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main.py
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#! /nv/hp10/adas30/bin/python
'''
----------------------------------------------------------------------------------------------------
DIRT 1.1 - An automatic high throughput root phenotyping platform
Web interface by Abhiram Das - adas30@biology.gatech.edu
http://dirt.iplantcollaborative.org
University of Georgia
The software is written in:
- python 2.7 (https://www.python.org)
The software depends on:
- the graphtools package (http://graph-tool.skewed.de)
- the mahotas package (http://luispedro.org/software/mahotas)
- the numpy package (http://sourceforge.net/projects/numpy/)
- the scipy package (http://www.scipy.org/SciPy)
Optionally binaries of can be used for tag recognition:
- tesseract (https://code.google.com/p/tesseract-ocr/)
- zbar (http://zbar.sourceforge.net)
The software uses free code that had no references when found on the net:
- http://www.daniweb.com/software-development/python/threads/31449/k-means-clustering
The software uses modified code from the scikit.image:
- adaptive thresholding in Masking.py (http://scikit-image.org)
The software uses modified code from Kyle Fox:
- fixOrientation.py: https://github.com/kylefox/python-image-orientation-patch
Please cite the DIRT Paper if you use the code for your scientific project.
Bucksch et al., 2014 "Image-based high-throughput field phenotyping of crop roots", Plant Physiology
----------------------------------------------------------------------------------------------------
Author: Alexander Bucksch
Department of Plant Biology
Warnell School of Forestry and Natural Resources
Institute of Bioinformatics
University of Georgia
Mail: bucksch@uga.edu
Web: http://www.computational-plant-science.org
----------------------------------------------------------------------------------------------------
Copyright (c) 2014 Alexander Bucksch
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are
met:
* Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above
copyright notice, this list of conditions and the following
disclaimer in the documentation and/or other materials provided
with the distribution.
* Neither the name of the DIRT Developers nor the names of its
contributors may be used to endorse or promote products derived
from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
'''
'''
# external library imports
'''
import scipy
'''
# internal library imports
'''
import dirtIO
import Segmentation
import Preprocessing
import Skeleton
import Analysis
import RootTipPaths
from fixImageOrientation import *
'''
# standard python imports
'''
import os
import pickle
import csv
import sys
import time
from collections import OrderedDict
'''
#global defs
'''
allCrown=[]
allPara=[]
f=[]
imgID=None
io=dirtIO.IO()
options=[]
scale=None
ID=None
stemCorrection=False
maxExRoot=None
traitDict= OrderedDict()
def init(fpath, io):
oldpath = os.getcwd()
io.setHomePath(fpath)
if not os.path.exists(fpath):
os.mkdir(fpath)
os.chdir(fpath)
print os.getcwd()
io.setServerPath(os.getcwd())
if not os.path.exists('tmp'):
os.mkdir('tmp')
if not os.path.exists('Mask'):
os.mkdir('Mask')
if not os.path.exists(os.path.join('Lateral','Plots')):
os.makedirs(os.path.join('Lateral','Plots'))
if not os.path.exists(os.path.join('Lateral','Result')):
os.makedirs(os.path.join('Lateral','Result'))
if not os.path.exists(os.path.join('Crown','Plots')):
os.makedirs(os.path.join('Crown','Plots'))
if not os.path.exists(os.path.join('Crown','Result')):
os.makedirs(os.path.join('Crown','Result'))
