<gh_stars>0
#!/usr/bin/env python

import os
import argparse
from plantcv import plantcv as pcv


# Parse command-line arguments
def options():
    parser = argparse.ArgumentParser(description="Imaging processing with opencv")
    parser.add_argument("-i", "--image", help="Input image file.", required=True)
    parser.add_argument("-o", "--outdir", help="Output directory for image files.", required=False)
    parser.add_argument("-r", "--result", help="result file.", required=False)
    parser.add_argument("-w", "--writeimg", help="write out images.", default=False, action="store_true")
    parser.add_argument("-D", "--debug",
                        help="can be set to 'print' or None (or 'plot' if in jupyter) prints intermediate images.",
                        default=None)
    args = parser.parse_args()
    return args


def main():
    # Initialize options
    args = options()
    # Set PlantCV debug mode to input debug method
    pcv.params.debug = args.debug

    # Use PlantCV to read in the input image. The function outputs an image as a NumPy array, the path to the file,
    # and the image filename
    img, path, filename = pcv.readimage(filename=args.image)

    # ## Segmentation

    # ### Saturation channel
    # Convert the RGB image to HSV colorspace and extract the saturation channel
    s = pcv.rgb2gray_hsv(rgb_img=img, channel='s')

    # Use a binary threshold to set an inflection value where all pixels in the grayscale saturation image below the
    # threshold get set to zero (pure black) and all pixels at or above the threshold get set to 255 (pure white)
    s_thresh = pcv.threshold.binary(gray_img=s, threshold=80, max_value=255, object_type='light')

    # ### Blue-yellow channel
    # Convert the RGB image to LAB colorspace and extract the blue-yellow channel
    b = pcv.rgb2gray_lab(rgb_img=img, channel='b')

    # Use a binary threshold to set an inflection value where all pixels in the grayscale blue-yellow image below the
    # threshold get set to zero (pure black) and all pixels at or above the threshold get set to 255 (pure white)
    b_thresh = pcv.threshold.binary(gray_img=b, threshold=134, max_value=255, object_type='light')

    # ### Green-magenta channel
    # Convert the RGB image to LAB colorspace and extract the green-magenta channel
    a = pcv.rgb2gray_lab(rgb_img=img, channel='a')

    # In the green-magenta image the plant pixels are darker than the background. Setting object_type="dark" will
    # invert the image first and then use a binary threshold to set an inflection value where all pixels in the
    # grayscale green-magenta image below the threshold get set to zero (pure black) and all pixels at or above the
    # threshold get set to 255 (pure white)
    a_thresh = pcv.threshold.binary(gray_img=a, threshold=122, max_value=255, object_type='dark')

    # Combine the binary images for the saturation and blue-yellow channels. The "or" operator returns a binary image
    # that is white when a pixel was white in either or both input images
    bs = pcv.logical_or(bin_img1=s_thresh, bin_img2=b_thresh)

    # Combine the binary images for the combined saturation and blue-yellow channels and the green-magenta channel.
    # The "or" operator returns a binary image that is white when a pixel was white in either or both input images
    bsa = pcv.logical_or(bin_img1=bs, bin_img2=a_thresh)

    # The combined binary image labels plant pixels well but the background still has pixels labeled as foreground.
    # Small white noise (salt) in the background can be removed by filtering white objects in the image by size and
    # setting a size threshold where smaller objects can be removed
    bsa_fill1 = pcv.fill(bin_img=bsa, size=15)  # Fill small noise

    # Before more stringent size filtering is done we want to connect plant parts that may still be disconnected from
    # the main plant. Use a dilation to expand the boundary of white regions. Ksize is the size of a box scanned
    # across the image and i is the number of times a scan is done
    bsa_fill2 = pcv.dilate(gray_img=bsa_fill1, ksize=3, i=3)

    # Remove small objects by size again but use a higher threshold
    bsa_fill3 = pcv.fill(bin_img=bsa_fill2, size=250)

    # Use the binary image to identify objects or connected components.
    id_objects, obj_hierarchy = pcv.find_objects(img=img, mask=bsa_fill3)

