diff --git a/.github/workflows/publish-package.yml b/.github/workflows/publish-package.yml
index 6a67c1355..914e31a45 100644
--- a/.github/workflows/publish-package.yml
+++ b/.github/workflows/publish-package.yml
@@ -33,7 +33,11 @@ jobs:
   deploy:
     needs: build
     runs-on: ubuntu-latest
-
+    environment:
+      name: pypi
+      url: https://pypi.org/p/plantcv
+    permissions:
+      id-token: write
     steps:
     - uses: actions/checkout@main
       with:
@@ -46,10 +50,8 @@ jobs:
       run: |
         python -m pip install --upgrade pip
         pip install setuptools wheel twine
-    - name: Build and publish
-      env:
-        TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }}
-        TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
+    - name: Build
       run: |
         python setup.py sdist bdist_wheel
-        twine upload dist/*
+    - name: Publish to PyPI
+      uses: pypa/gh-action-pypi-publish@release/v1
diff --git a/docs/analyze_grayscale.md b/docs/analyze_grayscale.md
index 82a986a94..90f093999 100644
--- a/docs/analyze_grayscale.md
+++ b/docs/analyze_grayscale.md
@@ -18,7 +18,7 @@ the values out to the [Outputs class](outputs.md). Can also return/plot/print ou
 - **Example use:**
     * [Grayscale Image Tutorial](tutorials/grayscale_tutorial.md)
 
-- **Output data stored:** Data ('gray_frequencies', 'gray_mean', 'gray_median', 'nir_stdev') automatically gets stored to
+- **Output data stored:** Data ('gray_frequencies', 'gray_mean', 'gray_median', 'gray_stdev') automatically gets stored to
 the [`Outputs` class](outputs.md) when this function is ran. These data can always get accessed during a workflow (example
 below). For more detail about data output see [Summary of Output Observations](output_measurements.md#summary-of-output-observations)
 
diff --git a/docs/annotate_click_count.md b/docs/annotate_click_count.md
new file mode 100644
index 000000000..bcc64c907
--- /dev/null
+++ b/docs/annotate_click_count.md
@@ -0,0 +1,208 @@
+## Click and count objects 
+
+`ClickCount` is a class that allows users to interactively count objects and other annotation tasks.
+
+*class* **plantcv.annotate.ClickCount**(*img*, *figsize*=(12, 6))
+
+- img - Image data
+- figsize - Interactive plot figure size (default = (12,6))
+
+### Attributes
+**img**: input image.
+
+**points**: a dictionary of coordinates for every class.
+
+**colors**: a dictionary of colors for every class.
+
+**counts**: a dictionary of count for every class.
+
+**figsize**: figure size.
+
+**events**: a list of events.
+
+**label**: current label.
+
+**color**: current color.
+
+**view_all**: a flag indicating whether or not view all labels.
+
+**fig**: matplotlib figure.
+
+**ax**: matplotlib axis.
+
+**p_not_current**: a list of matplotlib patches that are not current label.
+
+---
+### Methods
+---
+**view(*label*="total", *color*="c", *view_all*=False)**
+
+View marked image, and update markers/annotations if needed.
+
+- Parameters:
+    - label - class label to show on the marked image. By default `label="total`.
+    - color - desired color to show the class. By default `color="c"`.
+    - view_all - a flag indicating whether to show markers for all classes or not. 
+---
+**import_coords(*coords*, *label*="total")**
+
+Import coordinates from a list of coordinates.
+
+- Parameters:
+    - coords - a list of available coordintes.
+    - label - class label for imported coordinates. By default `label="total`.
+---
+**file_import**(*filename*)
+
+Import coordinates from file to a ClickCount object instance
+
+- **Parameters:**
+    - filename - path to a coordinates file  
+- **Context:**
+    - Loads coordinates from a file (probably created with the `.save_coords` method) to ClickCount object instance
+---
+**save_coords(*filename*)**
+
+Save the collected coordinates to a JSON file.
+
+- Parameters:
+    - filename - (json) file name to save the coordinates of collected points. 
+---
+**correct**(*bin_img*, *bin_img_recover*, *coords*)
+
+Make corrections to annotations 
+
+**returns** recovered image
+
+- **Parameters:**
+    - bin_img - binary image, image with selected objects (e.g. mask output of [`pcv.annotate.detect_discs`](annotate_detect_discs.md))
+    - bin_img_recover - binary image, image with all potential objects (binary image to recover objects from)  
+    - coords - list of coordinates of 'auto' detected points (e.g. coordinate output of `pcv.annotate.detect_discs`)
+- **Context:**
+    - Make corrections to the number of objects in a binary image with information from the ClickCount class object instance (both remove and recover objects). Also corrects the ClickCount object instance with coordinates at the center of each object (rather than click location).
+---
+**create_labels**(*gray_img*, *label='default'*)
+
+Label ClickCount Objects after they have been segmented 
+
+**returns** labeled object image, labeled class image, ordered list of names, number of objects
+
+- **Parameters:**
+    - gray_img - gray image with objects uniquely labeled (e.g. output of [pcv.watershed_segmentation](watershed.md))
+    - label - option to put in list of labels, defaults to 'default' if not included
+- **Context:**
+    - Labels each object with a class ID (e.g. germinated, and/or total) that matches classes from ClickCount, returns a list of names for input into analyze steps, renumbers objects to equal the total number of objects, and stores coordinates to `Outputs`. 
+- **Output data stored:** Data ('count') for each ClickCount category automatically gets stored to the [`Outputs` class](outputs.md) when this function is
+run. These data can be accessed during a workflow (example below). For more detail about data output see
+[Summary of Output Observations](output_measurements.md#summary-of-output-observations)
+---
+### Pollen Annotation Example
+- **Note**: used in Jupyter notebook.
+```python
+# Include the line of code below to allow interactive activities
+%matplotlib widget
+
+# Import packages
+from plantcv import plantcv as pcv
+import os
+
+# Define workflow inputs
+args = WorkflowInputs(images=["rgb_pollen_image.jpg",
+                      names="image1",
+                      result="imgID_results.json",
+                      outdir=".",
+                      writeimg=True,
+                      debug="plot")
+# Set global debug behavior to "plot" (Jupyter Notebooks or X11)
+pcv.params.debug = args.debug
+
+# Read image
+img, path, fname = pcv.readimage(filename=args.image1)
+```
+**Original RGB image**
+
+![Screenshot](img/documentation_images/annotate_clickcount_label/crop_pollen.png)
+
+```python
+# Segmentation, this might include many clean up functions 
+gray = pcv.rgb2gray_lab(img, channel='l')
+mask = pcv.threshold.mean(gray_img=gray, ksize=201, offset=20, object_type='dark')
+
+# Discard objects that are not circular
+discs, coords = pcv.annotate.detect_discs(img_l_post, ecc_thresh=0.5)
+```
+
+**Detect Disc mask output**
+
+![Screenshot](img/documentation_images/annotate_clickcount_correct/pollen_detectdisc_mask.png)
+
+```python
+# ClickCount Initialization
+counter = pcv.annotate.ClickCount(img)
+
+# Click on the plotted image to annotate  
+counter.view(label="total", color="r", view_all=False)
+```
+**View and Update ClickCount Coordinates**
+
+![screen-gif](img/documentation_images/annotate_click_count/clickcount_view.gif)
+
+```python 
+# Save out ClickCount coordinates file
+counter.save_coords(os.path.join(args.outdir, str(args.result) + '.coords'))
+
+# OPTIONALLY, import coordinates to ClickCount object 
+# (e.g. pick up where you left off)
+file = os.path.join(args.outdir, str(args.result) + ".coords") 
+counter.file_import(img=img, filename=file)
+# View "total" class
+counter.view(label="total", color="r", view_all=False)
+
+print(f"There are {counter.count['total']} selected objects")
+
+# Launch interactive tool to select objects with a different class
+counter.view(label="germinated", color="b", view_all=False)
+
+print(f"There are {counter.count['germinated']} selected objects")
+
+# Associate a unique label to each grain for segmentation, 
+# recover the missing grains, and create a complete mask
+completed_mask = counter.correct(bin_img=discs, bin_img_recover=mask, coords=coords)
+```
+
+**Recovered (after `.correct`) Objects Image**
+
+![Screenshot](img/documentation_images/annotate_clickcount_correct/Figure-4.png)
+
+```python 
+# Watershed egmentation to separate touching grains
+seg = pcv.watershed_segmentation(rgb_img=img, mask=completed_mask, distance=1)
+```
+
+**Output of Watershed Segementation**
+
+![Screenshot](img/documentation_images/annotate_clickcount_label/Figure6.png)
+
+```python
+# Assign a single label to each grain & store to outputs 
+class_label, class_count_dict, class_list, num = counter.create_labels(gray_img=seg, label="total")
+```
+**Labeled Objects Image**
+
+![Screenshot](img/documentation_images/annotate_clickcount_label/Figure7.png)
+![Screenshot](img/documentation_images/annotate_clickcount_label/Figure8.png)
+
+```python
+# Optional, run additional trait analysis 
+shape_img = pcv.analyze.size(img=img, labeled_mask=class_label, n_labels=num, label=class_list)
+
+# Save the results with "long" format. If using analyze.color this might get too long.
+pcv.outputs.save_results(filename=os.path.join(args.outdir, args.result + ".csv"), outformat="csv")
+pcv.outputs.clear()
+```
+
+**Size Analysis debug image**
+
+![Screenshot](img/documentation_images/annotate_click_count/pollen_shape_annotations.png)
+
+**Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/main/plantcv/plantcv/annotate/classes.py)
diff --git a/docs/annotate_get_centroids.md b/docs/annotate_get_centroids.md
new file mode 100644
index 000000000..126dba145
--- /dev/null
+++ b/docs/annotate_get_centroids.md
@@ -0,0 +1,46 @@
+## Get centroids
+
+Get the coordinates (row, column) of the centroid of each connected region
+in a binary image.
+
+**plantcv.annotate.get_centroids**(*bin_img*)
+
+**returns** list containing coordinates of centroids
+
+- **Parameters:**
+    - bin_img - Binary image containing the connected regions to consider
+- **Context:**
+    - Given an arbitrary mask of the objects of interest, `get_centroids`
+    returns a list of coordinates that can the be imported into the annotation classes
+    [ClickCount](annotate_click_count.md) and [Points](annotate_points.md).
+
+- **Example use:**
+    - Below
+
+**Binary image**
+
+![count_img](img/documentation_images/annotate_detect_discs/discs_mask_scaled.png)
+
+```python
+
+from plantcv import plantcv as pcv
+
+# Set global debug behavior to None (default), "print" (to file),
+# or "plot"
+pcv.params.debug = "plot"
+
+# Apply get centroids to the binary image
+coords = pcv.annotate.get_centroids(bin_img=binary_img)
+print(coords)
+# [[1902, 600], [1839, 1363], [1837, 383], [1669, 1977], [1631, 1889], [1590, 1372], [1550, 1525],
+# [1538, 1633], [1522, 1131], [1494, 2396], [1482, 1917], [1446, 1808], [1425, 726], [1418, 2392],
+# [1389, 198], [1358, 1712], [1288, 522], [1289, 406], [1279, 368], [1262, 1376], [1244, 1795],
+# [1224, 1327], [1201, 624], [1181, 725], [1062, 85], [999, 840], [885, 399], [740, 324], [728, 224],
+# [697, 860], [660, 650], [638, 2390], [622, 1565], [577, 497], [572, 2179], [550, 2230], [547, 1826],
+# [537, 892], [538, 481], [524, 2144], [521, 2336], [497, 201], [385, 1141], [342, 683], [342, 102],
+# [332, 1700], [295, 646], [271, 60], [269, 1626], [210, 1694], [189, 878], [178, 1570], [171, 2307],
+# [61, 286], [28, 2342]]
+
+```
+
+**Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/main/plantcv/plantcv/annotate/get_centroids.py)
diff --git a/docs/Points.md b/docs/annotate_points.md
similarity index 60%
rename from docs/Points.md
rename to docs/annotate_points.md
index cca4c602d..458dcaed7 100644
--- a/docs/Points.md
+++ b/docs/annotate_points.md
@@ -1,18 +1,27 @@
 ## Interactive Point Annotation Tool
 
-Using [Jupyter Notebooks](jupyter.md) 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](jupyter.md) 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
 closest collected point.
 
