Merge branch 'main' into 30-napari-draw-mask

.coveragerc

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

.github/PULL_REQUEST_TEMPLATE.md

@@ -23,6 +23,6 @@ See [this page](https://plantcv.readthedocs.io/en/latest/pr_review_process/) for
 - [ ] Test coverage remains 100%
 - [ ] Documentation tested
 - [ ] New documentation pages added to `plantcv/mkdocs.yml`
-- [ ] Changes to function input/output signatures added to `updating.md`
+- [ ] Changes to function input/output signatures added to `changelog.md`
 - [ ] Code reviewed
 - [ ] PR approved

.github/workflows/continuous-integration.yml

@@ -16,7 +16,7 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        python-version: ['3.8', '3.9', '3.10']
+        python-version: ['3.9', '3.10', '3.11']
         os: [ubuntu-latest]
     env:
       OS: ${{ matrix.os }}
@@ -30,8 +30,7 @@ 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
+        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
         python -m pip install --upgrade pip
         pip install flake8 pytest pytest-cov pytest-qt pytest-xvfb ipython anyio
     - name: Lint with flake8
@@ -48,8 +47,9 @@ jobs:
         pip uninstall -y opencv-python
         pip install opencv-python-headless
     - name: Tests
-      uses: aganders3/headless-gui@v2
+      uses: aganders3/headless-gui@v2.2
       with:
+        xvfb-screen-size: 1280x720x24
         run: pytest --cov-report=xml --cov=./
     - name: Upload coverage to Deepsource
       uses: deepsourcelabs/test-coverage-action@master

docs/changelog.md

@@ -1,3 +1,35 @@
 ## Changelog
 
 All notable changes to this project will be documented below.
+
+#### annotate.get_centroids
+
+* v0.1dev: coords_list = **annotate.get_centroids**(*bin_img*)
+
+#### annotate.napari_classes
+
+* v0.1dev: class_list = **annotate.napari_classes**(*viewer*)
+
+#### annotate.napari_join_labels
+
+* v0.1dev: relabeled_mask, mask_dict = **annotate.napari_join_labels**(*img, viewer*)
+
+#### annotate.napari_label_classes
+
+* v0.1dev: viewer = **annotate.napari_label_classes**(*img, classes, size=10, shape='square', importdata=False, show=True*)
+
+#### annotate.napari_open
+
+* v0.1dev: viewer = **annotate.napari_open**(*img, mode = 'native', show=True*)
+
+#### annotate.napari_read_coor
+
+* v0.1dev: data = **annotate.napari_read_coor**(*coor, dataformat='yx'*)
+
+#### annotate.napari_save_coor
+
+* v0.1dev: datadict = **annotate.napari_save_coor**(*viewer, filepath*)
+
+#### annotate.Points
+
+* v0.1dev: viewer = **annotate.Points**(*img, figsize=(12,6), label="dafault"*)

docs/get_centroids.md

@@ -0,0 +1,44 @@
+## Get Centroids 
+
+Extract the centroid coordinate (column,row) from regions in a binary image. 
+
+**plantcv.annotate.get_centroids**(*bin_img*)
+
+**returns** list containing coordinates of centroids 
+
+- **Parameters:**
+    - bin_img - Binary image containing the connected regions to consider
+- **Context:**
+    - Given an arbitrary mask of the objects of interest, `get_centroids`
+    returns a list of coordinates that can the be imported into the annotation class [Points](Points.md).
+
+- **Example use:**
+    - Below
+
+**Binary image**
+
+![count_img](img/documentation_images/get_centroids/discs_mask.png)
+
+```python
+
+from plantcv import plantcv as pcv
+
+# Set global debug behavior to None (default), "print" (to file),
+# or "plot"
+pcv.params.debug = "plot"
+
+# Apply get centroids to the binary image
+coords = pcv.annotate.get_centroids(bin_img=binary_img)
+print(coords)
+# [[1902, 600], [1839, 1363], [1837, 383], [1669, 1977], [1631, 1889], [1590, 1372], [1550, 1525],
+# [1538, 1633], [1522, 1131], [1494, 2396], [1482, 1917], [1446, 1808], [1425, 726], [1418, 2392],
+# [1389, 198], [1358, 1712], [1288, 522], [1289, 406], [1279, 368], [1262, 1376], [1244, 1795],
+# [1224, 1327], [1201, 624], [1181, 725], [1062, 85], [999, 840], [885, 399], [740, 324], [728, 224],
+# [697, 860], [660, 650], [638, 2390], [622, 1565], [577, 497], [572, 2179], [550, 2230], [547, 1826],
+# [537, 892], [538, 481], [524, 2144], [521, 2336], [497, 201], [385, 1141], [342, 683], [342, 102],
+# [332, 1700], [295, 646], [271, 60], [269, 1626], [210, 1694], [189, 878], [178, 1570], [171, 2307],
+# [61, 286], [28, 2342]]
+
+```
+
+**Source Code:** [Here](https://github.com/danforthcenter/plantcv-annotate/blob/main/plantcv/annotate/get_centroids.py)

docs/napari_join_labels.md

@@ -31,10 +31,10 @@ img, path, name = pcv.readimage("./grayimg.png")
 
 viewer = pcvan.napari_label_classes(img=img, ['background', 'wing','seed'])
 
-labeledmask, mask_dict = pcvan.napari_join_lables(img=img, viewer)
-
 # 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)

docs/napari_label_classes.md

@@ -1,24 +1,27 @@
 ## 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 annotate as long as viewer is open. 
+This function opens an image in Napari and then defines a set of Points layers with the user-defined labels called `classes`. A random `shape` of the annotation symbol is assigned to each of the `classes`. 
+Image can be annotated as long as viewer is open. 
 
