Napari join labels

.coveragerc

@@ -0,0 +1,4 @@
+[run]
+omit = 
+    config.py
+    config-3.py

.github/workflows/continuous-integration.yml

@@ -21,7 +21,7 @@ jobs:
     env:
       OS: ${{ matrix.os }}
       PYTHON: ${{ matrix.python-version }}
-
+      DISPLAY: ':99.0'
     steps:
     - uses: actions/checkout@main
     - name: Set up Python ${{ matrix.python-version }}
@@ -30,19 +30,27 @@ jobs:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
       run: |
+        sudo apt-get update
+        sudo apt-get install --no-install-recommends libyaml-dev libegl1-mesa libxkbcommon-x11-0 libxcb-icccm4 libxcb-image0 libxcb-keysyms1 libxcb-randr0 libxcb-render-util0 libxcb-xinerama0 libxcb-shape0 libxcb-cursor0 xserver-xephyr xvfb
         python -m pip install --upgrade pip
-        pip install flake8 pytest pytest-cov ipython
+        pip install flake8 pytest pytest-cov pytest-qt pytest-xvfb ipython anyio
     - 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
         # 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
-    - name: Test and generate coverage report
+    - uses: tlambert03/setup-qt-libs@v1
+    - name: Install plantcv-annotate
       # Run coverage analysis on pytest tests
       run: |
         pip install .
-        py.test --cov-report=xml --cov=./
+        pip uninstall -y opencv-python
+        pip install opencv-python-headless
+    - name: Tests
+      uses: aganders3/headless-gui@v2
+      with:
+        run: pytest --cov-report=xml --cov=./
     - name: Upload coverage to Deepsource
       uses: deepsourcelabs/test-coverage-action@master
       with:

docs/Points.md

@@ -1,6 +1,6 @@
 ## Interactive Point Annotation Tool
 
-Using [Jupyter Notebooks](jupyter.md) it is possible to interactively click to collect coordinates from an image, which can be used in various downstream applications. Left click on the image to collect a point. Right click removes the
+Using [Jupyter Notebooks](https://plantcv.readthedocs.io/en/stable/jupyter/) it is possible to interactively click to collect coordinates from an image, which can be used in various downstream applications. Left click on the image to collect a point. Right click removes the
 closest collected point.
 
 **plantcv.annotate.Points**(*img, figsize=(12,6), label="dafault"*)

docs/napari_classes.md

@@ -0,0 +1,32 @@
+## Open Image with Napari
+
+Get class names from Napari Viewer Object.
+
+**plantcv.annotate.napari_classes**(*viewer*)
+
+**returns** list of napari classes
+
+- **Parameters:**
+    - viewer - Napari viewer object
+
+- **Context:**
+    - Get names of Napari classes. This is mainly an internal function but can be useful in other context.
+
+- **Example use:**
+    - Get names of Napari classes/labels. 
+
+
+```python
+import plantcv.plantcv as pcv 
+import plantcv.annotate as pcvan
+
+# Create an instance of the Points class
+img, path, name = pcv.readimage("./grayimg.png")
+
+viewer = pcvan.napari_label_classes(img=img, classes=['background', 'wing', 'seed'])
+
+classes = pcvan.napari_classes(viewer)
+
+```
+
+**Source Code:** [Here](https://github.com/danforthcenter/plantcv-annotate/blob/main/plantcv/annotate/napari_classes.py)

docs/napari_join_labels.md

@@ -0,0 +1,45 @@
+## Join Labels with Napari
+
+This function joins classes with the same label. This function would be run after classes are labeled with napari_label_classes.
+
+**plantcv.annotate.napari_join_labels*(*img, viewer*)
+
+**returns** relabeled mask, dictionary of masks for each class
+
+- **Parameters:**
+    - img - image data (compatible with gray, RGB, and hyperspectral data. If data is hyperspecral it should be the array e.g. hyperspectral.array_data)
+    - viewer - viewer with labeled classes. If no points are selected for a class,
+        data without labels will default to this class when napari_join_labels
+        is run. If all classes have points labeled, any clusters not labeled
+        will default to the last class in the list if napari_join_labels is
+        run.
+
+- **Context:**
+    - This function would be run after labeling classes in Napari is complete. 
+
+- **Example use:**
+    - Joining classes labeled as the same, for example for joining classes from output of kmeans clustering
+
+
+```python
+import plantcv.plantcv as pcv 
+import plantcv.annotate as pcvan
+import napari
+
+# Create an instance of the Points class
+img, path, name = pcv.readimage("./grayimg.png")
+
+viewer = pcvan.napari_label_classes(img=img, ['background', 'wing','seed'])
+
+# Should open interactive napari viewer
+
+labeledmask, mask_dict = pcvan.napari_join_lables(img=img, viewer=viewer)
+
+```
+
+![Screenshot](img/documentation_images/napari_label_classes/napari_label_classes.png)
+
+![Screenshot](img/documentation_images/napari_join_labels/1_labeled_mask.png)
+
+
+**Source Code:** [Here](https://github.com/danforthcenter/plantcv-annotate/blob/main/plantcv/annotate/napari_label_classes.py)

