def detect_api():
print('detect Called')
msg = 'Sucess'
queue = -1
try:
JOIN = JOIN_
if request.method == 'POST':
global jsons
queue = str(getqueue())
addr = request.remote_addr
print("[{}] Remote addr: {}, Queue: {}".format(NOW(), addr, queue))
upload_path = os.path.join(config.upload_path, addr)
utils.checkDir(upload_path)
for f in request.files.getlist('files'):
print("[{}] HANDLING IMAGE: {}".format(NOW(), f.filename))
filename = JOIN(upload_path, secure_filename(f.filename))
f.save(filename)
timer = time()
h, w, dets = detect_by_path(filename)
check_results(h, w, dets, queue)
print("[+] SSD Detection time: {}".format(time() - timer))
print("[{}] Save result of {}".format(NOW(), f.filename))
except Exception as e:
print(e)
msg = 'INTERNAL ERROR'
return jsonify({'queue': queue})
def generate(request):
catalogs=Catalogs.objects.all()
lastExcutetime=ExcuteTime.objects.get(pk=1)
now=datetime.now()
xmlfileList=[]
patents=Patent.objects.filter(updateTime__gt=lastExcutetime.excuteTime).values()
# patents=Patent.objects.values()
impl=xml.dom.minidom.getDOMImplementation()
i=0
for patent in patents:
dom=impl.createDocument(None,'patent',None)
root=dom.documentElement
filename=os.path.join(settings.MEDIA_ROOT,'output',now.strftime('%Y%m%d%H'),'p'+str(i)+'.xml')
d=checkDir(filename)
for k,v in patent.iteritems():
if k == 'updateTime' or k == 'id' :
continue
if k == 'classid_id':
v=Catalogs.objects.get(pk=v).name
k=u'classid'
if k in ('patent_pic2file','patent_pic1file'):
if v:
shutil.copy(os.path.join(settings.MEDIA_ROOT,v),d)
v=os.path.basename(v)
item=makeEasyTag(dom,k,v)
root.appendChild(item)
i=i+1
f=file(filename,'w')
writer=codecs.lookup('utf8')[3](f)
dom.writexml(writer,encoding='utf-8',newl='\r\n')
xmlfileList.append(filename)
lastExcutetime.excuteTime=now
lastExcutetime.save()
return render_to_response("input/gen.html",add_csrf(request,xmls=xmlfileList,catalogs=catalogs))
def main(species,
abundances={},
out_pref=None,
constraints='european',
diet="HighFiber",
max_iters=10,
cobraonly=False,
with_essential=False):
#model_file_template = "%s/%%s.xml" % (SBML_DIR)
# start with the default 0.01 for all species
for s in species:
if s not in abundances:
abundances[s] = 0.01
#count = 0.0
SpeciesDict = {}
for s in species:
#model_file = model_file_template % (s)
model_file = "%s/%s.xml" % (SBML_Dict[constraints], s)
if not os.path.isfile(model_file):
#print("%s doesn't exist. copying it from %s" % (model_file, ALL_SBML_DIR))
print("%s doesn't exist. using it from %s" %
(model_file, ALL_SBML_DIR[constraints]))
model_file = "%s/%s.xml" % (ALL_SBML_DIR[constraints], s)
SpeciesDict[s] = {
'File': model_file,
'initAbundance': abundances[s],
'orig_name': s
}
#count += 1
out_file_pref = "%s%dmodels-%s-%s-%diters-" % (
out_pref, len(species), constraints, diet, max_iters)
out_dir = os.path.dirname(out_file_pref)
utils.checkDir(out_dir)
simulate_models(species,
SpeciesDict,
diet=diet,
out_file_pref=out_file_pref,
max_iters=max_iters,
cobraonly=cobraonly,
with_essential=with_essential)
def main(verbose):
samples = ['mg_pp_hh', 'py8_pp_minbias']
samples += ['mg_pp_tth']
#samples = ['py8_pp_minbias']
pileups = [0, 200, 1000]
pileups = [0, 1000]
trackBranchNames = [
"TruthTrack",
"Track",
"PBMatchedTracks",
"TracksFromHit30",
"SmearedTracksFromHits",
"PBMatchedHitTracks",
]
trackPlots = [
"track1Pt",
"track2Pt",
"track3Pt",
"track4Pt",
"electron1Pt",
"muon1Pt",
"lepton1Pt",
]
jetBranchNames = [
'TruthTrackJets',
'SmearedTrackJets',
'PBMatchedTrackJets',
'TruthHitTrackJets',
'SmearedHitTrackJets',
'PBMatchedHitTrackJets',
]
jetPlots = [
"jet1Pt",
"jet2Pt",
"jet3Pt",
"jet4Pt",
"jet5Pt",
"jet6Pt",
]
# signal acceptances for things from hits
plotSignalAcceptances(1000,
