Interactive object count

.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

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)
 

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)

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)

docs/Points.md → 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)