add save_counts and save_coords methods

docs/Points.md

@@ -25,10 +25,9 @@ closest collected point.
 
 - **Context:**
     - Used to define a list of coordinates of interest.
-    - 
 - **Example use:**
-    - (pcv.roi.multi)
-    - (pcv.roi.custom)
+    - pcv.roi.multi
+    - pcv.roi.custom
 
 
 ```python
@@ -45,5 +44,83 @@ roi = pcv.roi.custom(img=img, vertices=marker.coords['default'])
 
 ```
 
+**plantcv.annotate.Points.save_coords**()
+
+- **Context:**
+    - Once point annotations are collected, save the list of coordinates with their class label to Outputs and later saved out to file 
+
+**plantcv.annotate.Points.save_counts**()
+
+- **Context:**
+    - Once point annotations are collected, save the dictionary of counts with their class label to Outputs and later saved out to file
+
+- **Example use:**
+    - Remove noise from a microscopy image that is otherwise difficult to filter out with traditional computer vision
+    techniques, and recover stomata that were filtered out during mask cleaning. 
+
+**Original Image with Annotations: "total" pollen**
+
+![Screenshot](img/documentation_images/points_save/all_pollen.png)
+
+**Original Image with Annotations: "germinated" pollen**
+
+![Screenshot](img/documentation_images/points_save/germinated_pollen.png)
+
+```python
+import plantcv.plantcv as pcv 
+import plantcv.annotate as pcvan
+
+# Create an instance of the Points class
+img, path, name = pcv.readimage("stomata.tif")
+
+# Segmentation & mask clean up steps here 
+
+# Create an instance of the Points class & click on stomata
+marker = pcvan.Points(img=img, figsize=(12,6))
+
+marker.save_coords()
+marker.save_counts()
+
+pcv.outputs.observations
+
+{'total': {'coordinates': {'trait': 'collected coordinates',
+   'method': 'annotation',
+   'scale': 'none',
+   'datatype': "<class 'list'>",
+   'value': [(113, 58),
+    (353, 105),
+    (74, 240),
+    (103, 297),
+    (278, 333),
+    (185, 350),
+    (237, 335),
+    (244, 285),
+    (300, 192),
+    (295, 151),
+    (262, 178),
+    (321, 44),
+    (187, 158),
+    (160, 187),
+    (65, 186)],
+   'label': 'none'},
+  'object_count': {'trait': 'count of category',
+   'method': 'count',
+   'scale': 'count',
+   'datatype': "<class 'int'>",
+   'value': 15,
+   'label': 'none'}},
+ 'germinated': {'coordinates': {'trait': 'collected coordinates',
+   'method': 'annotation',
+   'scale': 'none',
+   'datatype': "<class 'list'>",
+   'value': [(64, 186), (244, 286)],
+   'label': 'none'},
+  'object_count': {'trait': 'count of category',
+   'method': 'count',
+   'scale': 'count',
+   'datatype': "<class 'int'>",
+   'value': 2,
+   'label': 'none'}}}
+```
 
 **Source Code:** [Here](https://github.com/danforthcenter/plantcv-annotate/blob/main/plantcv/annoate/classes.py)

mkdocs.yml

@@ -26,6 +26,7 @@ nav:
       - Napari Read Coor: napari_read_coor.md
       - Napari Save Coor: napari_save_coor.md
       - Points: Points.md
+      - Points Save: points_save.md
       - Get Centroids: get_centroids.md
 markdown_extensions:
   - toc:

plantcv/annotate/classes.py

@@ -5,8 +5,8 @@
 import json
 from math import floor
 import matplotlib.pyplot as plt
+from plantcv.plantcv import warn, outputs
 from plantcv.plantcv.annotate.points import _find_closest_pt
-from plantcv.plantcv import warn
 
 
 class Points:
@@ -79,6 +79,24 @@ def print_coords(self, filename):
             # Save the data in JSON format with indentation
             json.dump(obj=self.coords, fp=fp, indent=4)
 
+    def save_counts(self):
+        """Save collected coordinates to Outputs.observations"""
+        for key in self.count:
+            value = self.count[key]
+            outputs.add_observation(sample=key, variable="object_count",
+                                    trait='count of category',
+                                    method='count', scale='count', datatype=int,
+                                    value=value, label='none')
+
+    def save_coords(self):
+        """Save collected coordinates to Outputs.observations"""
+        for key in self.count:
+            value = self.coords[key]
+            outputs.add_observation(sample=key, variable="coordinates",
+                                    trait='collected coordinates',
+                                    method='annotation', scale='none', datatype=list,
+                                    value=value, label='none')
+
     def import_list(self, coords, label="default"):
         """Import coordinates.
 

tests/test_annotate_points.py

@@ -3,6 +3,7 @@
 import cv2
 import matplotlib
 from plantcv.annotate.classes import Points
+from plantcv.plantcv import outputs
 
 
 def test_points(test_data):
@@ -157,3 +158,22 @@ def test_points_view_warn(test_data):
     drawer_rgb.view(label="new", color='r')
 
     assert str(drawer_rgb.fig) == "Figure(500x500)"
+
+def test_points_save_coords(test_data):
+    img = cv2.imread(test_data.small_rgb_img)
+    drawer = Points(img=img)
+    # Populate attribute with coords
+    totalpoints1 = [(158, 531), (361, 112), (500, 418)]
+    drawer.coords = {'default': totalpoints1}
+    drawer.save_coords()
+
+    assert outputs.observations["default"]["coordinates"]["value"] == [(158, 531), (361, 112), (500, 418)]
+
+def test_points_save_counts(test_data):
+    img = cv2.imread(test_data.small_rgb_img)
+    drawer = Points(img=img)
+    # Populate attribute with coords
+    drawer.count = {'default': 1}
+    drawer.save_counts()
+
+    assert outputs.observations["default"]["object_count"]["value"] == 1