-**plantcv.Points**(*img, figsize=(12, 6)*)
+*class* **plantcv.annotate.Points**(*img, figsize=(12, 6)*)
 
-**returns** interactive image class
+- img - Image data
+- figsize - Interactive plot figure size (default = (12,6))
 
-- **Parameters:**
-    - img - Image data
-    - figsize - Interactive plot figure size (default = (12,6))
+### Attributes
+**points**: a dictionary of coordinates for every class.
 
-- **Attributes:**
-    - points - Coordinates (x,y) of the collected points as a list of tuples
+**fig**: matplotlib figure.
+
+**ax**: matplotlib axis.
+
+**events**: a list of events.
+
+### Methods
+**onclick(*event*)**
+
+Handles mouse click events.
 
 - **Context:**
     - Used to define a list of coordinates of interest.
@@ -23,10 +32,16 @@ closest collected point.
 
 
 ```python
+%matplotlib widget
+
 from plantcv import plantcv as pcv
 
+# Set global debug behavior to None (default), "print" (to file),
+# or "plot"
+pcv.params.debug = "plot"
+
 # Create an instance of the Points class
-marker = pcv.Points(img=img, figsize=(12,6))
+marker = pcv.annotate.Points(img=img, figsize=(12,6))
 
 # Click on the plotted image to collect coordinates
 
@@ -44,4 +59,4 @@ roi1 = pcv.roi.custom(img=img, vertices=marker.points)
 ![Screenshot](img/documentation_images/annotate_Points/custom_roi.jpg)
 
 
-**Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/master/plantcv/plantcv/classes.py)
+**Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/main/plantcv/plantcv/annotate/classes.py)
diff --git a/docs/detect_discs.md b/docs/detect_discs.md
new file mode 100644
index 000000000..59b5694db
--- /dev/null
+++ b/docs/detect_discs.md
@@ -0,0 +1,50 @@
+## Detect discs 
+
+Detects disc-shaped regions in a binary image based on eccentricity.
+A value of eccentricity between 0 and 1 corresponds to an ellipse.
+The closer the value to 0 the closer the shape is to a circle.
+
+**plantcv.detect_discs**(*bin_img, ecc_thresh=0*)
+
+**returns** mask
+
+- **Parameters:**
+    - bin_img - Binary image containing the connected regions to consider
+    - ecc_thresh - Eccentricity threshold below which a region is detected
+- **Context:**
+    - Used to isolate disc-shaped objects of interest in a binary image. The output mask can be used for further analysis.
+- **Example use:**
+    - Below
+
+**Original image**
+
+![ori_img](img/documentation_images/annotate_click_count/count_img.jpg)
+
+**Mask generated using binary threshold in the blue channel**
+![bin_img](img/documentation_images/annotate_detect_discs/discs_pre_scaled.png)
+
+```python
+
+from plantcv import plantcv as pcv
+
+# Set global debug behavior to None (default), "print" (to file),
+# or "plot"
+pcv.params.debug = "plot"
+
+# Apply detect discs to the binary image with an
+# eccentricity threshold of 0.9
+discs_mask_9 = pcv.detect_discs(bin_img=binary_img, ecc_thresh=0.9)
+
+# Apply detect discs to the binary image with an
+# eccentricity threshold of 0.5
+discs_mask_5 = pcv.detect_discs(bin_img=binary_img, ecc_thresh=0.5)
+
+```
+
+**Mask of detected objects with eccentricity threshold of 0.9**
+![count_img](img/documentation_images/annotate_click_count/count_mask.png)
+
+**Mask of detected objects with eccentricity threshold of 0.5**
+![count_img](img/documentation_images/detect_discs/discs_mask_scaled.png)
+
+**Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/main/plantcv/plantcv/annotate/detect_discs.py)
diff --git a/docs/get_color_matrix.md b/docs/get_color_matrix.md
index 8a15b70e4..6cac913e0 100644
--- a/docs/get_color_matrix.md
+++ b/docs/get_color_matrix.md
@@ -8,7 +8,7 @@ Computes the average *R*, *G*, *B* values for each region in the RGB image denot
 
 - **Parameters**
     - rgb_img - RGB image with color chips visualized
-    - mask    - a gray-scale img with unique values for each segmented space, representing unique, discrete color chips.
+    - mask    - a gray-scale img with unique values for each segmented space, representing unique, discrete color chips. Likely created with [`pcv.transform.detect_color_card`](transform_detect_color_card.md). 
 
 - **Returns**
     - color_matrix - a *n* x 4 matrix containing the average red value, average green value, and average blue value for each color chip.
diff --git a/docs/img/documentation_images/annotate_click_count/clickcount_view.gif b/docs/img/documentation_images/annotate_click_count/clickcount_view.gif
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index 000000000..357fc236a
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new file mode 100644
index 000000000..e6dcf3e2f
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diff --git a/docs/img/documentation_images/watershed/watershed-labels.png b/docs/img/documentation_images/watershed/watershed-labels.png
new file mode 100644
index 000000000..f016a0937
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diff --git a/docs/output_measurements.md b/docs/output_measurements.md
index f15c0016d..92dde927b 100644
--- a/docs/output_measurements.md
+++ b/docs/output_measurements.md
@@ -147,32 +147,35 @@ suggestions for additional metadata we should track that would be useful to you,
 
 Functions that automatically store data to the [`Outputs` class](outputs.md) are
 [analyze.bound_horizontal](analyze_bound_horizontal2.md), 
-[analyze.bound_vertical](analyze_bound_vertical2.md),
-[analyze.color](analyze_color2.md),
-[analyze.grayscale](analyze_grayscale.md),
-[analyze.size](analyze_size.md),
-[analyze.spectral_index](analyze_spectral_index.md),
-[analyze.spectral_reflectance](analyze_spectral_reflectance.md),
-[analyze.thermal](analyze_thermal.md),
+[analyze.bound_vertical](analyze_bound_vertical2.md), 
+[analyze.color](analyze_color2.md), 
+[analyze.grayscale](analyze_grayscale.md), 
+[analyze.size](analyze_size.md), 
+[analyze.spectral_index](analyze_spectral_index.md), 
+[analyze.spectral_reflectance](analyze_spectral_reflectance.md), 
+[analyze.thermal](analyze_thermal.md), 
 [analyze.yii](analyze_yii.md), 
-[analyze.npq](analyze_npq.md),
-[homology.landmark_reference_pt_dist](homology_landmark_reference_pt_dist.md),
+[analyze.npq](analyze_npq.md), 
+[homology.acute](homology_landmark_reference_pt_dist.md), 
+[homology.landmark_reference_pt_dist](homology_landmark_reference_pt_dist.md), 
 [homology.x_axis_pseudolandmarks](homology_x_axis_pseudolandmarks.md), 
-[homology.y_axis_pseudolandmarks](homology_y_axis_pseudolandmarks.md),
-[morphology.check_cycles](check_cycles.md),
+[homology.y_axis_pseudolandmarks](homology_y_axis_pseudolandmarks.md), 
+[morphology.analyze_stem](analyze_stem.md), 
+[morphology.check_cycles](check_cycles.md), 
 [morphology.fill_segments](fill_segments.md), 
-[morphology.find_tips](find_tips.md),
+[morphology.find_tips](find_tips.md), 
 [morphology.find_branch_pts](find_branch_pts.md), 
-[morphology.segment_angle](segment_angle.md),
+[morphology.segment_angle](segment_angle.md), 
 [morphology.segment_curvature](segment_curvature.md), 
-[morphology.segment_euclidean_length](segment_euclidean_length.md),
+[morphology.segment_euclidean_length](segment_euclidean_length.md), 
 [morphology.segment_insertion_angle](segment_insertion_angle.md), 
-[morphology.segment_path_length](segment_pathlength.md),
-[morphology.segment_tangent_angle](segment_tangent_angle.md),
-[report_size_marker_area](report_size_marker.md),
-[transform.find_color_card](find_color_card.md),
+[morphology.segment_path_length](segment_pathlength.md), 
+[morphology.segment_tangent_angle](segment_tangent_angle.md), 
+[report_size_marker_area](report_size_marker.md), 
+[transform.find_color_card](find_color_card.md), 
+[transform.detect_color_card](transform_detect_color_card.md)
 [watershed_segmentation](watershed.md), and
-[within_frame](within_frame.md)
+[within_frame](within_frame.md).
 
 All of these functions include an optional `label` parameter 
 that allows users to append custom prefixes to the unique variable identifier. 
diff --git a/docs/transform_affine_color_correction.md b/docs/transform_affine_color_correction.md
index 6320e64af..3ccfb5b11 100644
--- a/docs/transform_affine_color_correction.md
+++ b/docs/transform_affine_color_correction.md
@@ -15,7 +15,8 @@ Euclidean distance between the transformed source color values and the target co
     - rgb_img       - an RGB image with color chips visualized
     - source_matrix - array of RGB color values (intensity in the range [0-1]) from the image to be corrected where each row is one color reference and the columns are organized as index,R,G,B
     - target_matrix - array of target RGB color values (intensity in the range [0-1]) where each row is one color reference and the columns are organized as index,R,G,B
-
+- **Example use:**
+    - [Color Correction Tutorial](tutorials/transform_color_correction_tutorial.md)
 
 **Reference Images**
 
diff --git a/docs/transform_detect_color_card.md b/docs/transform_detect_color_card.md
index e57d34744..0bc669979 100644
--- a/docs/transform_detect_color_card.md
+++ b/docs/transform_detect_color_card.md
@@ -15,6 +15,8 @@ Automatically detects a color card and creates a labeled mask.
         - radius         - Radius of circle to make the color card labeled mask (default = 20).
 - **Returns**
     - labeled_mask     - Labeled color card mask (useful downstream of this step in `pcv.transform.get_color_matrix` and `pcv.transform.correct_color`)
+- **Example use:**
+    - [Color Correction Tutorial](tutorials/transform_color_correction_tutorial.md)
 
 !!! note
     Color chip size can only be used reasonably as a scaling factor (converting pixels to a known real world scale like cms)
@@ -31,6 +33,8 @@ rgb_img, path, filename = pcv.readimage("target_img.png")
 cc_mask = pcv.transform.detect_color_card(rgb_img=rgb_img)
 
 avg_chip_size = pcv.outputs.observations['default']['median_color_chip_size']['value']
+avg_chip_w = pcv.outputs.observations['default']['median_color_chip_width']['value']
+avg_chip_h = pcv.outputs.observations['default']['median_color_chip_height']['value']
 
