def findfaceswithtrigger(profile='picluster',threshold=15,n=100,serial='no',show=False):
import sys, cPickle, os,cv2,time,math,signal
pidfile=open('mypythonpid','w')
cPickle.dump(os.getpid(),pidfile)
pidfile.close()
runstate=open('runstate','w')
cPickle.dump(0,runstate)
runstate.close()
print os.getpid()
#context=zmq.Context()
#pubsocket=context.socket(zmq.PUB)
#port=5556
#pubsocket.bind("tcp://*:%s"%port)
#subsocket=context.socket(zmq.SUB)
#if sys.platform=='darwin':
# ips=open("/Users/isaac/Code/parastuff/ipaddresses").read()
#if sys.platform=='linux2':
# ips=open("/home/pi/ipaddresses").read()
#splitips=ips.split("\n")
#print splitips
#for ip in splitips:
# if ip:
# subsocket.connect("tcp://%s:%s"%(ip,port))
#subsocket.setsockopt(zmq.SUBSCRIBE,"need quiet?")
c=Client(profile=profile)
dview=c[:]
numnodes=len(c.ids)
dview.block=False
#dview.execute('import cv2,sys,time,math,os,cPickle,signal')
#dview.execute("mypid=os.getpid()")
def getmypid():
import cPickle, os
try:
file=open('mypythonpid','r')
mypid=cPickle.load(file)
file.close()
try:
os.kill(mypid,0)
except:
mypid=False
except:
mypid=False
return mypid
def getrunstate():
import cPickle
try:
file=open('runstate','r')
state=cPickle.load(file)
file.close()
except:
state=0
return state
dview["getrunstate"]=getrunstate
dview["getmypid"]=getmypid
dview.execute("mypid=getmypid()")
myippid=-1
for proc in psutil.process_iter():
if proc.name()=='ipengine':
myippid=proc.pid
ippids=[]
dview.execute("import os;enginepid=os.getpid()")
for i in range(numnodes):
if not myippid==c[c.ids[i]]['enginepid']:
ippids.append(c.ids[i])
dview=c.activate(ippids)
dview.execute('import cv2,sys,time,math,cPickle,signal')
print dview
numnodes=len(dview)
rejects=len(c.ids)-numnodes
print "configured for this pi by rejecting a certain engine"
print sys.platform
if sys.platform=='darwin':
facedetector=cv2.CascadeClassifier("/usr/local/Cellar/opencv/2.4.9/share/OpenCV/haarcascades/haarcascade_frontalface_alt2.xml")
elif sys.platform=='linux2':
facedetector=cv2.CascadeClassifier("/home/pi/opencv-2.4.9/data/haarcascades/haarcascade_frontalface_alt2.xml")
else:
print "something went wrong detecting the system"
cam=cv2.VideoCapture(0)
retval,img=cam.read()
gen.dumptimes(img,scale=1.1)
times=gen.loadtimes()
scale=1.1
dview["scale"]=scale
for i in ippids:
c[i].execute('if sys.platform=="darwin": haarface=cv2.CascadeClassifier("/usr/local/Cellar/opencv/2.4.9/share/OpenCV/haarcascades/haarcascade_frontalface_alt2.xml")')
c[i].execute('if sys.platform=="linux2": haarface=cv2.CascadeClassifier("/home/pi/opencv-2.4.9/data/haarcascades/haarcascade_frontalface_alt2.xml")')
numsizes=len(times)
trans=[None]*numsizes
for i in range(numsizes):
trans[i]=int(20*(scale**i))
mintime=min(times)
times=[thistime-mintime for thistime in times]
print "predicting even distribution..."
