Rebase + fixes

- Rebased on rc3 main
- Removed debug prints
- Added tqdm to reqs
- Added more error handling for stats to csv (region.axis_minor_length value errors)

Author: C-Achard

napari_cellseg3d/interface.py

@@ -496,10 +496,10 @@ def __init__(
         fixed: Optional[bool] = True,
     ):
         """Args:
-        min (Optional[int]): minimum value, defaults to 0
-        max (Optional[int]): maximum value, defaults to 10
-        default (Optional[int]): default value, defaults to 0
-        step (Optional[int]): step value, defaults to 1
+        min (Optional[float]): minimum value, defaults to 0
+        max (Optional[float]): maximum value, defaults to 10
+        default (Optional[float]): default value, defaults to 0
+        step (Optional[float]): step value, defaults to 1
         parent: parent widget, defaults to None
         fixed (bool): if True, sets the QSizePolicy of the spinbox to Fixed"""
 

napari_cellseg3d/model_instance_seg.py

@@ -194,15 +194,19 @@ def volume_stats(volume_image):
     """
 
     properties = regionprops(volume_image)
-    number_objects = np.amax(volume_image)
 
-    sphericity_va = []
-    sphericity_ax = [
-        sphericity_axis(
-            region.axis_major_length * 0.5, region.axis_minor_length * 0.5
-        )
-        for region in properties
-    ]
+    # sphericity_va = []
+    def sphericity(region):
+        try:
+            return sphericity_axis(
+                region.axis_major_length * 0.5, region.axis_minor_length * 0.5
+            )
+        except ValueError:
+            return (
+                np.nan
+            )  # FIXME better way ? inconsistent errors in region.axis_minor_length
+
+    sphericity_ax = [sphericity(region) for region in properties]
     # for region in properties:
     # object = (volume_image == region.label).transpose(1, 2, 0)
     # verts, faces, _, values = marching_cubes(

napari_cellseg3d/model_workers.py

@@ -3,7 +3,6 @@
 from pathlib import Path
 import importlib.util
 from typing import Optional
-import warnings
 
 import numpy as np
 from tifffile import imwrite
@@ -112,7 +111,7 @@ def show_progress(count, block_size, total_size):
             pretrained_folder_path, model_weights_filename
         )
         if os.path.exists(check_path):
-            message = f"Weight file {model_weights_filename} already exists, skipping download step"
+            message = f"Weight file {model_weights_filename} already exists, skipping download"
             if self.log_widget is not None:
                 self.log_widget.print_and_log(message, printing=False)
             print(message)
@@ -240,7 +239,6 @@ def __init__(
         self.layer = layer
         self.images_filepaths = images_filepaths
 
-
         """These attributes are all arguments of :py:func:~inference, please see that for reference"""
 
         self.downloader = WeightsDownloader()
@@ -362,8 +360,8 @@ def load_layer(self):
         dims_check = volume.shape
         self.log("\nChecking dimensions...")
         pad = utils.get_padding_dim(dims_check)
-        print(volume.shape)
-        print(volume.dtype)
+        # print(volume.shape)
+        # print(volume.dtype)
         load_transforms = Compose(
             [
                 ToTensor(),
@@ -534,10 +532,14 @@ def inference_on_list(self, inf_data, i, model, post_process_transforms):
 
     def stats_csv(self, instance_labels):
         if self.stats_to_csv:
+            # try:
             data_dict = volume_stats(
                 instance_labels
             )  # TODO test with area mesh function
             return data_dict
+        # except ValueError as e:
+        #     self.log(f"Error occurred during stats computing : {e}")
+        #     return None
         else:
             return None
 
@@ -677,7 +679,6 @@ def inference(self):
         if self.model_dict["name"] == "SegResNet":
             model = self.model_dict["class"].get_net()(
                 input_image_size=[
-
                     dims,
                     dims,
                     dims,
@@ -751,7 +752,7 @@ def inference(self):
 
         if is_layer and is_folder:
             raise ValueError(
-                "Both a layer and a folder have been specified, please specifiy only one of the two. Aborting."
+                "Both a layer and a folder have been specified, please specify only one of the two. Aborting."
             )
         elif is_folder:
             inference_loader = self.load_folder()
@@ -767,7 +768,7 @@ def inference(self):
             ##################
         elif is_layer:
             input_image = self.load_layer()
-            print(input_image.shape)
+            # print(input_image.shape)
 
         else:
             raise ValueError("No data has been provided. Aborting.")

napari_cellseg3d/plugin_model_inference.py

@@ -1,4 +1,3 @@
-import os
 import warnings
 
 import napari
@@ -220,8 +219,10 @@ def __init__(self, viewer: "napari.viewer.Viewer"):
             "Image size on which the SegResNet has been trained (default : 128)"
         )
 
-        thresh_desc = "Thresholding : all values in the image below the chosen probability" \
-                      " threshold will be set to 0, and all others to 1."
+        thresh_desc = (
+            "Thresholding : all values in the image below the chosen probability"
+            " threshold will be set to 0, and all others to 1."
+        )
         self.thresholding_checkbox.setToolTip(thresh_desc)
         self.thresholding_count.setToolTip(thresh_desc)
         self.window_infer_box.setToolTip(
@@ -712,14 +713,18 @@ def on_yield(data, widget):
 
         viewer = widget._viewer
 
-        widget.progress.setValue(100 * image_id // total)
+        pbar_value = image_id // total
+        if image_id == 0:
+            pbar_value = 1
+
+        widget.progress.setValue(100 * pbar_value)
 
         if widget.show_res and image_id <= widget.show_res_nbr:
 
             zoom = widget.zoom
 
             # print(data["original"].shape)
-            print(data["result"].shape)
+            # print(data["result"].shape)
 
             viewer.dims.ndisplay = 3
             viewer.scale_bar.visible = True

napari_cellseg3d/utils.py

@@ -67,7 +67,7 @@ def sphericity_axis(semi_major, semi_minor):
     a = semi_major
     b = semi_minor
 
-    root = (a**2 - b**2) ** (1 / 2)
+    root = np.sqrt(a**2 - b**2)
     try:
         result = (
             2
@@ -77,6 +77,9 @@ def sphericity_axis(semi_major, semi_minor):
     except ZeroDivisionError:
         print("Zero division in sphericity calculation was replaced by 0")
         result = 0
+    except ValueError as e:
+        print(f"Error encountered in calculation : {e}")
+        result = "Error in calculation"
 
     return result
 

setup.cfg

@@ -50,6 +50,7 @@ install_requires =
     tifffile>=2022.2.9
     imageio-ffmpeg>=0.4.5
     torch>=1.11
+    tqdm
     monai>=0.9.0
     nibabel
     scikit-image