Merge branch 'main' into zeeuqsze-grayscale-colab

.all-contributorsrc

@@ -627,7 +627,8 @@
       "profile": "https://github.com/jbraley1107",
       "contributions": [
         "review",
-        "userTesting"
+        "userTesting",
+        "bug"
       ]
     },
     {
@@ -638,6 +639,16 @@
       "contributions": [
         "bug"
       ]
+    },
+    {
+      "login": "mlohbihler",
+      "name": "Matthew Lohbihler",
+      "avatar_url": "https://avatars.githubusercontent.com/u/1182059?v=4",
+      "profile": "https://github.com/mlohbihler",
+      "contributions": [
+        "bug",
+        "doc"
+      ]
     }
   ],
   "contributorsPerLine": 7,

docs/roi_auto_wells.md

@@ -0,0 +1,44 @@
+## Autodetect Cicular Regions of Interest (ROI) 
+
+**plantcv.roi.auto_wells**(*gray_img, mindist, candec, accthresh, minradius, maxradius, nrows, ncols, radiusadjust=None*)
+
+**returns** roi_objects
+
+- **Parameters:**
+    - gray_img            = Gray single channel image data
+    - mindist             = minimum distance between detected circles
+    - candec              = higher threshold of canny edge detector
+    - accthresh           = accumulator threshold for the circl centers
+    - minradius           = minimum circle radius
+    - maxradius           = maximum circle radius
+    - nrows               = expected number of rows
+    - ncols               = expected number of columns
+    - radiusadjust        = amount to adjust the average radius, this can be desirable if you want ROI to sit inside a well, for example (in that case you might set it to a negative value).
+- **Context:**
+    - Uses a Hough Circle detector to find circular shapes, then uses a gaussian mixture model to sort found circular objects so they are ordered from 
+    top left to bottom right. We assume that circles are of approximately equal size because we calculate an average radius of all of the found circles.
+    The average radius size can be adjusted with the radius adjust parameter, for example in the case that you'd like the ROI to sit inside of the well.
+
+**Reference Image**
+
+![Screenshot](img/documentation_images/roi_auto_wells/circle-wells.png)
+
+```python
+
+from plantcv import plantcv as pcv
+
+# Set global debug behavior to None (default), "print" (to file), 
+# or "plot" (Jupyter Notebooks or X11)
+pcv.params.debug = "plot"
+
+# Detect Circular Shapes and Use as ROIs
+rois1 = pcv.roi.auto_wells(gray_img=gray_img, mindist = 20, candec = 50, 
+accthresh = 30, minradius = 40, maxradius = 50, nrows=4, ncols=6, radiusadjust=-10)
+
+```
+
+**Grid of ROIs**
+
+![Screenshot](img/documentation_images/roi_auto_wells/21_roi.png)
+
+**Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/main/plantcv/plantcv/roi/roi_methods.py)

docs/transform_auto_correct_color.md

@@ -0,0 +1,40 @@
+# Automatically detect a color card and color correct in one step
+
+Corrects the color of the input image based on the target color matrix using an affine transformation
+in the RGB space after automatic detection of a color card within the image. A one-step wrapper of
+[plantcv.transform.detect_color_card](transform_detect_color_card.md), [plantcv.transform.std_color_matrix](std_color_matrix.md),
+[plantcv.transform.get_color_matrix](get_color_matrix.md), and [plantcv.transform.affine_color_correction](transform_affine_color_correction.md).
+
+**plantcv.transform.auto_correct_color**(*rgb_img, label=None, \*\*kwargs*)
+
+**returns** corrected_img
+
+- **Parameters**
+    - rgb_img          - Input RGB image data containing a color card.
+    - label            - Optional label parameter, modifies the variable name of observations recorded. (default = `pcv.params.sample_label`)
+    - **kwargs         - Other keyword arguments passed to `cv2.adaptiveThreshold` and `cv2.circle`.
+        - adaptive_method  - Adaptive threhold method. 0 (mean) or 1 (Gaussian) (default = 1).
+        - block_size       - Size of a pixel neighborhood that is used to calculate a threshold value (default = 51). We suggest using 127 if using `adaptive_method=0`.
+        - radius           - Radius of circle to make the color card labeled mask (default = 20).
+        - min_size         - Minimum chip size for filtering objects after edge detection (default = 1000)
+- **Returns**
+    - corrected_img    - Color corrected image
+
+- **Example Use**
+    - Below
+
+```python
+
+from plantcv import plantcv as pcv
+
+rgb_img, imgpath, imgname = pcv.readimage(filename="top_view_plant.png")
+
+corrected_rgb = pcv.transform.auto_correct_color(rgb_img=old_card)
+```
+
+**Debug Image: automatically detected and masked the color card**
+
+![Screenshot](img/documentation_images/correct_color_imgs/detect_color_card.png)
+
+
+**Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/main/plantcv/plantcv/transform/auto_correct_color.py)