def predictdist(numnodes,times,numsizes,c):
timepernode=sum(times)/numnodes
thresholds=[None]*(numnodes+1)
thresholds[0]=0
for i in range(1,numnodes+1):
j=thresholds[i-1]+1
while sum(times[thresholds[i-1]:j])<timepernode and j<numsizes:
j=j+1
#print j
#time.sleep(3)
if sum(times[thresholds[i-1]:j])-timepernode>timepernode-sum(times[thresholds[i-1]:j-1]):
j=j-1 #makes more even, but last few take longest...otherwise even but last is really short
thresholds[i]=j
thresholds[numnodes]=numsizes-1
for i in range(numnodes):
c[ippids[i]]['thismin']=tuple([trans[thresholds[i]]-1]*2)
c[ippids[i]]['thismax']=tuple([trans[thresholds[i+1]]+1]*2)
predictdist(numnodes,times,numsizes,c)
def findfaces(img,scale,serial):
starttime=time.time()
numnodes=len(ippids)
img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
dview['img']=img
dview.execute('img=img.copy();mypid=getmypid()')
#dview.execute("mypid=getmypid()")
runstate=open('runstate','w')
cPickle.dump(1,runstate)
runstate.close()
dview.execute("state=getrunstate()") #may be redundant but avoids a lock
#for i in range(numnodes):
# print c[c.ids[i]]["state"]
dview.execute("if mypid and not state: os.kill(mypid,signal.SIGTSTP)")
dview.execute("exectime=time.time();myfaces=haarface.detectMultiScale(img,scale,4,1,thismin,thismax);exectime=time.time()-exectime;endtime=time.time()",block=False)
faces=set()
thistimes=[None]*numnodes
thistimesremote=[None]*numnodes
for i in range(numnodes):
thistimes[i]=time.time()
print "checking engine "+str(ippids[i])
thisfaces=c[ippids[i]]["myfaces"]
thistimesremote[i]=c[ippids[i]]["exectime"]
print len(thisfaces)
if len(thisfaces)>0:
for face in thisfaces:
faces.add(tuple(face))
thistimes[i]=time.time()-thistimes[i]
print thistimes
print thistimesremote
duptime=time.time()
facelist=list(faces)
donttest=[]
finallist=[]
testlen=len(facelist)
origlist=facelist
testlennew=0
print len(origlist)
while not testlennew==testlen:
testlen=len(facelist)
for first in range(len(facelist)):
if first not in donttest:
for second in range(first+1,len(facelist)):
sub=facelist[first]
j=facelist[second]
var=abs(j[3]-sub[3])/2
if ((j[0]+j[3]/2-var<sub[0]+sub[3]/2) and (j[0]+j[3]/2+var>sub[0]+sub[3]/2)) and ((j[1]+j[2]/2-var<sub[1]+sub[2]/2) and (j[1]+j[2]/2+var>sub[1]+sub[2]/2)) and min([float(j[3])/sub[3],float(sub[3])/j[3]])>1.0/2.0:
donttest.append(first)
donttest.append(second)
#facelist.append(tuple([(j[0]+sub[0])/2,(j[1]+sub[1])/2,(j[2]+sub[2])/2,(j[3]+sub[3])/2]))
inter=tuple([(j[0]+sub[0])/2,(j[1]+sub[1])/2,(j[2]+sub[2])/2,(j[3]+sub[3])/2])
finallist.append(inter)
break
if first not in donttest:
finallist.append(facelist[first])
testlennew=len(finallist)
facelist=finallist
finallist=[]
donttest=[]
finallist=facelist
duptime=time.time()-duptime
print len(finallist)
starttime=time.time()-starttime
print "min/avg/max/regtime/tottime: "+str(min(thistimesremote))+"/"+str(sum(thistimesremote)/numnodes)+"/"+str(max(thistimesremote))+"/"+str(starttime)+"/"+str(sum(thistimesremote))
if serial=='yes':
serialtime=time.time()
facedetector.detectMultiScale(img,scale,4,1)
serialtime=time.time()-serialtime
print serialtime
dview.execute("if mypid: os.kill(mypid,signal.SIGCONT)")
#for i in range(numnodes):
# print c[c.ids[i]]['mypid']
runstate=open('runstate','w')
cPickle.dump(0,runstate)
runstate.close()
return finallist
def calibcam(n,cam):
sumlist=[None]*n
for i in range(n):
retval,img=cam.read()
xstart=img.shape[0]/4
ystart=img.shape[1]/4
sumlist[i]=img[xstart:(xstart*3),ystart:(ystart*3),:].sum()
return sumlist
sumlist=calibcam(n,cam)
std=stats.tstd(sumlist)
avg=sum(sumlist)/n
framenum=0
oldids=ippids
print "starting detection"
while True:
retval,img=cam.read()
xstart=img.shape[0]/4
ystart=img.shape[1]/4
thisz=(img[xstart:(xstart*3),ystart:(ystart*3),:].sum()-avg)/std
#while True:
# print "testing for sleep needed"
# try:
# if subsocket.recv(flags=zmq.NOBLOCK)=="need quite? yes":
# print "need sleep"
# while True:
# try:
# if subsocket.recv(flags=zmq.NOBLOCK)=="need quite? not anymore":
# break
# except:
# pass
# time.sleep(.5)
# except:
# break
if abs(thisz)>threshold:
print "something weird, zscore="+str(thisz)
time.sleep(.5)
# pubsocket.send("need quiet? yes")
retval,newimg=cam.read() #works well on mac, maybe not pis...
#if not len(c.ids)==len(oldids): #done periodically, not ideal to update here...