    # Because the background still contains pixels labeled as foreground, the object list contains background.
    # Because these images were collected in an automated system the plant is always centered in the image at the
    # same position each time. Define a region of interest (ROI) to set the area where we expect to find plant
    # pixels. PlantCV can make simple ROI shapes like rectangles, circles, etc. but here we use a custom ROI to fit a
    # polygon around the plant area
    roi_custom, roi_hier_custom = pcv.roi.custom(img=img, vertices=[[1085, 1560], [1395, 1560], [1395, 1685],
                                                                    [1890, 1744], [1890, 25], [600, 25], [615, 1744],
                                                                    [1085, 1685]])

    # Use the ROI to filter out objects found outside the ROI. When `roi_type = "cutto"` objects outside the ROI are
    # cropped out. The default `roi_type` is "partial" which allows objects to overlap the ROI and be retained
    roi_objects, hierarchy, kept_mask, obj_area = pcv.roi_objects(img=img, roi_contour=roi_custom,
                                                                  roi_hierarchy=roi_hier_custom,
                                                                  object_contour=id_objects,
                                                                  obj_hierarchy=obj_hierarchy, roi_type='cutto')

    # Filter remaining objects by size again to remove any remaining background objects
    filled_mask1 = pcv.fill(bin_img=kept_mask, size=350)

    # Use a closing operation to first dilate (expand) and then erode (shrink) the plant to fill in any additional
    # gaps in leaves or stems
    filled_mask2 = pcv.closing(gray_img=filled_mask1)

    # Remove holes or dark spot noise (pepper) in the plant binary image
    filled_mask3 = pcv.fill_holes(filled_mask2)

    # With the clean binary image identify the contour of the plant
    id_objects, obj_hierarchy = pcv.find_objects(img=img, mask=filled_mask3)

    # Because a plant or object of interest may be composed of multiple contours, it is required to combine all
    # remaining contours into a single contour before measurements can be done
    obj, mask = pcv.object_composition(img=img, contours=id_objects, hierarchy=obj_hierarchy)

    # ## Measurements PlantCV has several built-in measurement or analysis methods. Here, basic measurements of size
    # and shape are done. Additional typical modules would include plant height (`pcv.analyze_bound_horizontal`) and
    # color (`pcv.analyze_color`)
    shape_img = pcv.analyze_object(img=img, obj=obj, mask=mask)

    # Save the shape image if requested
    if args.writeimg:
        outfile = os.path.join(args.outdir, filename[:-4] + "_shapes.png")
        pcv.print_image(img=shape_img, filename=outfile)

    # ## Morphology workflow

    # Update a few PlantCV parameters for plotting purposes
    pcv.params.text_size = 1.5
    pcv.params.text_thickness = 5
    pcv.params.line_thickness = 15

    # Convert the plant mask into a "skeletonized" image where each path along the stem and leaves are a single pixel
    # wide
    skel = pcv.morphology.skeletonize(mask=mask)

    # Sometimes wide parts of leaves or stems are skeletonized in the direction perpendicular to the main path. These
    # "barbs" or "spurs" can be removed by pruning the skeleton to remove small paths. Pruning will also separate the
    # individual path segments (leaves and stem parts)
    pruned, segmented_img, segment_objects = pcv.morphology.prune(skel_img=skel, size=30, mask=mask)
    pruned, segmented_img, segment_objects = pcv.morphology.prune(skel_img=pruned, size=3, mask=mask)

    # Leaf and stem segments above are separated but only into individual paths. We can sort the segments into stem
    # and leaf paths by identifying primary segments (stems; those that end in a branch point) and secondary segments
    # (leaves; those that begin at a branch point and end at a tip point)
    leaf_objects, other_objects = pcv.morphology.segment_sort(skel_img=pruned, objects=segment_objects, mask=mask)

    # Label the segment unique IDs
    segmented_img, labeled_id_img = pcv.morphology.segment_id(skel_img=pruned, objects=leaf_objects, mask=mask)

