# Break skeleton into segments

import os
import cv2
import numpy as np
from plantcv.plantcv import dilate
from plantcv.plantcv import params
from plantcv.plantcv import plot_image
from plantcv.plantcv import print_image
from plantcv.plantcv import find_objects
from plantcv.plantcv import color_palette
from plantcv.plantcv import image_subtract
from plantcv.plantcv.morphology import find_branch_pts


def segment_skeleton(skel_img, mask=None, ordered_colors=True):
    """ Segment a skeleton image into pieces

        Inputs:
        skel_img         = Skeletonized image
        mask             = (Optional) binary mask for debugging. If provided, debug image will be overlaid on the mask.
        ordered_colors   = if True, color gradient will correlate with segment ID number,
                           if False colors will be randomly assigned to segments.

        Returns:
        segmented_img       = Segmented debugging image
        segment_objects     = list of contours
        segment_hierarchies = contour hierarchy list

        :param skel_img: numpy.ndarray
        :param mask: numpy.ndarray
        :return segmented_img: numpy.ndarray
        :return segment_objects: list
        "return segment_hierarchies: numpy.ndarray
        """

    # Store debug
    debug = params.debug
    params.debug = None
    # Remove any previously saved color palettes, we need a new one for segment analysis
    params.saved_color_scale = None

    # Find branch points
    bp = find_branch_pts(skel_img)
    bp = dilate(bp, 3, 1)

    # Subtract from the skeleton so that leaves are no longer connected
    segments = image_subtract(skel_img, bp)

    # Gather contours of leaves
    segment_objects, _ = find_objects(segments, segments)

    # Reset debug mode
    params.debug = debug

    # Color each segment a different color
    rand_color = color_palette(len(segment_objects))

    if ordered_colors:
        rand_color = rand_color
    else:
        np.random.shuffle(rand_color)

    if mask is None:
        segmented_img = skel_img.copy()
    else:
        segmented_img = mask.copy()

    segmented_img = cv2.cvtColor(segmented_img, cv2.COLOR_GRAY2RGB)
    for i, cnt in enumerate(segment_objects):
        cv2.drawContours(segmented_img, segment_objects, i, rand_color[i], params.line_thickness, lineType=8)

    # Auto-increment device
    params.device += 1

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

    return segmented_img, segment_objects

# Break skeleton into segments

import os
import cv2
from plantcv.plantcv import dilate
from plantcv.plantcv import params
from plantcv.plantcv import plot_image
from plantcv.plantcv import print_image
from plantcv.plantcv import find_objects
from plantcv.plantcv import color_palette
from plantcv.plantcv import image_subtract
from plantcv.plantcv.morphology import find_branch_pts


def segment_skeleton(skel_img, mask=None):
    """ Segment a skeleton image into pieces

        Inputs:
        skel_img         = Skeletonized image
        mask             = (Optional) binary mask for debugging. If provided, debug image will be overlaid on the mask.

        Returns:
        segmented_img       = Segmented debugging image
        segment_objects     = list of contours

        :param skel_img: numpy.ndarray
        :param mask: numpy.ndarray
        :return segmented_img: numpy.ndarray
        :return segment_objects: list
        """

    # Store debug
    debug = params.debug
    params.debug = None
    # Remove any previously saved color palettes, we need a new one for segment analysis
    params.saved_color_scale = None

    # Find branch points
    bp = find_branch_pts(skel_img)
    bp = dilate(bp, 3, 1)

    # Subtract from the skeleton so that leaves are no longer connected
    segments = image_subtract(skel_img, bp)

    # Gather contours of leaves
    segment_objects, _ = find_objects(segments, segments)

    # Reset debug mode
    params.debug = debug

    # Color each segment a different color
    rand_color = color_palette(len(segment_objects))

    if mask is None:
        segmented_img = skel_img.copy()
    else:
        segmented_img = mask.copy()

    segmented_img = cv2.cvtColor(segmented_img, cv2.COLOR_GRAY2RGB)
    for i, cnt in enumerate(segment_objects):
        cv2.drawContours(segmented_img, segment_objects, i, rand_color[i], params.line_thickness, lineType=8)

    # Auto-increment device
    params.device += 1

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

    return segmented_img, segment_objects

Remove randomization