docs/tutorials/arabidopsis_multi_plant_tutorial.md

@@ -1,6 +1,9 @@
 ## Tutorial: Arabidopsis Multi-Plant Workflow
 
+Check out our interactive Arabidopsis multi-plant tutorial!
 
-[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/danforthcenter/plantcv-tutorial-arabidopsis-tray/HEAD?labpath=index.ipynb) Check out our interactive arabidopsis multi-plant tutorial! 
+[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/danforthcenter/plantcv-tutorial-arabidopsis-tray/HEAD?labpath=index.ipynb) 
+[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/danforthcenter/plantcv-tutorial-arabidopsis-tray/blob/main/index-Colab.ipynb)
+[![Static Badge](https://img.shields.io/badge/Open%20on%20GitHub-black?logo=github)](https://github.com/danforthcenter/plantcv-tutorial-arabidopsis-tray.git)
 
 <iframe src="https://nbviewer.jupyter.org/github/danforthcenter/plantcv-tutorial-arabidopsis-tray/blob/main/index.ipynb"  width="100%" height="1500px"></iframe>

docs/tutorials/morphology_tutorial.md

@@ -1,6 +1,9 @@
 ## Tutorial: Morphology Functions 
 
-[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/danforthcenter/plantcv-tutorial-morphology/HEAD?labpath=index.ipynb)
 Check out our interactive morphology tutorial! 
 
+[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/danforthcenter/plantcv-tutorial-morphology/HEAD)
+[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/danforthcenter/plantcv-tutorial-morphology/blob/main/index-Colab.ipynb)
+[![Static Badge](https://img.shields.io/badge/Open%20in%20GitHub-black?logo=github)](https://github.com/danforthcenter/plantcv-tutorial-morphology.git)
+
 <iframe src="https://nbviewer.jupyter.org/github/danforthcenter/plantcv-tutorial-morphology/blob/main/index.ipynb" width="100%" height="1500px"></iframe>

docs/tutorials/seed_analysis_tutorial.md

@@ -1,6 +1,9 @@
 ## Tutorial: Seed Analysis Workflow
 
+Check out our interactive tutorial! 
 
-[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/danforthcenter/plantcv-tutorial-seeds/HEAD?labpath=index.ipynb) Check out our interactive seed analysis tutorial! 
+[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/danforthcenter/plantcv-tutorial-seeds/HEAD)
+[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/danforthcenter/plantcv-tutorial-seeds/blob/main/index-Colab.ipynb)
+[![Static Badge](https://img.shields.io/badge/Open%20on%20GitHub-black?logo=github)](https://github.com/danforthcenter/plantcv-tutorial-seeds.git)
 