    # Measure leaf insertion angles. Measures the angle between a line fit through the stem paths and a line fit
    # through the first `size` points of each leaf path
    labeled_angle_img = pcv.morphology.segment_insertion_angle(skel_img=pruned, segmented_img=segmented_img,
                                                               leaf_objects=leaf_objects, stem_objects=other_objects,
                                                               size=22)

    # Save leaf angle image if requested
    if args.writeimg:
        outfile = os.path.join(args.outdir, filename[:-4] + "_leaf_insertion_angles.png")
        pcv.print_image(img=labeled_angle_img, filename=outfile)

    # ## Other potential morphological measurements There are many other functions that extract data from within the
    # morphology sub-package of PlantCV. For our purposes, we are most interested in the relative angle between each
    # leaf and the stem which we measure with `plantcv.morphology.segment_insertion_angle`. However, the following
    # cells show some of the other traits that we are able to measure from images that can be succesfully sorted into
    # primary and secondary segments.

    # Segment the plant binary mask using the leaf and stem segments. Allows for the measurement of individual leaf
    # areas
    # filled_img = pcv.morphology.fill_segments(mask=mask, objects=leaf_objects)

    # Measure the path length of each leaf (geodesic distance)
    # labeled_img2 = pcv.morphology.segment_path_length(segmented_img=segmented_img, objects=leaf_objects)

    # Measure the straight-line, branch point to tip distance (Euclidean) for each leaf
    # labeled_img3 = pcv.morphology.segment_euclidean_length(segmented_img=segmented_img, objects=leaf_objects)

    # Measure the curvature of each leaf (Values closer to 1 indicate that a segment is a straight line while larger
    # values indicate the segment has more curvature)
    # labeled_img4 = pcv.morphology.segment_curvature(segmented_img=segmented_img, objects=leaf_objects)

    # Measure absolute leaf angles (angle of linear regression line fit to each leaf object) Note: negative values
    # signify leaves to the left of the stem, positive values signify leaves to the right of the stem
    # labeled_img5 = pcv.morphology.segment_angle(segmented_img=segmented_img, objects=leaf_objects)

    # Measure leaf curvature in degrees
    # labeled_img6 = pcv.morphology.segment_tangent_angle(segmented_img=segmented_img, objects=leaf_objects, size=35)

    # Measure stem characteristics like stem angle and length
    # stem_img = pcv.morphology.analyze_stem(rgb_img=img, stem_objects=other_objects)

    # Remove unneeded observations (hack)
    _ = pcv.outputs.observations.pop("tips")
    _ = pcv.outputs.observations.pop("branch_pts")
    angles = pcv.outputs.observations["segment_insertion_angle"]["value"]
    remove_indices = []
    for i, value in enumerate(angles):
        if value == "NA":
            remove_indices.append(i)
    remove_indices.sort(reverse=True)
    for i in remove_indices:
        _ = pcv.outputs.observations["segment_insertion_angle"]["value"].pop(i)

    # ## Save the results out to file for downsteam analysis
    pcv.print_results(filename=args.result)


if __name__ == "__main__":
    main()

#!/usr/bin/env python

import os
import argparse
from plantcv import plantcv as pcv


# Parse command-line arguments
def options():
    parser = argparse.ArgumentParser(description="Imaging processing with opencv")
    parser.add_argument("-i", "--image", help="Input image file.", required=True)
    parser.add_argument("-o", "--outdir", help="Output directory for image files.", required=False)
    parser.add_argument("-r", "--result", help="result file.", required=False)
    parser.add_argument("-w", "--writeimg", help="write out images.", default=False, action="store_true")
    parser.add_argument("-D", "--debug",
                        help="can be set to 'print' or None (or 'plot' if in jupyter) prints intermediate images.",
                        default=None)
    args = parser.parse_args()
    return args


def main():
    # Initialize options
    args = options()
    # Set PlantCV debug mode to input debug method
    pcv.params.debug = args.debug