docs/napari_label_classes.md

@@ -0,0 +1,43 @@
+## Label Image with Napari
+
+This function opens an image in Napari and then defines a set of classes to label. A random shape label is assigned to each class. 
+Image can be annotated as long as viewer is open. 
+
+**plantcv.annotate.napari_label_classes*(*img, classes, show=True*)
+
+**returns** napari viewer object
+
+- **Parameters:**
+    - img - image data (compatible with gray, RGB, and hyperspectral data. If data is hyperspecral it should be the array e.g. hyperspectral.array_data)
+    - classes - list of classes to label. If no points are selected for a class,
+        data without labels will default to this class when napari_join_labels
+        is run. If all classes have points labeled, any clusters not labeled
+        will default to the last class in the list if napari_join_labels is
+        run.
+    - show - if show = True, viewer is launched. False setting is useful for test purposes.
+
+- **Context:**
+    - Adding class labels to images. Works best on an image that has objects segmented/classified with contours/clusters labeled with values (e.g. labeled mask, output of kmeans clustering).
+
+- **Example use:**
+    - Labeling output of kmeans clustering into classes. Labeling points.
+
+
+```python
+import plantcv.plantcv as pcv 
+import plantcv.annotate as pcvan
+import napari
+
+# Create an instance of the Points class
+img, path, name = pcv.readimage("./grayimg.png")
+
+viewer = pcvan.napari_label_classes(img=img, classes=['background', 'wing','seed'])
+
+# Should open interactive napari viewer
+
+```
+
+![Screenshot](img/documentation_images/napari_label_classes/napari_label_classes.png)
+
+
+**Source Code:** [Here](https://github.com/danforthcenter/plantcv-annotate/blob/main/plantcv/annotate/napari_label_classes.py)

docs/napari_open.md

@@ -0,0 +1,36 @@
+## Open Image with Napari
+
+Open image data (e.g. RGB, gray, hyperspectral) with an interactive Napari viewer. If a gray image is opened, the image will be pseudocolored for better visualization.
+
+**plantcv.annotate.napari_open**(*img, show=True*)
+
+**returns** napari viewer object
+
+- **Parameters:**
+    - img - image data (compatible with gray, RGB, and hyperspectral data. If data is hyperspecral it should be the array e.g. hyperspectral.array_data)
+    - show - if show = True, viewer is launched. False setting is useful for test purposes.
+
+- **Context:**
+    - Used to open image data with Napari.
+
+- **Example use:**
+    - Open image data to annotate it with other Napari functions (e.g. napari_label_classes)
+
+
+```python
+import plantcv.plantcv as pcv 
+import plantcv.annotate as pcvan
+
+# Create an instance of the Points class
+img, path, name = pcv.readimage("./grayimg.png")
+
+viewer = pcvan.napari_open(img=img)
+
+# Should open interactive napari viewer
+
+```
+
+![Screenshot](img/documentation_images/napari_open/napari_open.png)
+
+
+**Source Code:** [Here](https://github.com/danforthcenter/plantcv-annotate/blob/main/plantcv/annotate/napari_open.py)

environment.yml

@@ -0,0 +1,33 @@
+# run: conda env create --file environment.yml
+# optionally, change channel name with -n {plantcv-dev}
+name: plantcv
+dependencies:
+  - python=3.10
+  - matplotlib>=1.5
+  - numpy>=1.11
+  - pandas
+  - python-dateutil
+  - scipy
+  - scikit-image>=0.19
+  - scikit-learn
+  - dask
+  - dask-jobqueue
+  - opencv
+  - statsmodels
+  - xarray>=2022.11.0
+  - mkdocs
+  - pytest
+  - pytest-cov
+  - flake8
+  - ipympl
+  - nodejs
+  - jupyterlab
+  - altair
+  - vl-convert-python
+  - napari
+  - pyqt
+  - pytest-qt
+
+channels:
+  - conda-forge
+  - defaults

mkdocs.yml

@@ -17,6 +17,10 @@ nav:
     - 'Adding/Editing Documentation': documentation.md
   - 'PlantCV Namespace':
     - 'Annotation Tools':
+      - Napari Classes: napari_classes.md
+      - Napari Join: napari_join_labels.md
+      - Napari Label: napari_label_classes.md
+      - Napari Open: napari_open.md
       - Points: Points.md
       - Get Centroids: get_centroids.md
 markdown_extensions:

plantcv/annotate/__init__.py

@@ -1,11 +1,19 @@
 from importlib.metadata import version
 from plantcv.annotate.classes import Points
 from plantcv.annotate.get_centroids import get_centroids
+from plantcv.annotate.napari_classes import napari_classes
+from plantcv.annotate.napari_open import napari_open
+from plantcv.annotate.napari_label_classes import napari_label_classes
+from plantcv.annotate.napari_join_labels import napari_join_labels
 
 # Auto versioning
 __version__ = version("plantcv-annotate")
 
 __all__ = [
     "Points",
-    "get_centroids"
+    "get_centroids",
+    "napari_classes",
+    "napari_open",
+    "napari_label_classes",
+    "napari_join_labels"
     ]

plantcv/annotate/napari_classes.py

@@ -0,0 +1,31 @@
+# Get Napari Keys
+
+import re
+
+
+def napari_classes(viewer):
+    """
+    get names of napari keys
+
+    Inputs:
+    viewer  = napari viewer object
+
+    Returns:
+    classes  = napari class value names
+
+    :param viewer: napari.viewer.Viewer
+    :return labels: numpy.ndarray, list
+    """
+    keylist = list(viewer.layers)
+    keylist = ''.join(str(keylist))
+    keylist = keylist.split(',')
+
+    classes = []
+    for x in keylist:
+        if re.search('Image layer', x):
+            pass
+        else:
+            y = x.split(" ")
+            classes.append(y[3].strip("\'"))
+
+    return classes