reworked mentioned parts

Author: lufre1

synaptic_reconstruction/tools/distance_measure_widget.py

@@ -10,7 +10,6 @@
 
 from .base_widget import BaseWidget
 from .. import distance_measurements
-from .util import compute_scale_from_voxel_size
 
 try:
     from napari_skimage_regionprops import add_table
@@ -109,7 +108,7 @@ def on_measure_seg_to_object(self):
             resolution = [v for v in resolution.values()]
         # if user input is present override metadata
         if self.voxel_size_param.value() != 0.0:  # changed from default
-            resolution = [self.voxel_size_param.value() for _ in range(len(segmentation.shape))]
+            resolution = segmentation.ndim * [self.voxel_size_param.value()]
 
         (distances,
          endpoints1,
@@ -139,7 +138,7 @@ def on_measure_pairwise(self):
             resolution = [v for v in resolution.values()]
         # if user input is present override metadata
         if self.voxel_size_param.value() != 0.0:  # changed from default
-            resolution = [self.voxel_size_param.value() for _ in range(len(segmentation.shape))]
+            resolution = segmentation.ndim * [self.voxel_size_param.value()]
 
         (distances,
          endpoints1,
@@ -162,7 +161,7 @@ def _create_settings_widget(self):
 
         self.save_path, layout = self._add_path_param(name="Save Table", select_type="file", value="")
         setting_values.layout().addLayout(layout)
-        
+
         self.voxel_size_param, layout = self._add_float_param(
             "voxel_size", 0.0, min_val=0.0, max_val=100.0,
         )

synaptic_reconstruction/tools/segmentation_widget.py

@@ -3,7 +3,7 @@
 from qtpy.QtWidgets import QWidget, QVBoxLayout, QPushButton, QLabel, QComboBox
 
 from .base_widget import BaseWidget
-from .util import (run_segmentation, get_model, get_model_registry, _available_devices, get_device, 
+from .util import (run_segmentation, get_model, get_model_registry, _available_devices, get_device,
                    get_current_tiling, compute_scale_from_voxel_size)
 from synaptic_reconstruction.inference.util import get_default_tiling
 import copy
@@ -69,8 +69,8 @@ def on_predict(self):
             return
 
         # load current tiling
-        self.tiling = get_current_tiling(self.tiling, self.default_tiling, image.shape)
-        
+        self.tiling = get_current_tiling(self.tiling, self.default_tiling, model_type)
+
         # TODO: Use scale derived from the image resolution.
         # get avg image shape from training of the selected model
         # wichmann data avg voxel size = 17.53
@@ -92,8 +92,8 @@ def on_predict(self):
         if voxel_size:
             # calculate scale so voxel_size is the same as in training
             scale = compute_scale_from_voxel_size(voxel_size, model_type)
-            print(f"Rescaled the image by {scale} to optimize for the selected model.")
-        
+            show_info(f"Rescaled the image by {scale} to optimize for the selected model.")
+
         segmentation = run_segmentation(
             image, model=model, model_type=model_type, tiling=self.tiling, scale=scale
         )

synaptic_reconstruction/tools/util.py

@@ -176,7 +176,7 @@ def _available_devices():
     return available_devices
 
 
-def get_current_tiling(tiling: dict, default_tiling: dict, image_shape):
+def get_current_tiling(tiling: dict, default_tiling: dict, model_type: str):
     # get tiling values from qt objects
     for k, v in tiling.items():
         for k2, v2 in v.items():
@@ -185,8 +185,8 @@ def get_current_tiling(tiling: dict, default_tiling: dict, image_shape):
             tiling[k][k2] = v2.value()
     # check if user inputs tiling/halo or not
     if default_tiling == tiling:
-        if len(image_shape) == 2:
-            # if its 2d image expand x,y and set z to 1
+        if "2d" in model_type:
+            # if its a 2d model expand x,y and set z to 1
             tiling = {
                 "tile": {
                     "x": 512,
@@ -199,31 +199,14 @@ def get_current_tiling(tiling: dict, default_tiling: dict, image_shape):
                     "z": 1
                 }
             }
-    elif len(image_shape) == 2:
-        # if its a 2d image set z to 1
+    elif "2d" in model_type:
+        # if its a 2d model set z to 1
         tiling["tile"]["z"] = 1
         tiling["halo"]["z"] = 1
 
     return tiling
 
 
-def compute_average_voxel_size(voxel_size: dict) -> float:
-    """
-    Computes the average voxel size dynamically based on available dimensions.
-    
-    Args:
-        voxel_size (dict): Dictionary containing voxel dimensions (e.g., x, y, z).
-        
-    Returns:
-        float: Average voxel size.
-    """
-    # Extract all dimension values
-    dimensions = [voxel_size[key] for key in voxel_size if key in ["x", "y", "z"]]
-    
-    # Compute the average
-    return sum(dimensions) / len(dimensions)
-
-
 def compute_scale_from_voxel_size(
     voxel_size: dict,
     model_type: str
@@ -233,7 +216,7 @@ def compute_scale_from_voxel_size(
         voxel_size["x"] / training_voxel_size["x"],
         voxel_size["y"] / training_voxel_size["y"],
     ]
-    if len(voxel_size) == 3:
+    if len(voxel_size) == 3 and len(training_voxel_size) == 3:
         scale.append(
             voxel_size["z"] / training_voxel_size["z"]
         )

synaptic_reconstruction/tools/volume_reader.py

@@ -23,11 +23,11 @@ def get_reader(path: PathOrPaths) -> Optional[ReaderFunction]:
 def _read_mrc(path, fname):
     with open_file(path, mode="r") as f:
         data = f["data"][:]
-    
+    voxel_size = read_voxel_size(path)
     metadata = {
-        "file_path": path
+        "file_path": path,
+        "voxel_size": voxel_size
     }
-    read_voxel_size(path, metadata)
     layer_attributes = {
         "name": fname,
         "colormap": "gray",
@@ -74,22 +74,24 @@ def read_image_volume(path: PathOrPaths) -> List[LayerData]:
         return
 
 
-def read_voxel_size(input_path: str, layer_attributes: dict) -> None:
+def read_voxel_size(input_path: str) -> dict | None:
     """Read voxel size from mrc/rec file and store it in layer_attributes.
-    The original unit of voxel size is Angstrom and we convert it to nanometers 
-    by dividing it by ten. 
+    The original unit of voxel size is Angstrom and we convert it to nanometers
+    by dividing it by ten.
 
     Args:
         input_path (str): path to mrc/rec file
         layer_attributes (dict): napari layer attributes to store voxel size to
     """
+    new_voxel_size = None
     with mrcfile.open(input_path, permissive=True) as mrc:
         try:
             voxel_size = mrc.voxel_size
-            layer_attributes["voxel_size"] = {
+            new_voxel_size = {
                 "x": voxel_size.x / 10,
                 "y": voxel_size.y / 10,
                 "z": voxel_size.z / 10,
             }
         except Exception as e:
             print(f"Failed to read voxel size: {e}")
+    return new_voxel_size