/synapses/run_evaluation.py

@@ -1,4 +1,6 @@
 import os
+from glob import glob
+from pathlib import Path
 
 import pandas as pd
 from elf.io import open_file
@@ -48,7 +50,7 @@ def visualize_synapse_detections(pred, gt, heatmap_path=None, ctbp2_path=None):
 
     pred = pd.read_csv(pred, sep="\t")[["z", "y", "x"]].values
     gt = pd.read_csv(gt, sep="\t")[["z", "y", "x"]].values
-    tps_pred, tps_gt, fps, fns = match_detections(pred, gt, max_dist=5)
+    tps_pred, tps_gt, fps, fns = match_detections(pred, gt, max_dist=4)
 
     tps = pred[tps_pred]
     fps = pred[fps]
@@ -91,15 +93,20 @@ def main():
     parser.add_argument("--visualize", action="store_true")
     args = parser.parse_args()
 
-    pred_files = [
-        "/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/AnnotatedImageCrops/SynapseValidation/m226l_midp330_vglut3-ctbp2/filtered_synapse_detection.tsv",  # noqa
-    ]
-    gt_files = [
-        "/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/training_data/synapses/test_data/v3/labels/m226l_midp330_vglut3-ctbp2_filtered.tsv",  # noqa
-    ]
-    ctbp2_files = [
-        "/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/training_data/synapses/test_data/v3/images/m226l_midp330_vglut3-ctbp2.zarr",  # noqa
-    ]
+    pred_root = "/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/AnnotatedImageCrops/SynapseValidation"
+    gt_root = "/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/training_data/synapses/test_data/v3/labels"
+    ctbp2_root = "/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/training_data/synapses/test_data/v3/images"  # noqa
+
+    ctbp2_files = sorted(glob(os.path.join(ctbp2_root, "*.zarr")))
+    gt_files = sorted(glob(os.path.join(gt_root, "*_filtered.tsv")))
+    assert len(ctbp2_files) == len(gt_files)
+
+    pred_files = []
+    for ff in ctbp2_files:
+        fname = Path(ff).stem
+        pred_file = os.path.join(pred_root, fname, "filtered_synapse_detection.tsv")
+        assert os.path.exists(pred_file), pred_file
+        pred_files.append(pred_file)
 
     if args.visualize:
         visualize_evaluation(pred_files, gt_files, ctbp2_files)