def GetSpeech(self, useTextAreas=False):
speech = ''
print 'Speak now'
util.beep()
if useTextAreas and len(self.textAreas) > 0:
areaText = [a.text for a in self.textAreas]
speech = self.speech.listen(phrases=areaText)
else:
speech = self.speech.listen()
if speech is not None and speech != '':
print 'You said: %s' % speech
# search for a match
areaText = [a.text for a in self.textAreas]
found = False
for area in areaText:
if area.text.startswith(speech):
self.speech.Say('Locating target %s' % area.link.text)
self.directionMode = True
self.tracker.setTarget(util.Centroid(area.link.box), area.link.text and not self.directionMode)
plt.hist([m['genant_0'] for m in all_metadata], bins=25)
plt.legend(['#vertebra with genant 0'])
savefig('./img/statistics/genant_0_{0}'.format(ds_name))
plt.clf()
plt.hist([m['genant_1'] for m in all_metadata], bins=25)
plt.legend(['#vertebra with genant 1'])
savefig('./img/statistics/genant_1_{0}'.format(ds_name))
plt.clf()
plt.hist([m['genant_2'] for m in all_metadata], bins=25)
plt.legend(['#vertebra with genant 2'])
savefig('./img/statistics/genant_2_{0}'.format(ds_name))
plt.clf()
plt.hist([m['genant_3'] for m in all_metadata], bins=25)
plt.legend(['#vertebra with genant 3'])
savefig('./img/statistics/genant_3_{0}'.format(ds_name))
plt.clf()
if __name__ == '__main__':
dummy_computation(memory_control)
# noinspection PyBroadException
try:
generate_statistics()
except Exception:
print_exc_plus()
exit(-1)
finally:
frequency = 2000
duration = 500
util.beep(frequency, duration)
def main():
global ocrResults
logger.debug('Started')
speech = speechManager.SpeechManager()
# create the images we need
imSize = (camSmall[1],camSmall[0]) if rotate == -90 or rotate == 90 else (camSmall[0],camSmall[1])
imgCopy = cv.CreateImage(imSize, vidDepth, 3) # a rotated copy
imgYCC = cv.CreateImage(imSize, vidDepth, 3) # ycc for skin
imgSkin = cv.CreateImage(imSize, vidDepth, 1)
imgFG = cv.CreateImage(imSize, vidDepth, 1)
imgHSV = cv.CreateImage(imSize, vidDepth, 3)
imgGray = cv.CreateImage(imSize, vidDepth, 1)
imgEdge = cv.CreateImage(imSize, vidDepth, 1)
imgFinger = cv.CreateImage(imSize, vidDepth, 1)
# big images
bigRot = (bigClip[3],bigClip[2]) if rotate == -90 or rotate == 90 else (bigClip[2],bigClip[3])
imgBigCropped = cv.CreateImage((bigClip[2],bigClip[3]), vidDepth, 3)
imgBigRotated = cv.CreateImage(bigRot, vidDepth, 3)
# set up cam
camera = cv.CaptureFromCAM(0)
cv.SetCaptureProperty(camera, cv.CV_CAP_PROP_FRAME_WIDTH, camSmall[X])
cv.SetCaptureProperty(camera, cv.CV_CAP_PROP_FRAME_HEIGHT, camSmall[Y])
# SetResolution(camSmall)
img = cv.QueryFrame(camera)
util.RotateImage(img, imgCopy, rotate)
cv.NamedWindow(windowTitle, 1)
counter = 0