 <iframe src="https://nbviewer.jupyter.org/github/danforthcenter/plantcv-tutorial-seeds/blob/main/index.ipynb" width="100%" height="1500px"></iframe>

docs/tutorials/watershed_segmentation_tutorial.md

@@ -1,5 +1,9 @@
 ## Tutorial: Watershed Segmentation Workflow
 
-[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/danforthcenter/plantcv-tutorial-watershed/HEAD?labpath=index.ipynb) Check out our interactive watershed tutorial! 
+Check out our interactive watershed tutorial! This tutorial uses the analyze watershed segmentation function to roughly segment individual leaves. In this case, the number of objects reported from the analyze watershed function is a rough estimation of the number of leaves. The Watershed Segmentation Function is a PlantCV function based on code contributed by Suxing Liu, Arkansas State University. For more information see https://github.com/lsx1980/Leaf_count.
+
+[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/danforthcenter/plantcv-tutorial-watershed/HEAD?labpath=index.ipynb)
+[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/danforthcenter/plantcv-tutorial-watershed/blob/main/index-Colab.ipynb)
+[![Static Badge](https://img.shields.io/badge/Open%20on%20GitHub-black?logo=github)](https://github.com/danforthcenter/plantcv-tutorial-watershed.git)
 
 <iframe src="https://nbviewer.jupyter.org/github/danforthcenter/plantcv-tutorial-watershed/blob/main/index.ipynb" width="100%" height="1500px"></iframe>

docs/updating.md

@@ -145,7 +145,7 @@ roi = pcv.roi.rectangle(img=img, x=100, y=100, h=100, w=100)
 
 # Filter binary image to make a clean mask based on ROI 
 # (no longer needs `pcv.find_objects` or `pcv.object_composition`)
-mask = pcv.roi.filter(mask=bin_img, roi=roi, roi_type="partial")
+mask = pcv.roi.filter(mask=bin_mask, roi=roi, roi_type="partial")
 
 # Extract shape traits from plant
 shape_img = pcv.analyze.size(img=img,labeled_mask=mask, n_labels=1)
@@ -934,6 +934,11 @@ pages for more details on the input and output variable types.
 * pre v4.0: NA
 * post v4.0: roi_objects = **pcv.roi.auto_grid**(*mask, nrows, ncols, radius=None, img=None*)
 
+#### plantcv.roi.auto_wells
+
+* pre v4.6: NA
+* post v4.6: roi_objects = **pcv.roi.auto_wells**(*gray_img, mindist, candec, accthresh, minradius, maxradius, nrows, ncols, radiusadjust=None*)
+
 #### plantcv.roi.multi
 
 * pre v3.1: NA
@@ -1210,6 +1215,11 @@ pages for more details on the input and output variable types.
 * pre v3.0dev1: NA
 * post v3.0dev2: determinant, transformation_matrix = **plantcv.transform.calc_transformation_matrix**(*matrix_m, matrix_b*)
 
+#### plantcv.transform.auto_correct_color
+
+* pre v4.6: NA
+* post v4.6: corrected_img = **plantcv.transform.auto_correct_color**(*rgb_img, label=None, **kwargs*)
+
 #### plantcv.transform.correct_color
 
 * pre v3.0dev1: NA

mkdocs.yml

@@ -153,6 +153,7 @@ nav:
         - 'Create ROI from Binary Image': roi_from_binary_image.md
         - 'Create Multi ROIs': 'roi_multi.md'
         - 'Create Grid of ROIs Automatically': roi_auto_grid.md
+        - 'Create Grid of Circular ROIs Automatically': roi_auto_wells.md
         - 'Filter a mask by ROI': roi_filter.md
         - 'Filter a mask by ROI (quickly)': roi_quick_filter.md
         - 'Convert ROI to Mask': roi2mask.md
@@ -175,6 +176,7 @@ nav:
       - 'Transformation Methods':
           - 'Auto-Detect Color Card': transform_detect_color_card.md
           - 'Auto-Detect Color Card (old)': find_color_card.md
+          - 'Auto Correct Color': transform_auto_correct_color.md
           - 'Calibrate Camera': transform_calibrate_camera.md
           - 'Checkerboard Calibration': transform_checkerboard_calibration.md
           - 'Create Color Card Mask': create_color_card_mask.md

