bugfix open_file_dialog import

Author: vidalmaxime

src/napari_cellseg_annotator/interface.py

@@ -1,15 +1,15 @@
-from qtpy.QtCore import QUrl
 from qtpy.QtCore import Qt
+from qtpy.QtCore import QUrl
 from qtpy.QtGui import QDesktopServices
 from qtpy.QtWidgets import QFileDialog
 from qtpy.QtWidgets import QGridLayout
 from qtpy.QtWidgets import QGroupBox
 from qtpy.QtWidgets import QLabel
-from qtpy.QtWidgets import QSpinBox
 from qtpy.QtWidgets import QLayout
 from qtpy.QtWidgets import QPushButton
 from qtpy.QtWidgets import QScrollArea
 from qtpy.QtWidgets import QSizePolicy
+from qtpy.QtWidgets import QSpinBox
 from qtpy.QtWidgets import QVBoxLayout
 from qtpy.QtWidgets import QWidget
 

src/napari_cellseg_annotator/model_framework.py

@@ -14,12 +14,12 @@
 from qtpy.QtWidgets import QTabWidget
 
 # local
-from napari_cellseg_annotator import utils
 from napari_cellseg_annotator import interface as ui
+from napari_cellseg_annotator import utils
 from napari_cellseg_annotator.log_utility import Log
-from napari_cellseg_annotator.models import TRAILMAP_test as TMAP
 from napari_cellseg_annotator.models import model_SegResNet as SegResNet
 from napari_cellseg_annotator.models import model_VNet as VNet
+from napari_cellseg_annotator.models import TRAILMAP_test as TMAP
 
 warnings.formatwarning = utils.format_Warning
 

src/napari_cellseg_annotator/model_instance_seg.py

@@ -1,16 +1,20 @@
-from __future__ import print_function, division
+from __future__ import division
+from __future__ import print_function
+
 import os
-from dask_image.imread import imread
-from PIL import Image
 
 import numpy as np
+from dask_image.imread import imread
+from PIL import Image
 from skimage.measure import label
 from skimage.morphology import remove_small_objects
 from skimage.segmentation import watershed
 from skimage.transform import resize
 
 
-def binary_connected(volume, thres=0.5, thres_small=3, scale_factors=(1.0, 1.0, 1.0)):
+def binary_connected(
+    volume, thres=0.5, thres_small=3, scale_factors=(1.0, 1.0, 1.0)
+):
     r"""Convert binary foreground probability maps to instance masks via
     connected-component labeling.
 
@@ -21,20 +25,35 @@ def binary_connected(volume, thres=0.5, thres_small=3, scale_factors=(1.0, 1.0,
         scale_factors (tuple): scale factors for resizing in :math:`(Z, Y, X)` order. Default: (1.0, 1.0, 1.0)
     """
     semantic = volume[0]
-    foreground = (semantic > int(255 * thres))
+    foreground = semantic > int(255 * thres)
     segm = label(foreground)
     segm = remove_small_objects(segm, thres_small)
 
     if not all(x == 1.0 for x in scale_factors):
-        target_size = (int(semantic.shape[0] * scale_factors[0]),
-                       int(semantic.shape[1] * scale_factors[1]),
-                       int(semantic.shape[2] * scale_factors[2]))
-        segm = resize(segm, target_size, order=0, anti_aliasing=False, preserve_range=True)
+        target_size = (
+            int(semantic.shape[0] * scale_factors[0]),
+            int(semantic.shape[1] * scale_factors[1]),
+            int(semantic.shape[2] * scale_factors[2]),
+        )
+        segm = resize(
+            segm,
+            target_size,
+            order=0,
+            anti_aliasing=False,
+            preserve_range=True,
+        )
 
     return segm
 
 
-def binary_watershed(volume, thres1=0.9, thres2=0.3, thres_small=3, scale_factors=(1.0, 1.0, 1.0), seed_thres=3):
+def binary_watershed(
+    volume,
+    thres1=0.9,
+    thres2=0.3,
+    thres_small=3,
+    scale_factors=(1.0, 1.0, 1.0),
+    seed_thres=3,
+):
     r"""Convert binary foreground probability maps to instance masks via
     watershed segmentation algorithm.
 
@@ -58,10 +77,18 @@ def binary_watershed(volume, thres1=0.9, thres2=0.3, thres_small=3, scale_factor
     segm = remove_small_objects(segm, thres_small)
 
     if not all(x == 1.0 for x in scale_factors):
-        target_size = (int(semantic.shape[0] * scale_factors[0]),
-                       int(semantic.shape[1] * scale_factors[1]),
-                       int(semantic.shape[2] * scale_factors[2]))
-        segm = resize(segm, target_size, order=0, anti_aliasing=False, preserve_range=True)
+        target_size = (
+            int(semantic.shape[0] * scale_factors[0]),
+            int(semantic.shape[1] * scale_factors[1]),
+            int(semantic.shape[2] * scale_factors[2]),
+        )
+        segm = resize(
+            segm,
+            target_size,
+            order=0,
+            anti_aliasing=False,
+            preserve_range=True,
+        )
 
     return segm
 
@@ -89,12 +116,16 @@ def write_tiff_stack(vol, fname):
 y_pred = y_pred.astype("uint8")
 