# before we do anything, create our background model
bgModel = bg2.BackgroundModel(imSize[0], imSize[1], fitThreshold=fitThreshold, shadowColorDiff=shadowColorDiff, shadowMaxDarken=shadowMaxDarken)
for i in range(0, trainImages):
smFrame = cv.QueryFrame(camera)
util.RotateImage(smFrame, imgCopy, rotate)
bg2.FindBackground(imgCopy, imgFG, bgModel)
cv.ShowImage(windowTitle, imgFG)
cv.WaitKey(10)
# keep state here
documentOnTable = False
doingOcr = False
ocrDone = False
accumulator = 0
documentCorners = [] # corners in small view
aspectRatio = None
transform = None
transformInv = None
docWidth = None
docHeight = None
bigBoxes = []
smallBoxes = []
overlays = []
overlayIndex = [] # map overlay to box index
tracking = False
trackingTarget = None
finger = None
lastTouched = None
colorMode = False
lastColor = None
tableMode = False
topRow = []
leftRow = []
showBG = False
showSkin = False
dicto = dict.DictionaryManager('dict/wordfreq.txt','dict/userdict.txt')
# main loop
while True:
key = cv.WaitKey(10) # key handler
if key == 27: break
char = None if key == -1 else chr(key)
# get image
img = cv.QueryFrame(camera)
util.RotateImage(img, imgCopy, rotate)
util.GetYCC(imgCopy,imgYCC)
bg2.FindBackground(imgCopy, imgFG, bgModel, update=0,findShadow=findShadow)
# for i in range(0, erodeIterations):
# cv.Erode(imgFG,imgFG)
for i in range(0, erodeIterations):
cv.Dilate(imgFG,imgFG)
element = cv.CreateStructuringElementEx(3,3,1,1,cv.CV_SHAPE_RECT)
bg2.FindSkin(imgYCC,imgSkin,doCleanup=False,showMaybeSkin=True)
if ocrDone:
pass
cv.And(imgFG,imgSkin,imgFG)
#cv.MorphologyEx(imgFG,imgFG,None,element,cv.CV_MOP_CLOSE, erodeIterations)
#cv.MorphologyEx(imgFG,imgFG,None,element,cv.CV_MOP_OPEN, erodeIterations)
### STEP ONE: DETECT DOCUMENT
# now, start looking for documents
if not doingOcr and not documentOnTable:
if len(sys.argv) > 1:
smallBoxes, documentCorners, aspectRatio, ocrResults, docWidth, docHeight, transform, transformInv = LoadState(sys.argv[1])
doingOcr = True
ocrDone = True
documentOnTable = True
print 'Loaded'
util.GetGrayscale(imgCopy, imgGray)
util.GetEdge(frame=imgGray, edge=imgEdge)
rect = util.FindLargestRectangle(imgFG, imgGray)
if len(rect) == 4 and util.BoundingRectArea(rect) > 25000:
accumulator += 1
if accumulator >= 60:
accumulator = 0
speech.Say('Document detected')
documentOnTable = True
documentCorners = rect
else:
accumulator = 0
# if we have a document, wait for it to go away
elif not doingOcr and documentOnTable:
util.GetGrayscale(imgCopy, imgGray)
util.GetEdge(frame=imgGray, edge=imgEdge)
rect = util.FindLargestRectangle(imgFG, imgGray)
if len(rect) != 4 or util.BoundingRectArea(rect) < 25000:
accumulator += 1
if accumulator >= 30:
speech.Say('Document removed')