if not os.path.exists(os.path.join('Crown','Skeleton')):
os.makedirs(os.path.join('Crown','Skeleton'))
os.chdir(oldpath)
readTraits(options[12][1])
def readTraits(myFilePath='./traits.csv'):
global traitDict
print "TRAITS DIRECTORY: "+ os.getcwd()
#check to make sure its a file not a sub folder
if (os.path.isfile(myFilePath) and myFilePath.endswith(".csv")):
with open(myFilePath, 'U') as csvfile:
#sniff to find the format
fileDialect = csv.Sniffer().sniff(csvfile.read(1024))
csvfile.seek(0)
#read the CSV file into a dictionary
dictReader = csv.reader(csvfile, dialect=fileDialect)
for row in dictReader:
try:
traitDict[row[0]]=bool(int(row[1]))
except:
print 'invalid entry in trait file: '+ str(row)
pass
print traitDict
return
def readOptions():
global options
if len(sys.argv)==13:
options.append([0,os.path.dirname(sys.argv[1])])
options.append([0,os.path.basename(sys.argv[1])])
options.append([0,sys.argv[2]])
options.append([0,sys.argv[3]])
options.append([0,sys.argv[4]])
options.append([0,sys.argv[5]])
options.append([0,sys.argv[6]])
options.append([0,sys.argv[7]])
options.append([0,sys.argv[8]])
options.append([0,sys.argv[9]])
options.append([0,sys.argv[10]])
options.append([0,sys.argv[11]])
options.append([0,sys.argv[12]])
else:
with open('./options.csv','U') as csvfile:
filedata= csv.reader(csvfile)
for i in filedata:
options.append(i)
return options
def ifAnyKeyIsTrue(listOfKeys):
for i in listOfKeys:
if traitDict[i]==True:
return True
return False
def threadSegmentation(filepath,imgFile,imgID,maxExRoot,rootCrown,marker):
global io
global scale
global stemCorrection
stemCorrection=bool(int(options[8][1]))
io.setFileName(imgFile)
io.setidIdx(imgID)
prep=Preprocessing.Preprocessing(io)
print 'segmenting file: '+imgFile +'\n'
image_file_path = os.path.join(options[0][1], imgFile)
if os.path.isfile(image_file_path):
# fix orientation of the image in tiff and Jpg files
try:
fix_orientation(image_file_path, save_over=True)
except:
pass
img= scipy.misc.imread(image_file_path, flatten=True)
else:
print 'Image not readable'
img=[]
if len(img)>0:
currT=time.time()
Failed,tagExtract,circleRatio, circleWidth, circleHeight = prep.prepocess(img,rootCrown,scale=float(options[3][1]),nrExRoot=maxExRoot,marker=marker,stemCorrection=stemCorrection)
print 'Segmentation finished in '+str(time.time()-currT)+'s'
if Failed == False:
xScale=scale/float(circleWidth)
yScale=scale/float(circleHeight)
if xScale<=0.0: xScale=1.
if yScale<=0.0: yScale=1.
para=[int(imgID),io.getFileName(),Failed,tagExtract,circleRatio, circleWidth, circleHeight,xScale,yScale,-1,-1]
if maxExRoot>1:
for _ in range(maxExRoot):
allPara.append(para)
else: allPara.append(para)
else:
xScale=scale/float(1.0)
yScale=scale/float(1.0)
circleRatio=1.0
circleWidth=1.0
circleHeight=1.0
para=[int(imgID),io.getFileName(),Failed,tagExtract,circleRatio, circleWidth, circleHeight,xScale,yScale,-1,-1]
if maxExRoot>1:
for _ in range(maxExRoot):
allPara.append(para)
else: allPara.append(para)
def threadCrown(filepath):
global io
rtpSkel=-1
crownT=OrderedDict()
imgL=[]
stemCorrection=bool(int(options[8][1]))
print io.getHomePath()
oldHome=io.getHomePath()
os.chdir(io.getHomePath())
io.setHomePath('./Crown/')
f=io.scanDir()
for (counter,i) in enumerate(f):
io.setFileName(os.path.basename(i))
io.setidIdx(imgID)
print 'processing Crown file: '+i
xScale=allPara[counter][7]
yScale=allPara[counter][8]
analysis=Analysis.Analysis(io,(xScale+yScale)/2)
rtp=RootTipPaths.RootTipPaths(io)
try:
img=scipy.misc.imread(i,flatten=True)
except:
print 'Image not readable'