['SmearedTracksFromHits', 'PBMatchedHitTracks'],
True)
plotSignalAcceptances(1000,
['SmearedHitTrackJets', 'PBMatchedHitTrackJets'],
False)
# signal acceptances for things from delphes objects
plotSignalAcceptances(1000, ['Track', 'PBMatchedTracks'], True)
plotSignalAcceptances(1000, ['SmearedTrackJets', 'PBMatchedTrackJets'],
False)
#return
# signal acceptances for things from hits
plotSignalAcceptances(0, ['SmearedTracksFromHits', 'PBMatchedHitTracks'],
True)
plotSignalAcceptances(0, ['SmearedHitTrackJets', 'PBMatchedHitTrackJets'],
False)
# signal acceptances for things from delphes objects
plotSignalAcceptances(0, ['TruthTrack', 'Track'], True)
plotSignalAcceptances(0, ['Track', 'PBMatchedTracks'], True)
plotSignalAcceptances(0, ['SmearedTrackJets', 'PBMatchedTrackJets'], False)
can = TCanvas("can", "can", 500, 500)
for sample in samples:
print 'Plotting for sample', sample
for pileup in pileups:
print '\tPlotting for pileup', pileup
iFileName = INPUT_DIR + '{0}_pu{1}.root'.format(sample, pileup)
iFile = TFile.Open(iFileName)
plotDict = {}
outputBaseDir = OUTPUD_BASE_DIR + '/{0}_pu{1}/'.format(
sample, pileup)
checkDir(outputBaseDir)
for branch in trackBranchNames + jetBranchNames:
print '\t\tPlotting for branch', branch
outputDir = outputBaseDir + appendSlash(branch)
checkDir(outputDir)
plotDict[branch] = {}
if branch in trackBranchNames:
plotList = trackPlots
else:
plotList = jetPlots
for plot in plotList:
histoName = branch + "_" + plot
print 'plotting', histoName
h0 = iFile.Get(histoName)
xTitle = plot.replace('track', 'Track ').replace(
'jet', 'Track Jet ').replace('Pt', ' p_{T}')
#print xTitle
h0.GetXaxis().SetTitle(xTitle + " [GeV]")
h0.GetYaxis().SetTitle("Fraction of events")
h0.Draw()
nbins = h0.GetNbinsX()
#print h0.Integral()
#print h0.Integral(1, nbins)
#print h0.Integral(1, nbins+1) # should include overflow
#print ''
# plot acceptance
can.SaveAs(outputDir + plot + ".pdf")
#acceptance = h0.GetCumulative(False)
acceptance = getReverseCumulativeHisto(h0)
xaxis = acceptance.GetXaxis()
yaxis = acceptance.GetYaxis()
xaxis.SetTitle(xTitle + " [GeV]")
yaxis.SetTitle("Acceptance")
if 'jet' in branch.lower():
xaxis.SetRangeUser(200, 600)
else:
xaxis.SetRangeUser(0, 100)
#acceptance.DrawNormalized()
acceptance.Draw()
can.SaveAs(outputDir + "acceptance_" + plot + ".pdf")
efficiency = h0.GetCumulative(True)
#efficiency.DrawNormalized()
#efficiency.DrawNormalized()
#can.SaveAs(outputDir+"efficiency_"+plot+".pdf")
plotDict[branch][plot] = {
'efficiency': efficiency,
'acceptance': acceptance
}
#print plotDict
# plot trigger rates (for minbias) in the different scenarios
s1 = {
#"TruthTrack",
"Track": {
'marker': 20,
'colour': colours.black,
'leg': 'All tracks'
},
"PBMatchedTracks": {
'marker': 23,
'colour': colours.orange,
'leg': 'Tracks from PB'
}
}
s2 = {
'SmearedTrackJets': {
'marker': 20,
'colour': colours.black,
'leg': 'All tracks'
},
'PBMatchedTrackJets': {
'marker': 23,
'colour': colours.orange,
'leg': 'Tracks from PB'
}
}
s3 = {
'SmearedHitTrackJets': {
'marker': 20,
'colour': colours.black,
'leg': 'All tracks'
},
'PBMatchedHitTrackJets': {
'marker': 23,
'colour': colours.orange,
'leg': 'Tracks from PB'
}
}
s4 = {
'SmearedTracksFromHits': {
'marker': 20,
'colour': colours.black,
'leg': 'All tracks'
},
'PBMatchedHitTracks': {
'marker': 23,
'colour': colours.orange,
'leg': 'Tracks from PB'
}
}
s00 = [
"TracksFromHit30",
"SmearedTracksFromHits",
"PBMatchedHitTracks",
]
############################
# Make plots of trigger rate
############################
makeRatePlot(s1, True, outputBaseDir, plotDict, sample, pileup)
makeRatePlot(s2, False, outputBaseDir, plotDict, sample, pileup)
makeRatePlot(s3, False, outputBaseDir, plotDict, sample, pileup)
makeRatePlot(s4, True, outputBaseDir, plotDict, sample, pileup)
