Merge branch 'main' into feature/progress_bars

.github/workflows/test_and_deploy.yml

@@ -1,9 +1,12 @@
 # This workflows will upload a Python Package using Twine when a release is created
 # For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries
 
+### test workflow from Talley Lambert's napari-omero! thanks!
+# https://github.com/tlambert03/napari-omero/blob/main/.github/workflows/ci.yml
+
 name: tests
 
-on: 
+on:
   push:
     branches:
       - main
@@ -16,22 +19,25 @@ on:
 
 jobs:
   test:
-    name: ${{ matrix.platform }} py${{ matrix.python-version }}
+    name: ${{ matrix.platform }} ${{ matrix.python-version }}
     runs-on: ${{ matrix.platform }}
+    defaults:
+      run:
+        shell: bash -l {0}
     strategy:
       fail-fast: false
       matrix:
-        platform: [ubuntu-latest, windows-latest, macos-latest]
-        python-version: ['3.8', '3.9', '3.10']
-        exclude: # torch doesn't support windows python 3.10 https://pytorch.org/get-started/locally/#windows-python
-          - platform: windows-latest
-            python-version: '3.10'
-    steps:
-      - uses: actions/checkout@v3
+        platform: [ubuntu-latest]#, macos-latest, windows-latest]
+        python-version: ["3.9", "3.10", "3.11"]
+        backend: [pyqt5]
 
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
+    steps:
+      - uses: actions/checkout@v4
+      - uses: conda-incubator/setup-miniconda@v2
         with:
+          miniconda-version: "latest"
+          channels: conda-forge
+          channel-priority: strict
           python-version: ${{ matrix.python-version }}
 
       - uses: tlambert03/setup-qt-libs@v1
@@ -41,27 +47,25 @@ jobs:
         run: |
           git clone --depth 1 https://github.com/pyvista/gl-ci-helpers.git
           powershell gl-ci-helpers/appveyor/install_opengl.ps1
-          if (Test-Path -Path "C:\Windows\system32\opengl32.dll" -PathType Leaf) {Exit 0} else {Exit 1}
 
-
-      # note: if you need dependencies from conda, considering using
-      # setup-miniconda: https://github.com/conda-incubator/setup-miniconda
-      # and
-      # tox-conda: https://github.com/tox-dev/tox-conda
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          python -m pip install setuptools tox tox-gh-actions
-      # this runs the platform-specific tests declared in tox.ini
+          python -m pip install --upgrade setuptools tox tox-conda tox-gh-actions
+
       - name: Test with tox
-        uses: GabrielBB/xvfb-action@v1
+        uses: aganders3/headless-gui@v1.2
         with:
+          shell: bash -el {0}
           run: python -m tox
         env:
           PLATFORM: ${{ matrix.platform }}
+          PYTHON: ${{ matrix.python-version }}
+          PYVISTA_OFF_SCREEN: True
+          BACKEND: ${{ matrix.backend }}
 
-      - name: Coverage
-        uses: codecov/codecov-action@v2
+      - name: Codecov
+        uses: codecov/codecov-action@v3
 
   deploy:
     # this will run when you have tagged a commit, starting with "v*"
@@ -71,9 +75,9 @@ jobs:
     runs-on: ubuntu-latest
     if: contains(github.ref, 'tags')
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v4
       - name: Set up Python
-        uses: actions/setup-python@v2
+        uses: actions/setup-python@v4
         with:
           python-version: "3.x"
       - name: Install dependencies
@@ -87,4 +91,5 @@ jobs:
         run: |
           git tag
           python -m build
+          twine check dist/*
           twine upload dist/*

cellpose_napari/_dock_widget.py

@@ -15,6 +15,10 @@
 from magicgui import magicgui
 import sys
 
+CP_models = ["cyto3", "cyto2", "cyto", "nuclei", "tissuenet_cp3", 
+             "livecell_cp3", "yeast_PhC_cp3", "yeast_BF_cp3", "bact_phase_cp3", 
+             "bact_fluor_cp3", "deepbacs_cp3", "cyto2_cp3"]
+
 # initialize logger
 # use -v or --verbose when starting napari to increase verbosity
 logger = logging.getLogger(__name__)
@@ -59,12 +63,11 @@ def _deco(func):
     @thread_worker(progress=True)
     @no_grad()
     def run_cellpose(image, model_type, custom_model, channels, channel_axis, diameter,
-                    net_avg, resample, cellprob_threshold, 
-                    model_match_threshold, do_3D, stitch_threshold):
+                    resample, cellprob_threshold, 
+                    flow_threshold, do_3D, stitch_threshold):
         from cellpose import models
 