img=-1
if len(img)>0:
seg=Segmentation.Segmentation(img,io)
imgL=seg.label()
print 'compute root profile'
currT=time.time()
if ifAnyKeyIsTrue(['AVG_DENSITY','WIDTH_MED','WIDTH_MAX','DIA_STM_SIMPLE','D10','D20','D30','D40','D50','D60','D70','D80','D90','DS10','DS20','DS30','DS40','DS50','DS60','DS70','DS80','DS90','AREA','ANG_TOP','ANG_BTM']):
crownT['AVG_DENSITY'],crownT['WIDTH_MED'],crownT['WIDTH_MAX'],crownT['D10'],crownT['D20'],crownT['D30'],crownT['D40'],crownT['D50'],crownT['D60'],crownT['D70'],crownT['D80'],crownT['D90'],crownT['DS10'],crownT['DS20'],crownT['DS30'],crownT['DS40'],crownT['DS50'],crownT['DS60'],crownT['DS70'],crownT['DS80'],crownT['DS90'],crownT['AREA'],crownT['DIA_STM_SIMPLE'],crownT['ANG_TOP'],crownT['ANG_BTM']=analysis.getWidthOverHeight(imgL,xScale,yScale)
print 'Mask traits computed '+str(time.time()-currT)+'s'
if ifAnyKeyIsTrue(['DIA_STM','TD_MED','TD_AVG','STA_RANGE','STA_DOM_I','STA_DOM_II','STA_25_I','STA_25_II','STA_50_I','STA_50_II','STA_75_I','STA_75_II','STA_90_I','STA_90_II','RTA_DOM_I','RTA_DOM_II','STA_MIN','STA_MAX','STA_MED','RTA_RANGE','RTA_MIN','RTA_MAX','RTA_MED','NR_RTP_SEG_I','NR_RTP_SEG_II','ADVT_COUNT','BASAL_COUNT','ADVT_ANG','BASAL_ANG','HYP_DIA','TAP_DIA','MAX_DIA_90','DROP_50','CP_DIA25','CP_DIA50','CP_DIA75','CP_DIA90','SKL_DEPTH','SKL_WIDTH']):
currT=time.time()
skel=Skeleton.Skeleton(imgL)
testSkel,testDia=skel.skel(imgL)
scipy.misc.imsave(os.path.join(io.getHomePath(), 'Skeleton', io.getFileName() + '_skel.png'), testSkel)
print 'Medial axis computed '+str(time.time()-currT)+'s'
currT=time.time()
path,skelGraph,crownT['DIA_STM'],skelSize=seg.findThickestPath(testSkel,testDia,xScale,yScale)
allPara[counter][10]=skelSize
print 'Central path computed '+str(time.time()-currT)+'s'
if ifAnyKeyIsTrue(['TD_MED','TD_AVG','STA_RANGE','STA_DOM_I','STA_DOM_II','STA_25_I','STA_25_II','STA_50_I','STA_50_II','STA_75_I','STA_75_II','STA_90_I','STA_90_II','RTA_DOM_I','RTA_DOM_II','STA_MIN','STA_MAX','STA_MED','RTA_RANGE','RTA_MIN','RTA_MAX','RTA_MED','NR_RTP_SEG_I','NR_RTP_SEG_II','ADVT_COUNT','BASAL_COUNT','ADVT_ANG','BASAL_ANG','HYP_DIA','TAP_DIA','MAX_DIA_90','DROP_50','CP_DIA25','CP_DIA50','CP_DIA75','CP_DIA90','SKL_DEPTH','SKL_WIDTH','RTP_COUNT']):
print 'Compute RTP skeleton'
currT=time.time()
rtpSkel,crownT['RTP_COUNT'], crownT['TD_MED'],crownT['TD_AVG'],crownT['MAX_DIA_90'], rtps, tips, crownT['SKL_WIDTH'], crownT['SKL_DEPTH'] =rtp.getRTPSkeleton(path,skelGraph,True)
seg.setTips(tips)
print 'RTP Skeleton computed '+str(time.time()-currT)+'s'
allPara[len(allPara)-1][2]=seg.getFail()
if ifAnyKeyIsTrue(['RDISTR_X','RDISTR_Y']):
print 'Compute spatial root distribution'
currT=time.time()
crownT['RDISTR_X'],crownT['RDISTR_Y']=analysis.getSymmetry(rtps,rtpSkel)
print 'Symmetry computed '+str(time.time()-currT)+'s'
if rtpSkel!=-1:
if ifAnyKeyIsTrue(['NR_RTP_SEG_I','NR_RTP_SEG_II','ADVT_COUNT','BASAL_COUNT','ADVT_ANG','BASAL_ANG','HYP_DIA','TAP_DIA']):
print 'searching for hypocotyl'
currT=time.time()
branchRad,nrPaths=seg.findHypocotylCluster(path,rtpSkel)
print 'hypocotyl computed '+str(time.time()-currT)+'s'
print 'starting kmeans'
try:
currT=time.time()
c1x,c1y,c2x,c2y = analysis.plotDiaRadius(nrPaths, branchRad,path,2)
print '2 clusters computed in '+str(time.time()-currT)+'s'
currT=time.time()
segImg=seg.makeSegmentationPicture(path,rtpSkel,img,xScale,yScale,c1x,c1y,c2x,c2y)
scipy.misc.imsave(io.getHomePath()+'/Result/' +io.getFileName()+ 'Seg2.png', segImg)
crownT['ADVT_COUNT'],crownT['BASAL_COUNT'],crownT['NR_RTP_SEG_I'],crownT['NR_RTP_SEG_II'], crownT['HYP_DIA'], crownT['TAP_DIA'] =analysis.countRootsPerSegment(c1y,c2y,c1x,c2x)
