Update napari_label_classes.md

docs/napari_label_classes.md

@@ -1,26 +1,26 @@
 ## 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. 
+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, shape =10, 'square', 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
-    - shape - can be 'o', 'arrow', 'clobber', 'cross', 'diamond', 'disc', 'hbar', 'ring', 'square', 'star', 'tailed_arrow', 
-    'triangle_down', 'triangle_up', 'vbar', 'x'.
+    - 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
@@ -31,9 +31,8 @@ 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)
 
 ```