-        flow_threshold = (31.0 - model_match_threshold) / 10.
-        if model_match_threshold==0.0:
+        if flow_threshold==0.0:
             flow_threshold = 0.0
             logger.debug('flow_threshold=0 => no masks thrown out due to model mismatch')
         logger.debug(f'computing masks with cellprob_threshold={cellprob_threshold}, flow_threshold={flow_threshold}')
@@ -82,7 +85,6 @@ def run_cellpose(image, model_type, custom_model, channels, channel_axis, diamet
                                     channels=channels, 
                                     channel_axis=channel_axis,
                                     diameter=diameter,
-                                    net_avg=net_avg,
                                     resample=resample,
                                     cellprob_threshold=cellprob_threshold,
                                     flow_threshold=flow_threshold,
@@ -101,23 +103,24 @@ def run_cellpose(image, model_type, custom_model, channels, channel_axis, diamet
     @thread_worker(progress=True)
     def compute_diameter(image, channels, model_type):
         from cellpose import models
+        model_type0 = model_type if model_type in CP_models[:4] else "cyto3"
 
-        CP = models.Cellpose(model_type = model_type, gpu=True)
+        CP = models.Cellpose(model_type = model_type0, gpu=True)
         diam = CP.sz.eval(image, channels=channels, channel_axis=-1)[0]
         diam = np.around(diam, 2)
         del CP
         return diam
 
-    @thread_worker(progress=True) 
-    def compute_masks(masks_orig, flows_orig, cellprob_threshold, model_match_threshold):
+    @thread_worker(progress=True)
+    def compute_masks(masks_orig, flows_orig, cellprob_threshold, flow_threshold):
         import cv2
         from cellpose.utils import fill_holes_and_remove_small_masks
         from cellpose.dynamics import get_masks
         from cellpose.transforms import resize_image
 