except:
c1x=None
c1y=None
c2x=None
c2y=None
pass
crownT['DROP_50']=analysis.RTPsOverDepth(path,rtpSkel)
print 'count roots per segment'
print 'Root classes computed in '+str(time.time()-currT)+'s'
if ifAnyKeyIsTrue(['ADVT_ANG','BASAL_ANG','STA_RANGE','STA_DOM_I','STA_DOM_II','STA_25_I','STA_25_II','STA_50_I','STA_50_II','STA_75_I','STA_75_II','STA_90_I','STA_90_II','RTA_DOM_I','RTA_DOM_II','STA_MIN','STA_MAX','STA_MED','RTA_RANGE','RTA_MIN','RTA_MAX','RTA_MED']):
currT=time.time()
lat,corrBranchpts=seg.findLaterals(rtps, rtpSkel,(xScale+yScale)/2, None)
print 'seg.findLaterals computed in '+str(time.time()-currT)+'s'
print 'Compute angles at 2cm'
currT=time.time()
if c1x!=None and c1y!=None and c2x!=None and c2y!=None: crownT['ADVT_ANG'],crownT['BASAL_ANG']=analysis.anglesPerClusterAtDist(c1y, c2y, rtpSkel, path, lat, corrBranchpts, (xScale+yScale)/2, dist=20)
else:
crownT['ADVT_ANG']='nan'
crownT['BASAL_NG']='nan'
print 'angles at 2cm computed in '+str(time.time()-currT)+'s'
if ifAnyKeyIsTrue(['STA_25_I','STA_25_II','STA_50_I','STA_50_II','STA_75_I','STA_75_II','STA_90_I','STA_90_II']):
try:
print 'compute quantile angles'
currT=time.time()
a25,a50,a75,a90=analysis.calculateAngleQuantiles(path,lat,corrBranchpts,rtpSkel)
print 'angles computed in '+str(time.time()-currT)+'s'
except:
a25=['nan']
a50=['nan']
a75=['nan']
a90=['nan']
print 'ERROR: No quantile angles calculated'
if ifAnyKeyIsTrue(['RTA_RANGE','RTA_MIN','RTA_MAX','RTA_MED']):
try:
print 'compute angles'
currT=time.time()
crownT['RTA_MED'],crownT['RTA_MIN'],crownT['RTA_MAX'],crownT['RTA_RANGE'],anglesN=analysis.calculateAngles(path,lat,corrBranchpts,rtpSkel)
print 'RTA angle characteristics computed in '+str(time.time()-currT)+'s'
except:
print 'ERROR: No RTA angles calculated'
if ifAnyKeyIsTrue(['STA_RANGE','STA_MIN','STA_MAX','STA_MED']):
try:
print 'compute STA angles'
currT=time.time()
crownT['STA_RANGE'],crownT['STA_MED'],crownT['STA_MIN'],crownT['STA_MAX'],angles=analysis.getLateralAngles(path,lat,corrBranchpts,rtpSkel)
print 'STA angles characteristics computed in '+str(time.time()-currT)+'s'
except:
print 'ERROR: No STA angles calculated'
if ifAnyKeyIsTrue(['CP_DIA25','CP_DIA50','CP_DIA75','CP_DIA90']):
try:
print 'compute diameter quantils'
currT=time.time()
crownT['CP_DIA25'],crownT['CP_DIA50'],crownT['CP_DIA75'],crownT['CP_DIA90']=analysis.getDiameterQuantilesAlongSinglePath(path,rtpSkel)
print 'Tap diameters computed in '+str(time.time()-currT)+'s'
except:
print 'ERROR: No quantile diameters calculated'
if ifAnyKeyIsTrue(['STA_DOM_I','STA_DOM_II']):
try:
print 'compute STA dominant angles'
currT=time.time()
crownT['STA_DOM_I'],crownT['STA_DOM_II']=analysis.findHistoPeaks(angles)
print 'STA dominant angles computed in '+str(time.time()-currT)+'s'
except:
print 'ERROR: No dominant angles calculated (STA)'
if ifAnyKeyIsTrue(['STA_25_I','STA_25_II']):
try:
currT=time.time()
crownT['STA_25_I'],crownT['STA_25_II']=analysis.findHistoPeaks(a25)
print 'STA 25 angles computed in '+str(time.time()-currT)+'s'
except:
print 'ERROR: No dominant angles25 calculated'
if ifAnyKeyIsTrue(['STA_50_I','STA_50_II']):
try:
currT=time.time()
crownT['STA_50_I'],crownT['STA_50_II']=analysis.findHistoPeaks(a50)
print 'STA 50 angles computed in '+str(time.time()-currT)+'s'
except:
print 'ERROR: No dominant angles50 calculated'
if ifAnyKeyIsTrue(['STA_75_I','STA_75_II']):
try:
currT=time.time()
crownT['STA_75_I'],crownT['STA_75_II']=analysis.findHistoPeaks(a75)