def makeRatePlot(scenarioSet, doTrack, outputBaseDir, plotDict, sample,
pileup):
gStyle.SetGridStyle(3)
gStyle.SetGridColor(kGray)
can = TCanvas("can3", "", 500, 500)
can.SetGrid()
can.SetLogy()
#can.SetLogx()
# scenario set contains the "nominal" objects and the PB matched objects
outputDir = outputBaseDir + "Rates/"
checkDir(outputDir)
outputDir = outputDir + scenarioSet.keys()[0] + '/'
checkDir(outputDir)
plots = plotDict[scenarioSet.keys()[0]].keys()
for plot in plots:
pCounter = 0
#leg = prepareLegend('topRight')
if doTrack:
#leg = TLegend(0.55, 0.55, 0.8, 0.70)
# legend for modified boarders
leg = TLegend(0.65, 0.65, 0.9, 0.80)
else:
leg = TLegend(0.3, 0.5, 0.8, 0.70)
leg.SetTextSize(TEXT_SIZE)
for scenario in scenarioSet.keys():
# get style dict
style = scenarioSet[scenario]
# scale to trigger rate
rate = plotDict[scenario][plot]['acceptance']
titleInfo = sampleInfo[sample]
rate.Scale(
40 * 1e3
) # scale to 40 MHz (appropriate for minbias, not for other samples)
rate.SetTitleSize(TITLE_SIZE, 'X')
rate.SetLabelSize(TITLE_SIZE, 'X')
rate.SetTitleSize(TITLE_SIZE, 'Y')
rate.SetLabelSize(TITLE_SIZE, 'Y')
# style
#rate.SetTitle("{0} #LT#mu#GT = {1}".format(titleInfo['title'], pileup))
xaxis = rate.GetXaxis()
yaxis = rate.GetYaxis()
yaxis.SetTitle('Rate [kHz]')
rate.SetMinimum(300)
# settings for larger font
xaxis.SetNdivisions(5, 5, 0)
xaxis.SetTitleOffset(X_AXIS_OFFSET)
yaxis.SetTitleOffset(Y_AXIS_OFFSET)
if doTrack:
xaxis.SetRangeUser(0, 175)
if '1Pt' in plot:
xaxis.SetRangeUser(0, 175)
if '2Pt' in plot:
xaxis.SetRangeUser(0, 150)
if '3Pt' in plot:
xaxis.SetRangeUser(0, 70)
if '4Pt' in plot:
xaxis.SetRangeUser(0, 50)
myText(0.65, 0.90, '#sqrt{s} = 100 TeV', TEXT_SIZE)
myText(0.65, 0.85, '{0}'.format(titleInfo['title']), TEXT_SIZE)
myText(0.65, 0.80, "#LT#mu#GT = {0}".format(pileup), TEXT_SIZE)
else:
myText(0.3, 0.80, '#sqrt{s} = 100 TeV', TEXT_SIZE)
myText(0.3, 0.75, '{0}'.format(titleInfo['title']), TEXT_SIZE)
myText(0.3, 0.70, "#LT#mu#GT = {0}".format(pileup), TEXT_SIZE)
xaxis.SetRangeUser(X_MIN, 300)
rate.SetMarkerStyle(style['marker'])
rate.SetMarkerColor(style['colour'])
rate.SetLineColor(style['colour'])
if pCounter == 0:
rate.Draw()
#elif 'jet' in plot: # only draw PU supressed for jet histograms
else:
rate.Draw('same')
pCounter += 1
leg.AddEntry(rate, style['leg'], 'lp')
leg.Draw()
can.SaveAs(outputDir + 'rate_{0}.pdf'.format(plot))
def main(inputFile, outputDirBase):
outputDirBase = appendSlash(outputDirBase)
checkDir(outputDirBase)
# open file
iFile = TFile.Open(inputFile)
ptRanges = [
#[0,2],
[2, 5],
[6, 10],
[11, 30],
[31, 50]
]
etaRanges = [[0, 0.2], [0.2, 0.4], [0.4, 0.8], [0.8, 1.0], [1.0, 1.2],
[1.2, 1.4], [1.4, 1.6], [1.6, 1.8], [1.8, 2.0]]
parameters = {
'ptRes': {
'title': 'p_{T} resolution',
'units': '',
'label': '#deltap_{T}/p_{T}'
},
'z0Res': {
'title': 'z0 resolution',
'units': '[mm]',
'label': '#deltaz_{0}'
},
'ptResRaw': {
'title': 'p_{T} resolution',
'units': '[GeV]',
'label': '#deltap_{T}'
},
#'d0Res' : {'title' : 'd0 resolution' , 'units' : '[mm]', 'label': '#deltad_{0}'},
##'phiRes' : {'title' : '#phi resolution', 'units' : '[deg]', 'label' : '#delta#phi'},
##'CtgThetaRes': {'title' : 'cot(#theta) resolution', 'units' : '', 'label' : '#deltacot(#theta)'},
}
branchNames = [
#"TruthTrack",
"Track",
#"PBMatchedTracks",
"TracksFromHit30",
"SmearedTracksFromHits",
#"PBMatchedHitTracks",
]
for branch in branchNames:
outputDir = appendSlash(outputDirBase + branch)
checkDir(outputDir)
for par in parameters.keys():
##############################################
# Make 1d resolution plot across all eta range
##############################################
basicCan = TCanvas(branch + par + "basicCan", "", 500, 500)