    # Use PlantCV to read in the input image. The function outputs an image as a NumPy array, the path to the file,
    # and the image filename
    img, path, filename = pcv.readimage(filename=args.image)

    # ## Segmentation

    # ### Saturation channel
    # Convert the RGB image to HSV colorspace and extract the saturation channel
    s = pcv.rgb2gray_hsv(rgb_img=img, channel='s')

    # Use a binary threshold to set an inflection value where all pixels in the grayscale saturation image below the
    # threshold get set to zero (pure black) and all pixels at or above the threshold get set to 255 (pure white)
    s_thresh = pcv.threshold.binary(gray_img=s, threshold=80, max_value=255, object_type='light')

    # ### Blue-yellow channel
    # Convert the RGB image to LAB colorspace and extract the blue-yellow channel
    b = pcv.rgb2gray_lab(rgb_img=img, channel='b')

    # Use a binary threshold to set an inflection value where all pixels in the grayscale blue-yellow image below the
    # threshold get set to zero (pure black) and all pixels at or above the threshold get set to 255 (pure white)
    b_thresh = pcv.threshold.binary(gray_img=b, threshold=134, max_value=255, object_type='light')

    # ### Green-magenta channel
    # Convert the RGB image to LAB colorspace and extract the green-magenta channel
    a = pcv.rgb2gray_lab(rgb_img=img, channel='a')

    # In the green-magenta image the plant pixels are darker than the background. Setting object_type="dark" will
    # invert the image first and then use a binary threshold to set an inflection value where all pixels in the
    # grayscale green-magenta image below the threshold get set to zero (pure black) and all pixels at or above the
    # threshold get set to 255 (pure white)
    a_thresh = pcv.threshold.binary(gray_img=a, threshold=122, max_value=255, object_type='dark')

    # Combine the binary images for the saturation and blue-yellow channels. The "or" operator returns a binary image
    # that is white when a pixel was white in either or both input images
    bs = pcv.logical_or(bin_img1=s_thresh, bin_img2=b_thresh)

    # Combine the binary images for the combined saturation and blue-yellow channels and the green-magenta channel.
    # The "or" operator returns a binary image that is white when a pixel was white in either or both input images
    bsa = pcv.logical_or(bin_img1=bs, bin_img2=a_thresh)

    # The combined binary image labels plant pixels well but the background still has pixels labeled as foreground.
    # Small white noise (salt) in the background can be removed by filtering white objects in the image by size and
    # setting a size threshold where smaller objects can be removed
    bsa_fill1 = pcv.fill(bin_img=bsa, size=15)  # Fill small noise

    # Before more stringent size filtering is done we want to connect plant parts that may still be disconnected from
    # the main plant. Use a dilation to expand the boundary of white regions. Ksize is the size of a box scanned
    # across the image and i is the number of times a scan is done
    bsa_fill2 = pcv.dilate(gray_img=bsa_fill1, ksize=3, i=3)

    # Remove small objects by size again but use a higher threshold
    bsa_fill3 = pcv.fill(bin_img=bsa_fill2, size=250)

    # Use the binary image to identify objects or connected components.
    id_objects, obj_hierarchy = pcv.find_objects(img=img, mask=bsa_fill3)

    # Because the background still contains pixels labeled as foreground, the object list contains background.
    # Because these images were collected in an automated system the plant is always centered in the image at the
    # same position each time. Define a region of interest (ROI) to set the area where we expect to find plant
    # pixels. PlantCV can make simple ROI shapes like rectangles, circles, etc. but here we use a custom ROI to fit a
    # polygon around the plant area
    roi_custom, roi_hier_custom = pcv.roi.custom(img=img, vertices=[[1085, 1560], [1395, 1560], [1395, 1685],
                                                                    [1890, 1744], [1890, 25], [600, 25], [615, 1744],
                                                                    [1085, 1685]])