-**plantcv.annotate.napari_label_classes*(*img, classes, size=10, importdata=False, show=True*)
+
+**plantcv.annotate.napari_label_classes*(*img, classes, size=10, shape='square', importdata=False, 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. This option is not necessary if data is data is imported.
-    - size - integer pixel size of label, best if even number
+    - size - integer pixel size of label (also adjustable from the interactive Napari viewer)
+    - shape - shape of the annotation symbol. Can be 'o', 'arrow', 'clobber', 'cross', 'diamond', 'disc', 'hbar', 'ring', 'square' (default), 'star', 'tailed_arrow', 
+    'triangle_down', 'triangle_up', 'vbar', or 'x' (also adjustable from the interactive Napari viewer)
     - importdata - dictionary of data, data saved from napari_save_coor or data imported from napari_read_coor
-    - show - if show = True, viewer is launched. False setting is useful for test purposes.
+    - 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).
+    - Adding one or more classes of points layer for annotation of the image.
 
 - **Example use:**
-    - Labeling output of kmeans clustering into classes. Labeling points.
+    - Ground truth counting, labeling classes of objects of interest.
 
 
 ```python
@@ -29,9 +32,8 @@ 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'], size = 30)
-
-# Should open interactive napari viewer
+# Opens interactive napari viewer
+viewer = pcvan.napari_label_classes(img=img, classes=['background', 'wing','seed'], size=30)
 
 ```
 

docs/napari_open.md

@@ -1,13 +1,14 @@
 ## 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.
+Open image data (e.g. RGB, gray, hyperspectral) with an interactive Napari viewer. Labeled masks may be colorized for better visualization. 
 
-**plantcv.annotate.napari_open**(*img, show=True*)
+**plantcv.annotate.napari_open**(*img, mode='native', 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)
+    - img - image data (compatible with gray, RGB, and hyperspectral data. If data is hyperspecral it should be the array e.g. `hyperspectral.array_data`)
+    - mode - 'native' or 'colorize'. If 'colorized' is selected gray images will be colorized.
     - show - if show = True, viewer is launched. False setting is useful for test purposes.
 
 - **Context:**
@@ -24,7 +25,7 @@ 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)
+viewer = pcvan.napari_open(img=img, mode='colorize')
 
 # Should open interactive napari viewer
 

docs/napari_read_coor.md

@@ -7,14 +7,14 @@ Save Points Labeled in Napari to a File
 **returns** dictionary of points labeled by class
 
 - **Parameters:**
-    - coor - dictionary object of coordinates or a path to json datafile with dictionary of point coordinates
+    - coor - dictionary object of coordinates, or a path to json datafile with dictionary of point coordinates
     - dataformat - either 'yx' or 'xy', Napari takes data as y,x format. If data is 'xy' data is converted from x,y to y,x
 
 - **Context:**
-    - Import data from a file and convert to Napari format data if necessary
+    - Import previously labeled points, or points from other functions (e.g. [`pcvan.napari_read_coor`](napari_read_coor.md))
 
 - **Example use:**
-    - Import previously labeled points, or points from other functions (e.g. detect_centroid)
+    - Below
 
 
 ```python

docs/napari_save_coor.md

@@ -11,7 +11,7 @@ Save Points Labeled in Napari to a File
     - Filepath - File to save data. If the file exits an extension will be added.
 
 - **Context:**
-    - Save points labeled in Napari to a file in case the same points need to be used.
+    - Save points labeled in Napari to a file to checkpoint annotation progress or reuse.
 
 - **Example use:**
     - Save points labeled to a file 
@@ -23,13 +23,11 @@ import plantcv.annotate as pcvan
 
 # Create an instance of the Points class
 img, path, name = pcv.readimage("./grayimg.png")
-
+# Should open interactive napari viewer
 viewer = pcvan.napari_label_classes(img=img, classes=['background', 'wing','seed'])
 
 dictobj = pcvan.napari_save_coor(viewer, 'testdata.txt')
 
-# Should open interactive napari viewer
-
 ```
 
 ![Screenshot](img/documentation_images/napari_label_classes/napari_label_classes.png)

mkdocs.yml

@@ -25,7 +25,7 @@ nav:
       - Napari Read Coor: napari_read_coor.md
       - Napari Save Coor: napari_save_coor.md
       - Points: Points.md
-
+      - Get Centroids: get_centroids.md
 markdown_extensions:
   - toc:
       permalink: True

plantcv/annotate/__init__.py

@@ -1,3 +1,6 @@
+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
@@ -6,8 +9,12 @@
 from plantcv.annotate.napari_read_coor import napari_read_coor
 from plantcv.annotate.napari_points_mask import napari_points_mask
 
+# Auto versioning
+__version__ = version("plantcv-annotate")
 
 __all__ = [
+    "Points",
+    "get_centroids",
     "napari_classes",
     "napari_open",
     "napari_label_classes",