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terra.plantcv.py
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terra.plantcv.py
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#!/usr/bin/env python
import sys
import re
import os
import logging
from config import *
import pyclowder.extractors as extractors
import PlantcvClowderIndoorAnalysis as pci
import numpy as np
def main():
global extractorName, messageType, rabbitmqExchange, rabbitmqURL, registrationEndpoints
#set logging
logging.basicConfig(format='%(levelname)-7s : %(name)s - %(message)s', level=logging.WARN)
logging.getLogger('pyclowder.extractors').setLevel(logging.INFO)
#connect to rabbitmq
extractors.connect_message_bus(extractorName=extractorName, messageType=messageType, processFileFunction=process_dataset,
checkMessageFunction=check_message, rabbitmqExchange=rabbitmqExchange, rabbitmqURL=rabbitmqURL)
# ----------------------------------------------------------------------
def check_message(parameters):
# Expect at least 10 files to execute this processing
if len(parameters['filelist']) >= 10:
return True
else:
return False
def process_dataset(parameters):
# TODO: re-enable once this is merged into Clowder: https://opensource.ncsa.illinois.edu/bitbucket/projects/CATS/repos/clowder/pull-requests/883/overview
# fetch metadata from dataset to check if we should remove existing entry for this extractor first
md = extractors.download_dataset_metadata_jsonld(parameters['host'], parameters['secretKey'], parameters['datasetId'], extractorName)
if len(md) > 0:
for m in md:
if 'agent' in m and 'name' in m['agent']:
if m['agent']['name'].find(extractorName) > -1:
print("skipping, already done")
return
#extractors.remove_dataset_metadata_jsonld(parameters['host'], parameters['secretKey'], parameters['datasetId'], extractorName)
# pass
# Compiled traits table
fields = ('plant_barcode', 'genotype', 'treatment', 'imagedate', 'sv_area', 'tv_area', 'hull_area',
'solidity', 'height', 'perimeter')
traits = {'plant_barcode' : '',
'genotype' : '',
'treatment' : '',
'imagedate' : '',
'sv_area' : [],
'tv_area' : '',
'hull_area' : [],
'solidity' : [],
'height' : [],
'perimeter' : []}
nir_traits = {}
vis_traits = {}
# build img paths list
img_paths = []
# get imgs paths, filter out the json paths
for p in parameters['files']:
if p[-4:] == '.jpg' or p[-4:] == '.png':
img_paths.append(p)
print "printing img_paths..."
print img_paths
# build file objs - list of dicts
file_objs = []
for f in parameters['filelist']:
fmd = (extractors.download_file_metadata_jsonld(parameters['host'], parameters['secretKey'], f['id'], extractorName))[0]
#print "printing fmd..."
#print fmd
angle = fmd['content']['rotation_angle'] # -1, 0, 90, 180, 270
perspective = fmd['content']['perspective'] # 'side-view' / 'top-view'
if perspective == 'top-view': angle = -1 # set tv angle to be -1 for later sorting
camera_type = fmd['content']['camera_type'] # 'visible/RGB' / 'near-infrared'
image_id = f['id']
for p in img_paths:
print "printing p.."
print p
path = (re.findall(str(image_id), p))
print "printing path founded.."
print path
if path != []:
file_objs.append({'perspective':perspective, 'angle':angle, 'camera_type':camera_type, 'image_path': p, 'image_id': image_id})
print "printing file objs.."
print file_objs
# sort file objs by angle
file_objs = sorted(file_objs, key=lambda k: k['angle'])
print "printing sorted.."
print file_objs
# process images by matching angles with plantcv
for i in [0,2,4,6,8]:
if file_objs[i]['camera_type'] == 'visible/RGB':
vis_src = file_objs[i]['image_path']
nir_src = file_objs[i+1]['image_path']
vis_id = file_objs[i]['image_id']
nir_id = file_objs[i+1]['image_id']
else:
vis_src = file_objs[i+1]['image_path']
nir_src = file_objs[i]['image_path']
vis_id = file_objs[i+1]['image_id']
nir_id = file_objs[i]['image_id']
print 'vis src: ' + vis_src
print 'nir src: ' + nir_src
if i == 0:
vn_traits = pci.process_tv_images(vis_src, nir_src, traits)
else:
vn_traits = pci.process_sv_images(vis_src, nir_src, traits)
print "finished processing..."
# upload the individual file metadata
metadata = {
"@context": {
"@vocab": "https://clowder.ncsa.illinois.edu/clowder/assets/docs/api/index.html#!/files/uploadToDataset"
},
"content": vn_traits[0],
"agent": {
"@type": "cat:extractor",
"extractor_id": parameters['host'] + "/api/extractors/" + extractorName
}
}
parameters["fileid"] = vis_id
extractors.upload_file_metadata_jsonld(mdata=metadata, parameters=parameters)
metadata = {
"@context": {
"@vocab": "https://clowder.ncsa.illinois.edu/clowder/assets/docs/api/index.html#!/files/uploadToDataset"
},
"content": vn_traits[1],
"agent": {
"@type": "cat:extractor",
"extractor_id": parameters['host'] + "/api/extractors/" + extractorName
}
}
parameters["fileid"] = nir_id
extractors.upload_file_metadata_jsonld(mdata=metadata, parameters=parameters)
# compose the summary traits
trait_list = [ traits['plant_barcode'],
traits['genotype'],
traits['treatment'],
traits['imagedate'],
np.mean(traits['sv_area']),
traits['tv_area'],
np.mean(traits['hull_area']),
np.mean(traits['solidity']),
np.mean(traits['height']),
np.mean(traits['perimeter'])]
outfile = 'avg_traits.csv'
with open(outfile, 'w') as csv:
csv.write(','.join(map(str, fields)) + '\n')
csv.write(','.join(map(str, trait_list)) + '\n')
csv.flush()
extractors.upload_file_to_dataset(outfile, parameters)
os.remove(outfile)
# debug
csv_data = ','.join(map(str, fields)) + '\n' + ','.join(map(str, trait_list)) + '\n'
print csv_data
metadata = {
"@context": {
"@vocab": "https://clowder.ncsa.illinois.edu/clowder/assets/docs/api/index.html#!/files/uploadToDataset"
},
"dataset_id": parameters["datasetId"],
"content": {"status": "COMPLETED", "csv": csv_data},
"agent": {
"@type": "cat:extractor",
"extractor_id": parameters['host'] + "/api/extractors/" + extractorName
}
}
extractors.upload_dataset_metadata_jsonld(mdata=metadata, parameters=parameters)
if __name__ == "__main__":
main()