    # Use the ROI to filter out objects found outside the ROI. When `roi_type = "cutto"` objects outside the ROI are
    # cropped out. The default `roi_type` is "partial" which allows objects to overlap the ROI and be retained
    roi_objects, hierarchy, kept_mask, obj_area = pcv.roi_objects(img=img, roi_contour=roi_custom,
                                                                  roi_hierarchy=roi_hier_custom,
                                                                  object_contour=id_objects,
                                                                  obj_hierarchy=obj_hierarchy, roi_type='cutto')

    # Filter remaining objects by size again to remove any remaining background objects
    filled_mask1 = pcv.fill(bin_img=kept_mask, size=350)

    # Use a closing operation to first dilate (expand) and then erode (shrink) the plant to fill in any additional
    # gaps in leaves or stems
    filled_mask2 = pcv.closing(gray_img=filled_mask1)

    # Remove holes or dark spot noise (pepper) in the plant binary image
    filled_mask3 = pcv.fill_holes(filled_mask2)

    # With the clean binary image identify the contour of the plant
    id_objects, obj_hierarchy = pcv.find_objects(img=img, mask=filled_mask3)

    # Because a plant or object of interest may be composed of multiple contours, it is required to combine all
    # remaining contours into a single contour before measurements can be done
    obj, mask = pcv.object_composition(img=img, contours=id_objects, hierarchy=obj_hierarchy)

    # ## Measurements PlantCV has several built-in measurement or analysis methods. Here, basic measurements of size
    # and shape are done. Additional typical modules would include plant height (`pcv.analyze_bound_horizontal`) and
    # color (`pcv.analyze_color`)
    shape_img = pcv.analyze_object(img=img, obj=obj, mask=mask)

    # Save the shape image if requested
    if args.writeimg:
        outfile = os.path.join(args.outdir, filename[:-4] + "_shapes.png")
        pcv.print_image(img=shape_img, filename=outfile)

    # ## Morphology workflow

    # Update a few PlantCV parameters for plotting purposes
    pcv.params.text_size = 1.5
    pcv.params.text_thickness = 5
    pcv.params.line_thickness = 15

    # Convert the plant mask into a "skeletonized" image where each path along the stem and leaves are a single pixel
    # wide
    skel = pcv.morphology.skeletonize(mask=mask)

    # Sometimes wide parts of leaves or stems are skeletonized in the direction perpendicular to the main path. These
    # "barbs" or "spurs" can be removed by pruning the skeleton to remove small paths. Pruning will also separate the
    # individual path segments (leaves and stem parts)
    pruned, segmented_img, segment_objects = pcv.morphology.prune(skel_img=skel, size=30, mask=mask)
    pruned, segmented_img, segment_objects = pcv.morphology.prune(skel_img=pruned, size=3, mask=mask)

    # Leaf and stem segments above are separated but only into individual paths. We can sort the segments into stem
    # and leaf paths by identifying primary segments (stems; those that end in a branch point) and secondary segments
    # (leaves; those that begin at a branch point and end at a tip point)
    leaf_objects, other_objects = pcv.morphology.segment_sort(skel_img=pruned, objects=segment_objects, mask=mask)

    # Label the segment unique IDs
    segmented_img, labeled_id_img = pcv.morphology.segment_id(skel_img=pruned, objects=leaf_objects, mask=mask)

    # Measure leaf insertion angles. Measures the angle between a line fit through the stem paths and a line fit
    # through the first `size` points of each leaf path
    labeled_angle_img = pcv.morphology.segment_insertion_angle(skel_img=pruned, segmented_img=segmented_img,
                                                               leaf_objects=leaf_objects, stem_objects=other_objects,
                                                               size=22)

    # Save leaf angle image if requested
    if args.writeimg:
        outfile = os.path.join(args.outdir, filename[:-4] + "_leaf_insertion_angles.png")
        pcv.print_image(img=labeled_angle_img, filename=outfile)

    # ## Other potential morphological measurements There are many other functions that extract data from within the
    # morphology sub-package of PlantCV. For our purposes, we are most interested in the relative angle between each
    # leaf and the stem which we measure with `plantcv.morphology.segment_insertion_angle`. However, the following
    # cells show some of the other traits that we are able to measure from images that can be succesfully sorted into
    # primary and secondary segments.