 # Run post process
-output_watershed_path = base_path + "/data/testing/instance-segmentation-w.tiff"
-output_connected_path = base_path + "/data/testing/instance-segmentation-c.tiff"
+output_watershed_path = (
+    base_path + "/data/testing/instance-segmentation-w.tiff"
+)
+output_connected_path = (
+    base_path + "/data/testing/instance-segmentation-c.tiff"
+)
 
 bw_result = binary_watershed(y_pred)
 bc_result = binary_connected(y_pred)
 
 # Save instance predictions
 write_tiff_stack(bw_result, output_watershed_path)
-write_tiff_stack(bc_result, output_connected_path)
+write_tiff_stack(bc_result, output_connected_path)

src/napari_cellseg_annotator/model_workers.py

@@ -4,12 +4,12 @@
 
 import numpy as np
 import torch
+from monai.data import CacheDataset
 from monai.data import DataLoader
 from monai.data import Dataset
 from monai.data import decollate_batch
 from monai.data import pad_list_data_collate
 from monai.data import PatchDataset
-from monai.data import CacheDataset
 from monai.inferers import sliding_window_inference
 from monai.metrics import DiceMetric
 from monai.transforms import AsDiscrete
@@ -100,7 +100,7 @@ def __init__(
         """
 
         super().__init__(self.inference)
-        self._signals = LogSignal() # add custom signals
+        self._signals = LogSignal()  # add custom signals
         self.log_signal = self._signals.log_signal
         ###########################################
         ###########################################
@@ -554,10 +554,7 @@ def train(self):
                 transform=Compose(load_single_images, train_transforms),
             )
             print("Cache dataset : val")
-            val_ds = CacheDataset(
-                data=val_files,
-                transform=load_single_images
-            )
+            val_ds = CacheDataset(data=val_files, transform=load_single_images)
         print("Dataloader")
         train_loader = DataLoader(
             train_ds,

src/napari_cellseg_annotator/plugin_base.py

@@ -7,7 +7,6 @@
 from qtpy.QtWidgets import QSizePolicy
 from qtpy.QtWidgets import QWidget
 
-from napari_cellseg_annotator import utils
 from napari_cellseg_annotator import interface as ui
 
 
@@ -90,7 +89,7 @@ def show_filetype_choice(self):
 
     def show_file_dialog(self):
         """Open file dialog and process path depending on single file/folder loading behaviour"""
-        f_or_dir_name = utils.open_file_dialog(
+        f_or_dir_name = ui.open_file_dialog(
             self, self._default_path, self.file_handling_box.isChecked()
         )
         if not self.file_handling_box.isChecked():

src/napari_cellseg_annotator/plugin_crop.py

@@ -13,8 +13,8 @@
 from qtpy.QtWidgets import QVBoxLayout
 from tifffile import imwrite
 
-from napari_cellseg_annotator import utils
 from napari_cellseg_annotator import interface as ui
+from napari_cellseg_annotator import utils
 from napari_cellseg_annotator.plugin_base import BasePlugin
 
 DEFAULT_CROP_SIZE = 64

src/napari_cellseg_annotator/plugin_helper.py

@@ -7,8 +7,8 @@
 from qtpy.QtWidgets import QWidget
 
 # local
-from napari_cellseg_annotator import utils
 from napari_cellseg_annotator import interface as ui
+from napari_cellseg_annotator import utils
 
 
 class Helper(QWidget):

src/napari_cellseg_annotator/plugin_model_inference.py

@@ -11,8 +11,8 @@
 from qtpy.QtWidgets import QWidget
 
 # local
-from napari_cellseg_annotator import utils
 from napari_cellseg_annotator import interface as ui
+from napari_cellseg_annotator import utils
 from napari_cellseg_annotator.model_framework import ModelFramework
 from napari_cellseg_annotator.model_workers import InferenceWorker
 

src/napari_cellseg_annotator/plugin_model_training.py

@@ -17,18 +17,18 @@
 from monai.losses import FocalLoss
 from monai.losses import GeneralizedDiceLoss
 from monai.losses import TverskyLoss
-from qtpy.QtWidgets import QCheckBox
 
 # Qt
+from qtpy.QtWidgets import QCheckBox
 from qtpy.QtWidgets import QComboBox
 from qtpy.QtWidgets import QLabel
 from qtpy.QtWidgets import QProgressBar
 from qtpy.QtWidgets import QSizePolicy
 from qtpy.QtWidgets import QSpinBox
 
 # local
-from napari_cellseg_annotator import utils
 from napari_cellseg_annotator import interface as ui
+from napari_cellseg_annotator import utils
 from napari_cellseg_annotator.model_framework import ModelFramework
 from napari_cellseg_annotator.model_workers import TrainingWorker
 

src/napari_cellseg_annotator/plugin_review.py

@@ -13,8 +13,8 @@
 from qtpy.QtWidgets import QSizePolicy
 from qtpy.QtWidgets import QVBoxLayout
 
-from napari_cellseg_annotator import utils
 from napari_cellseg_annotator import interface as ui
+from napari_cellseg_annotator import utils
 from napari_cellseg_annotator.launch_review import launch_review
 from napari_cellseg_annotator.plugin_base import BasePlugin
 
@@ -90,7 +90,8 @@ def build(self):
         data_group_w, data_group_l = ui.make_group("Data")
 
         data_group_l.addWidget(
-            ui.combine_blocks(self.filetype_choice, self.file_handling_box), alignment = ui.LEFT_AL
+            ui.combine_blocks(self.filetype_choice, self.file_handling_box),
+            alignment=ui.LEFT_AL,
         )
         self.filetype_choice.setVisible(False)