Initial CI setup

.github/workflows/main.yml

@@ -0,0 +1,63 @@
+# This is a basic workflow to help you get started with Actions
+
+name: CI
+
+# Controls when the action will run. 
+on:
+  # Triggers the workflow on push or pull request events but only for the master branch
+  push:
+    branches: [ master ]
+  pull_request:
+    branches: [ master ]
+
+  # Allows you to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+# A workflow run is made up of one or more jobs that can run sequentially or in parallel
+jobs:
+  # This workflow contains a single job called "build"
+  build:
+    # The type of runner that the job will run on
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: [3.6, 3.7, 3.8]
+
+    steps:
+      - name: Check out repo
+        uses: actions/checkout@v2
+      - name: Install VTK dependencies
+        run: |
+          sudo apt-get update
+          sudo apt-get install libsm6 libgl1-mesa-glx
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Cache pip
+        uses: actions/cache@v2
+        with:
+          # This path is specific to Ubuntu
+          path: ~/.cache/pip
+          # Look to see if there is a cache hit for the corresponding requirements file
+          key: ${{ runner.os }}-pip-${{ hashFiles('envs/requirements.txt') }}
+          restore-keys: |
+            ${{ runner.os }}-pip-
+            ${{ runner.os }}-
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip setuptools wheel
+          pip install flake8
+          export QT_QPA_PLATFORM=offscreen
+          if [ -f envs/requirements.txt ]; then pip install -r envs/requirements.txt; fi
+      - name: Lint with flake8
+        run: |
+          # stop the build if there are Python syntax errors or undefined names
+          flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics --exclude stitch
+          # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+          flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics --exclude stitch
+      - name: Test with pytest
+        run: |
+          # TODO: add image artifact
+          #python -u -m magmap.tests.unit_testing
+          python -u -m magmap.tests.test_libmag

envs/requirements.txt

@@ -16,7 +16,7 @@ imageio==2.9.0
 importlib-metadata==3.10.0
 importlib-resources==5.1.2
 iniconfig==1.1.1
-javabridge==1.0.18.post21+g4526f53
+javabridge==1.0.19.post4+gbebed64
 Jinja2==2.11.3
 jmespath==0.10.0
 joblib==1.0.1
@@ -53,7 +53,7 @@ s3transfer==0.3.6
 scikit-image==0.17.2
 scikit-learn==0.24.1
 scipy==1.5.4
-SimpleITK==1.2.0rc2.dev1162+g2a79d
+SimpleITK==2.0.2rc2.dev785+g8ac4f
 six==1.15.0
 threadpoolctl==2.1.0
 tifffile==2020.9.3
@@ -62,5 +62,5 @@ traits==6.2.0
 traitsui==7.1.1
 typing-extensions==3.7.4.3
 urllib3==1.26.5
-vtk==8.1.2
+vtk==9.0.1
 zipp==3.4.1

magmap/cv/segmenter.py

@@ -142,6 +142,7 @@ def segment_ws(roi, channel, thresholded=None, blobs=None):
         of labels given as an image of the same shape as ``roi``.
     """
     labels = []
+    labels_ws = None
     multichannel, channels = plot_3d.setup_channels(roi, channel, 3)
     for i in channels:
         roi_segment = roi[..., i] if multichannel else roi
@@ -157,6 +158,7 @@ def segment_ws(roi, channel, thresholded=None, blobs=None):
         if blobs is None:
             # default to finding peaks of distance transform if no blobs 
             # given, using an anisotropic footprint
+            distance = ndimage.distance_transform_edt(thresholded)
             try:
                 local_max = feature.peak_local_max(
                     distance, indices=False, footprint=np.ones((1, 3, 3)), 

magmap/gui/roi_editor.py

@@ -1204,11 +1204,11 @@ def on_motion(event):
             if labels is not None:
                 for i in range(len(labels)):
                     label = labels[i]
-                    if z_relative >= 0 and z_relative < label.shape[0]:
-                        ax.imshow(
-                            label[z_relative], cmap=cmap_labels,
-                            norm=cmap_labels.norm)
-                        #ax.imshow(label[z_relative]) # showing only threshold
+                    if 0 <= z_relative < label.shape[0]:
+                        img = label[z_relative]
+                        if img.ndim > 1:
+                            ax.imshow(
+                                img, cmap=cmap_labels, norm=cmap_labels.norm)
 
             if ((segs_in is not None or segs_out is not None)
                     and not circles == self.CircleStyles.NO_CIRCLES):