Morphology Tutorial¶

Morphology sub-package functions can be used with a clean mask of a plant (see VIS tutorial for examples of masking background. This tutorial will start with a binary mask (after object segmentation has been completed) but in a complete workflow users will need to use other functions to achieve plant isolation. Skeletonizing is very sensitive to any pepper noise remaining within a binary mask. Morphology functions are intended to be one type of object analysis. These functions can potentially return information about leaf length, leaf angle, and leaf curvature.

In [1]:

# Import libraries
from plantcv import plantcv as pcv 

In [2]:

class options:
    def __init__(self):
        self.image = "./img/mask.png"
        self.debug = "plot"
        self.writeimg= False 
        self.result = "morphology_tutorial_results.txt"
        self.outdir = "."

# Get options
args = options()

# Set debug to the global parameter 
pcv.params.debug = args.debug

In [3]:

# Read image (sometimes you need to run this line twice to see the image) 

# Inputs:
#   filename - Image file to be read in 
#   mode - How to read in the image; either 'native' (default), 'rgb', 'gray', or 'csv'
img, path, filename = pcv.readimage(filename=args.image)

# Crop the mask 
cropped_mask = img[1150:1750, 900:1550]

In [4]:

# Skeletonize the mask 
#%matplotlib notebook
# To enable the zoom feature to better see fine lines, uncomment the line above ^^ 

# Inputs:
#   mask = Binary image data
skeleton = pcv.morphology.skeletonize(mask=cropped_mask)

In [5]:

# Prune the skeleton  
# Generally, skeletonized images will have barbs (this image is particularly ideal, 
# that's why it's the example image in the tutorial!), 
# representing the width, that need to get pruned off. 

# Inputs:
#   skel_img = Skeletonized image
#   size     = Size to get pruned off each branch
#   mask     = (Optional) binary mask for debugging. If provided, debug image will be overlaid on the mask.
img1, seg_img, edge_objects = pcv.morphology.prune(skel_img=skeleton, size=10, mask=cropped_mask)

In [6]:

# Fill in segments (also stores out area data)  

# Inputs:
# mask         = Binary image, single channel, object = 1 and background = 0
# objects      = List of contours
# label        = Optional label parameter, modifies the variable name of observations recorded. (default `label="default"`)filled_img = pcv.morphology.fill_segments(mask=cropped_mask, objects=edge_objects)

filled_img = pcv.morphology.fill_segments(mask=cropped_mask, objects=edge_objects, label="default")

In [7]:

# Identify branch points   

# Inputs:
#   skel_img = Skeletonized image
#   mask     = (Optional) binary mask for debugging. If provided, debug image will be overlaid on the mask.
#   label    = Optional label parameter, modifies the variable name of observations recorded. (default `label="default"`)filled_img = pcv.morphology.fill_segments(mask=cropped_mask, objects=edge_objects)

branch_pts_mask = pcv.morphology.find_branch_pts(skel_img=skeleton, mask=cropped_mask, label="default")

In [8]:

# Identify tip points   

# Inputs:
#   skel_img = Skeletonized image
#   mask     = (Optional) binary mask for debugging. If provided, debug 
#              image will be overlaid on the mask
#   label    = Optional label parameter, modifies the variable name of observations recorded. (default `label="default"`)filled_img = pcv.morphology.fill_segments(mask=cropped_mask, objects=edge_objects)

tip_pts_mask = pcv.morphology.find_tips(skel_img=skeleton, mask=None, label="default")

In [9]:

# Adjust line thickness with the global line thickness parameter (default = 5),
# and provide binary mask of the plant for debugging. NOTE: the objects and
# hierarchies returned will be exactly the same but the debugging image (segmented_img)
# will look different.
pcv.params.line_thickness = 3 

In [10]:

# Sort segments into primary (stem) objects and secondary (leaf) objects. 
# Downstream steps can be performed on just one class of objects at a time, 
# or all objects (output from segment_skeleton) 
  
# Inputs:
#   skel_img  = Skeletonized image
#   objects   = List of contours
#   mask      = (Optional) binary mask for debugging. If provided, debug image will be overlaid on the mask.
leaf_obj, stem_obj = pcv.morphology.segment_sort(skel_img=skeleton, 
                                                 objects=edge_objects,
                                                 mask=cropped_mask)

In [11]:

# Identify segments     

# Inputs:
#   skel_img  = Skeletonized image
#   objects   = List of contours
#   mask      = (Optional) binary mask for debugging. If provided, 
#               debug image will be overlaid on the mask.
segmented_img, labeled_img = pcv.morphology.segment_id(skel_img=skeleton,
                                                       objects=leaf_obj,
                                                       mask=cropped_mask)

In [12]:

# Similar to line thickness, there are optional text size and text thickness parameters 
# that can be adjusted to better suit images or varying sizes.
pcv.params.text_size=.8 # (default text_size=.55)
pcv.params.text_thickness=3 # (defaul text_thickness=2) 

segmented_img, labeled_img = pcv.morphology.segment_id(skel_img=skeleton,
                                                       objects=leaf_obj,
                                                       mask=cropped_mask)

In [13]:

# Measure path lengths of segments     

# Inputs:
#   segmented_img = Segmented image to plot lengths on
#   objects       = List of contours
#   label           = Optional label parameter, modifies the variable name of observations recorded. (default `label="default"`)filled_img = pcv.morphology.fill_segments(mask=cropped_mask, objects=edge_objects)

labeled_img  = pcv.morphology.segment_path_length(segmented_img=segmented_img, 
                                                  objects=leaf_obj, label="default")

In [14]:

# Measure euclidean distance of segments      

# Inputs:
#   segmented_img = Segmented image to plot lengths on
#   objects       = List of contours
#   label         = Optional label parameter, modifies the variable name of observations recorded. (default `label="default"`)filled_img = pcv.morphology.fill_segments(mask=cropped_mask, objects=edge_objects)

labeled_img = pcv.morphology.segment_euclidean_length(segmented_img=segmented_img, 
                                                      objects=leaf_obj, label="default")

In [15]:

# Measure curvature of segments      

# Inputs:
#   segmented_img = Segmented image to plot curvature on
#   objects       = List of contours
#   label         = Optional label parameter, modifies the variable name of observations recorded. (default `label="default"`)filled_img = pcv.morphology.fill_segments(mask=cropped_mask, objects=edge_objects)

labeled_img = pcv.morphology.segment_curvature(segmented_img=segmented_img, 
                                               objects=leaf_obj, label="default")

In [16]:

# Measure the angle of segments      

# Inputs:
#   segmented_img = Segmented image to plot angles on
#   objects       = List of contours
#   label         = Optional label parameter, modifies the variable name of observations recorded. (default `label="default"`)filled_img = pcv.morphology.fill_segments(mask=cropped_mask, objects=edge_objects)

labeled_img = pcv.morphology.segment_angle(segmented_img=segmented_img, 
                                           objects=leaf_obj, label="default")