 ```
 
diff --git a/docs/tutorials.md b/docs/tutorials.md
index 71e95958c..3d199c9da 100644
--- a/docs/tutorials.md
+++ b/docs/tutorials.md
@@ -278,6 +278,19 @@
     </div>
 </div>
 
+<!--Interactive Seed Count Tutorial-->
+<div class="card" style="display:block">
+    <a target="_blank" href="" onclick="location.href=this.href.replace('tutorials', 'tutorials/interactive_ClickCount_tutorial');return false;">
+        <img src="https://github.com/danforthcenter/plantcv-interactive-tutorials/blob/main/interactive_seed_count_card.png?raw=true" alt="Interactive Seed Count tutorial" width="600" height="auto">
+    </a>
+    <div class="card-title">
+        <h6>Interactive Seed Count Tutorial</h6>
+    </div>
+    <div class="card-tags">
+        <span><strong>Tags</strong>: seed, seed analysis, human-in-the-loop, RGB, seed count, camilina</span>
+    </div>
+</div>
+
 <script type="text/javascript">
     function getTags() {
         var tag_list = [];
diff --git a/docs/tutorials/interactive_ClickCount_tutorial.md b/docs/tutorials/interactive_ClickCount_tutorial.md
new file mode 100644
index 000000000..0b3ebda5c
--- /dev/null
+++ b/docs/tutorials/interactive_ClickCount_tutorial.md
@@ -0,0 +1,6 @@
+## Tutorial: Seed Analysis Workflow
+
+
+[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/danforthcenter/plantcv-interactive-tutorials/HEAD?filepath=index.ipynb) Check out the interactive tutorial! 
+
+<iframe src="https://nbviewer.jupyter.org/github/danforthcenter/plantcv-interactive-tutorials/blob/main/index.ipynb" width="100%" height="1500px"></iframe>
diff --git a/docs/updating.md b/docs/updating.md
index 20ff28842..3f1158ad6 100644
--- a/docs/updating.md
+++ b/docs/updating.md
@@ -218,6 +218,7 @@ pages for more details on the input and output variable types.
 * post v3.11: analysis_image = **plantcv.analyze_bound_vertical**(*img, obj, mask, line_position, label="default"*)
 * post v4.0: DEPRECATED see plantcv.analyze.bound_vertical
 
+
 #### plantcv.analyze_color
 
 * pre v3.0dev2: device, hist_header, hist_data, analysis_images = **plantcv.analyze_color**(*img, imgname, mask, bins, device, debug=None, hist_plot_type=None, pseudo_channel='v', pseudo_bkg='img', resolution=300, filename=False*)
@@ -261,7 +262,6 @@ pages for more details on the input and output variable types.
 * pre v4.0: (see plantcv.analyze_bound_horizontal)
 * post v4.0: analysis_image = **plantcv.analyze.bound_horizontal**(*img, labeled_mask, line_position, n_labels=1, label=None*)
 
-
 #### plantcv.analyze.bound_vertical
 
 * pre v4.0: (see plantcv.analyze_bound_vertical)
@@ -312,6 +312,57 @@ pages for more details on the input and output variable types.
 * pre v4.0: NA
 * post v4.0: yii, yii_hist = **plantcv.analyze.yii**(*ps_da, labeled_mask, n_labels=1, auto_fm=False, measurement_labels=None, label=None*)
 
+#### plantcv.annotate.ClickCount
+
+* pre v4.1: NA
+* post v4.1: counter = **plantcv.annotate.ClickCount**(*img, figsize=(6,12)*)
+
+#### plantcv.annotate.clickcount.correct
+
+* pre v4.1: NA
+* post v4.1: corrected = **plantcv.annotate.clickcount.correct**(*bin_img*, *bin_img_recover*, *coords*)
+
+
+#### plantcv.annotate.clickcount.create_labels
+
+* pre v4.1: NA
+* post v4.1: corrected_label, corrected_class, corrected_name, num = **plantcv.annotate.clickcount.create_labels**(*gray_img, label='default'*)
+
+
+#### plantcv.annotate.clickcount.file_import
+
+* pre v4.1: NA
+* post v4.1: **plantcv.annotate.clickcount.file_import**(*filename*)
+
+
+#### plantcv.annotate.clickcount.import_coords
+
+* pre v4.1: NA
+* post v4.1: **plantcv.annotate.clickcount.import_coords**(*coords, label="total"*)
+
+
+#### plantcv.annotate.clickcount.save_coords
+
+* pre v4.1: NA
+* post v4.1: **plantcv.annotate.clickcount.save_coords**(*filename*)
+
+
+#### plantcv.annotate.clickcount.view
+
+* pre v4.1: NA
+* post v4.1: **plantcv.annotate.clickcount.view**(*label="total", color="c", view_all=False*)
+
+
+#### plantcv.annotate.get_centroids
+
+* pre v4.1: NA
+* post v4.1: coords = **plantcv.annotate.get_centroids**(*bin_img*)
+
+#### plantcv.annotate.Points
+
+* pre v4.1: NA
+* post v4.1: marker = **plantcv.annotate.Points**(*img, figsize=(6,12)*)
+
 #### plantcv.apply_mask
 
 * pre v3.0dev2: device, masked_img = **plantcv.apply_mask**(*img, mask, mask_color, device, debug=None*)
@@ -383,6 +434,7 @@ pages for more details on the input and output variable types.
 * pre v3.0dev2: device, newmask = **plantcv.crop_position_mask**(*img, mask, device, x, y, v_pos="top", h_pos="right", debug=None*)
 * post v3.0dev2: newmask = **plantcv.crop_position_mask**(*img, mask, x, y, v_pos="top", h_pos="right"*)
 
+
 #### plantcv.define_roi
 
 * pre v3.0dev2: device, contour, hierarchy = **plantcv.define_roi**(*img, shape, device, roi=None, roi_input='default', debug=None, adjust=False, x_adj=0, y_adj=0, w_adj=0, h_adj=0*)
@@ -392,6 +444,13 @@ pages for more details on the input and output variable types.
     * roi_contour, roi_hierarchy = **plantcv.roi.from_binary_image**(*img, bin_img*)
     * roi_contour, roi_hierarchy = **plantcv.roi.rectangle**(*img, x, y, h, w*)
 
+
+#### plantcv.detect_discs
+
+* pre v4.1: NA
+* post v4.1: discs_mask = **plantcv.detect_discs**(*bin_img, ecc_thresh=0*)
+
+
 #### plantcv.dilate
 
 * pre v3.0dev2: device, dil_img = **plantcv.dilate**(*img, kernel, i, device, debug=None*)
@@ -780,6 +839,7 @@ pages for more details on the input and output variable types.
 
 * pre v4.0: NA
 * post v4.0: marker = **plantcv.Points**(*img, figsize=(6,12)*)
+* post v4.1: DEPRECATED, see plantcv.annotate.Points
 
 #### plantcv.print_image
 
diff --git a/docs/watershed.md b/docs/watershed.md
index 8287966be..060bc260e 100644
--- a/docs/watershed.md
+++ b/docs/watershed.md
@@ -41,6 +41,7 @@ analysis_image = pcv.watershed_segmentation(rgb_img=crop_img, mask=bin_mask, dis
 
 **Watershed Segmentation**
 
+![Screenshot](img/documentation_images/watershed/watershed-labels.png)
 ![Screenshot](img/documentation_images/watershed/watershed.jpg)
 
 **Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/main/plantcv/plantcv/watershed.py)
diff --git a/mkdocs.yml b/mkdocs.yml
index 976f3b31e..2a7d65910 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -42,7 +42,12 @@ nav:
         - 'Analyze YII': analyze_yii.md
         - 'Analyze NPQ': analyze_npq.md
       - 'Annotation Tools':
-          - 'Points': Points.md
+          - 'ClickCount': annotate_click_count.md
+          - 'File Import Coordinates': annotate_clickcount_file_import.md
+          - 'Correct ClickCount': annotate_clickcount_correct.md
+          - 'Label ClickCount': annotate_clickcount_label.md
+          - 'Get Centroids': annotate_get_centroids.md
+          - 'Points': annotate_points.md
       - 'Apply Mask': apply_mask.md
       - 'Auto Crop': auto_crop.md
       - 'Background Subtraction': background_subtraction.md
@@ -57,6 +62,7 @@ nav:
       - 'Create Labels': create_labels.md
       - 'Crop': crop.md
       - 'Crop and Position Mask': crop_position_mask.md
+      - 'Detect Discs': detect_discs.md
       - 'Dilation': dilate.md
       - 'Distance Transform': distance_transform.md
       - 'Erosion': erode.md
diff --git a/plantcv/plantcv/__init__.py b/plantcv/plantcv/__init__.py
index 637fa9284..494f61bf7 100644
--- a/plantcv/plantcv/__init__.py
+++ b/plantcv/plantcv/__init__.py
@@ -3,9 +3,9 @@
 from plantcv.plantcv.classes import Outputs
 from plantcv.plantcv.classes import Spectral_data
 from plantcv.plantcv.classes import PSII_data
-from plantcv.plantcv.classes import Points
 from plantcv.plantcv.classes import Objects
 
+
 # Initialize an instance of the Params and Outputs class with default values
 # params and outputs are available when plantcv is imported
 params = Params()
@@ -74,6 +74,7 @@
 from plantcv.plantcv.create_labels import create_labels
 from plantcv.plantcv.floodfill import floodfill
 from plantcv.plantcv import analyze
+from plantcv.plantcv.detect_discs import detect_discs
 # add new functions to end of lists
 
