Add .correct_mask method

docs/Points.md

@@ -1,6 +1,6 @@
 ## Interactive Point Annotation Tool
 
-Using [Jupyter Notebooks](https://plantcv.readthedocs.io/en/stable/jupyter/) it is possible to interactively click to collect coordinates from an image, which can be used in various downstream applications. Left click on the image to collect a point. Right click removes the
+Using [Jupyter Notebooks](https://plantcv.readthedocs.io/en/stable/jupyter/) it is possible to interactively click on an image to collect coordinates, which can be used in various downstream applications. Left click on the image to collect a point. Right click removes the
 closest collected point.
 
 **plantcv.annotate.Points**(*img, figsize=(12,6), label="dafault"*)
@@ -13,9 +13,9 @@ closest collected point.
  - label - The current label (default = "default")
 
 - **Attributes:**
- - coords - dictionary of all coordinates per group label
+ - coords - dictionary of all coordinates per sample label
  - events - includes right and left click events
- - count - dictionary that save the counts of different groups (labels)
+ - count - dictionary that save the counts of different groups (sample_labels)
  - label - the current label
  - sample_labels - list of all sample labels, one to one with coordinates collected 
  - view_all - flag indicating whether or not to view all labels 
@@ -25,18 +25,18 @@ closest collected point.
 
 - **Context:**
  - Used to define a list of coordinates of interest.
- - 
+ - Can be helpful to ground truth counting algorithms, and exported coordinates could be used in other image analysis workflows.
 - **Example use:**
- - (pcv.roi.multi)
- - (pcv.roi.custom)
+ - [pcv.roi.multi](https://plantcv.readthedocs.io/en/stable/roi_multi/)
+ - Shown below [pcv.roi.custom](https://plantcv.readthedocs.io/en/stable/roi_custom/)
 
 
 ```python
 import plantcv.plantcv as pcv 
 import plantcv.annotate as an
 
 # Create an instance of the Points class
-marker = an.Points(img=img, figsize=(12,6))
+marker = an.Points(img=img, figsize=(12,6), label='default')
 
 # Click on the plotted image to collect coordinates
 
@@ -45,5 +45,71 @@ roi = pcv.roi.custom(img=img, vertices=marker.coords['default'])
 
 ```
 
+## Methods
+### Correct a Mask using Point Annotations
+
+Using [Jupyter Notebooks](https://plantcv.readthedocs.io/en/stable/jupyter/) it is possible to interactively click to collect coordinates from an image, then use these coordinate to remove and recover objects from a binary mask.
+
+**plantcv.annotate.Points.correct_mask**(*mask*)
+
+**returns** corrected_mask, lbls, num
+
+- **Parameters:**
+ - mask - binary or labeled mask image, filtered mask image with selected objects, or the output from [`plantcv.watershed`](https://plantcv.readthedocs.io/en/stable/watershed/)
+
+- **Returns:**
+ - corrected_mask - A labeled mask with non-annotated objects filtered out, and unresolvable annotations marked with a labeled pixel. 
+ - lbls - A list of class labels ordered the same as the corrected mask object IDs
+ - num - The number of unique objects in the `corrected_mask`.
+
+- **Context:**
+ - Filters objects from the `bin_mask` if they do not overlap with an annotation in the `Points` class instance. 
+ - Adds a labeled pixel to the corrected mask if an object cannot be resolved for any annotations (false negatives can be counted but cannot have their size measured downstream). 
+ - Returns the number of unique objects in the `corrected_mask` which is useful for downstream analysis.
+ - Debug image is a colorized representation of the labeled mask. The "unresolved" annotation replicates are plotted with a radius of `pcv.params.line_thickness` (default = 5). 
+ - Hint: set `pcv.params.text_size=0` to skip ID labeling and instead only plot the annotation coordinate.
+
+- **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. 
+
+**bin_img**
+
+![Screenshot](img/documentation_images/points_correct_mask/bin_mask.png)
+
+**Original Image with "auto-detected" Annotations**
+
+![Screenshot](img/documentation_images/points_correct_mask/auto_annotated_stomata.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, get_centroids steps here 
+
+# Create an instance of the Points class & click on stomata
+marker = pcvan.Points(img=img, figsize=(12,6))
+marker.import_list(coords=centroid_coords, label="stomata")
+
+# Filter the binary mask based on corrected annotations
+corrected_mask, lbls, num = marker.correct_mask(mask=bin_mask)
+
+# Analysis steps here
+size_img = pcv.analyze.size(img=img, labeled_mask=corrected_mask, n_labels=num, label=lbls)
+```
+**Annotations After Human Corrections**
+
+![Screenshot](img/documentation_images/points_correct_mask/annotated_stomata.png)
+
+**Corrected & Labeled Mask**
+
+![Screenshot](img/documentation_images/points_correct_mask/colorized_label_img.png)
+
+**[Size](https://plantcv.readthedocs.io/en/stable/analyze_size/) Analysis Image**
+
+![Screenshot](img/documentation_images/points_correct_mask/shape_img.png)
 
 **Source Code:** [Here](https://github.com/danforthcenter/plantcv-annotate/blob/main/plantcv/annoate/classes.py)