# Find geodesic lengths of skeleton segments

import os
import cv2
from plantcv.plantcv import params
from plantcv.plantcv import outputs
from plantcv.plantcv import plot_image
from plantcv.plantcv import print_image


def segment_path_length(segmented_img, objects, label=None):
    """ Use segments to calculate geodesic distance per segment

        Inputs:
        segmented_img = Segmented image to plot lengths on
        objects       = List of contours

        Returns:
        labeled_img        = Segmented debugging image with lengths labeled

        :param segmented_img: numpy.ndarray
        :param objects: list
        :return labeled_img: numpy.ndarray

        """

    label_coord_x = []
    label_coord_y = []
    segment_lengths = []
    labeled_img = segmented_img.copy()

    for i, cnt in enumerate(objects):
        # Calculate geodesic distance, divide by two since cv2 seems to be taking the perimeter of the contour
        segment_lengths.append(float(cv2.arcLength(objects[i], False) / 2))
        # Store coordinates for labels
        label_coord_x.append(objects[i][0][0][0])
        label_coord_y.append(objects[i][0][0][1])

    segment_ids = []
    # Put labels of length
    for c, value in enumerate(segment_lengths):
        text = "{:.2f}".format(value)
        w = label_coord_x[c]
        h = label_coord_y[c]
        cv2.putText(img=labeled_img, text=text, org=(w, h), fontFace=cv2.FONT_HERSHEY_SIMPLEX,
                    fontScale=params.text_size, color=(150, 150, 150), thickness=params.text_thickness)
        # segment_label = "ID" + str(c)
        segment_ids.append(c)

    outputs.add_observation(variable='segment_path_length', trait='segment path length',
                            method='plantcv.plantcv.morphology.segment_path_length', scale='pixels', datatype=list,
                            value=segment_lengths, label=segment_ids)

    # Auto-increment device
    params.device += 1

    if params.debug == 'print':
        print_image(labeled_img, os.path.join(params.debug_outdir, str(params.device) + '_segment_path_lengths.png'))
    elif params.debug == 'plot':
        plot_image(labeled_img)

    return labeled_img

# Find geodesic lengths of skeleton segments

import os
import cv2
from plantcv.plantcv import params
from plantcv.plantcv import outputs
from plantcv.plantcv import plot_image
from plantcv.plantcv import print_image


def segment_path_length(segmented_img, objects, label=None):
    """ Use segments to calculate geodesic distance per segment

        Inputs:
        segmented_img = Segmented image to plot lengths on
        objects       = List of contours
        label        = optional label parameter, modifies the variable name of observations recorded

        Returns:
        labeled_img        = Segmented debugging image with lengths labeled

        :param segmented_img: numpy.ndarray
        :param objects: list
        :param label: str
        :return labeled_img: numpy.ndarray

        """

    label_coord_x = []
    label_coord_y = []
    segment_lengths = []
    labeled_img = segmented_img.copy()

    for i, cnt in enumerate(objects):
        # Calculate geodesic distance, divide by two since cv2 seems to be taking the perimeter of the contour
        segment_lengths.append(float(cv2.arcLength(objects[i], False) / 2))
        # Store coordinates for labels
        label_coord_x.append(objects[i][0][0][0])
        label_coord_y.append(objects[i][0][0][1])

    segment_ids = []
    # Put labels of length
    for c, value in enumerate(segment_lengths):
        text = "{:.2f}".format(value)
        w = label_coord_x[c]
        h = label_coord_y[c]
        cv2.putText(img=labeled_img, text=text, org=(w, h), fontFace=cv2.FONT_HERSHEY_SIMPLEX,
                    fontScale=params.text_size, color=(150, 150, 150), thickness=params.text_thickness)
        segment_ids.append(c)

    if label == None:
        prefix = ""
    else:
        prefix = label + "_"

    outputs.add_observation(variable=prefix + 'segment_path_length', trait='segment path length',
                            method='plantcv.plantcv.morphology.segment_path_length', scale='pixels', datatype=list,
                            value=segment_lengths, label=segment_ids)

    # Auto-increment device
    params.device += 1

    if params.debug == 'print':
        print_image(labeled_img, os.path.join(params.debug_outdir, str(params.device) + '_segment_path_lengths.png'))
    elif params.debug == 'plot':
        plot_image(labeled_img)

    return labeled_img

Update segment_path_length.py