basicRes = iFile.Get(branch + '_' + par)
basicRes.Fit(
"gaus", "MQ"
) # M: "improve fit", Q: "quiet" (no printing), O: "Don't draw fit or histo
theFit = basicRes.GetFunction("gaus")
basicCan.SetLogy()
basicRes.Draw()
theFit.Draw('same')
basicCan.SaveAs(outputDir + par + '_basic.pdf')
##########################################################
# Make proper resolution plots in pT and eta slices from 3D plot
##########################################################
plotName = branch + '_' + par + '_pt_eta'
resPlot = iFile.Get(plotName)
if not is_TH3(resPlot):
print plotName
print 'ERROR, {0} is not of type TH3, it is {1}'.format(
resPlot.GetName(), type(resPlot))
sys.exit()
# Create legend
#leg = prepareLegend('bottomRight')
leg = prepareLegend('topLeft')
leg.SetHeader('Track p_{T} [GeV]')
# Fit resolution, extract sigma
graphs = []
for n, ptRange in enumerate(ptRanges):
graphs.append(TGraphErrors())
for i, etaRange in enumerate(etaRanges):
xposition = (etaRange[0] + etaRange[1]) / 2.0
fitResults = extractResolution(resPlot, ptRange, etaRange,
par, outputDir)
yVal = fitResults['Sigma'][0]
yErr = fitResults['Sigma'][1]
if par == "phiRes": # want phi resolution to be in degrees (to compare with tkLayout)
yVal *= 180.0 / PI
yErr *= 180.0 / PI
graphs[n].SetPoint(i, xposition, yVal)
graphs[n].SetPointError(i, 0, yErr)
graphs[n].SetMarkerColor(colours[n])
graphs[n].SetLineColor(colours[n])
graphs[n].SetMarkerStyle(markers[n])
leg.AddEntry(graphs[n],
'{0} < pT < {1}'.format(ptRange[0],
ptRange[1]), 'lp')
# Draw graphs
can = TCanvas(branch + par + 'can', 'can', 500, 500)
can.SetGrid()
can.SetLogy()
mg = TMultiGraph()
for g in graphs:
mg.Add(g, 'p')
# set plot title
plotHeader = '{0} from Delphes'.format(parameters[par]['title'])
plotHeader = ''
xTitle = '#eta'
yTitle = '{0} {1}'.format(parameters[par]['label'],
parameters[par]['units'])
mg.SetTitle('{0};{1};{2}'.format(plotHeader, xTitle, yTitle))
mg.Draw('a')
leg.Draw()
# Change the axis limits
mg.GetHistogram().GetXaxis().SetNdivisions(5, 5, 0)
mgMin = mg.GetHistogram().GetYaxis().GetXmin()
mgMax = mg.GetHistogram().GetYaxis().GetXmax()
if mgMax / 10 < mgMin or True:
mg.GetHistogram().GetYaxis().SetRangeUser(
mgMin * 0.5, mgMax * 5) # make space for legend
can.SaveAs(outputDir + par + '_graphs.pdf')
import os
import cv2
import json
import utils
import requests
from config import *
from time import time
if __name__ == '__main__':
JOIN = os.path.join
images = [
img for img in os.listdir(image_dir) if img.upper().endswith('.JPG')
]
utils.checkDir(output_dir)
timer = time()
for image in images:
print('image: {}'.format(image))
# detect
url = base_url + '/detect'
img = cv2.imread(JOIN(image_dir, image))
files = [('files', open(JOIN(image_dir, image), 'rb'))] # For Object
ret = requests.post(url=url, files=files).json()
queue = ret['queue']
# askResult
url = base_url + '/askResult'
files = {'data': json.dumps({'queue': queue})}
ret = requests.post(url=url, files=files)
results = ret.json()['result']
for r in results:
import theano
import theano.tensor as T
import theano.tensor.nlinalg as nlinalg
import nodes.iaf2 as iaf
import model.mars1 as M
from optimisor.adagrad import opt as adagrad
from optimisor.sgd import opt as sgd
from optimisor.sgdMom import opt as sgdMom
f32 = theano.config.floatX
SAMPLING = int(10)
DECAY = 1e-3
LR0 = 0.05
MAXITER = 3000
VISUAL = True
utils.checkDir('./tmp', build=True)
print('... constructing model')
VALX = -1.2
x = utils.sharedf([VALX, VALX])
model = M.banana()
print('... variational distribution and symbol flows')
lr = T.fscalar()
qiaf = iaf.experiment(lr, 2)
e = qiaf.getNoise(SAMPLING)
z = qiaf.forward(e, interout=False)
logqz = qiaf.mcLogQZ(e)
logpz = T.mean(model.logPrior(z))
logpxz = T.mean(model.logLik(x, z))