 # Auto versioning
@@ -86,7 +87,6 @@
     "Outputs",
     "Spectral_data",
     'PSII_data',
-    'Points',
     "Objects",
     "deprecation_warning",
     "warn",
@@ -150,5 +150,6 @@
     "segment_image_series",
     "create_labels",
     "analyze",
-    "floodfill"
+    "floodfill",
+    "detect_discs"
 ]
diff --git a/plantcv/plantcv/annotate/__init__.py b/plantcv/plantcv/annotate/__init__.py
index e69de29bb..8ebbf7717 100644
--- a/plantcv/plantcv/annotate/__init__.py
+++ b/plantcv/plantcv/annotate/__init__.py
@@ -0,0 +1,6 @@
+from plantcv.plantcv.annotate.get_centroids import get_centroids
+from plantcv.plantcv.annotate.classes import Points, ClickCount
+
+
+# add new functions to end of lists
+__all__ = ["get_centroids", "Points", "ClickCount"]
diff --git a/plantcv/plantcv/annotate/classes.py b/plantcv/plantcv/annotate/classes.py
new file mode 100644
index 000000000..b188f678c
--- /dev/null
+++ b/plantcv/plantcv/annotate/classes.py
@@ -0,0 +1,440 @@
+"""Annotation classes."""
+import cv2
+import os
+import json
+import numpy as np
+from math import floor
+import matplotlib.pyplot as plt
+from scipy.spatial import distance
+from plantcv.plantcv import warn, params, outputs
+from plantcv.plantcv._debug import _debug
+from plantcv.plantcv.visualize import colorize_label_img 
+
+
+# Class helpers
+def _find_closest_pt(pt, pts):
+    """Find the closest (Euclidean) point to a given point from a list of points.
+
+    :param pt: (tuple) coordinates of a point
+    :param pts: (a list of tuples) coordinates of a list of points
+    :return: index of the closest point and the coordinates of that point
+    """
+    if pt in pts:
+        idx = pts.index(pt)
+        return idx, pt
+
+    dists = distance.cdist([pt], pts, 'euclidean')
+    idx = np.argmin(dists)
+    return idx, pts[idx]
+
+def _recover_circ(bin_img, c):
+    """function to recover circular objects"""
+    # Generates a binary image of a disc based on the coordinates c
+    # and the surrounding white pixels in bin_img
+    # make bin_img 1-0
+    bin_img = 1*(bin_img != 0)
+    h, w = bin_img.shape
+    # coordinates of pixels in the shape of the bin_image
+    X, Y = np.meshgrid(np.linspace(0, w-1, w), np.linspace(0, h-1, h))
+
+    # Alternate two steps:
+    # 1 - growing the disc centered in c until it overlaps with black pixels in bin_img.
+    # 2 - move c a step of at least one pixel towards the center of mass (mean of coordinate values)
+    # of the white pixels of bin_img that overlap with the disc.
+    # It terminates when the direction of the step in both axis has changed once.
+    # radius
+    r = 0
+    # sum of previous directions
+    dir_x_hist = 0
+    dir_y_hist = 0
+    # flags indicate if there has been a change in direction in each axis
+    chg_dir_x = False
+    chg_dir_y = False
+
+    while (chg_dir_x and chg_dir_y) is False:
+        # image of concentric circles centered in c
+        circ = np.sqrt((X-c[1])**2+(Y-c[0])**2)
+        # growing radius until it reaches black pixels
+        inside = True
+        while inside is True:
+            circ_mask = 1*(circ < r)
+            circ_mask_area = np.sum(circ_mask)
+            masked_circ = bin_img*circ_mask
+            # if masked_circ has a smaller count it means the circle is
+            # overlapping black pixels -> no longer inside the white part
+            if np.abs(np.sum(masked_circ) - circ_mask_area) > 100:
+                inside = False
+            else:
+                r += 1
+        # moving center towards the center of mass
+        Cx = np.mean(X[masked_circ == 1])
+        Cy = np.mean(Y[masked_circ == 1])
+
+        dir_x = np.sign(Cx - c[1])
+        dir_y = np.sign(Cy - c[0])
+
+        stepx = dir_x*np.ceil(np.abs(Cx - c[1]))
+        stepy = dir_y*np.ceil(np.abs(Cy - c[0]))
+
+        dir_x_hist += dir_x
+        dir_y_hist += dir_y
+        # register that a change in direction has occurred to terminate when
+        # is has happened in both directions
+        if np.sign(dir_x_hist) != dir_x or dir_x == 0:
+            chg_dir_x = True
+
+        if np.sign(dir_y_hist) != dir_y or dir_y == 0:
+            chg_dir_y = True
+
+        c[1] += stepx.astype(np.int32)
+        c[0] += stepy.astype(np.int32)
+
+    circ_mask = 1*(circ < r)
+    masked_circ = (bin_img*circ_mask).astype(np.uint8)
+    masked_circ[c[0], c[1]] = 1  # center of mass always part of the mask
+
+    return masked_circ, c
+
+def _clickcount_labels(self):
+    """Function to get label names"""
+    labels = list(self.count)
+
+    return labels
+
+def _remove_points(autolist, confirmedlist):
+    """Function to remove points if interactively removed by user"""
+    # internal function to remove to remove points specified by a user
+    removecoor = []
+
+    for element in autolist:
+        if element not in confirmedlist:
+            removecoor.append(element)
+
+    return removecoor
+
+
+class Points:
+    """Point annotation/collection class to use in Jupyter notebooks. It allows the user to
+    interactively click to collect coordinates from an image. Left click collects the point and
+    right click removes the closest collected point
+    """
+
+    def __init__(self, img, figsize=(12, 6)):
+        """Initialization
+        :param img: image data
+        :param figsize: desired figure size, (12,6) by default
+        :attribute points: list of points as (x,y) coordinates tuples
+        """
+        self.fig, self.ax = plt.subplots(1, 1, figsize=figsize)
+        self.ax.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
+
+        self.points = []
+        self.events = []
+
+        self.fig.canvas.mpl_connect('button_press_event', self.onclick)
+
+    def onclick(self, event):
+        """Handle mouse click events."""
+        self.events.append(event)
+        if event.button == 1:
+
+            self.ax.plot(event.xdata, event.ydata, 'x', c='red')
+            self.points.append((floor(event.xdata), floor(event.ydata)))
+
+        else:
+            idx_remove, _ = _find_closest_pt((event.xdata, event.ydata), self.points)
+            # remove the closest point to the user right clicked one
+            self.points.pop(idx_remove)
+            self.ax.lines[idx_remove].remove()
+        self.fig.canvas.draw()
+
+
+class ClickCount:
+    def __init__(self, img, figsize=(12, 6)):
+        print("If you have coordinates to import, the label represent for total count should be 'total'!")
+        self.img = img
+        self.points = {}
+        self.colors = {}
+        self.count = {}  # a dictionary that saves the counts of different classes (labels)
+        self.figsize = figsize
+        self.events = []
+        self.label = None  # current label
+        self.color = None  # current color
+        self.view_all = None  # a flag indicating whether or not view all labels
+        self.fig = None
+        self.ax = None
+        self.p_not_current = None
+
+    def import_coords(self, coords, label="total"):
+        """Import center coordinates of already detected objects
+        Inputs:
+        coords = list of center coordinates of already detected objects.
+        label = class label for imported coordinates, by default label="total".
+
+        Returns:
+        :param coords: list
+        :param label: string
+        :return:
+        """
+        if label not in self.points:
+            self.points[label] = []
+            for (y, x) in coords:
+                self.points[label].append((x, y))
+            self.count[label] = len(self.points[label])
+
+        else:
+            warn(f"{label} already included and counted, nothing is imported!")
+
+    def file_import(self, filename):
+        """
+        Method to import ClickCount coordinate file to ClickCount object
+        
+        Inputs:
+        filename = filename of stored coordinates and classes
+
+        :param filename: ndarray
+        :return: 
+        """
+        # img - img file to initialize ClickCount class
+        # coor_file - file of coordinates and classes
+        coords = open(filename, "r")
+        coords = json.load(coords)
+
+        keys = list(coords.keys())
+
+        for key in keys:
+            keycoor = coords[key]
+            keycoor = list(map(lambda sub: (sub[1], sub[0]), keycoor))
+            self.import_coords(keycoor, label=key)
+
+    def save_coords(self, filename):
+        """Save collected coordinates to a file.
+        Input variables:
+        filename = Name of the file to save collected coordinate
+        :param filename: str
+        """
+        # Open the file for writing
+        with open(filename, "w") as fp:
+            # Save the data in JSON format with indentation
+            json.dump(obj=self.points, fp=fp, indent=4)
+
+    def view(self, label="total", color="c", view_all=False):
+        """
+        View the label for a specific class label
+        Inputs:
+        label = (optional) class label, by default label="total"
+        color = desired color, by default color="c"
+        view_all = indicator of whether view all classes, by default view_all=False
+
+        :param label: string
+        :param color: string
+        :param view_all: boolean
+        :return:
+        """
+        if label not in self.points and color in self.colors.values():
+            warn("The color assigned to the new class label is already used, if proceeding, "
+                 "items from different classes will not be distinguishable in plots!")
+
+        self.label = label
+        self.color = color
+        self.view_all = view_all
+
+        if label not in self.points:
+            self.points[label] = []
+            self.count[label] = 0
+        self.colors[label] = color
+
+        self.fig, self.ax = plt.subplots(1, 1, figsize=self.figsize)
+
+        self.events = []
+        self.fig.canvas.mpl_connect('button_press_event', self.onclick)
+
+        self.ax.imshow(cv2.cvtColor(self.img, cv2.COLOR_BGR2RGB))
+        self.ax.set_title("Please left click on missing objects\n Right click on those you want to remove")
+        self.p_not_current = 0
+        # if view_all is True, show all already marked markers
+        if view_all:
+            for k in self.points:
+                for (x, y) in self.points[k]:
+                    self.ax.plot(x, y, marker='x', c=self.colors[k])
+                    if self.label not in self.points or len(self.points[self.label]) == 0:
+                        self.p_not_current += 1
+        else:
+            for (x, y) in self.points[label]:
+                self.ax.plot(x, y, marker='x', c=color)
+
+    def onclick(self, event):
+        """Handle mouse click events."""
+        self.events.append(event)
+        if event.button == 1:
+            self.ax.plot(event.xdata, event.ydata, marker='x', c=self.color)
+            self.points[self.label].append((floor(event.xdata), floor(event.ydata)))
+            self.count[self.label] += 1
+        else:
+            idx_remove, _ = _find_closest_pt((event.xdata, event.ydata), self.points[self.label])
+            self.points[self.label].pop(idx_remove)
+            idx_remove = idx_remove + self.p_not_current
+            self.ax.lines[idx_remove].remove()
+            self.count[self.label] -= 1
+        self.fig.canvas.draw()
+
+    def correct(self, bin_img, bin_img_recover, coords):
+        """
+        Method to correct ClickCount object instance by removing or recovering points
+        
+        Inputs:
+        bin_img = binary image, image with selected objects
+        bin_img_recover = binary image, image with all potential objects
+        coords = coordinates of 'auto' detected points (coordinate output of detect_discs)
+
+        :param bin_img: ndarray
+        :param bin_img_recover = ndarray
+        :param coords = list
+        :return completed_mask: ndarray
+        """
+        from plantcv.plantcv import floodfill
+
+        debug = params.debug
+        params.debug = None
+
+        labelnames = _clickcount_labels(self)
+
+        completed_mask = np.copy(bin_img)
+
+        totalcoor = []
+
+        for names in labelnames:
+            for i, (x, y) in enumerate(self.points[names]):
+                x = int(x)
+                y = int(y)
+                totalcoor.append((y, x))
+
+        removecoor = _remove_points(coords, totalcoor)
+        removecoor = list(map(lambda sub: (sub[1], sub[0]), removecoor))
+        completed_mask = floodfill(completed_mask, removecoor, 0)
+
+        # points in class used for recovering and labeling
+        for names in labelnames:
+            for i, (x, y) in enumerate(self.points[names]):
+                x = int(x)
+                y = int(y)
+                # corrected coordinates
