refactoring to add show as an option

docs/napari_classes.md

@@ -23,9 +23,7 @@ import plantcv.annotate as pcvan
 # Create an instance of the Points class
 img, path, name = pcv.readimage("./grayimg.png")
 
-viewer= napari.Viewer()
-
-viewer = pcvan.napari_label_classes(img=img, viewer= viewer, classes=['background', 'wing', 'seed'])
+viewer = pcvan.napari_label_classes(img=img, classes=['background', 'wing', 'seed'])
 
 classes = pcvan.napari_classes(viewer)
 

docs/napari_label_classes.md

@@ -3,18 +3,18 @@
 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. 
 
-**plantcv.annotate.napari_label_classes*(*img, viewer, classes*)
+**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)
-    - viewer - Napari viewer object
     - 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).
@@ -31,8 +31,7 @@ import napari
 # Create an instance of the Points class
 img, path, name = pcv.readimage("./grayimg.png")
 
-viewer = napari.Viewer()
-viewer = pcvan.napari_label_classes(img=img, viewer= viewer, classes=['background', 'wing','seed'])
+viewer = pcvan.napari_label_classes(img=img, classes=['background', 'wing','seed'])
 
 # Should open interactive napari viewer
 

docs/napari_open.md

@@ -2,13 +2,13 @@
 
 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, viewer*)
+**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)
-    - viewer - Napari viewer object
+    - show - if show = True, viewer is launched. False setting is useful for test purposes.
 
 - **Context:**
     - Used to open image data with Napari.
@@ -24,9 +24,7 @@ import plantcv.annotate as pcvan
 # Create an instance of the Points class
 img, path, name = pcv.readimage("./grayimg.png")
 
-viewer = napari.Viewer()
-
-viewer = pcvan.napari_open(img=img, viewer=viewer)
+viewer = pcvan.napari_open(img=img)
 
 # Should open interactive napari viewer
 

plantcv/annotate/napari_label_classes.py

@@ -5,7 +5,7 @@
 from plantcv.annotate import napari_open
 
 
-def napari_label_classes(img, classes):
+def napari_label_classes(img, classes, show=True):
     """
     open img in napari and label classes
 
@@ -19,6 +19,8 @@ def napari_label_classes(img, classes):
         is run. If all classes have points labeled, any clusters not labeled
         will default to the last class in the list when napari_join_labels is
         run.
+    show = if show is True the viewer is launched. This opetion is useful for
+    running tests without triggering the viewer.
 
     Returns:
     viewer  = Napari viewer object
@@ -27,7 +29,8 @@ def napari_label_classes(img, classes):
     :return viewer: napari viewer object
 
     """
-    viewer = napari_open(img)
+    showcall = show
+    viewer = napari_open(img, show=showcall)
 
     symbols = ['arrow', 'clobber', 'cross', 'diamond', 'disc', 'hbar', 'ring',
                'square', 'star', 'tailed_arrow', 'triangle_down',

plantcv/annotate/napari_open.py

@@ -6,7 +6,7 @@
 import napari
 
 
-def napari_open(img):
+def napari_open(img, show=True):
     """
     open img in napari and label classes
 
@@ -33,7 +33,7 @@ def napari_open(img):
             img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
         if shape[2] > 3:
             img = img.transpose(2, 0, 1)
-    viewer = napari.Viewer()
+    showcall = show
+    viewer = napari.Viewer(show=showcall)
     viewer.add_image(img)
-
     return viewer

tests/test_napari_label_classes.py

@@ -7,7 +7,7 @@ def test_napari_label_classes_gray(test_data):
     """Test for PlantCV.Annotate"""
     # Read in test data
     img, _, _ = readimage(test_data.kmeans_seed_gray_img)
-    viewer = napari_label_classes(img, ['seed'])
+    viewer = napari_label_classes(img, ['seed'], show=False)
     coor = [(25, 25)]
     viewer.add_points(np.array(coor), symbol="o", name='background',
                       face_color="red", size=1)

tests/test_napari_open.py

@@ -7,7 +7,7 @@ def test_napari_open_rgb(test_data):
     """Test for PlantCV.Annotate"""
     # Read in test data
     img, _, _ = readimage(test_data.small_rgb_img)
-    viewer = napari_open(img)
+    viewer = napari_open(img, show=False)
     coor = [(25, 25), (50, 50)]
     viewer.add_points(np.array(coor), symbol="o", name="total",
                       face_color="red", size=1)
@@ -19,7 +19,7 @@ def test_napari_open_gray(test_data):
     """Test for PlantCV.Annotate"""
     # Read in test data
     img, _, _ = readimage(test_data.kmeans_seed_gray_img)
-    viewer = napari_open(img)
+    viewer = napari_open(img, show=False)
     coor = [(25, 25), (50, 50)]
     viewer.add_points(np.array(coor), symbol="o", name="total",
                       face_color="red", size=1)
@@ -32,7 +32,7 @@ def test_napari_open_envi(test_data):
     # Read in test data
     img = readimage(test_data.envi_sample_data, mode='envi')
     img = img.array_data
-    viewer = napari_open(img)
+    viewer = napari_open(img, show=False)
     coor = [(25, 25), (50, 50)]
     viewer.add_points(np.array(coor), symbol="o", name="total",
                       face_color="red", size=1)