def do_process(data, settings):
# Preparing directories
utils.checkDir(utils.GET("object_dir"), "Object")
if utils.GET("toposort_verbose_logging_dir") is not None and utils.GET(
"toposort_verbose_logging_dir") != "":
utils.checkDir(utils.GET("toposort_verbose_logging_dir"),
"Toposort verbose logging")
originalCXX = utils.GET("original_cxx_executable")
originalCC = utils.GET("original_cc_executable")
finishedList = Manager().list()
totalLength = len(data["compile"])
compileTaskPool = Pool()
console.log("Compiling .o (total: {})".format(totalLength))
for r in range(totalLength):
i = data["compile"][r]
execname = "(unknown)"
cmdline = list(filter(lambda x: x != "", i.split(" ")))
filehashpath = ["0" for i in range(0, 40)]
for argnum in range(len(cmdline)):
if cmdline[argnum] == originalCXX:
cmdline[argnum] = utils.GET("targeted_cxx_executable")
cmdline[argnum] += " -emit-llvm"
elif cmdline[argnum] == originalCC:
cmdline[argnum] = utils.GET("targeted_cc_executable")
cmdline[argnum] += " -emit-llvm"
elif cmdline[argnum] == "-o":
filepath = realpath(cmdline[argnum + 1])
filehashpath = utils.sha1sum(filepath)
sha1Table[filehashpath] = filepath
cmdline[argnum + 1] = realpath(
utils.GET("object_dir") + "/" + filehashpath)
execname = utils.findName(filepath)
elif cmdline[argnum] == "-c":
cmdline[argnum] = "-S"
elif cmdline[argnum] == "-g":
cmdline[argnum] = ""
command = " ".join(cmdline)
compileTaskPool.apply_async(single_compile,
args=(command, filehashpath, execname, r,
totalLength, finishedList,
settings.clean),
error_callback=console_error_and_exit)
compileTaskPool.close()
compileTaskPool.join()
# Construct the graph
console.success("All object files are compiled.")
console.info("Preparing linking relationships")
graphData = data["scripts"]
for i in graphData:
itemPath = i["target"]["abs_path"]
hashedItemPath = utils.sha1sum(itemPath)
sha1Table[hashedItemPath] = itemPath
itemDependencies = i["target"]["dependencies"]
dependencyList[hashedItemPath] = utils.deduplicate(
utils.pathToSha1(itemDependencies, sha1Table))
if hashedItemPath in dependencyList[hashedItemPath]:
console.warn("Self-circle found. Ignoring.")
dependencyList[hashedItemPath].remove(hashedItemPath)
preserveProcess = utils.GET("preserve_process")
if preserveProcess != None and preserveProcess != "":
console.info("Saving metadata")
sha1FilePath = utils.GET("object_dir") + "/" + preserveProcess
try:
json.dump(
sha1Table,
open(utils.GET("object_dir") + "/" + preserveProcess, "w"))
console.success("Metadata saved.")
except PermissionError:
console.warn(
"Process file {} is not writable, while preseve_process is on."
.format(sha1FilePath))
console.info("Calculating linking sequence")
try:
currList = utils.topoSort(dependencyList, finishedList, sha1Table)
except ValueError:
console.error("Topo sort failed to complete. Please check your data.")
sys.exit(1)
console.success("Linking sequence calculated.")
if settings.clean or settings.clean_linking:
console.info("Cleaning linking targets")
for i in dependencyList.keys():
if os.access(utils.GET("object_dir") + "/" + i, os.W_OK):
os.unlink(utils.GET("object_dir") + "/" + i)
console.success("Linking targets cleaned.")
if len(currList) != len(graphData):
console.warn("Bad consistance on linking recipe")
console.debug("Linking sequence:", currList, "or",
list(map(lambda x: sha1Table[x], currList)))
console.info("Start linking")
ctrLen = len(currList)
p = Pool()
for idx, obj in enumerate(currList):
console.info("Linking {} ({}) [{}/{}]".format(sha1Table[obj], obj,
idx + 1, ctrLen))
p.apply_async(single_linking,
args=(obj, finishedList),
error_callback=console_error_and_exit)
p.close()
p.join()
console.success("All targets are linked.")
console.success("Finished.")