documentOnTable = False
documentCorners = []
ocrDone = False
accumulator = 0
### STEP TWO: IF THE USER PRESSES THE O KEY, DO OCR
if documentOnTable and not doingOcr and not ocrDone and char == ' ':
speech.Say('Starting OCR')
char = -1
aspectRatio = GetAspectRatio(documentCorners)
imgRect, transform, transformInv = CreateTransform(documentCorners, imgCopy, aspectRatio)
# we'll use this later, for overlays
docWidth = imgRect.width
docHeight = imgRect.height
accumulator = 0
doingOcr = True
timestamp = int(time.time()*1000)
# save small image
cv.SaveImage('logs/small-%d.png' % timestamp, imgCopy)
cv.SaveImage('logs/smallrect-%d.png' % timestamp, imgRect)
# get big image
print 'Getting big image'
cv.SetCaptureProperty(camera, cv.CV_CAP_PROP_FRAME_WIDTH, camLarge[X])
cv.SetCaptureProperty(camera, cv.CV_CAP_PROP_FRAME_HEIGHT, camLarge[Y])
# get a couple frames to warm up
for i in range(0,10):
bigFrame = cv.QueryFrame(camera)
cv.WaitKey(10)
bigFrame = cv.QueryFrame(camera)
cv.SaveImage('logs/big-%d.png' % timestamp, bigFrame)
# crop big image
cropRegion = cv.GetSubRect(bigFrame, bigClip)
cv.Copy(cropRegion, imgBigCropped)
cv.SaveImage('logs/bigcropped-%d.png' % timestamp, imgBigCropped)
# rotate big image
util.RotateImage(imgBigCropped, imgBigRotated, rotate)
cv.SaveImage('logs/bigrotated-%d.png' % timestamp, imgBigRotated)
# rectify big image
scaledCorners = [(p[0]*bigScale,p[1]*bigScale) for p in documentCorners]
bigRectified, bigTransform, bigTransformInv = CreateTransform(scaledCorners, imgBigRotated, aspectRatio)
cv.SaveImage('logs/bigrectified-%d.png' % timestamp, bigRectified)
# get text regions
print 'Getting text regions'
ocr.ClearOCRTempFiles()
bigBoxes = FindTextAreas(bigRectified)
# start OCR of text regions
ocrResults.clear()
bigGray = util.GetGrayscale(bigRectified)
# do the OCR for each box
for i in range(0, len(bigBoxes)):
ocr.CreateTempFile(bigGray,bigBoxes[i],i)
pool = multiprocessing.Pool(4)
for i in range(0, len(bigBoxes)):
ocrResults[i] = None
pool.apply_async(ocr.CallOCREngine, args = (i,'./ocrtemp/',ocrEngine), callback = ocrCallback)
pool.close()
pool.join()
# now we are done with OCR
# restore small image
cv.SetCaptureProperty(camera, cv.CV_CAP_PROP_FRAME_WIDTH, camSmall[X])
cv.SetCaptureProperty(camera, cv.CV_CAP_PROP_FRAME_HEIGHT, camSmall[Y])
# get a couple frames to warm up
for i in range(0,3):
smFrame = cv.QueryFrame(camera)
cv.WaitKey(10)
# retrain bg to hide the document
for i in range(0, trainImages):
smFrame = cv.QueryFrame(camera)
util.RotateImage(smFrame, imgCopy, rotate)
bg2.FindBackground(imgCopy, imgFG, bgModel)
cv.ShowImage(windowTitle, imgFG)
cv.WaitKey(10)
### STEP THREE: WAIT FOR OCR TO HAPPEN
if doingOcr and not ocrDone:
# did we recognize everything?
done = None not in ocrResults.values()
if done:
ocrDone = True
### OCR IS DONE ###
# remove empty boxes
print bigBoxes
print ocrResults
if autocorrect:
print 'autocorrect'
# correct words
for key in ocrResults.keys():
word = ocrResults[key]
if len(word) > 3:
if not dicto.WordInDictionary(word):
correct = dicto.BestMatch(word,2)
if correct is None:
print 'Removed %s' % word
ocrResults[key] = ''
else:
print '%s=>%s' % (word,correct)
ocrResults[key] = correct.word
else:
print 'Keeping %s' % word
print 'autocorrect done'
newBoxes = []
newOcrResults = {}
for i in range(0, len(bigBoxes)):
if ocrResults.has_key(i) and (ocrResults[i] is not None and len(ocrResults[i]) > 0):
print 'Adding %s' % ocrResults[i]
newBoxes.append(bigBoxes[i])
newOcrResults[len(newBoxes)-1] = ocrResults[i]
bigBoxes = newBoxes
global ocrResults
ocrResults.clear()
for key in newOcrResults.keys():
ocrResults[key] = newOcrResults[key]
print bigBoxes
print ocrResults
speech.Say('OCR complete. Found %d items' %len([v for v in ocrResults.values() if v is not None and v != '']))
print 'ocrresults %d boxes %d' % (len(bigBoxes),len(ocrResults.keys()))
smallBoxes = [(p[0]*1./bigScale,p[1]*1./bigScale,p[2]*1./bigScale,p[3]*1./bigScale) for p in bigBoxes]
SaveState(smallBoxes, documentCorners, aspectRatio, ocrResults, docWidth, docHeight, transform, transformInv)
print 'Saved state'
# auto add overlay
overlays, overlayIndex = CreateOverlays(smallBoxes, docWidth, docHeight, sides=overlayNumSides)
# # we can save this state
# state = {}
# state['ocrResults'] = ocrResults
# state['documentCorners'] = documentCorners
# state['aspectRatio'] = aspectRatio
# state['transform'] = transform
# state['transformInv'] = transformInv
# state['smallBoxes'] = smallBoxes
# state['docWidth'] = docWidth
# state['docHeight'] = docHeight
# state['overlays'] = overlays
# timestamp = int(time.time())
# pickle.dump(state, open('logs/data-%d.pickle' % timestamp, 'wb'))
### STEP FOUR: IF WE HAVE A DOCUMENT, TRACK TOUCH
if ocrDone:
finger = GetFingerPosition(imgFG,imgFinger)
if finger is None: lastTouched = None
else:
fingerTrans = util.Transform(finger, transform)
if not tracking:
if colorMode:
cv.CvtColor(imgCopy,imgHSV,cv.CV_BGR2HSV)
color = cv.Get2D(imgHSV, max(finger[1]-20,0), finger[0])
colorName = GetColorName(color)
# if colorName != lastColor:
speech.Say(colorName)
# lastColor = colorName
else:
handledFinger = False
# first, check to see if we are in an overlay
for overlay in overlays:
if not handledFinger and util.PointInsideRect(fingerTrans, overlay):
index = overlays.index(overlay)
if lastTouched == overlay:
if not tableMode:
accumulator += 1
if accumulator > 60: # start tracking
speech.Say('Locating %s' % ocrResults[index])
trackingTarget = smallBoxes[index]
tracking = True
accumulator = 0
else:
try:
speech.Say('Shortcut to %s' % ocrResults[index])
accumulator = 1
except KeyError:
print 'whoops'
lastTouched = overlay
handledFinger = True
# if that doesn't work, see if we are in the paper
if not handledFinger and util.PointInsideRect(fingerTrans, (0,0,docWidth,docHeight)) and len(smallBoxes) > 0:
# if so, get the closest box
# are we inside a box?
boxesInside = [b for b in smallBoxes if util.PointInsideRect(fingerTrans,b)]
closestBox = None
if len(boxesInside) > 0:
closestBox = boxesInside[0]
else:
closestBox = min(smallBoxes, key=lambda b: util.distance(fingerTrans, (b[0]+b[2]/2,b[1]+b[3]/2)))
index = smallBoxes.index(closestBox)
box = smallBoxes[index]
if lastTouched == box:
pass
else: # we are in a box
if not tableMode:
try:
speech.Say(ocrResults[index])
except KeyError:
print 'whoops'
else: # get the row and column
row, col = GetTableHeaders(box,leftRow,topRow)
speech.Say('%s, row %s, column %s' % (ocrResults[index],row,col))