+                self.points[names][i] = (x, y)
+                # if the coordinates point to 0 in the binary image, recover the grain and coordinates of center
+                if completed_mask[y, x] == 1 or completed_mask[y, x] == 0:
+                    print(f"Recovering grain at coordinates: x = {x}, y = {y}")
+                    masked_circ, [a, b] = _recover_circ(bin_img_recover, [y, x])
+                    completed_mask = completed_mask + masked_circ
+                    self.points[names][i] = (b, a)
+
+        completed_mask1 = 1*((completed_mask + 1*(completed_mask == 255)) != 0).astype(np.uint8)
+
+        params.debug = debug
+
+        _debug(visual=completed_mask1, filename=os.path.join(params.debug_outdir, f"{params.device}_clickcount-corrected.png"))
+
+        return completed_mask
+
+    def create_labels(self, gray_img, label='default'):
+        """
+        Saves out ClickCount labeled category count to Outputs 
+        
+        Inputs:
+        gray_img = gray image with objects labeled (e.g.watershed output)
+        label = label parameter, modifies the variable name of
+        observations recorded (defaults to label="default") 
+
+        Outputs:
+        corrected_label = labeled object image
+        corrected_class = labeled class image
+        corrected_name = ordered list of names
+        num = number of objects 
+
+        :param gray_img: ndarray
+        :label = str, list
+        :return corrected_label = ndarray
+        :return corrected_class = ndarray
+        :return corrected_name = list
+        :return num: int
+        """
+
+        debug = params.debug
+        params.debug = None
+
+        labelnames = _clickcount_labels(self)
+
+        dict_class_labels = {}
+
+        # creating dictionary with label: replicate ID number 
+        for i, x in enumerate(labelnames):
+            dict_class_labels[x] = i+1
+
+        shape = np.shape(gray_img)
+        class_label = np.zeros((shape[0], shape[1]), dtype=np.uint8)
+
+        class_number = []
+        class_name = []
+
+        # Only keep watersed results that overlap with a clickpoint and do not ==0
+        for cl in list(dict_class_labels.keys()):
+            for (y, x) in self.points[cl]:
+                x = int(x)
+                y = int(y)
+                seg_label = gray_img[x, y] # grab intensity value from watershed labeled mask 
+                if seg_label != 0:
+                    class_number.append(seg_label)
+                    class_name.append(cl)
+                    class_label[gray_img == seg_label] = seg_label
+
+        # Get corrected name
+        corrected_number = []
+        corrected_name = []
+
+        for i, x in enumerate(class_number):
+            if x in corrected_number:
+                ind = (corrected_number.index(x))
+                y = corrected_name[ind]+"_"+str(class_name[i])
+                corrected_name[ind] = y
+            else:
+                corrected_number.append(x)
+                y = str(class_name[i])
+                corrected_name.append(y)
+
+        classes = np.unique(corrected_name)
+        class_dict = {}
+        count_class_dict = {}
+
+        for i, x in enumerate(classes):
+            class_dict[x] = i+1
+            count_class_dict[x] = corrected_name.count(x)
+
+        corrected_label = np.zeros((shape[0], shape[1]))
+        corrected_class = np.zeros((shape[0], shape[1]))
+
+        for i, value in enumerate(corrected_number):
+            if value != 0:
+                corrected_label[gray_img == value] = i+1
+                corrected_class[gray_img == value] = class_dict[corrected_name[i]]
+                corrected_name[i] = str(i+1)+"_"+corrected_name[i]
+
+        num = len(corrected_name)
+
+        vis_labeled = colorize_label_img(corrected_label)
+        vis_class = colorize_label_img(corrected_class)
+
+        params.debug = debug
+        _debug(visual=vis_labeled,
+            filename=os.path.join(params.debug_outdir, str(params.device) + '_corrected__labels_img.png'))
+        _debug(visual=vis_class,
+            filename=os.path.join(params.debug_outdir, str(params.device) + '_corrected_class.png'))
+
+        for i, x in enumerate(count_class_dict.keys()):
+            variable = x
+            value = count_class_dict[x]
+            outputs.add_observation(sample=label, variable=variable,
+                                    trait='count of category',
+                                    method='count', scale='count', datatype=int,
+                                    value=value, label=variable)
+
+        return corrected_label, corrected_class, corrected_name, num
+
diff --git a/plantcv/plantcv/annotate/get_centroids.py b/plantcv/plantcv/annotate/get_centroids.py
new file mode 100644
index 000000000..bba883a35
--- /dev/null
+++ b/plantcv/plantcv/annotate/get_centroids.py
@@ -0,0 +1,31 @@
+# Get centroids.
+
+import cv2
+import numpy as np
+from plantcv.plantcv._helpers import _cv2_findcontours
+
+
+def get_centroids(bin_img):
+    """Get the coordinates (row,column) of the centroid of each connected region in a binary image.
+
+    Inputs:
+    bin_img       = Binary image containing the connected regions to consider
+
+    Returns:
+    coords  = List of coordinates (row,column) of the centroids of the regions
+
+    :param bin_img: numpy.ndarray
+    :return coords: list
+    """
+    # find contours in the binary image
+    contours, _ = _cv2_findcontours(bin_img.astype(np.uint8))
+    coords = []
+    for c in contours:
+        # calculate moments for each contour
+        M = cv2.moments(c)
+        # calculate row,col coordinates of centroid
+        col = int(M["m10"] / M["m00"])
+        row = int(M["m01"] / M["m00"])
+        coords.append([row, col])
+
+    return coords
diff --git a/plantcv/plantcv/annotate/temp_file.py b/plantcv/plantcv/annotate/temp_file.py
new file mode 100644
index 000000000..c5613bfab
--- /dev/null
+++ b/plantcv/plantcv/annotate/temp_file.py
@@ -0,0 +1,492 @@
+"""Annotation classes."""
+import cv2
+import os
+import json
+import numpy as np
+from math import floor
+import matplotlib.pyplot as plt
+from scipy.spatial import distance
+from plantcv.plantcv import warn, params, outputs
+from plantcv.plantcv._debug import _debug
+from plantcv.plantcv.visualize import colorize_label_img 
+from plantcv.plantcv import create_labels
+
+
+
+# Class helpers
+def _find_closest_pt(pt, pts):
+    """Find the closest (Euclidean) point to a given point from a list of points.
+
+    :param pt: (tuple) coordinates of a point
+    :param pts: (a list of tuples) coordinates of a list of points
+    :return: index of the closest point and the coordinates of that point
+    """
+    if pt in pts:
+        idx = pts.index(pt)
+        return idx, pt
+    dists = distance.cdist([pt], pts, 'euclidean')
+    idx = np.argmin(dists)
+    return idx, pts[idx]
+
+def _recover_circ(bin_img, c):
+    """function to recover circular objects"""
+    # Generates a binary image of a disc based on the coordinates c
+    # and the surrounding white pixels in bin_img
+    # make bin_img 1-0
+    bin_img = 1*(bin_img != 0)
+    h, w = bin_img.shape
+    # coordinates of pixels in the shape of the bin_image
+    X, Y = np.meshgrid(np.linspace(0, w-1, w), np.linspace(0, h-1, h))
+
+    # Alternate two steps:
+    # 1 - growing the disc centered in c until it overlaps with black pixels in bin_img.
+    # 2 - move c a step of at least one pixel towards the center of mass (mean of coordinate values)
+    # of the white pixels of bin_img that overlap with the disc.
+    # It terminates when the direction of the step in both axis has changed once.
+    # radius
+    r = 0
+    # sum of previous directions
+    dir_x_hist = 0
+    dir_y_hist = 0
+    # flags indicate if there has been a change in direction in each axis
+    chg_dir_x = False
+    chg_dir_y = False
+
+    while (chg_dir_x and chg_dir_y) is False:
+        # image of concentric circles centered in c
+        circ = np.sqrt((X-c[1])**2+(Y-c[0])**2)
+        # growing radius until it reaches black pixels
+        inside = True
+        while inside is True:
+            circ_mask = 1*(circ < r)
+            circ_mask_area = np.sum(circ_mask)
+            masked_circ = bin_img*circ_mask
+            # if masked_circ has a smaller count it means the circle is
+            # overlapping black pixels -> no longer inside the white part
+            if np.abs(np.sum(masked_circ) - circ_mask_area) > 100:
+                inside = False
+            else:
+                r += 1
+        # moving center towards the center of mass
+        Cx = np.mean(X[masked_circ == 1])
+        Cy = np.mean(Y[masked_circ == 1])
+
+        dir_x = np.sign(Cx - c[1])
+        dir_y = np.sign(Cy - c[0])
+
+        stepx = dir_x*np.ceil(np.abs(Cx - c[1]))
+        stepy = dir_y*np.ceil(np.abs(Cy - c[0]))
+
+        dir_x_hist += dir_x
+        dir_y_hist += dir_y
+        # register that a change in direction has occurred to terminate when
+        # is has happened in both directions
+        if np.sign(dir_x_hist) != dir_x or dir_x == 0:
+            chg_dir_x = True
+
+        if np.sign(dir_y_hist) != dir_y or dir_y == 0:
+            chg_dir_y = True
+
+        c[1] += stepx.astype(np.int32)
+        c[0] += stepy.astype(np.int32)
+
+    circ_mask = 1*(circ < r)
+    masked_circ = (bin_img*circ_mask).astype(np.uint8)
+    masked_circ[c[0], c[1]] = 1  # center of mass always part of the mask
+
+    return masked_circ, c
+
+def _clickcount_labels(self):
+    """Function to get label names"""
+    labels = list(self.count)
+
+    return labels
+
+def _remove_points(autolist, confirmedlist):
+    """Function to remove points if interactively removed by user"""
+    # internal function to remove to remove points specified by a user
+    removecoor = []
+    for element in autolist:
+        if element not in confirmedlist:
+            removecoor.append(element)
+    return removecoor
+
+def _view(self, label="default", color="c", view_all=False):
+    """
+    View the label for a specific class label
+    Inputs:
+    label = (optional) class label, by default label="total"
+    color = desired color, by default color="c"
+    view_all = indicator of whether view all classes, by default view_all=False
+
+    :param label: string
+    :param color: string
+    :param view_all: boolean
+    :return:
+    """
+    if label not in self.points and color in self.colors.values():
+        warn("The color assigned to the new class label is already used, if proceeding, "
+             "items from different classes will not be distinguishable in plots!")
+    if label is not None:
+        self.label = label
+    self.color = color
+    self.view_all = view_all
+
+    if label not in self.points:
+        self.points[label] = []
+        self.count[label] = 0
+    self.colors[label] = color
+
+    self.fig, self.ax = plt.subplots(1, 1, figsize=self.figsize)
+
+    self.events = []
+    self.fig.canvas.mpl_connect('button_press_event', self.onclick)
+
+    self.ax.imshow(cv2.cvtColor(self.img, cv2.COLOR_BGR2RGB))
+    self.ax.set_title("Please left click on objects\n Right click to remove")
+    self.p_not_current = 0
+    # if view_all is True, show all already marked markers
+    if view_all:
+        for k in self.points:
+            for (x, y) in self.points[k]:
+                self.ax.plot(x, y, marker='x', c=self.colors[k])
+                if self.label not in self.points or len(self.points[self.label]) == 0:
+                    self.p_not_current += 1
+    else:
+        for (x, y) in self.points[label]:
+            self.ax.plot(x, y, marker='x', c=color)
+
+class Click:
+    def __init__(self, img, label="default", bin_img=None, bin_img_recover=None, figsize=(12, 6)):
+        #print("If you have coordinates to import, the label represent for total count should be 'total'!")
+        self.img = img
+        self.bin_img = None # optional binary mask, necesssary for the .correct method
+        self.bin_img_recover = None # binary image with all potential objects (binary image to recover objects) necesssary for the .correct method
+        self.bin_img_current = None # current binary mask, updated by _recover_circ 
+        self.points = {}
+        self.colors = {}
+        self.count = {}  # a dictionary that saves the counts of different classes (labels)
+        self.figsize = figsize
+        self.events = []
+        self.label = label  # current label
+        self.sample_labels = [] # list of all sample labels, one to one with points collected 
+        self.color = None  # current color
+        self.view_all = None  # a flag indicating whether or not view all labels