#outputDir = "FakeRate_phiEta_tolerance1mm_phi2GeV/"
#outputDir = "FakeRate_phiEta_tolerance01mm_phi2GeV/"
#outputDir = "FakeRate_phiEta_tolerance01mm_phi2GeV_curvature0005/"
#outputDir = "FakeRate_phiEta_tolerance05mm_phi2GeV_curvature0005/"
#outputDir = "FakeRate_phiEta_tolerance05mm_phi2GeV_curvature0005_nVertexSigma5/"
#outputDir = "processedTracks/"
##outputDir = "FakeRate_tolerance05mm_phi2GeV_multiCurvature_nVertexSigma5/"
#outputDir = "processedTracks_kappa_deltaPhi/"
#outputDir = "FakeRate_phiEta_tolerance01mm_phi2GeV_curvature001/"
#outputDir = "processedTracks_kappa_deltaPhi_zresiduum_BDT/"
#outputDir = "processedTracks_kappa_deltaPhi_zresiduum/"
outputDir = path.split('/')[-2] + '/'
print outputDir, path
checkDir(outputDir)
cols = {
10: colours.blue,
20: colours.orange,
30: colours.red,
40: colours.green,
50: colours.grey
}
binslist = range(0, 21) + range(22, 31, 2) + [35.0, 40.0, 50.0, 100.0]
print binslist
# binslist for CDR
binslist = [0.0, 5.0, 10.0, 15.0, 30.0, 40.0, 60.0, 100.0]
binsarray = array('d', binslist)
def extractTagForStudy(study, data_folder, out_images_dir, tag_list, non_tag_us,
tag_bounding_box, server_path, greedy=False):
logging.info("=========== PROCESSING SUBJECT: {} ===============".format(study.name))
out_dir = getStudyOutputFolder(study, data_folder, out_images_dir)
utils.checkDir(out_dir, delete = (not greedy))
# look for the info.csv file in the output folder.
# if exists, read the file names and the tags
i=1
file_paths = list( study.glob('**/*.dcm') )
csvrows=[]
unknown = 0
tag_manager = taginfo.TagInfoFile(out_dir)
tag_statistic = {}
prev_tags = {}
if greedy and tag_manager.exists():
# If this is in greedy mode, try to get the names of the files
# that have an undecided tag:
tag_manager.read()
# get a copy of the previously created tags for all files
prev_tags = tag_manager.getDictWithNameKeys()
# clear the tags stored in the file. (This does not delete the file, just empties the data structure)
tag_manager.clear()
tag_manager.deleteTagFile()
for file_path in file_paths:
logging.debug("FILE {}: {}".format(i, str(file_path)))
# Make sure path exists
if not file_path.exists():
logging.warning('File: {} does not exist, skipping'.format(file_path))
continue
# if in greedy mode, look at the tag, look at the tag and decide if we wamt to extract tag
getTag = True
tag = None
us_type = None
if greedy and len(prev_tags) > 0:
name = file_path.name
if name in prev_tags:
tag = prev_tags[name][1]
us_type = prev_tags[name][0]
if tag in ['Unknown', 'Undecided', 'No tag']:
getTag = True
else:
getTag = False
if getTag:
start = time.time()
# Get the representative frame
np_frame, us_type, capture_model = extractImageArrayFromUS(file_path, out_dir=out_dir)
end = time.time()
logging.debug('Preprocessing took : {} seconds'.format(end-start))
if len(capture_model)>0 and capture_model not in tag_bounding_box.keys():
logging.warning('US Model: {} not supported for file: {}'.format(capture_model, file_path))
del np_frame
continue
# Extract the tag
start = time.time()
tag = 'Unknown'
if np_frame is not None and \
us_type not in non_tag_us and \
capture_model in tag_bounding_box.keys():
# Run tag extraction
tag = tess.extractTagFromFrame(np_frame, tag_bounding_box[capture_model], tag_list)
end = time.time()
logging.debug('Tag extraction took : {} seconds'.format(end-start))
del np_frame
if tag in ['Unknown', 'Undecided', 'No tag']:
unknown += 1
else:
logging.debug('Skipping the file: {}, tag: {}, type: {}, because it was known'.format(file_path, tag, us_type))
tag_manager.addTag(file_path.parent, server_path, file_path.name, us_type, tag, write=True)
i+=1
gc.collect()