lastTouched = box
else: # tracking
# are we in are target?
if util.PointInsideRect(fingerTrans, trackingTarget):
index = smallBoxes.index(trackingTarget)
name = ocrResults[index]
util.beep()
speech.Say('Located %s' % name)
tracking = False
else:
box = trackingTarget
trackPoint = [box[0]+box[2]/2,box[1]+box[3]/2]
dx = trackPoint[0] - fingerTrans[0]
dy = trackPoint[1] - fingerTrans[1]
# get dir
direction = ''
if abs(dx) > abs(dy):
if dx > 0: direction = 'right'
else: direction = 'left'
else:
if dy > 0: direction = 'down'
else: direction = 'up'
if counter % 10 == 0:
speech.Say(direction)
# mini key handler
if char == 'b':
showBG = not showBG
print 'Show bg? %s' % showBG
elif char == 's':
showSkin = not showSkin
print 'Show skin? %s' % showSkin
elif char == 'c':
colorMode = not colorMode
print 'Color mode? %s' % colorMode
elif char == 'o':
overlays, overlayIndex = CreateOverlays(smallBoxes, docWidth, docHeight, sides=overlayNumSides)
print 'Created overlay'
elif char == 'a': #all
# get all results
items = [ocrResults[i] for i in range(0, len(smallBoxes)) if ocrResults[i] is not None and ocrResults[i] != '']
whatToSay = '%d items. ' % len(items)
for i in items:
whatToSay += 'Item %s. ' %i
speech.Say(whatToSay)
elif char == 'u':
print 'Updating bg'
for i in range(0, trainImages):
smFrame = cv.QueryFrame(camera)
util.RotateImage(smFrame, imgCopy, rotate)
bg2.FindBackground(imgCopy, imgFG, bgModel)
cv.ShowImage(windowTitle, imgFG)
cv.WaitKey(10)
elif char == ' ':
speech.StopSpeaking()
tracking = False
### STEP FOUR AND A HALF: CHECK FOR VOICE COMMANDS
if ocrDone and not tracking and char == 'v':
# available voice commands: color mode, text mode, find, rerecognize, cancel
util.beep()
print 'Waiting for voice command'
commands = ['color','text','find','recognize','cancel', 'list','help','overlay']
input = speech.listen(commands, 5)
if input is None:
util.beep()
speech.Say('Canceled')
elif input == 'help':
speech.Say('You can say ' + ', '.join(commands))
colorMode = True
elif input == 'color':
speech.Say('Color mode selected')
colorMode = True
elif input == 'text':
speech.Say('Text mode selected')
colorMode = False
elif input == 'recognize':
if lastTouched is None: speech.Say('No text selected')
else:
speech.Say('rerecognizing')
proc = multiprocessing.Process(target = ocr.CallOCREngine, args = (i, './ocrtemp/', 'abbyy'), callback = ocrCallback)
proc.start()
proc.join()
elif input == 'cancel':
speech.Say('Canceled')
elif input == 'find':
util.beep()
speech.Say('Say the first word of the section you wish to find')
# get first words
firstWords = []
phrases = []
for i in range(0, len(ocrResults)):
if ocrResults.has_key(i):
phrase = ocrResults[i]
phrases.append(phrase)
if phrase is None:
firstWords.append(None)
else:
firstWords.append(phrase.split(' ')[0].lower())
wordFind = speech.listen(firstWords, 10)
if wordFind is None:
util.beep()
speech.Say('Not found')
else:
match = firstWords.index(wordFind.lower())
speech.Say('Tracking %s' % ocrResults[match])
tracking = True
trackingTarget = smallBoxes[match]
elif input == 'overlay':
overlays, overlayIndex = CreateOverlays(smallBoxes, docWidth, docHeight, sides=overlayNumSides)
speech.Say('Added overlays')
elif input == 'table':
speech.Say('Set table mode')
overlays, overlayIndex = CreateTableOverlays(smallBoxes, docWidth, docHeight)
elif input == 'list':