    # Segment the plant binary mask using the leaf and stem segments. Allows for the measurement of individual leaf
    # areas
    # filled_img = pcv.morphology.fill_segments(mask=mask, objects=leaf_objects)

    # Measure the path length of each leaf (geodesic distance)
    # labeled_img2 = pcv.morphology.segment_path_length(segmented_img=segmented_img, objects=leaf_objects)

    # Measure the straight-line, branch point to tip distance (Euclidean) for each leaf
    # labeled_img3 = pcv.morphology.segment_euclidean_length(segmented_img=segmented_img, objects=leaf_objects)

    # Measure the curvature of each leaf (Values closer to 1 indicate that a segment is a straight line while larger
    # values indicate the segment has more curvature)
    # labeled_img4 = pcv.morphology.segment_curvature(segmented_img=segmented_img, objects=leaf_objects)

    # Measure absolute leaf angles (angle of linear regression line fit to each leaf object) Note: negative values
    # signify leaves to the left of the stem, positive values signify leaves to the right of the stem
    # labeled_img5 = pcv.morphology.segment_angle(segmented_img=segmented_img, objects=leaf_objects)

    # Measure leaf curvature in degrees
    # labeled_img6 = pcv.morphology.segment_tangent_angle(segmented_img=segmented_img, objects=leaf_objects, size=35)

    # Measure stem characteristics like stem angle and length
    # stem_img = pcv.morphology.analyze_stem(rgb_img=img, stem_objects=other_objects)

    # Remove unneeded observations (hack)
    _ = pcv.outputs.observations.pop("tips")
    _ = pcv.outputs.observations.pop("branch_pts")
    angles = pcv.outputs.observations["segment_insertion_angle"]["value"]
    remove_indices = []
    for i, value in enumerate(angles):
        if value == "NA":
            remove_indices.append(i)
    remove_indices.sort(reverse=True)
    for i in remove_indices:
        _ = pcv.outputs.observations["segment_insertion_angle"]["value"].pop(i)