+        self.fig = None
+        self.ax = None
+        self.p_not_current = None
+
+        _view(self, label=label, color="r", view_all=True)
+
+    def import_coords(self, coords, label="total"):
+        """Import center coordinates of already detected objects
+        Inputs:
+        coords = list of center coordinates of already detected objects.
+        label = class label for imported coordinates, by default label="total".
+
+        Returns:
+        :param coords: list
+        :param label: string
+        :return:
+        """
+        if label not in self.points:
+            self.points[label] = []
+            for (y, x) in coords:
+                self.points[label].append((x, y))
+            self.count[label] = len(self.points[label])
+            for coord in coords:
+                self.sample_labels.append(label)
+
+        else:
+            warn(f"{label} already included and counted, nothing is imported!")
+
+        _view(self, label=label, color="c", view_all=True)
+
+    def file_import(self, filename):
+        """
+        Method to import ClickCount coordinate file to ClickCount object
+        
+        Inputs:
+        filename = filename of stored coordinates and classes
+
+        :param filename: ndarray
+        :return: 
+        """
+        # img - img file to initialize ClickCount class
+        # coor_file - file of coordinates and classes
+        coords = open(filename, "r")
+        coords = json.load(coords)
+
+        keys = list(coords.keys())
+
+        for key in keys:
+            keycoor = coords[key]
+            keycoor = list(map(lambda sub: (sub[1], sub[0]), keycoor))
+            self.import_coords(keycoor, label=key)
+
+        #_view(self, label=self.label, color="c", view_all=True)
+
+    def print_coords(self, filename):
+        """Save collected coordinates to a file.
+        Input variables:
+        filename = Name of the file to save collected coordinate
+        :param filename: str
+        """
+        # Open the file for writing
+        with open(filename, "w") as fp:
+            # Save the data in JSON format with indentation
+            json.dump(obj=self.points, fp=fp, indent=4)
+
+    def save_counts(self, label=None):
+        """Save collected coordinates to Outputs.observations""" 
+        if label is None:
+            label = params.sample_label
+        for i, x in enumerate(self.count.keys()):
+            variable = x
+            value = self.count[x]
+            outputs.add_observation(sample=label, variable=str(variable) + "_count",
+                                    trait='count of category',
+                                    method='count', scale='count', datatype=int,
+                                    value=value, label='none')
+
+    def save_coords(self, label=None):
+        """Save collected coordinates to Outputs.observations""" 
+        if label is None:
+            label = params.sample_label
+        for i, x in enumerate(self.count.keys()):
+            variable = x
+            value = self.points[x]
+            outputs.add_observation(sample=label, variable=str(variable) + "_coords",
+                                    trait='collected coordinates',
+                                    method='annotation', scale='count', datatype=list,
+                                    value=value, label='none')
+
+    def onclick(self, event):
+        """Handle mouse click events."""
+        self.events.append(event)
+        if event.button == 1:
+            self.ax.plot(event.xdata, event.ydata, marker='x', c=self.color)
+            self.points[self.label].append((floor(event.xdata), floor(event.ydata)))
+            self.count[self.label] += 1
+            self.sample_labels.append(self.label)
+        else:
+            idx_remove, _ = _find_closest_pt((event.xdata, event.ydata), self.points[self.label])
+            self.points[self.label].pop(idx_remove)
+            idx_remove = idx_remove + self.p_not_current
+            self.ax.lines[idx_remove].remove()
+            self.count[self.label] -= 1
+            self.sample_labels.pop(idx_remove)
+        self.fig.canvas.draw()
+
+    def correct(self, bin_img, bin_img_recover, coords):
+        """
+        Method to correct ClickCount object instance by removing or recovering points
+        
+        Inputs:
+        bin_img = binary image, image with selected objects
+        bin_img_recover = binary image, image with all potential objects
+        coords = coordinates of 'auto' detected points (coordinate output of detect_discs)
+
+        :param bin_img: ndarray
+        :param bin_img_recover = ndarray
+        :param coords = list
+        :return completed_mask: ndarray
+        """
+        from plantcv.plantcv import floodfill
+
+        debug = params.debug
+        params.debug = None
+
+        labelnames = _clickcount_labels(self)
+
+        completed_mask = np.copy(bin_img)
+
+        totalcoor = []
+
+        for names in labelnames:
+            for i, (x, y) in enumerate(self.points[names]):
+                x = int(x)
+                y = int(y)
+                totalcoor.append((y, x))
+
+        removecoor = _remove_points(coords, totalcoor)
+        removecoor = list(map(lambda sub: (sub[1], sub[0]), removecoor))
+        completed_mask = floodfill(completed_mask, removecoor, 0)
+
+        # points in class used for recovering and labeling
+        for names in labelnames:
+            for i, (x, y) in enumerate(self.points[names]):
+                x = int(x)
+                y = int(y)
+                # corrected coordinates
+                self.points[names][i] = (x, y)
+                # if the coordinates point to 0 in the binary image, recover the grain and coordinates of center
+                if completed_mask[y, x] == 1 or completed_mask[y, x] == 0:
+                    print(f"Recovering object at coordinates: x = {x}, y = {y}")
+                    masked_circ, [a, b] = _recover_circ(bin_img_recover, [y, x])
+                    completed_mask = completed_mask + masked_circ
+                    self.points[names][i] = (b, a)
+
+        completed_mask1 = 1*((completed_mask + 1*(completed_mask == 255)) != 0).astype(np.uint8)
+
+        params.debug = debug
+
+        _debug(visual=completed_mask1, filename=os.path.join(params.debug_outdir, f"{params.device}_clickcount-corrected.png"))
+
+        return completed_mask
+
+    def correct_labels(self, gray_img, label='default'):
+        """
+        Saves out ClickCount labeled category count to Outputs 
+        
+        Inputs:
+        gray_img = gray image with objects labeled (e.g.watershed output)
+        label = label parameter, modifies the variable name of
+        observations recorded (defaults to label="default") 
+
+        Outputs:
+        corrected_label = labeled object image
+        corrected_class = labeled class image
+        corrected_name = ordered list of names
+        num = number of objects 
+
+        :param gray_img: ndarray
+        :label = str, list
+        :return corrected_label = ndarray
+        :return corrected_class = ndarray
+        :return corrected_name = list
+        :return num: int
+        """
+
+        debug = params.debug
+        params.debug = None
+
+        labelnames = _clickcount_labels(self)
+
+        dict_class_labels = {}
+
+        # creating dictionary with label: replicate ID number 
+        for i, x in enumerate(labelnames):
+            dict_class_labels[x] = i+1
+
+        shape = np.shape(gray_img)
+        class_label = np.zeros((shape[0], shape[1]), dtype=np.uint8)
+
+        class_number = []
+        class_name = []
+
+        # Only keep watersed results that overlap with a clickpoint and do not ==0
+        for cl in list(dict_class_labels.keys()):
+            for (y, x) in self.points[cl]:
+                x = int(x)
+                y = int(y)
+                seg_label = gray_img[x, y] # grab intensity value/object from watershed labeled mask 
+                if seg_label != 0:
+                    class_number.append(seg_label)
+                    class_name.append(cl)
+                    class_label[gray_img == seg_label] = seg_label
+
+        # Get corrected name
+        corrected_number = []
+        corrected_name = []
+
+        for i, x in enumerate(class_number):
+            if x in corrected_number:
+                ind = (corrected_number.index(x))
+                y = corrected_name[ind]+"_"+str(class_name[i])
+                corrected_name[ind] = y
+            else:
+                corrected_number.append(x)
+                y = str(class_name[i])
+                corrected_name.append(y)
+
+        classes = np.unique(corrected_name)
+        class_dict = {}
+        count_class_dict = {}
+
+        for i, x in enumerate(classes):
+            class_dict[x] = i+1
+            count_class_dict[x] = corrected_name.count(x)
+
+        corrected_label = np.zeros((shape[0], shape[1]))
+        corrected_class = np.zeros((shape[0], shape[1]))
+
+        for i, value in enumerate(corrected_number):
+            if value != 0:
+                corrected_label[gray_img == value] = i+1
+                corrected_class[gray_img == value] = class_dict[corrected_name[i]]
+                corrected_name[i] = str(i+1)+"_"+corrected_name[i]
+
+        num = len(corrected_name)
+
+        vis_labeled = colorize_label_img(corrected_label)
+        vis_class = colorize_label_img(corrected_class)
+
+        params.debug = debug
+        _debug(visual=vis_labeled,
+            filename=os.path.join(params.debug_outdir, str(params.device) + '_corrected__labels_img.png'))
+        _debug(visual=vis_class,
+            filename=os.path.join(params.debug_outdir, str(params.device) + '_corrected_class.png'))
+
+        for i, x in enumerate(count_class_dict.keys()):
+            variable = x
+            value = count_class_dict[x]
+            outputs.add_observation(sample=label, variable=variable,
+                                    trait='count of category',
+                                    method='count', scale='count', datatype=int,
+                                    value=value, label=variable)
+
+        return corrected_label, corrected_class, corrected_name, num
+    ### Keep at bottom for now for import order 
+
+    def view(self, label="total", color="c", view_all=False):
+        # call helper 
+        _view(self, label=label, color=color, view_all=view_all)
+        
+        debug = params.debug
+        params.debug = None
+
+        labelnames = _clickcount_labels(self)
+        if self.bin_img_current is not None: 
+            gray_img, _ = create_labels(mask=self.bin_img_current)
+    
+            dict_class_labels = {}
+    
+            # creating dictionary with label: replicate ID number 
+            for i, x in enumerate(labelnames):
+                dict_class_labels[x] = i+1
+    
+            shape = np.shape(gray_img)
+            class_label = np.zeros((shape[0], shape[1]), dtype=np.uint8)
+    
+            class_number = []
+            class_name = []
+    
+            # Only keep watersed results that overlap with a clickpoint and do not ==0
+            for cl in list(dict_class_labels.keys()):
+                for (y, x) in self.points[cl]:
+                    x = int(x)
+                    y = int(y)
+                    seg_label = gray_img[x, y] # grab intensity value/object from watershed labeled mask 
+                    if seg_label != 0:
+                        class_number.append(seg_label)
+                        class_name.append(cl)
+                        class_label[gray_img == seg_label] = seg_label
+    
+            # Get corrected name
+            corrected_number = []
+            corrected_name = []
+    
+            for i, x in enumerate(class_number):
+                if x in corrected_number:
+                    ind = (corrected_number.index(x))
+                    y = corrected_name[ind]+"_"+str(class_name[i])
+                    corrected_name[ind] = y
+                else:
+                    corrected_number.append(x)
+                    y = str(class_name[i])
+                    corrected_name.append(y)
+            # overwrite sample labels with corrected name list 
+            self.sample_labels = corrected_name
+        params.debug = debug
+        
\ No newline at end of file
diff --git a/plantcv/plantcv/classes.py b/plantcv/plantcv/classes.py
index 4c971e366..fa4f6d73f 100644
--- a/plantcv/plantcv/classes.py
+++ b/plantcv/plantcv/classes.py
@@ -1,6 +1,5 @@
 # PlantCV classes
 import os
-import cv2
 import json
 import numpy as np
 from plantcv.plantcv import fatal_error
@@ -11,6 +10,7 @@
 import pandas as pd
 
