Merge pull request #29 from danforthcenter/18-napari-import-coor

Updates to napari_label_classes

docs/changelog.md

@@ -16,7 +16,7 @@ All notable changes to this project will be documented below.
 
 #### annotate.napari_label_classes
 
-* v0.1dev: viewer = **annotate.napari_label_classes**(*img, classes, show=True*)
+* v0.1dev: viewer = **annotate.napari_label_classes**(*img, classes, size=10, shape='square', importdata=False, show=True*)
 
 #### annotate.napari_open
 

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, 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. 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.
+    - classes - list of classes to label. This option is not necessary if data is data is imported.
+    - 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.
 
 - **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'])
-
-# Should open interactive napari viewer
+# Opens interactive napari viewer
+viewer = pcvan.napari_label_classes(img=img, classes=['background', 'wing','seed'], size=30)
 
 ```
 

plantcv/annotate/napari_label_classes.py

@@ -3,9 +3,11 @@
 import numpy as np
 import random
 from plantcv.annotate import napari_open
+from plantcv.annotate import napari_classes
 
 
-def napari_label_classes(img, classes, show=True):
+def napari_label_classes(img, classes=False, size=10, shape='square',
+                         importdata=False, show=True):
     """
     open img in napari and label classes
 
@@ -19,26 +21,83 @@ def napari_label_classes(img, classes, show=True):
         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 option is useful for
+    size = size of marker in pixels
+    shape = either 'square' or 'circle'
+    importdata = dictionary of values in Napari format (y,x).
+        Output of napari_read_coor
+    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
 
     :param img: numpy.ndarray
     :param classes: list
+    :param size: int
+    :param shape: str
+    :param importdata: dict
+    :param show: str
+
     :return viewer: napari viewer object
 
     """
     showcall = show
     viewer = napari_open(img, show=showcall)
 
-    symbols = ['arrow', 'clobber', 'cross', 'diamond', 'disc', 'hbar', 'ring',
-               'square', 'star', 'tailed_arrow', 'triangle_down',
-               'triangle_up', 'vbar']
+    color = ["aliceblue", "antiquewhite", "aqua", "aquamarine", "azure",
+             "beige", "bisque", "black", "blanchedalmond", "blue",
+             "blueviolet", "brown", "burlywood", "cadetblue",
+             "chartreuse", "chocolate", "coral", "cornflowerblue",
+             "cornsilk", "crimson", "cyan", "darkblue", "darkcyan",
+             "darkgoldenrod", "darkgray", "darkgreen", "darkkhaki",
+             "darkmagenta", "darkolivegreen", "darkorange", "darkorchid",
+             "darkred", "darksalmon", "darkseagreen", "darkslateblue",
+             "darkslategray", "darkturquoise", "darkviolet", "deeppink",
+             "deepskyblue", "dimgray", "dodgerblue", "firebrick",
+             "floralwhite", "forestgreen", "fuchsia", "gainsboro",
+             "ghostwhite", "gold", "goldenrod", "gray", "green",
+             "greenyellow", "honeydew", "hotpink", "indianred",
+             "indigo", "ivory", "khaki", "lavender", "lavenderblush",
+             "lawngreen", "lemonchiffon", "lightblue", "lightcoral",
+             "lightcyan", "lightgoldenrodyellow", "lightgray", "lightgreen",
+             "lightpink", "lightsalmon", "lightseagreen", "lightskyblue",
+             "lightslategray", "lightsteelblue", "lightyellow", "lime",
+             "limegreen", "linen", "magenta", "maroon", "mediumaquamarine",
+             "mediumblue", "mediumorchid", "mediumpurple", "mediumseagreen",
+             "mediumslateblue", "mediumspringgreen", "mediumturquoise",
+             "mediumvioletred", "midnightblue", "mintcream", "mistyrose",
+             "moccasin", "navajowhite", "navy", "oldlace", "olive",
+             "olivedrab", "orange", "orangered", "orchid", "palegoldenrod",
+             "palegreen", "paleturquoise", "palevioletred", "papayawhip",
+             "peachpuff", "peru", "pink", "plum", "powderblue", "purple",
+             "red", "rosybrown", "royalblue", "saddlebrown",
+             "salmon", "sandybrown", "seagreen", "seashell", "sienna",
+             "silver", "skyblue", "slateblue", "slategray", "snow",
+             "springgreen", "steelblue", "tan", "teal", "thistle", "tomato",
+             "turquoise", "violet", "wheat", "white", "whitesmoke",
+             "yellow", "yellowgreen"]
+
+    keys = napari_classes(viewer)
+
+    if classes is not False:
+        for x in classes:
+            if x not in keys:
+                viewer.add_points(np.array([]), name=x, symbol=shape,
+                                  edge_color=random.choice(color),
+                                  face_color=random.choice(color), size=size)
+        keys = napari_classes(viewer)
 
-    for x in classes:
-        viewer.add_points(np.array([]), name=x, symbol=random.choice(symbols),
-                          face_color='white', size=10)
+    if importdata is not False:
+        importkeys = list(importdata.keys())
+        for key in importkeys:
+            if len(importdata[key]) != 0:
+                if key in keys:
+                    viewer.layers[key].add(importdata[key])
+                else:
+                    viewer.add_points(importdata[key], name=key, symbol=shape,
+                                      edge_color=random.choice(color),
+                                      face_color=random.choice(color),
+                                      size=size)
 
     return viewer

tests/test_napari_label_classes.py

@@ -7,10 +7,13 @@ 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'], show=False)
-    coor = [(25, 25)]
-    viewer.add_points(np.array(coor), symbol="o", name='background',
-                      face_color="red", size=1)
+    data = {'total': [(25, 25)], 'background': [(50, 50)]}
+    viewer = napari_label_classes(img, ['total'], size=5, importdata=data,
+                                  show=False)
+    coor = [(50, 25)]
+    viewer.add_points(np.array(coor), symbol="o", name='coor',
+                      face_color="red", size=5)
+
+    assert len(viewer.layers['total'].data) == 1
 
-    assert len(viewer.layers['background'].data) == 1
     viewer.close()