         #print(flows_orig[3].shape, flows_orig[2].shape, masks_orig.shape)
-        flow_threshold = (31.0 - model_match_threshold) / 10.
-        if model_match_threshold==0.0:
+        flow_threshold = (31.0 - flow_threshold) / 10.
+        if flow_threshold==0.0:
             flow_threshold = 0.0
             logger.debug('flow_threshold=0 => no masks thrown out due to model mismatch')
         logger.debug(f'computing masks with cellprob_threshold={cellprob_threshold}, flow_threshold={flow_threshold}')
@@ -131,17 +134,16 @@ def compute_masks(masks_orig, flows_orig, cellprob_threshold, model_match_thresh
     @magicgui(
         call_button='run segmentation',  
         layout='vertical',
-        model_type = dict(widget_type='ComboBox', label='model type', choices=['cyto', 'nuclei', 'cyto2', 'custom'], value='cyto', tooltip='there is a <em>cyto</em> model, a new <em>cyto2</em> model from user submissions, and a <em>nuclei</em> model'),
+        model_type = dict(widget_type='ComboBox', label='model type', choices=[*CP_models, 'custom'], value='cyto3', tooltip='there is a <em>cyto</em> model, a new <em>cyto2</em> model from user submissions, and a <em>nuclei</em> model'),
         custom_model = dict(widget_type='FileEdit', label='custom model path: ', tooltip='if model type is custom, specify file path to it here'),
         main_channel = dict(widget_type='ComboBox', label='channel to segment', choices=main_channel_choices, value=0, tooltip='choose channel with cells'),
         optional_nuclear_channel = dict(widget_type='ComboBox', label='optional nuclear channel', choices=optional_nuclear_channel_choices, value=0, tooltip='optional, if available, choose channel with nuclei of cells'),
         diameter = dict(widget_type='LineEdit', label='diameter', value=30, tooltip='approximate diameter of cells to be segmented'),
         compute_diameter_shape  = dict(widget_type='PushButton', text='compute diameter from shape layer', tooltip='create shape layer with circles and/or squares, select above, and diameter will be estimated from it'),
         compute_diameter_button  = dict(widget_type='PushButton', text='compute diameter from image', tooltip='cellpose model will estimate diameter from image using specified channels'),
         cellprob_threshold = dict(widget_type='FloatSlider', name='cellprob_threshold', value=0.0, min=-8.0, max=8.0, step=0.2, tooltip='cell probability threshold (set lower to get more cells and larger cells)'),
-        model_match_threshold = dict(widget_type='FloatSlider', name='model_match_threshold', value=27.0, min=0.0, max=30.0, step=0.2, tooltip='threshold on gradient match to accept a mask (set lower to get more cells)'),
+        flow_threshold = dict(widget_type='FloatSlider', name='flow_threshold', value=0.4, min=0.0, max=3.0, step=0.05, tooltip='threshold on gradient match to accept a mask (set higher to get more cells, or to zero to turn off)'),
         compute_masks_button  = dict(widget_type='PushButton', text='recompute last masks with new cellprob + model match', enabled=False),
-        net_average = dict(widget_type='CheckBox', text='average 4 nets', value=True, tooltip='average 4 different fit networks (default) or if not checked run only 1 network (fast)'),
         resample_dynamics = dict(widget_type='CheckBox', text='resample dynamics', value=False, tooltip='if False, mask estimation with dynamics run on resized image with diameter=30; if True, flows are resized to original image size before dynamics and mask estimation (turn on for more smooth masks)'),
         process_3D = dict(widget_type='CheckBox', text='process stack as 3D', value=False, tooltip='use default 3D processing where flows in X, Y, and Z are computed and dynamics run in 3D to create masks'),
         stitch_threshold_3D = dict(widget_type='LineEdit', label='stitch threshold slices', value=0, tooltip='across time or Z, stitch together masks with IoU threshold of "stitch threshold" to create 3D segmentation'),
@@ -161,9 +163,8 @@ def widget(#label_logo,
         compute_diameter_shape,
         compute_diameter_button,
         cellprob_threshold,
-        model_match_threshold,
+        flow_threshold,
         compute_masks_button,
-        net_average,
         resample_dynamics,
         process_3D,
         stitch_threshold_3D,
@@ -264,10 +265,9 @@ def _new_segmentation(segmentation):
                                             max(0, optional_nuclear_channel)],
                                 channel_axis=widget.channel_axis, 
                                 diameter=float(diameter),
-                                net_avg=net_average,
                                 resample=resample_dynamics,
                                 cellprob_threshold=cellprob_threshold,
-                                model_match_threshold=model_match_threshold,
+                                flow_threshold=flow_threshold,
                                 do_3D=(process_3D and float(stitch_threshold_3D)==0 and image_layer.ndim>2),
                                 stitch_threshold=float(stitch_threshold_3D) if image_layer.ndim>2 else 0.0)
         cp_worker.returned.connect(_new_segmentation)
@@ -298,7 +298,7 @@ def _compute_masks(e: Any):
         mask_worker = compute_masks(widget.masks_orig, 
                                     widget.flows_orig, 
                                     widget.cellprob_threshold.value, 
-                                    widget.model_match_threshold.value)
+                                    widget.flow_threshold.value)
         mask_worker.returned.connect(update_masks)
         mask_worker.start()
 

tests/test_plugin.py

@@ -48,7 +48,7 @@ def check_widget():
     assert "cp_masks" in viewer.layers[-1].name
 
     # check that the segmentation was proper, should yield 11 cells
-    assert viewer.layers[-1].data.max() == 11
+    assert viewer.layers[-1].data.max() == 10
 
 @pytest.mark.skipif(sys.platform.startswith('linux'), reason="ubuntu stalls with two cellpose tests")
 def test_compute_diameter(qtbot, viewer_widget):