Merge pull request #34 from volume-em/empanada_v1.1.1

Empanada v1.1.1

README.md

@@ -44,7 +44,7 @@ empanada-napari works with python=3.9 or lower
 It's recommended to have installed napari through [conda](https://docs.conda.io/en/latest/miniconda.html). Then to install this plugin:
 
 ```shell
-pip install empanada-napari==1.1.0
+pip install empanada-napari==1.1.1
 ```
 
 Launch napari:
@@ -68,7 +68,7 @@ pip uninstall empanada-napari
 Then install the newest version:
 
 ```shell
-pip install empanada-napari==1.1.0
+pip install empanada-napari==1.1.1
 ```
 
 

empanada_napari/_filter_small_labels.py

@@ -104,9 +104,18 @@ def widget(
             plane = viewer.dims.current_step[0]
 
             if remove_opt == 'Small labels':
-                labels[plane], labels_removed = filter_out_small_label_areas(labels[plane], min_area)
+                labels_, labels_removed = filter_out_small_label_areas(labels[plane] if labels.ndim > 2 else labels, min_area)
+                if labels.ndim > 2:
+                    labels[plane] = labels_
+
+                else:
+                    labels = labels_
             elif remove_opt == 'Boundary labels':
-                labels[plane], labels_removed = remove_boundary_labels(labels[plane])
+                labels_, labels_removed = remove_boundary_labels(labels[plane] if labels.ndim > 2 else labels)
+                if labels.ndim > 2:
+                    labels[plane] = labels_
+                else:
+                    labels = labels_
 
         elif labels.ndim == 3 and apply_to == '2D patches':
             for label in tqdm(range(labels.shape[0])):

empanada_napari/_label_counter_widget.py

@@ -110,9 +110,9 @@ def count_labels(label_values, label_divisor):
 
     class_ids = np.unique(np.floor_divide(label_values, label_divisor)).tolist()
     for ci in class_ids:
-        min_id = ci * label_divisor + 1
+        min_id = ci * label_divisor
         max_id = (ci + 1) * label_divisor
-        label_ids = label_values[np.logical_and(label_values >= min_id, label_values < max_id)]
+        label_ids = label_values[(label_values >= min_id) & (label_values < max_id)]
         label_queue[ci] = label_ids.tolist()
 
     return label_queue, class_ids
@@ -161,6 +161,7 @@ def widget(
         if export_xlsx:
             # folder_name = f'{labels_layer.name}_label_ids'
             folder_path = os.path.join(save_dir, folder_name)
+
             # Create the save directory if it doesn't exist
             save_dir = folder_path
             if not os.path.exists(save_dir):
@@ -174,20 +175,60 @@ def widget(
 
         labels = labels_layer.data
 
+        def _get_current_image(labels_layer):
+            cursor_pos = viewer.cursor.position
+
+            labels_ = labels_layer.data
+
+            y, x = 0, 0
+            if labels.ndim == 4:
+                axis = tuple(viewer.dims.order[:2])
+                plane = (
+                    int(labels_layer.world_to_data(cursor_pos)[axis[0]]),
+                    int(labels_layer.world_to_data(cursor_pos)[axis[1]])
+                )
+
+                slices = [slice(None), slice(None), slice(None), slice(None)]
+
+                slices[axis[0]] = plane[0]
+                slices[axis[1]] = plane[1]
+
+            elif labels.ndim == 3:
+                axis = viewer.dims.order[0]
+                plane = int(labels_layer.world_to_data(cursor_pos)[axis])
+
+                slices = [slice(None), slice(None), slice(None)]
+                slices[axis] = plane
+
+            else:
+                slices = [slice(None), slice(None)]
+                axis = None
+                plane = None
+
+            labels_slice = labels_[tuple(slices)]
+            unique_labels = np.unique(labels_slice)
+
+            return labels_slice, unique_labels, axis, plane, y, x
+
         if labels.ndim > 2 and label_type == 'Current image':
-            # assert viewer.dims.order[0] == 0, "Must be viewing axis 0 (xy plane)!"
             plane = viewer.dims.current_step[0]
+            # print(f'Viewing plane: {plane}')
         else:
             plane = 'null'
 
         if plane != 'null':
-            labels = labels[plane]
+            label_slice, unique_labels, axis, plane, y, x = _get_current_image(labels_layer)
+            labels = unique_labels
 
         class_names = {}
         for seg_class in label_text.split():
             class_id, class_name = seg_class.split(',')
             class_num = class_id.strip()
             class_name = class_name.strip()
+
+            if not class_num.isdigit():
+                print(f'The class number you entered is invalid. Please provide an integer value!')
+                return
             class_names[int(class_num)] = class_name
 
         print(f'Class names: {class_names}')

empanada_napari/_volume_inference.py

@@ -4,7 +4,7 @@
 from napari.layers import Image 
 from napari_plugin_engine import napari_hook_implementation
 from magicgui import magicgui, widgets
-#from qtpy.QtWidgets import QScrollArea
+from qtpy.QtWidgets import QScrollArea
 
 def volume_inference_widget():
     from napari.qt.threading import thread_worker
@@ -48,7 +48,7 @@ def orthoplane_inference(engine, volume):
         label_head=dict(widget_type='Label', label=f'<h1 style="text-align:center"><img src="{logo}"></h1>'),
         call_button='Run 3D Inference',
         layout='vertical',
-        #scrollable=True,
+        scrollable=True,
         
         model_config=dict(widget_type='ComboBox', label='model', choices=list(model_configs.keys()), value=list(model_configs.keys())[0], tooltip='Model to use for inference'),
         use_gpu=dict(widget_type='CheckBox', text='Use GPU', value=device_count() >= 1, tooltip='If checked, run on GPU 0'),
@@ -318,12 +318,13 @@ def start_consensus_worker(trackers_dict):
         pbar.show()
 
     # make the scroll available
-    #scroll = QScrollArea()
-    #scroll.setWidget(widget._widget._qwidget)
-    #widget._widget._qwidget = scroll
+    scroll = QScrollArea()
+    scroll.setWidget(widget._widget._qwidget)
+    widget._widget._qwidget = scroll
 
     return widget
 
+
 @napari_hook_implementation(specname='napari_experimental_provide_dock_widget')
 def volume_dock_widget():
-    return volume_inference_widget, {'name': '3D Inference'}
+    return volume_inference_widget, {'name': '3D Inference'}

requirements.txt

@@ -1,5 +1,8 @@
-napari[all]>=0.4.15
+opencv-python==4.9.0.80
+opencv-python-headless==4.9.0.80
+napari[all]==0.4.18
 scikit-image>=0.18
 empanada-dl>=0.1.5
 napari-plugin-engine>=0.1.4
 imagecodecs
+numba==0.59.0

setup.cfg

@@ -1,6 +1,6 @@
 [metadata]
 name = empanada-napari
-version = 1.1.0
+version = 1.1.1
 author = Madeline Barry, Abhishek Bhardwaj, Ryan Conrad
 author_email = [email protected]
 url = https://github.com/volume-em/empanada-napari
@@ -33,11 +33,14 @@ setup_requires = setuptools_scm
 include_package_data = True
 # add your package requirements here
 install_requires =
-    napari>=0.4.16
+    opencv-python==4.9.0.80
+    opencv-python-headless==4.9.0.80
+    napari==0.4.18
     numpy==1.22
     napari-plugin-engine>=0.1.4
     scikit-image>=0.19
     empanada-dl>=0.1.7
+    numba==0.59.0
     imagecodecs
     openpyxl
     imagehash