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workflow_script.py
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workflow_script.py
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#!/usr/bin/python
import sys, traceback
import cv2
import numpy as np
import argparse
import string
from plantcv import plantcv as pcv
import os
import json
### 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 createMask(img):
b_img = pcv.rgb2gray_lab(img, 'b')
thresh = pcv.threshold.binary(b_img, 135, 255, 'light')
fill = pcv.fill(thresh, 100)
seedmask = pcv.dilate(fill, 3, 3)
roots = pcv.apply_mask(img, pcv.invert(seedmask), 'black')
l_img = pcv.rgb2gray_lab(roots, 'l')
thresh_l = pcv.threshold.binary(l_img, 85, 255, 'light')
mask = pcv.fill(thresh_l, 275)
mask = pcv.dilate(mask, 3, 2)
mask = pcv.gaussian_blur(mask, (9,9))
return (mask)
### Main workflow
def main():
# Get options
args = options()
pcv.params.debug = args.debug #set debug mode
pcv.params.debug_outdir = 'debug_1'
os.makedirs(pcv.params.debug_outdir, exist_ok=True)
pcv.params.outdir = args.outdir #set output directory
pcv.params.line_thickness = 4
pcv.params.text_size = 2
pcv.params.text_thickness = 4
pcv.params.dpi = 150
# The result file should exist if plantcv-workflow.py was run
if os.path.exists(args.result):
# Open the result file
results = open(args.result, "r")
# The result file would have image metadata in it from plantcv-workflow.py, read it into memory
metadata = json.load(results)
# Close the file
results.close()
#
plantbarcode = metadata['metadata']['plantbarcode']['value']
timestamp = metadata['metadata']['timestamp']['value']
print(timestamp)
img, pn, fn = pcv.readbayer(args.image)
# img = pcv.rotate(img, 90, False)
mask = createMask(img)
masked_img = pcv.apply_mask(img, mask, 'black')
# Find roots in image
rootr, rooth = pcv.find_objects(img, mask)
# Filter objects in image based on ROI filter
dishr, dishh = pcv.roi.circle(mask, 1750, 1300, 400)
obj, obj_h, obj_mask, _ = pcv.roi_objects(img, dishr, dishh, rootr, rooth)
pcv.print_image(
obj_mask, filename=os.path.join(args.outdir, timestamp+'_mask.png')
) # (os.path.join(pn,os.path.splitext(fn)[0]+'_masked.png'))
if len(np.unique(obj_mask)) > 1:
# Create skeleton of roots
skeleton = pcv.morphology.skeletonize(mask=obj_mask)
# Prune the skeleton
pruned, seg_img, edge_objects = pcv.morphology.prune(skel_img=skeleton,
size=10,
mask=obj_mask)
if len(edge_objects) > 0:
# Identify branch points
branch_pts_mask = pcv.morphology.find_branch_pts(skel_img=pruned,
mask=obj_mask)
# Identify tip points
tip_pts_mask = pcv.morphology.find_tips(skel_img=pruned,
mask=obj_mask)
# Sort segments vs stem
leaf_obj, stem_obj = pcv.morphology.segment_sort(skel_img=pruned,
objects=edge_objects,
mask=obj_mask)
# Analyze Stem
stem_img = pcv.morphology.analyze_stem(img, stem_obj)
if args.writeimg:
pcv.print_image(stem_img,
filename=os.path.join(args.outdir,
timestamp+'_stem.png'))
# Identify segments
segmented_img, labeled_img = pcv.morphology.segment_id(
skel_img=pruned, objects=edge_objects, mask=obj_mask)
# Measure path lengths of segments
labeled_img2 = pcv.morphology.segment_path_length(
segmented_img=segmented_img, objects=edge_objects)
if args.writeimg:
pcv.print_image(labeled_img2,
filename=os.path.join(args.outdir,
timestamp+'_length.png'))
# Measure angle of roots
labeled_img4 = pcv.morphology.segment_angle(
segmented_img=segmented_img, objects=edge_objects)
if args.writeimg:
pcv.print_image(labeled_img4,
filename=os.path.join(args.outdir,
timestamp+'_angle.png'))
pcv.print_results(args.result)
if __name__ == '__main__':
main()