plantcv/plantcv/_helpers.py

@@ -3,6 +3,94 @@
 from plantcv.plantcv.logical_and import logical_and
 from plantcv.plantcv import fatal_error, warn
 from plantcv.plantcv import params
+import pandas as pd
+
+
+def _hough_circle(gray_img, mindist, candec, accthresh, minradius, maxradius, maxfound=None):
+    """
+    Hough Circle Detection
+
+    Keyword inputs:
+    gray_img = gray image (np.ndarray)
+    mindist = minimum distance between detected circles
+    candec = higher threshold of canny edge detector
+    accthresh = accumulator threshold for the circl centers
+    minradius = minimum circle radius
+    maxradius = maximum circle radius
+    maxfound = maximum number of circles to find
+
+    :param gray_img: np.ndarray
+    :param mindist: int
+    :param candec: int
+    :param accthresh: int
+    :param minradius: int
+    :param maxradius: int
+    :param maxfound: None or int
+    :return dataframe: pandas dataframe
+    :return img: np.ndarray
+    """
+    # Store debug
+    debug = params.debug
+    params.debug = None
+
+    circles = cv2.HoughCircles(gray_img, cv2.HOUGH_GRADIENT,
+                               dp=1, minDist=mindist,
+                               param1=candec, param2=accthresh,
+                               minRadius=minradius, maxRadius=maxradius)
+
+    cimg = cv2.cvtColor(gray_img, cv2.COLOR_GRAY2BGR)
+    x = []
+    y = []
+    radius = []
+    if circles is None:
+        fatal_error('number of circles found is None with these parameters')
+    circles = np.uint16(np.around(circles))
+    if maxfound is not None:
+        if maxfound >= len(circles[0, :]):
+            for i in circles[0, :]:
+                # draw the outer circle
+                cv2.circle(cimg, (i[0], i[1]), i[2], (0, 255, 0),
+                           params.line_thickness)
+                # draw the center of the circle
+                cv2.circle(cimg, (i[0], i[1]), 2, (0, 0, 255),
+                           params.line_thickness)
+                x.append(i[0])
+                y.append(i[1])
+                radius.append(i[2])
+        else:
+            for n, i in enumerate(circles[0, :]):
+                if n <= (maxfound-1):
+                    # draw the outer circle
+                    cv2.circle(cimg, (i[0], i[1]), i[2], (0, 255, 0),
+                               params.line_thickness)
+                    # draw the center of the circle
+                    cv2.circle(cimg, (i[0], i[1]), 2, (0, 0, 255),
+                               params.line_thickness)
+                    x.append(i[0])
+                    y.append(i[1])
+                    radius.append(i[2])
+            warn('Number of found circles is ' +
+                 str(len(circles[0, :])) +
+                 ' Change Parameters. Only drawing first '+str(maxfound))
+    else:
+        for i in circles[0, :]:
+            # draw the outer circle
+            cv2.circle(cimg, (i[0], i[1]), i[2], (0, 255, 0),
+                       params.line_thickness)
+            # draw the center of the circle
+            cv2.circle(cimg, (i[0], i[1]), 2, (0, 0, 255),
+                       params.line_thickness)
+            x.append(i[0])
+            y.append(i[1])
+            radius.append(i[2])
+
+    data = {'x': x, 'y': y, 'radius': radius}
+    df = pd.DataFrame(data)
+
+    # Reset debug mode
+    params.debug = debug
+
+    return df, cimg
 
 
 def _cv2_findcontours(bin_img):

plantcv/plantcv/roi/__init__.py

@@ -6,8 +6,10 @@
 from plantcv.plantcv.roi.roi_methods import multi
 from plantcv.plantcv.roi.roi_methods import custom
 from plantcv.plantcv.roi.roi_methods import filter
+from plantcv.plantcv.roi.roi_methods import auto_wells
 from plantcv.plantcv.roi.roi2mask import roi2mask
 from plantcv.plantcv.roi.quick_filter import quick_filter
 
-__all__ = ["circle", "ellipse", "from_binary_image", "rectangle", "auto_grid", "multi", "custom",
+__all__ = ["circle", "ellipse", "from_binary_image", "rectangle", "auto_grid",
+           "multi", "custom", "auto_wells",
            "filter", "roi2mask", "quick_filter"]