# get all results
items = [ocrResults[i] for i in range(0, len(smallBoxes)) if ocrResults.has_key(i) and ocrResults[i] is not None and ocrResults[i] != '']
whatToSay = '%d items. ' % len(items)
for i in items:
whatToSay += 'Item %s. ' %i
speech.Say(whatToSay)
### STEP FIVE: DRAW EVERYTHING
# doc corners
util.DrawPoints(imgCopy, documentCorners, color=(255,0,0))
# boxes and overlays
for i in range(0,len(smallBoxes)):
b = smallBoxes[i]
if ocrResults.has_key(i) and (ocrResults[i] is not None and ocrResults[i] != ''):
util.DrawRect(imgCopy, b, color=(0,0,255), transform=transformInv)
pbox = util.Transform((b[X],b[Y]), transformInv)
util.DrawText(imgCopy, ocrResults[i], pbox[X], pbox[Y], color=(0,0,255))
for i in range(0, len(overlays)):
o = overlays[i]
# get relevant box index
box = overlayIndex[i]
util.DrawRect(imgCopy, o, color=(0,100,255), transform=transformInv)
if ocrResults.has_key(i) and (ocrResults[i] is not None and ocrResults[i] != ''):
po = util.Transform((o[X],o[Y]), transformInv)
util.DrawText(imgCopy, ocrResults[box], po[X], po[Y], color=(0,100,255))
if finger is not None:
util.DrawPoint(imgCopy, finger, color=(0,0,255))
imgToShow = imgCopy
if showSkin: imgToShow = imgSkin
elif showBG: imgToShow = imgFG
cv.ShowImage(windowTitle, imgToShow)
### STEP END: INCREMENT THE COUNTER
counter += 1
### END OF MAIN LOOP
logger.debug('Ended')
def run():
lastRunMaxTime=0 # for cache overwrite after this
last_cache_time_file = LastCachedRumTimeFile
if os.path.exists(last_cache_time_file):
with open(last_cache_time_file,"r") as f:
try:
lastRunMaxTime = int(f.readline())
except: pass
if SetCacheLastTime is not None: lastRunMaxTime = SetCacheLastTime
print(My_Ip, SUMO_PORT)
traci.init(int( SUMO_PORT))
var2str, varlist = get_sumo_vars(traci)
badTls, badCross= [],[]
Car.traci = traci
tlslist=traci.trafficlights.getIDList()
print('# tls', len(tlslist))
tlsSet = set(tlslist)
id2tls=dict()
e2tls =dict() # controlled edge to tls.
Tls.ofid = open(OutputDir+"tlslog.txt","w") if _Do_Output else None
Tls.ofidname = OutputDir+"tlslog.txt"
Tls.traci = traci
Tls.feedbackData = feedbackData
unlearnedTls =[]
for tlsid in tlsSet:
tmp = Tls(tlsid, loadStages = True, stageFolder= TlsLoadStageDir)
id2tls[tlsid]=tmp
ctrlanes = tmp.ctrlanes
tmp.output_file = _Do_Output # writePhaseLog?
for lane in ctrlanes:
e2tls[lane.split("_",1)[0]]=tmp
if len(tmp.stageList)==0: # not learned
unlearnedTls.append(tmp)
alledges = [eid for eid in traci.edge.getIDList() if not eid.startswith(":")]
edgeSet = set(alledges)
print('# edges', len(edgeSet), "tls-ctr edges:",len(e2tls))
if _Do_Output:
with open(OutputDir+"netinfo.txt","w") as f:
f.write("nedge=%d nctredge=%d ntls=%d"%(len(edgeSet),len(e2tls),len(id2tls)))
lane2vq={} # vehicle queue at cross.
id2car = dict()
id2group = dict()
Car.ofid = open(OutputDir+"carlog.txt","w") if _Do_Output else None
Car.ofidname = OutputDir+"carlog.txt"
Car.mvfd = open(OutputDir+"carmov.txt","w") if _Do_Output else None
Car.mvfdname = OutputDir+"carmov.txt"
Car.spdfd = open(OutputDir+"carspds.txt","w") if _Do_Output else None
Car.spdfdname = OutputDir+"carspds.txt"
Car.lane2vq = lane2vq
Car.id2car = id2car
Car.id2group = id2group
Car.feedbackData = feedbackData
Car.congestedAreas = congestedAreas
Car.RemoveWongLaneThresh = 2
print("!!! [cache] lastRunMaxTime",lastRunMaxTime)
curtime=0
sumoRunTime=0.