tag_statistic = tag_manager.tag_statistic
# If all unknown, delete the tag file.
if unknown == len(file_paths):
tag_manager.deleteTagFile()
tag_manager.clear()
return tag_statistic
def main(args):
data_folder = Path(args.dir)
out_images_dir = Path(args.out_dir)
utils.checkDir(out_images_dir, False)
utils.setupLogFile(out_images_dir, args.debug)
studies = []
for dirname, dirnames, __ in os.walk(str(data_folder)):
if len(dirnames) == 0:
studies.append(Path(dirname))
logging.info('Found {} studies '.format(len(studies)))
print('Found {} studies '.format(len(studies)))
# read the list of acceptable tags in the ultrasound file
tag_list = utils.getTagsList()
tag_statistic = dict.fromkeys(tag_list, 0)
tag_statistic['Unknown'] = 0
tag_statistic['Undecided'] = 0
tag_statistic['No tag'] = 0
# Approximate bounding box of where the tag is written acoording to the
# us model
tag_bounding_box = { 'V830':[[40,75], [255,190]],
'LOGIQe': [[0,55], [200,160]],
'Voluson S': [[40,75], [255,190]],
'LOGIQeCine': [[0,0],[135,90]],
'Turbo': [[75,20,],[230,80]],
'Voluson E8': [[40,75], [255,250]]
}
# list of ultrasound image types whose tags we do not care about right now.
non_tag_us = ['Unknown', 'Secondary capture image report',
'Comprehensive SR', '3D Dicom Volume']
# Also read in study directories that might have been finished by a previous run - do not want to rerun them again
finished_study_file = out_images_dir/'finished_studies.txt'
finished_studies = None
if finished_study_file.exists():
with open(finished_study_file) as f:
finished_studies = f.read().splitlines()
finished_studies = [study for study in finished_studies if study.strip()]
if finished_studies is not None:
logging.info('Found {} finished studies'.format(len(finished_studies)))
cleaned_studies = [study for study in studies if str(study) not in finished_studies]
# Get statistics for the finished studies
for study in finished_studies:
logging.info('Will skip: {}'.format(study))
try:
infocsv_dir = getStudyOutputFolder(Path(study), data_folder, out_images_dir)
logging.info('Opening: {}'.format(infocsv_dir))
tag_file_man =taginfo.TagInfoFile(infocsv_dir)
tag_file_man.read()
if tag_file_man.getNumFiles() > 0:
for tag in tag_file_man.tag_statistic:
if tag not in tag_statistic:
tag_statistic[tag] = 0
tag_statistic[tag] += tag_file_man.tag_statistic[tag]
except (OSError, ValueError) as err:
logging.warning('Error reading previously created tags.csv for subject: {}: {}'.format(study, err))
except:
logging.warning('Error reading previously created tags.csv for subject: {}'.format(study))
logging.warning('Unknown except while reading csvt: {}'.format(sys.exc_info()[0]))
else:
cleaned_studies = studies
del studies
if args.use_threads:
with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:
# Start the load operations and mark each future with its URL
future_tags = {executor.submit(extractTagForStudy, study,
data_folder, out_images_dir, tag_list,
non_tag_us, tag_bounding_box,
Path(args.server_path), args.greedy ): study for study in cleaned_studies}
for future in concurrent.futures.as_completed(future_tags):
d = future_tags[future]
logging.info('Finished processing: {}'.format(d))
this_tag_statistic = future.result()
#logging.info(future.result())
for key, value in this_tag_statistic.items():
tag_statistic[key] += value
with open(finished_study_file, "a+") as f:
f.write(str(d)+os.linesep)
else:
i=1
for study in cleaned_studies:
this_tag_statistic = extractTagForStudy(study, data_folder, out_images_dir,
tag_list, non_tag_us, tag_bounding_box,
Path(args.server_path), args.greedy)
logging.info('Finished processing: {}'.format(study))
for key, value in this_tag_statistic.items():
tag_statistic[key] += value
endstr = "\n" if i%50 == 0 else "."
print("",end=endstr)
with open(finished_study_file, "a+") as f:
f.write(str(study)+os.linesep)
i+=1
pprint(tag_statistic)
with open(out_images_dir/"NumberOfTags.json", "w") as outfile:
json.dump(tag_statistic, outfile, indent=4)
logging.info(pformat(tag_statistic))
logging.info('---- DONE ----')
print('------DONE-----------')
def main(args):
data_folder = Path(args.dir)
out_folder = Path(args.out_dir)
utils.checkDir(out_folder, False)
# Setup logging:
utils.setupLogFile(out_folder, args.debug)
if args.cine_mode:
tags = utils.getCineTagsList(args.tags)
else:
tags = utils.getTagsList(args.tags)
print('Tags: {}'.format(tags))
try:
for tag in tags:
out_folder_tag, out_tag_list_file_path, out_tag = getTagDirPathListFile(
out_folder, tag)
