Merge branch 'more-inner-ear-analysis' of https://github.com/computational-cell-analytics/synaptic-reconstruction into more-inner-ear-analysis

Author: constantinpape

scripts/inner_ear/processing/run_analyis.py

@@ -52,7 +52,7 @@ def _load_segmentation(seg_path, tomo_shape):
     return seg
 
 
-def compute_distances(segmentation_paths, save_folder, resolution, force, tomo_shape):
+def compute_distances(segmentation_paths, save_folder, resolution, force, tomo_shape, use_corrected_vesicles=True):
     os.makedirs(save_folder, exist_ok=True)
 
     vesicles = None
@@ -61,9 +61,10 @@ def _require_vesicles():
         vesicle_path = segmentation_paths["vesicles"]
 
         if vesicles is None:
-            vesicle_pool_path = os.path.join(os.path.split(save_folder)[0], "vesicle_pools.tif")
-            if os.path.exists(vesicle_pool_path):
-                vesicle_path = vesicle_pool_path
+            if use_corrected_vesicles:
+                vesicle_pool_path = os.path.join(os.path.split(save_folder)[0], "vesicle_pools.tif")
+                if os.path.exists(vesicle_pool_path):
+                    vesicle_path = vesicle_pool_path
             return _load_segmentation(vesicle_path, tomo_shape)
 
         else:
@@ -394,14 +395,22 @@ def analyze_folder(folder, version, n_ribbons, force, use_corrected_vesicles):
     with open_file(data_path, "r") as f:
         tomo_shape = f["data"].shape
 
-    out_distance_folder = os.path.join(output_folder, "distances")
+    if use_corrected_vesicles:
+        out_distance_folder = os.path.join(output_folder, "distances")
+    else:
+        out_distance_folder = os.path.join(output_folder, "distances_uncorrected")
     distance_paths, skip = compute_distances(
         segmentation_paths, out_distance_folder, resolution, force=force, tomo_shape=tomo_shape,
+        use_corrected_vesicles=use_corrected_vesicles
     )
     if skip:
         return
 
     if force or not os.path.exists(result_path):
+
+        if not use_corrected_vesicles:
+            pool_correction_path = None
+
         analyze_distances(
             segmentation_paths, distance_paths, resolution, result_path, tomo_shape,
             pool_correction_path=pool_correction_path

scripts/inner_ear/training/postprocessing_and_evaluation.py

@@ -13,8 +13,8 @@
 
 from train_structure_segmentation import get_train_val_test_split
 
-ROOT = "/home/pape/Work/data/synaptic_reconstruction/moser"
-# ROOT = "/mnt/lustre-emmy-hdd/projects/nim00007/data/synaptic-reconstruction/moser"
+# ROOT = "/home/pape/Work/data/synaptic_reconstruction/moser"
+ROOT = "/mnt/lustre-emmy-hdd/projects/nim00007/data/synaptic-reconstruction/moser"
 MODEL_PATH = "/mnt/lustre-emmy-hdd/projects/nim00007/models/synaptic-reconstruction/vesicle-DA-inner_ear-v2"
 OUTPUT_ROOT = "./predictions"
 
@@ -187,8 +187,8 @@ def segment_train_domain():
     name = "train_domain"
     run_vesicle_segmentation(paths, MODEL_PATH, name, is_nested=True)
     postprocess_structures(paths, name, is_nested=True)
-    visualize(paths, name, is_nested=True)
-    results = evaluate(paths, name, is_nested=True, save_path="./results/train_domain_postprocessed.csv")
+    # visualize(paths, name, is_nested=True)
+    results = evaluate(paths, name, is_nested=True, save_path="./results/train_domain_postprocessed_v2.csv")
     print(results)
     print("Ribbon segmentation:", results["ribbon"].mean(), "+-", results["ribbon"].std())
     print("PD segmentation:", results["PD"].mean(), "+-", results["PD"].std())

scripts/inner_ear/training/structure_prediction_and_evaluation.py

@@ -143,10 +143,10 @@ def predict_and_evaluate_train_domain():
     print("Run evaluation on", len(paths), "tomos")
 
     name = "train_domain"
-    model_path = "./checkpoints/inner_ear_structure_model"
+    model_path = "./checkpoints/inner_ear_structure_model_v2"
 
     run_prediction(paths, model_path, name, is_nested=True)
-    evaluate(paths, name, is_nested=True, save_path="./results/train_domain.csv")
+    evaluate(paths, name, is_nested=True, save_path="./results/train_domain_v2.csv")
     visualize(paths, name, is_nested=True)
 
 
@@ -187,9 +187,9 @@ def predict_and_evaluate_rat():
 
 
 def main():
-    # predict_and_evaluate_train_domain()
+    predict_and_evaluate_train_domain()
     # predict_and_evaluate_vesicle_pools()
-    predict_and_evaluate_rat()
+    # predict_and_evaluate_rat()
 
 
 if __name__ == "__main__":