 
+# Class definitions
 class Params:
     """PlantCV parameters class."""
 
@@ -350,47 +350,8 @@ def add_data(self, protocol):
         self.__dict__[protocol.name] = protocol
 
 
-class Points:
-    """Point annotation/collection class to use in Jupyter notebooks. It allows the user to
-    interactively click to collect coordinates from an image. Left click collects the point and
-    right click removes the closest collected point
-    """
-
-    def __init__(self, img, figsize=(12, 6)):
-        """
-        Initialization
-        :param img: image data
-        :param figsize: desired figure size, (12,6) by default
-        :attribute points: list of points as (x,y) coordinates tuples
-        """
-
-        self.fig, self.ax = plt.subplots(1, 1, figsize=figsize)
-        self.ax.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
-
-        self.points = []
-        self.events = []
-
-        self.fig.canvas.mpl_connect('button_press_event', self.onclick)
-
-    def onclick(self, event):
-        """Handle mouse click events"""
-        self.events.append(event)
-        if event.button == 1:
-
-            self.ax.plot(event.xdata, event.ydata, 'x', c='red')
-            self.points.append((floor(event.xdata), floor(event.ydata)))
-
-        else:
-            idx_remove, _ = _find_closest_pt((event.xdata, event.ydata), self.points)
-            # remove the closest point to the user right clicked one
-            self.points.pop(idx_remove)
-            self.ax.lines[idx_remove].remove()
-        self.fig.canvas.draw()
-
-
 class Objects:
     """Class for managing image contours/objects and their hierarchical relationships."""
-
     def __init__(self, contours: list = None, hierarchy: list = None):
         self.contours = contours
         self.hierarchy = hierarchy
@@ -406,18 +367,20 @@ def __next__(self):
         if self.n < len(self.contours):
             self.n += 1
             return Objects(contours=[self.contours[self.n-1]], hierarchy=[self.hierarchy[self.n-1]])
-        else:
-            raise StopIteration
+        raise StopIteration
 
     def append(self, contour, h):
+        """Method to append objects to existing list."""
         self.contours.append(contour)
         self.hierarchy.append(h)
 
     def save(self, filename):
+        """Method to save objects to a file."""
         np.savez(filename, contours=self.contours, hierarchy=self.hierarchy)
 
     @staticmethod
     def load(filename):
+        """Method to load objects from a file."""
         file = np.load(filename)
         obj = Objects(file['contours'].tolist(), file['hierarchy'])
         return obj
diff --git a/plantcv/plantcv/detect_discs.py b/plantcv/plantcv/detect_discs.py
new file mode 100644
index 000000000..ee64c594a
--- /dev/null
+++ b/plantcv/plantcv/detect_discs.py
@@ -0,0 +1,42 @@
+# Detect dics based on object eccentricity
+import os
+import numpy as np
+from skimage.measure import label, regionprops
+from plantcv.plantcv import params
+from plantcv.plantcv._debug import _debug
+
+
+def detect_discs(bin_img, ecc_thresh=0):
+    """Detect/filter disc-shaped regions in a binary image based on eccentricity.
+
+    A value of eccentricity between 0 and 1 corresponds to an ellipse.
+    The closer the value to 0 the closer the shape is to a circle.
+
+    Inputs:
+    bin_img       = Binary image containing the connected regions to consider
+    ecc_thresh    = Eccentricity threshold below which a region is kept
+
+
+    Returns:
+    discs_mask  = Binary image that contains only the detected discs
+
+    :param bin_img: numpy.ndarray
+    :param ecc_thresh: float
+    :return discs_mask: numpy.ndarray
+    """
+    params.device += 1
+    # label connected regions
+    labeled_img = label(bin_img)
+    # measure region properties
+    obj_measures = regionprops(labeled_img)
+    # blank mask to draw discs onto
+    discs_mask = np.zeros(labeled_img.shape, dtype=np.uint8)
+    # Store the list of coordinates (row,col) for the objects that pass 
+    for obj in obj_measures:
+        if obj.eccentricity < ecc_thresh:
+            # Convert coord values to int
+            discs_mask += np.where(labeled_img == obj.label, 255, 0).astype(np.uint8)
+
+    _debug(visual=discs_mask, filename=os.path.join(params.debug_outdir, f"{params.device}_discs_mask_{ecc_thresh*10}.png"))
+
+    return discs_mask
diff --git a/plantcv/plantcv/photosynthesis/reassign_frame_labels.py b/plantcv/plantcv/photosynthesis/reassign_frame_labels.py
index 0d427594a..ee7e725ea 100644
--- a/plantcv/plantcv/photosynthesis/reassign_frame_labels.py
+++ b/plantcv/plantcv/photosynthesis/reassign_frame_labels.py
@@ -1,3 +1,4 @@
+"""Reassign PSII frame labels based on induction curve"""
 import numpy as np
 from plantcv.plantcv import fatal_error
 from plantcv.plantcv.classes import PSII_data
diff --git a/plantcv/plantcv/visualize/__init__.py b/plantcv/plantcv/visualize/__init__.py
index b293f0610..d73c10486 100644
--- a/plantcv/plantcv/visualize/__init__.py
+++ b/plantcv/plantcv/visualize/__init__.py
@@ -15,4 +15,3 @@
 __all__ = ["pseudocolor", "colorize_masks", "histogram", "colorspaces", "auto_threshold_methods",
            "overlay_two_imgs", "colorize_label_img", "obj_size_ecdf", "obj_sizes", "hyper_histogram",
            "pixel_scatter_plot", "time_lapse_video", "chlorophyll_fluorescence"]
-
diff --git a/plantcv/plantcv/watershed.py b/plantcv/plantcv/watershed.py
index 3c380a6b2..ca8e90897 100755
--- a/plantcv/plantcv/watershed.py
+++ b/plantcv/plantcv/watershed.py
@@ -68,9 +68,8 @@ def watershed_segmentation(rgb_img, mask, distance=10, label=None):
 
     # Reset debug mode
     params.debug = debug
-    _debug(visual=dist_transform,
-           filename=os.path.join(params.debug_outdir, str(params.device) + '_watershed_dist_img.png'),
-           cmap='gray')
+    _debug(visual=labels,
+           filename=os.path.join(params.debug_outdir, str(params.device) + '_watershed__labels_img.png'))
     _debug(visual=joined,
            filename=os.path.join(params.debug_outdir, str(params.device) + '_watershed_img.png'))
 
@@ -79,6 +78,6 @@ def watershed_segmentation(rgb_img, mask, distance=10, label=None):
                             value=estimated_object_count, label='none')
 
     # Store images
-    outputs.images.append([dist_transform, joined])
+    outputs.images.append([labels, joined])
 
-    return joined
+    return labels
diff --git a/tests/conftest.py b/tests/conftest.py
index 38cfa1f4d..1e3053276 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -81,6 +81,7 @@ def __init__(self):
         # Clustered contours names file with too many labels
         self.cluster_names_too_many = os.path.join(self.datadir, "cluster_names_too_many.txt")
 
+
     @staticmethod
     def load_hsi(pkl_file):
         """Load PlantCV Spectral_data pickled object."""
diff --git a/tests/plantcv/annotate/conftest.py b/tests/plantcv/annotate/conftest.py
index 72630630f..851906f56 100644
--- a/tests/plantcv/annotate/conftest.py
+++ b/tests/plantcv/annotate/conftest.py
@@ -13,6 +13,15 @@ def __init__(self):
         self.datadir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "..", "testdata")
         # RGB image
         self.small_rgb_img = os.path.join(self.datadir, "setaria_small_plant_rgb.png")
+        # Disc image mask
+        self.discs_mask = os.path.join(self.datadir, "discs_binary.png")
+        # Clickcount_correct image masks
+        self.pollen = os.path.join(self.datadir, "crop_pollen.png")
+        self.pollen_all = os.path.join(self.datadir, "pollen_all_mask.png")
+        self.pollen_discs = os.path.join(self.datadir, "pollen_detectdisc_mask.png")
+        self.pollen_watershed = os.path.join(self.datadir, "pollen_watershed.png")
+        self.pollen_recovered = os.path.join(self.datadir, "pollen_recovered.png")
+        self.pollen_coords = os.path.join(self.datadir, "clickcount_file_import.coords")
 