print 'STA 75 angles computed in '+str(time.time()-currT)+'s'
except:
print 'ERROR: No dominant angles75 calculated'
if ifAnyKeyIsTrue(['STA_90_I','STA_90_II']):
try:
currT=time.time()
crownT['STA_90_I'],crownT['STA_90_II']=analysis.findHistoPeaks(a90)
print 'STA 90 angles computed in '+str(time.time()-currT)+'s'
except:
print 'ERROR: No dominant angles90 calculated'
if ifAnyKeyIsTrue(['RTA_DOM_I','RTA_DOM_II']):
try:
currT=time.time()
crownT['RTA_DOM_I'],crownT['RTA_DOM_II']=analysis.findHistoPeaks(anglesN)
print 'angles computed in '+str(time.time()-currT)+'s'
except:
print 'ERROR: No dominant RTA angles calculated'
io.setHomePath(oldHome)
if maxExRoot >= 1:
rtpSkel=-1
os.chdir(io.getHomePath())
io.setHomePath('./Lateral/')
f=io.scanDir()
for (counter,i) in enumerate(f):
print 'processing lateral file: '+i
if maxExRoot>0:
xScale=allPara[counter/maxExRoot][7]
yScale=allPara[counter/maxExRoot][8]
io.setFileName(os.path.basename(i))
else:
xScale=allPara[counter][7]
yScale=allPara[counter][8]
io.setFileName(os.path.basename(i))
io.setidIdx(counter)
rtp=RootTipPaths.RootTipPaths(io)
analysis=Analysis.Analysis(io,(xScale+yScale)/2)
try:
img=scipy.misc.imread(i,flatten=True)
except:
print 'Image not readable'
img=[]
pass
if len(img)>0:
seg=Segmentation.Segmentation(img,io=io)
imgL=seg.label()
if imgL!=None:
skel=Skeleton.Skeleton(imgL)
testSkel,testDia=skel.skel(imgL)
path,skelGraph=seg.findThickestPathLateral(testSkel,testDia,xScale,yScale)
if ifAnyKeyIsTrue(['LT_AVG_LEN','NODAL_LEN','LT_BRA_FRQ','NODAL_AVG_DIA','LT_AVG_ANG','LT_ANG_RANGE','LT_MIN_ANG','LT_MAX_ANG','LT_DIST_FIRST','LT_MED_DIA','LT_AVG_DIA']):
rtpSkel,_,crownT['LT_MED_DIA'],crownT['LT_AVG_DIA'],_,rtps,_,_,_=rtp.getRTPSkeleton(path,skelGraph,True)
if rtpSkel!=-1:
if ifAnyKeyIsTrue(['LT_BRA_FRQ']):
crownT['LT_BRA_FRQ']=analysis.getBranchingfrequencyAlongSinglePath(rtps,path)
crownT['NODAL_AVG_DIA'],_=analysis.getDiametersAlongSinglePath(path,rtpSkel,(xScale+yScale)/2)
crownT['NODAL_LEN']=analysis.getLengthOfPath(path)
if ifAnyKeyIsTrue(['LT_DIST_FIRST','LT_AVG_LEN','LT_BRA_FRQ','LT_ANG_RANGE','LT_AVG_ANG','LT_MIN_ANG','LT_MAX_ANG']):
lat,corrBranchpts,crownT['LT_DIST_FIRST']=seg.findLaterals(rtps, rtpSkel,(xScale+yScale)/2,path)
if ifAnyKeyIsTrue(['LT_AVG_LEN']):
crownT['LT_AVG_LEN']=analysis.getLateralLength(lat,path,rtpSkel)
if ifAnyKeyIsTrue(['LT_ANG_RANGE','LT_AVG_ANG','LT_MIN_ANG','LT_MAX_ANG']):
crownT['LT_ANG_RANGE'],crownT['LT_AVG_ANG'],crownT['LT_MIN_ANG'],crownT['LT_MAX_ANG'],_=analysis.getLateralAngles(path,lat,corrBranchpts,rtpSkel)
allCrown.append(crownT.copy())
else:
allCrown.append(crownT.copy())
io.setHomePath(oldHome)
#os.chdir('../')
def printHeader():
if not os.path.exists('./options.csv') and len(sys.argv)!=13:
print '------------------------------------------------------------'
print 'DIRT 1.1 - An automatic highthroughput root phenotyping platform'
print '(c) 2014 Alexander Bucksch - bucksch@uga.edu'
print 'Web application by Abhiram Das - abhiram.das@gmail.com'
print ' '
print 'http://dirt.iplantcollaborative.org'
print ' '
print 'University of Georgia'
print '------------------------------------------------------------'
print 'Program usage: python main.py (please configure the program with the otions.csv file)'
print '<run file path> full path to file with the root image'
print '<unique id> ID which will be a folder name in theworking directory. Integer value needed'
print '<mask threshold> multiplier for the automatically determned mask threshold. 1.0 works fine and is default. If flashlight is used, the 0.6 is a good choice.'
