Update synapse detection scripts

Author: constantinpape

scripts/synapse_marker_detection/check_synapse_prediction.py

@@ -1,9 +1,11 @@
 import os
 from glob import glob
+from pathlib import Path
 
 import h5py
 import imageio.v3 as imageio
 import napari
+import numpy as np
 import pandas as pd
 import zarr
 
@@ -14,7 +16,10 @@
 from train_synapse_detection import get_paths
 from tqdm import tqdm
 
+# INPUT_ROOT = "/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/training_data/synapses/test_crops"
+INPUT_ROOT = "./data/test_crops"
 OUTPUT_ROOT = "./predictions"
+DETECTION_OUT_ROOT = "./detections"
 
 
 def run_prediction(val_image):
@@ -40,19 +45,22 @@ def require_prediction(image_data, output_path):
 def run_postprocessing(pred):
     # print("Running local max ...")
     # coords = blob_dog(pred)
-    coords = peak_local_max(pred, min_distance=2, threshold_abs=0.2)
+    coords = peak_local_max(pred, min_distance=2, threshold_abs=0.5)
     # print("... done")
     return coords
 
 
-def visualize_results(image_data, pred, coords=None, val_coords=None):
+def visualize_results(image_data, pred, coords=None, val_coords=None, title=None):
     v = napari.Viewer()
     v.add_image(image_data)
+    pred = pred.clip(0, pred.max())
     v.add_image(pred)
-    if coords is None:
-        v.add_points(coords, name="predicted_synapses")
-    if val_coords is None:
+    if coords is not None:
+        v.add_points(coords, name="predicted_synapses", face_color="yellow")
+    if val_coords is not None:
         v.add_points(val_coords, face_color="green", name="synapse_annotations")
+    if title is not None:
+        v.title = title
     napari.run()
 
 
@@ -68,9 +76,8 @@ def check_val_image():
     visualize_results(val_image, pred)
 
 
-def check_new_images():
-    input_root = "/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/training_data/synapses/test_crops"
-    inputs = glob(os.path.join(input_root, "*.tif"))
+def check_new_images(view=False, save_detection=False):
+    inputs = glob(os.path.join(INPUT_ROOT, "*.tif"))
     output_folder = os.path.join(OUTPUT_ROOT, "new_crops")
     os.makedirs(output_folder, exist_ok=True)
     for path in tqdm(inputs):
@@ -80,13 +87,27 @@ def check_new_images():
             continue
         image_data = imageio.imread(path)
         output_path = os.path.join(output_folder, name.replace(".tif", ".h5"))
-        require_prediction(image_data, output_path)
+        # if not os.path.exists(output_path):
+        #     continue
+        pred = require_prediction(image_data, output_path)
+        if view or save_detection:
+            coords = run_postprocessing(pred)
+        if view:
+            print("Number of synapses:", len(coords))
+            visualize_results(image_data, pred, coords=coords, title=name)
+        if save_detection:
+            os.makedirs(DETECTION_OUT_ROOT, exist_ok=True)
+            coords = np.concatenate([np.arange(0, len(coords))[:, None], coords], axis=1)
+            coords = pd.DataFrame(coords, columns=["index", "axis-0", "axis-1", "axis-2"])
+            fname = Path(path).stem
+            detection_save_path = os.path.join(DETECTION_OUT_ROOT, f"{fname}.csv")
+            coords.to_csv(detection_save_path, index=False)
 
 
 # TODO update to support post-processing and showing annotations for the val data
 def main():
     # check_val_image()
-    check_new_images()
+    check_new_images(view=False, save_detection=True)
 
 
 if __name__ == "__main__":

scripts/synapse_marker_detection/train_synapse_detection.py

@@ -3,7 +3,7 @@
 
 from detection_dataset import DetectionDataset
 
-# sys.path.append("/home/pape/Work/my_projects/czii-protein-challenge")
+sys.path.append("/home/pape/Work/my_projects/czii-protein-challenge")
 sys.path.append("/user/pape41/u12086/Work/my_projects/czii-protein-challenge")
 
 from utils.training.training import supervised_training  # noqa