fix a few things

Author: SarahMuth

run_sbatch_revision.sbatch

@@ -10,5 +10,5 @@
 
 source ~/.bashrc
 conda activate synapse-net
-python /user/muth9/u12095/synapse-net/scripts/cooper/revision/az_prediction.py -v 7 --names 20241019_Tomo-eval_MF_Synapse 20241019_Tomo-eval_PS_Synapse  \
+python /user/muth9/u12095/synapse-net/scripts/cooper/revision/az_prediction.py -v 7 --names 20241019_Tomo-eval_MF_Synapse 20241019_Tomo-eval_PS_Synapse 20241019_Tomo-eval_SC_Synapse  \
     -i /mnt/lustre-emmy-hdd/projects/nim00007/data/synaptic-reconstruction/cooper/ground_truth/04Dataset_for_vesicle_eval 

scripts/cooper/revision/az_prediction.py

@@ -10,34 +10,47 @@
 from common import get_file_names, get_split_folder, ALL_NAMES, INPUT_ROOT, OUTPUT_ROOT
 
 
-def run_prediction(model, name, split_folder, version, split_names, input_path):
-    if input_path:
-        file_names =glob(os.path.join(input_path, name, "*.h5"))
+def run_prediction(model, name, split_folder, version, split_names, in_path):
+    if in_path:
+        file_paths = glob(os.path.join(in_path, name, "*.h5"))
+        file_names = [os.path.basename(path) for path in file_paths]
     else:
         file_names = get_file_names(name, split_folder, split_names=split_names)
 
     output_folder = os.path.join(OUTPUT_ROOT, name)
     os.makedirs(output_folder, exist_ok=True)
     output_key = f"predictions/az/v{version}"
+    output_key_seg = f"predictions/az/seg_v{version}"
 
     for fname in tqdm(file_names):
-        input_path = os.path.join(INPUT_ROOT, name, fname)
+        if in_path:
+            input_path=os.path.join(in_path, name, fname)
+        else:
+            input_path = os.path.join(INPUT_ROOT, name, fname)
         print(f"segmenting {input_path}")
 
         output_path = os.path.join(output_folder, fname)
 
         if os.path.exists(output_path):
             with h5py.File(output_path, "r") as f:
-                if output_key in f:
-                    print(f"skipping, because {output_key} already exists in {output_path}")
+                if output_key in f and output_key_seg in f:
+                    print(f"skipping, because {output_key} and {output_key_seg} already exists in {output_path}")
                     continue
 
         with h5py.File(input_path, "r") as f:
             raw = f["raw"][:]
 
-        _, pred = segment_active_zone(raw, model=model, verbose=False, return_predictions=True)
+        seg, pred = segment_active_zone(raw, model=model, verbose=False, return_predictions=True)
         with h5py.File(output_path, "a") as f:
-            f.create_dataset(output_key, data=pred, compression="lzf")
+            if output_key in f:
+                print(f"{output_key} already saved")
+            else:
+                f.create_dataset(output_key, data=pred, compression="lzf")
+            if output_key_seg in f:
+                print(f"{output_key_seg} already saved")
+            else:
+                f.create_dataset(output_key_seg, data=seg, compression="lzf")
+                
 
 
 def get_model(version):

scripts/cooper/revision/check_prediction.py

@@ -18,13 +18,16 @@ def check_predictions(name, split, version):
             gt = f["labels/az"][:] if version == 3 else f["labels/az_thin"][:]
 
         with h5py.File(seg_path) as f:
-            seg_key = f"predictions/az/v{version}"
-            pred = f[seg_key][:]
+            pred_key = f"predictions/az/v{version}"
+            seg_key = f"predictions/az/seg_v{version}"
+            pred = f[pred_key][:]
+            seg = f[seg_key][:]
 
         v = napari.Viewer()
         v.add_image(raw)
         v.add_image(pred, blending="additive")
         v.add_labels(gt)
+        v.add_labels(seg)
         v.title = f"{name}/{os.path.basename(seg_path)}"
         napari.run()
 

scripts/cooper/revision/evaluation_results/v7.xlsx

@@ -17,7 +17,7 @@
         # Recreate the dataset with the new data
         f.create_dataset("labels/az_thin", data=gt)
 '''
-import os
+'''import os
 import h5py
 from glob import glob
 import numpy as np
@@ -41,4 +41,32 @@
             f.create_dataset("/labels/az_merged_v6", data=az_merged, compression="lzf")
             print(f"Updated file: {fname}")
         else:
-            print(f"Dataset not found in: {fname}")
+            print(f"Dataset not found in: {fname}")'''
+
+import os
+import h5py
+
+# List of target folders
+base_path = "/mnt/lustre-emmy-hdd/projects/nim00007/data/synaptic-reconstruction/cooper/ground_truth/04Dataset_for_vesicle_eval"
+folders = [
+    "20241019_Tomo-eval_MF_Synapse",
+    "20241019_Tomo-eval_PS_Synapse",
+    "20241019_Tomo-eval_SC_Synapse"
+]
+
+# Keys to delete
+keys_to_delete = ["/predictions/az/seg_v7", "/predictions/az/v7", "/predictions/az", "/predictions"]
+
+for folder in folders:
+    folder_path = os.path.join(base_path, folder)
+    for filename in os.listdir(folder_path):
+        if filename.endswith(".h5"):
+            file_path = os.path.join(folder_path, filename)
+            print(f"Processing: {file_path}")
+            with h5py.File(file_path, 'a') as h5file:
+                for key in keys_to_delete:
+                    if key in h5file:
+                        print(f"  Deleting key: {key}")
+                        del h5file[key]
+                    else:
+                        print(f"  Key not found: {key}")

scripts/cooper/revision/remove_az_thin.py

@@ -23,7 +23,7 @@ def run_az_evaluation(args):
         results.append(result)
 
     results = pd.concat(results)
-    output_path = f"./results/v{args.version}.xlsx"
+    output_path = f"/user/muth9/u12095/synapse-net/scripts/cooper/revision/evaluation_results/v{args.version}.xlsx"
     results.to_excel(output_path, index=False)