 
 @pytest.fixture(scope="session")
diff --git a/tests/plantcv/annotate/test_click_count.py b/tests/plantcv/annotate/test_click_count.py
new file mode 100644
index 000000000..7797c90c9
--- /dev/null
+++ b/tests/plantcv/annotate/test_click_count.py
@@ -0,0 +1,61 @@
+import os
+import numpy as np
+import matplotlib
+from plantcv.plantcv.transform import rescale
+from plantcv.plantcv.annotate import ClickCount
+
+
+def test_plantcv_visualize_click_count(tmpdir):
+    """Test for PlantCV."""
+    # Create a test tmp directory
+    cache_dir = tmpdir.mkdir("cache")
+    # generate fake testing image
+    temp = rescale(np.random.rand(5, 5))
+    img = np.stack((temp,)*3, axis=-1)
+    counter = ClickCount(img, figsize=(12, 6))
+    assert len(counter.count) == 0
+
+    # import coordinates
+    counter.import_coords([(0, 0)], label="total")
+    counter.import_coords([(0, 1)], label="c1")
+    counter.import_coords([(0, 2)], label="total")
+    assert len(counter.count) == 2
+
+    # save coordinates
+    coord_file = os.path.join(cache_dir, 'coord.json')
+    counter.save_coords(coord_file)
+    assert os.path.exists(coord_file)
+
+    # view different classes
+    counter.view()
+    counter.view(label="c1", color="r", view_all=True)
+    counter.view(label="c2", color="r", view_all=True)
+    counter.view(label="c2", color="k", view_all=True)
+    assert len(counter.points) == 3
+
+    # create mock events
+    # left click
+    e1 = matplotlib.backend_bases.MouseEvent(name="button_press_event", canvas=counter.fig.canvas, x=0, y=0, button=1)
+    e1.xdata = 0
+    e1.ydata = 0
+
+    # right click
+    e1_ = matplotlib.backend_bases.MouseEvent(name="button_press_event", canvas=counter.fig.canvas, x=0, y=0, button=3)
+    e1_.xdata = 0
+    e1_.ydata = 0
+
+    # left click
+    e2 = matplotlib.backend_bases.MouseEvent(name="button_press_event", canvas=counter.fig.canvas, x=0, y=0, button=1)
+    e2.xdata = 1
+    e2.ydata = 1
+
+    # right click
+    e2_ = matplotlib.backend_bases.MouseEvent(name="button_press_event", canvas=counter.fig.canvas, x=0, y=0, button=3)
+    e2_.xdata = 1.2
+    e2_.ydata = 1.2
+
+    counter.onclick(e1)
+    counter.onclick(e1_)
+    counter.onclick(e2)
+    counter.onclick(e2_)
+    assert len(counter.events) == 4
diff --git a/tests/plantcv/annotate/test_clickcount_correct.py b/tests/plantcv/annotate/test_clickcount_correct.py
new file mode 100644
index 000000000..6ef61814e
--- /dev/null
+++ b/tests/plantcv/annotate/test_clickcount_correct.py
@@ -0,0 +1,27 @@
+import cv2
+import numpy as np
+from plantcv.plantcv.annotate import ClickCount
+
+
+def test_plantcv_click_count_correct(annotate_test_data):
+    """Test for PlantCV."""
+    # Create a test tmp directory
+    # generate fake testing image
+    img = cv2.imread(annotate_test_data.pollen, -1)
+    allmask = cv2.imread(annotate_test_data.pollen_all, -1)
+    discs = cv2.imread(annotate_test_data.pollen_discs, -1)
+    
+    coor = [(158, 531), (265, 427), (361, 112), (500, 418)]
+    totalpoints1 = [(158, 531), (361, 112), (500, 418), (269.25303806488864, 385.69839981447126),
+    (231.21964288863632, 445.995245825603), (293.37177646934134, 448.778177179963), (240.49608073650273, 277.1640769944342),
+    (279.4571196975417, 240.05832560296852), (77.23077461405376, 165.84682282003712), (423.24190633947126, 364.3625927643785),
+    (509.5127783246289, 353.2308673469388), (527, 275), (445.50535717435065, 138.94515306122452)]
+    germinated1= [(283.3633696975417, 92.56296382189242), (429.93108769383116, 369.0008116883117)]
+    counter = ClickCount(img, figsize=(8, 6))
+    counter.import_coords(totalpoints1, label="total")
+    counter.import_coords(germinated1, label="germinated")
+
+    corrected_mask = counter.correct(discs, allmask, coor)
+
+    assert np.count_nonzero(discs)< np.count_nonzero(corrected_mask) 
+    assert np.count_nonzero(corrected_mask) < np.count_nonzero(allmask)
\ No newline at end of file
diff --git a/tests/plantcv/annotate/test_clickcount_file_import.py b/tests/plantcv/annotate/test_clickcount_file_import.py
new file mode 100644
index 000000000..0432b0601
--- /dev/null
+++ b/tests/plantcv/annotate/test_clickcount_file_import.py
@@ -0,0 +1,12 @@
+import cv2
+from plantcv.plantcv.annotate import ClickCount
+
+
+def test_plantcv_clickcount_file_import(annotate_test_data):
+    """Test for PlantCV."""
+    img = cv2.imread(annotate_test_data.discs_mask, -1)
+    counter = ClickCount(img, figsize=(8, 6))
+    file  = annotate_test_data.pollen_coords
+    counter.file_import(file)
+
+    assert counter.count['total'] == 70
diff --git a/tests/plantcv/annotate/test_clickcount_label.py b/tests/plantcv/annotate/test_clickcount_label.py
new file mode 100644
index 000000000..2a0003c23
--- /dev/null
+++ b/tests/plantcv/annotate/test_clickcount_label.py
@@ -0,0 +1,26 @@
+import cv2
+from plantcv.plantcv.annotate import ClickCount
+
+
+def test_clickcount_label(annotate_test_data):
+    """Test for PlantCV."""
+    # Read in test data
+    img = cv2.imread(annotate_test_data.pollen, -1)
+    watershed = cv2.imread(annotate_test_data.pollen_watershed, -1)
+
+    totalpoints1 = [(158, 531), (361, 112), (500, 418), (269.25303806488864, 385.69839981447126),
+                  (231.21964288863632, 445.995245825603), (293.37177646934134, 448.778177179963), (240.49608073650273, 277.1640769944342), 
+                  (279.4571196975417, 240.05832560296852), (77.23077461405376, 165.84682282003712), (420, 364),
+                  (509.5127783246289, 353.2308673469388), (527.1380102355752, 275.3087894248609), (445.50535717435065, 138.94515306122452)]
+    germinated1 = [(283.3633696975417, 92.56296382189242), (420, 364)]
+    counter = ClickCount(img, figsize=(8, 6))
+    counter.import_coords(totalpoints1, label="total")
+    counter.import_coords(germinated1, label="germinated")
+
+    imagesname = "test"
+
+    _, _, _, num = counter.create_labels(gray_img=watershed, label=imagesname)
+
+    assert num == 14
+
+  
\ No newline at end of file
diff --git a/tests/plantcv/annotate/test_get_centroids.py b/tests/plantcv/annotate/test_get_centroids.py
new file mode 100644
index 000000000..96762b2ec
--- /dev/null
+++ b/tests/plantcv/annotate/test_get_centroids.py
@@ -0,0 +1,12 @@
+import cv2
+from plantcv.plantcv.annotate import get_centroids
+
+
+def test_get_centroids(annotate_test_data):
+    """Test for PlantCV."""
+    # Read in test data
+    mask = cv2.imread(annotate_test_data.discs_mask, -1)
+
+    coor = get_centroids(bin_img=mask)
+
+    assert len(coor) == 5
diff --git a/tests/plantcv/annotate/test_points.py b/tests/plantcv/annotate/test_points.py
index 8378addc8..5d1c27f2d 100644
--- a/tests/plantcv/annotate/test_points.py
+++ b/tests/plantcv/annotate/test_points.py
@@ -1,6 +1,6 @@
 import cv2
 import matplotlib
-from plantcv.plantcv import Points
+from plantcv.plantcv.annotate import Points
 
 
 def test_points_interactive(annotate_test_data):
diff --git a/tests/plantcv/test_detect_discs.py b/tests/plantcv/test_detect_discs.py
new file mode 100644
index 000000000..a2997474e
--- /dev/null
+++ b/tests/plantcv/test_detect_discs.py
@@ -0,0 +1,13 @@
+import cv2
+import numpy as np 
+from plantcv.plantcv import detect_discs
+
+
+def test_detect_discs(test_data):
+    """Test for PlantCV."""
+    # Read in test data
+    mask = cv2.imread(test_data.small_bin_img, -1)
+
+    filtered_mask = detect_discs(bin_img=mask, ecc_thresh=0.8)
+
+    assert  np.sum(mask) > np.sum(filtered_mask) 
diff --git a/tests/testdata/clickcount_file_import.coords b/tests/testdata/clickcount_file_import.coords
new file mode 100644
index 000000000..2be1117a1
--- /dev/null
+++ b/tests/testdata/clickcount_file_import.coords
@@ -0,0 +1,306 @@
+{
+    "total": [
+        [
+            1524,
+            59
+        ],
+        [
+            1642,
+            78
+        ],
+        [
+            723,
+            159
+        ],
+        [
+            1711,
+            218
+        ],
+        [
+            736,
+            263
+        ],
+        [
+            1431,
+            275
+        ],
+        [
+            1573,
+            338
+        ],
+        [
+            531,
+            358
+        ],
+        [
+            1437,
+            365
+        ],
+        [
+            904,
+            380
+        ],
+        [
+            1255,
+            387
+        ],
+        [
+            1140,
+            417
+        ],
+        [
+            112,
+            561
+        ],
+        [
+            2056,
+            690
+        ],
+        [
+            418,
+            700
+        ],
+        [
+            1738,
+            703
+        ],
+        [
+            1255,
+            959
+        ],
+        [
+            1791,
+            1042
+        ],
+        [
+            1310,
+            1065
+        ],
+        [
+            902,
+            1084
+        ],
+        [
+            1319,
+            1131
+        ],
+        [
+            1084,
+            1156
+        ],
+        [
+            457,
+            1183
+        ],
+        [
+            1763,
+            1190
+        ],
+        [
+            1407,
+            1232
+        ],
+        [
+            2053,
+            1283
+        ],
+        [
+            1140,
+            1383
+        ],
+        [
+            566,
+            1393
+        ],
+        [
+            240,
+            1403
+        ],
+        [
+            1681,
+            1421
+        ],
+        [
+            398,
+            470
+        ],
+        [
+            438,
+            432
+        ],
+        [
+            446,
+            502
+        ],
+        [
+            919,
+            252
+        ],
+        [
+            947,
+            204
+        ],
+        [
+            997,
+            219
+        ],
+        [
+            967,
+            267
+        ],
+        [
+            1034,
+            400
+        ],
+        [
+            1074,
+            364
+        ],
+        [
+            1104,
+            329
+        ],
+        [
+            1165,
+            267
+        ],
+        [
+            1235,
+            304
+        ],
+        [
+            1272,
+            262
+        ],
+        [
+            1297,
+            299
+        ],
+        [
+            1350,
+            249
+        ],
+        [
+            1387,
+            229
+        ],
+        [
+            1488,
+            492
+        ],
+        [
+            1565,
+            202
+        ],
+        [
+            1595,
+            247
+        ],
+        [
+            1633,
+            274
+        ],
+        [
+            839,
+            101
+        ],
+        [
+            861,
+            162
+        ],
+        [
+            914,
+            46
+        ],
+        [
+            1052,
+            56
+        ],
+        [
+            1102,
+            149
+        ],
+        [
+            1150,
+            127
+        ],
+        [
+            280,
+            442
+        ],
+        [
+            240,
+            477
+        ],
+        [
+            93,
+            490
+        ],
+        [
+            270,
+            733
+        ],
+        [
+            351,
+            713
+        ],
+        [
+            363,
+            627
+        ],
+        [
+            215,
+            1036
+        ],
+        [
+            498,
+            973
+        ],
+        [
+            606,
+            1224
+        ],
+        [
+            646,
+            1264
+        ],
+        [
+            353,
+            1244
+        ],
+        [
+            343,
+            1284
+        ],
+        [
+            300,
+            1244
+        ],
+        [
+            323,
+            1196
+        ]
+    ],
+    "germinated": [
+        [
+            1485,
+            494
+        ],
+        [
+            95,
+            481
+        ],
+        [
+            1588,
+            974
+        ],
+        [
+            1994,
+            1039
+        ],
+        [
+            1901,
+            198
+        ]
+    ]
+}
\ No newline at end of file
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index 000000000..88242bc47
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new file mode 100644
index 000000000..849ec6b14
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diff --git a/tests/tests.py b/tests/tests.py
deleted file mode 100755
index e69de29bb..000000000