    # ## Save the results out to file for downsteam analysis
    pcv.print_results(filename=args.result)


if __name__ == "__main__":
    main()
#!/usr/bin/env python # Parse command-line arguments # Initialize options # Set PlantCV debug mode to input debug method # Use PlantCV to read in the input image. The function outputs an image as a NumPy array, the path to the file, # and the image filename # ## Segmentation # ### Saturation channel # Convert the RGB image to HSV colorspace and extract the saturation channel # Use a binary threshold to set an inflection value where all pixels in the grayscale saturation image below the # threshold get set to zero (pure black) and all pixels at or above the threshold get set to 255 (pure white) # ### Blue-yellow channel # Convert the RGB image to LAB colorspace and extract the blue-yellow channel # Use a binary threshold to set an inflection value where all pixels in the grayscale blue-yellow image below the # threshold get set to zero (pure black) and all pixels at or above the threshold get set to 255 (pure white) # ### Green-magenta channel # Convert the RGB image to LAB colorspace and extract the green-magenta channel # In the green-magenta image the plant pixels are darker than the background. Setting object_type="dark" will # invert the image first and then use a binary threshold to set an inflection value where all pixels in the # grayscale green-magenta image below the threshold get set to zero (pure black) and all pixels at or above the # threshold get set to 255 (pure white) # Combine the binary images for the saturation and blue-yellow channels. The "or" operator returns a binary image # that is white when a pixel was white in either or both input images # Combine the binary images for the combined saturation and blue-yellow channels and the green-magenta channel. # The "or" operator returns a binary image that is white when a pixel was white in either or both input images # The combined binary image labels plant pixels well but the background still has pixels labeled as foreground. # Small white noise (salt) in the background can be removed by filtering white objects in the image by size and # setting a size threshold where smaller objects can be removed # Fill small noise # Before more stringent size filtering is done we want to connect plant parts that may still be disconnected from # the main plant. Use a dilation to expand the boundary of white regions. Ksize is the size of a box scanned # across the image and i is the number of times a scan is done # Remove small objects by size again but use a higher threshold # Use the binary image to identify objects or connected components. # Because the background still contains pixels labeled as foreground, the object list contains background. # Because these images were collected in an automated system the plant is always centered in the image at the # same position each time. Define a region of interest (ROI) to set the area where we expect to find plant # pixels. PlantCV can make simple ROI shapes like rectangles, circles, etc. but here we use a custom ROI to fit a # polygon around the plant area # Use the ROI to filter out objects found outside the ROI. When `roi_type = "cutto"` objects outside the ROI are # cropped out. The default `roi_type` is "partial" which allows objects to overlap the ROI and be retained # Filter remaining objects by size again to remove any remaining background objects # Use a closing operation to first dilate (expand) and then erode (shrink) the plant to fill in any additional # gaps in leaves or stems # Remove holes or dark spot noise (pepper) in the plant binary image # With the clean binary image identify the contour of the plant # Because a plant or object of interest may be composed of multiple contours, it is required to combine all # remaining contours into a single contour before measurements can be done # ## Measurements PlantCV has several built-in measurement or analysis methods. Here, basic measurements of size # and shape are done. Additional typical modules would include plant height (`pcv.analyze_bound_horizontal`) and # color (`pcv.analyze_color`) # Save the shape image if requested # ## Morphology workflow # Update a few PlantCV parameters for plotting purposes # Convert the plant mask into a "skeletonized" image where each path along the stem and leaves are a single pixel # wide # Sometimes wide parts of leaves or stems are skeletonized in the direction perpendicular to the main path. These # "barbs" or "spurs" can be removed by pruning the skeleton to remove small paths. Pruning will also separate the # individual path segments (leaves and stem parts) # Leaf and stem segments above are separated but only into individual paths. We can sort the segments into stem # and leaf paths by identifying primary segments (stems; those that end in a branch point) and secondary segments # (leaves; those that begin at a branch point and end at a tip point) # Label the segment unique IDs # Measure leaf insertion angles. Measures the angle between a line fit through the stem paths and a line fit # through the first `size` points of each leaf path # Save leaf angle image if requested # ## Other potential morphological measurements There are many other functions that extract data from within the # morphology sub-package of PlantCV. For our purposes, we are most interested in the relative angle between each # leaf and the stem which we measure with `plantcv.morphology.segment_insertion_angle`. However, the following # cells show some of the other traits that we are able to measure from images that can be succesfully sorted into # primary and secondary segments. # Segment the plant binary mask using the leaf and stem segments. Allows for the measurement of individual leaf # areas # filled_img = pcv.morphology.fill_segments(mask=mask, objects=leaf_objects) # Measure the path length of each leaf (geodesic distance) # labeled_img2 = pcv.morphology.segment_path_length(segmented_img=segmented_img, objects=leaf_objects) # Measure the straight-line, branch point to tip distance (Euclidean) for each leaf # labeled_img3 = pcv.morphology.segment_euclidean_length(segmented_img=segmented_img, objects=leaf_objects) # Measure the curvature of each leaf (Values closer to 1 indicate that a segment is a straight line while larger # values indicate the segment has more curvature) # labeled_img4 = pcv.morphology.segment_curvature(segmented_img=segmented_img, objects=leaf_objects) # Measure absolute leaf angles (angle of linear regression line fit to each leaf object) Note: negative values # signify leaves to the left of the stem, positive values signify leaves to the right of the stem # labeled_img5 = pcv.morphology.segment_angle(segmented_img=segmented_img, objects=leaf_objects) # Measure leaf curvature in degrees # labeled_img6 = pcv.morphology.segment_tangent_angle(segmented_img=segmented_img, objects=leaf_objects, size=35) # Measure stem characteristics like stem angle and length # stem_img = pcv.morphology.analyze_stem(rgb_img=img, stem_objects=other_objects) # Remove unneeded observations (hack) # ## Save the results out to file for downsteam analysis