utils.checkDir(out_folder_tag, args.delete_existing)
except Exception as e:
logging.error("Couldn't split the tags string: {}".format(e))
return
gt_ga = {}
if args.gt_ga_list:
try:
with open(args.gt_ga_list) as f:
csv_reader = csv.DictReader(f)
for line in csv_reader:
gt_ga[line['StudyID']] = {}
if line['ga_boe'] != ".":
gt_ga[line['StudyID']]['ga_boe'] = int(line['ga_boe'])
else:
gt_ga[line['StudyID']]['ga_boe'] = -1
if line['ga_avua'] != ".":
gt_ga[line['StudyID']]['ga_avua'] = int(
line['ga_avua'])
else:
gt_ga[line['StudyID']]['ga_avua'] = -1
except OSError as e:
logging.error(
'Error reading the gt ga file {} \n Error: {}'.format(
args.gt_ga_list, e))
gt_ga = {}
print('Found {} studies with GT Ga'.format(len(gt_ga)))
bounding_box = [[0, 0], [255, 250]]
# Find all the info.csv files:
tag_file_names = list(
data_folder.glob('**/' + taginfo.TagInfoFile.file_name))
tag_file_list_rows = []
for tag_file in tag_file_names:
logging.info('--- PROCESSING: {}'.format(tag_file))
files_to_copy = []
tag_file_info = taginfo.TagInfoFile(tag_file.parent)
tag_file_info.read()
file_tag_pairs = tag_file_info.getFileNamesWithTags(tags)
if len(file_tag_pairs) == 0:
continue
# print(file_tag_pairs[0])
for file_tag_dict in file_tag_pairs:
file_name = Path(file_tag_dict['File']).name
name_no_suffix = Path(file_name).stem
jpg_file_name = tag_file.parent / (name_no_suffix + '.jpg')
cropped = None
if jpg_file_name.exists():
simage = sitk.ReadImage(str(jpg_file_name))
if args.crop_images:
size = simage.GetSize()
cropped = sitk.Crop(
simage, bounding_box[0],
[size[i] - bounding_box[1][i] for i in range(2)])
else:
cropped = simage
tag = file_tag_dict['tag']
tag_folder, out_tag_list_file_path, out_tag = getTagDirPathListFile(
out_folder, tag)
target_simlink_name = tag_folder / file_name
# Get the data for the global list
if args.create_global_list:
if target_simlink_name.exists():
tag_file_row = {}
study_name = (tag_file.parent).name
pos = study_name.find('_')
if pos == -1:
logging.warning(
"Study name in path {} not in the correct format for a valid study"
.format(study_path))
continue
study_id = study_name[:pos]
study_date = study_name[pos + 1:pos + 9]
tag_file_row['study_id'] = study_id
tag_file_row['study_date'] = study_date
if len(gt_ga) > 0 and study_id in gt_ga:
tag_file_row['ga_boe'] = str(
gt_ga[study_id]['ga_boe']
) if gt_ga[study_id]['ga_boe'] > 0 else ''
tag_file_row['ga_avua'] = str(
gt_ga[study_id]['ga_avua']
) if gt_ga[study_id]['ga_avua'] > 0 else ''
else:
tag_file_row['ga_boe'] = ''
tag_file_row['ga_avua'] = ''
tag_file_row['file_path'] = target_simlink_name
tag_file_row['tag'] = out_tag
tag_file_list_rows.append(tag_file_row)
else:
logging.info(
'The file: {}, study id: {} does not exist'.format(
target_simlink_name, (tag_file.parent).name))
continue
# If not in global list generation mode, deal with the file based on what has been requested.
out_jpg_name = tag_folder / (name_no_suffix + '.jpg')
if os.path.exists(target_simlink_name):
# count all files with that link
logging.info('<---Found duplicates! ----> ')
ext = Path(file_name).suffix
all_target_simlink_files = list(
Path(tag_folder).glob(stem + '*' + ext))
new_name = stem + '_' + str(
len(all_target_simlink_files)) + ext
target_simlink_name = tag_folder / new_name
new_name = stem + '_' + str(
len(all_target_simlink_files)) + '.jpg'
out_jpg_name = tag_folder / (new_name + '.jpg')
if cropped is not None:
logging.info('Writing jpg image: {}'.format(out_jpg_name))
sitk.WriteImage(cropped, str(out_jpg_name))
source_file = Path(args.som_home_dir) / Path(file_tag_dict['File'])
if not args.create_only_lists:
logging.info('Copying file: {} -> {}, study:{}'.format(
file_name, target_simlink_name, (tag_file.parent).stem))
try:
shutil.copyfile(source_file, target_simlink_name)
except FileNotFoundError:
logging.warning("Couldn't find file: {}".format(file))
continue
except PermissionError:
logging.warning(
"Didn't have enough permissions to copy to target: {}".
format(target_simlink_name))
continue
else:
with open(out_tag_list_file_path, "a") as fh:
fh.write(str(source_file) + "\n")
if args.create_global_list and len(tag_file_list_rows) > 0:
logging.info('Number of tag file rows: {}, writing'.format(
len(tag_file_list_rows)))
outfilepath = out_folder / 'all_files_gt_ga.csv'
try:
csvwrap.CSVWrap.writeCSV(tag_file_list_rows, outfilepath)
except IOError as e:
logging.error(
'Error writing the output file: {} \n Error: {}'.format(
outfilepath, e))
logging.info('----- DONE -----')