print '<excised root> 1 - excised root analysis is on, 0 - excised root analysis is off'
print '<crown root> 1 - crown root analysis is on, 0 - crown root analysis is off'
print '<segmentation> 1 - is on, 0 - is off'
print '<marker diameter> a simple decimal e.g. 25.4. If 0.0 is used, then the output will have pixels as unit.'
print '<stem reconstruction> 1 - reconstruction is turned on, 0 - reconstruction is turned off'
print '<plots> 1 - plotting data is stored, 0 - plotting data is not stored'
print '<output format> 1 - the full trait set is put into one excel file containing empty cells for traits that were not computed, 0 - only computed files are written to the output file'
print '<working directory> full path to folder were the result is stored'
print '<trait file path> full path to .csv file containing the traits to be computed'
print ' '
print 'Example: '
print '/Documents/image_name.jpg 8 25.0 1 1 1 25.1 0 0 0 /Documents/image_folder/ /Documents/traits.csv'
sys.exit()
else:
print '------------------------------------------------------------'
print 'DIRT 1.1 - An automatic highthroughput root phenotyping platform'
print '(c) 2014 Alexander Bucksch - bucksch@uga.edu'
print 'Web application by Abhiram Das - abhiram.das@gmail.com'
print ' '
print 'http://dirt.iplantcollaborative.org'
print ' '
print 'University of Georgia'
print '------------------------------------------------------------'
print ' '
print 'Initializing folder structure'
def main(opt=None):
global io
global ID
global scale
global allPara
global allLat
global allCrown
global options
global maxExRoot
printHeader()
allStart=time.time()
if opt is None:
options = readOptions()
else: options=opt
ID=int(options[2][1])
try: scale = float(options[7][1])
except: scale =1.
rootCrown=int(options[5][1])
maxExRoot=int(options[4][1])
io.__init__(options[0][1],ID=ID,plots=bool(int(options[9][1])))
init(os.path.join(options[11][1], str(ID)), io)
#Run analysis
if int(options[6][1]) == 0:
io.setHomePath(os.path.join(options[11][1], str(ID)))
print os.getcwd()
infile=open(os.path.join(io.getHomePath(), 'tmp', 'para.sav'), 'rb')
allPara=pickle.load(infile)
infile.close()
print 'Saved parameters loaded'
infile.close()
elif int(options[6][1]) == 1:
threadSegmentation(options[11][1],options[1][1],ID,int(options[4][1]),rootCrown,float(options[7][1])>0.0)
outfile=open(os.path.join(io.getHomePath(), 'tmp', 'para.sav'), 'wb')
pickle.dump(allPara,outfile)
outfile.close()
else: print'The segmentation switch must be 0 or 1'
if int(options[5][1]) != 0 or int(options[4][1]) != 0:
print 'Start Root Analysis'
threadCrown(os.path.join(options[11][1], str(ID)))
print "Exiting Root Analysis"
compTime=int((time.time() - allStart))
print 'All done in just ' + str(compTime) + ' s!'
print 'Write output.csv file'
r=len(allCrown)
if r==0: r=len(allCrown)
for i in range(r):
allPara[i][9]=compTime
io.writeFile(allPara[i], allCrown[i],traitDict,int(options[10][1]))
return 0
if __name__ == '__main__':
sys.exit(main())