manageRunTime=0.
processRunTime=0.
is_showing = None
if Has_GUI_Device:
Car.traci.gui.setZoom('View #0',1000)
vprint = set([]) # to track this vehicle
else: vprint=set([])
while curtime<config.simulationEndTime+1:
startTime = time.time()
traci.simulationStep()
sumoRunTime += time.time()-startTime
curtime = traci.simulation.getTime() -1
if Has_GUI_Device and curtime == -1:
beep(5)
raw_input("\nEnter any to Continue...") # debug purpose
if curtime%30 ==0 or curtime==config.simulationEndTime:
print("\nT=%d Vnum=%d sT=%.2f mT=%.2f pT=%.2f"%(curtime,len(id2car),1000*sumoRunTime/(curtime+1),1000*manageRunTime/(curtime+1),1000*processRunTime/(curtime+1)))
if curtime>lastRunMaxTime:
lastRunMaxTime = curtime
with open(last_cache_time_file,"w") as f:
f.write("%d"%lastRunMaxTime)
if curtime%31==0 or is_showing is not None:
badTls= get_bad_list(feedbackData, "tls",TlsBadRateThresh, id2tls=id2tls)
badCross= get_bad_list(feedbackData, "cross", CrossBadRateThresh)
for tid in badTls:
print("Checkout bad tls "+tid)
for nid in badCross:
print("bad cross "+nid)
congestedAreas["cross"].add(nid)
for vid in traci.simulation.getStartingTeleportIDList():# stuck
try:
id2car[vid].remove(curtime,output=False) # traci remove + setDead
except: pass
try:
del id2car[vid]
except: pass
startTime = time.time()
for vid in traci.simulation.getArrivedIDList():
if vid in id2car:
id2car[vid].setDead(curtime) # already auto removed.
del id2car[vid]
for vid in traci.simulation.getDepartedIDList(): # new born cars
tmp = Car(vid, modelPredictFuel=True)
tmp.subscribe(varlist)
tmp.setStartTime(curtime)
rid = tmp.getRouteID()
tmp.route = construct_segment(tmp.rou, e2tls, route_id=rid, print_str=vid in vprint, overwrite_cache=curtime>=lastRunMaxTime, prefix=config.osmfile )
tmp.route.id = rid
tmp.route.car = tmp
tmp.e2tls= e2tls
if vid.startswith('a'):
tmp.do_Tls1 = R['tls1']>0 # query 1st tls ahead
tmp.do_Tls2 = R['tls2']>0 # query two tls ahead
tmp.do_coor = R['co']>0 # codrive
tmp.do_optimizeSpeed = True
else:
tmp.do_Tls1 = False
tmp.do_Tls2 = False
tmp.do_coor = False
tmp.do_optimizeSpeed = False # false will not do lane change.
tmp.output_file = _Do_Output
id2car[vid]=tmp
if vid in vprint:
tmp.iprintSet |= set([2,3,9]) # track this car, and print more
tmp.route.print_str = True
tmp.setColor((255,255,0))
print(tmp.rou)
pprint.pprint(tmp.route)
manageRunTime += time.time()-startTime
startTime = time.time()
for tlsid in tlsSet: # proc tls, called before car.process
id2tls[tlsid].process(curtime)
for vid,car in id2car.items():
car.process(curtime) # fill in basic variables
for lid,que in lane2vq.items():
que.sort(key=lambda car: car.lanePos) # sort by lane-pos.
for vid,car in id2car.items():
car.formGroup()
for vid,car in id2car.items():
car.optimize() # run optimization.
for vid,car in id2car.items():
car.transaction() # if codrive
processRunTime += time.time()-startTime
traci.close()
Car.ofid.close()
Tls.ofid.close()
Car.mvfd.close()
Car.spdfd.close()