# print "cluster changed"
# numnodes=len(c.ids)
# dview=c[:]
# print "repredicting even distribution..."
# predictdist(numnodes,times,numsizes,c)
# for id in c.ids:
# if id not in oldids:
# c[id].execute('import cv2,sys,time,math,os,cPickle,signal;myfaces=[]')
# c[id]['getrunstate']=getrunstate
# c[id]['getmypid']=getmypid
# c[id]['scale']=scale
# c[id].execute('haarface=cv2.CascadeClassifier("/home/pi/opencv-2.4.9/data/haarcascades/haarcascade_frontalface_alt2.xml")')
#else:
# c[id].execute('myfaces=[]')
# oldids=c.ids
print "searching for faces..."
faces=findfaces(newimg,scale,serial)
# pubsocket.send("need quiet? not anymore")
if len(faces)>0:
print "FOUND A FACEZ!!!!!11!"
if show==True:
for (x,y,h,w) in faces:
cv2.rectangle(newimg,(x,y),(x+w,y+h),(0,255,255),1)
#cv2.imshow("obj num "+str(i),newimg)
cv2.imshow("obj found",newimg)
else:
print "no facez :("
if show==True:
cv2.imshow("obj not found",newimg)
if show==True:
cv2.waitKey(1)
sumlist[0]=img[xstart:(xstart*3),ystart:(ystart*3),:].sum()
std=stats.tstd(sumlist)
avg=sum(sumlist)/n
#else:
# pubsocket.send("need quiet? no")
framenum=framenum+1
if framenum%10==0:
sumlist[0]=img[xstart:(xstart*3),ystart:(ystart*3),:].sum()
std=stats.tstd(sumlist)
avg=sum(sumlist)/n
#if framenum%100==0:
# print framenum
if framenum%(10*numnodes)==0:
if not len(c.ids)-len(oldids)==rejects:
print "cluster changed"
numnodes=len(oldids)
dview=c[:]
print "repredicting even distribution..."
#predictdist(numnodes,times,numsizes,c)
for id in c.ids:
if id not in oldids:
c[id].execute('import cv2,sys,time,math,os,cPickle,signal;myfaces=[]')
c[id]['getrunstate']=getrunstate
c[id]['getmypid']=getmypid
c[id]['scale']=scale
c[id].execute('haarface=cv2.CascadeClassifier("/home/pi/opencv-2.4.9/data/haarcascades/haarcascade_frontalface_alt2.xml")')
#else:
# c[id].execute('myfaces=[]')
#oldids=c.ids
#c.spin()
#trying reseting the client
c=Client(profile=profile)
dview=c[:]
myippid=-1
for proc in psutil.process_iter():
if proc.name()=='ipengine':
myippid=proc.pid
ippids=[]
dview.execute("import os;enginepid=os.getpid()")
for i in range(len(c.ids)):
if not myippid==c[c.ids[i]]['enginepid']:
ippids.append(c.ids[i])
dview=c.activate(ippids)
numnodes=len(ippids)
oldids=ippids
rejects=len(c.ids)-numnodes
predictdist(numnodes,times,numsizes,c)
def picamtrigger(profile='picluster',threshold=15,n=100,serial='no',show=False,width=720,height=480,scale=1.1,cascade="haarcascade_frontalface_alt2.xml"):
import sys, cPickle, os,cv2,time,math,signal
pidfile=open('mypythonpid','w')
cPickle.dump(os.getpid(),pidfile)
pidfile.close()
runstate=open('runstate','w')
cPickle.dump(0,runstate)
runstate.close()
print os.getpid()
c=Client(profile=profile)
dview=c[:]
numnodes=len(c.ids)
dview.block=False
def getmypid():
import cPickle, os
try:
file=open('mypythonpid','r')
mypid=cPickle.load(file)
file.close()
try:
os.kill(mypid,0)
except:
mypid=False
except:
mypid=False
return mypid
def getrunstate():
import cPickle
try:
file=open('runstate','r')
state=cPickle.load(file)
file.close()
except:
state=0
return state
dview["getrunstate"]=getrunstate
dview["getmypid"]=getmypid
dview.execute("mypid=getmypid()")
myippid=-1
for proc in psutil.process_iter():
if proc.name()=='ipengine':
myippid=proc.pid
ippids=[]
dview.execute("import os;enginepid=os.getpid()")
for i in range(numnodes):
if not myippid==c[c.ids[i]]['enginepid']:
ippids.append(c.ids[i])
dview=c.activate(ippids)
dview.execute('import cv2,sys,time,math,cPickle,signal')
print dview
numnodes=len(dview)
rejects=len(c.ids)-numnodes
print "configured for this pi by rejecting a certain engine"
#print sys.platform
if sys.platform=='darwin':
facedetector=cv2.CascadeClassifier("/usr/local/Cellar/opencv/2.4.9/share/OpenCV/haarcascades/"+cascade)
elif sys.platform=='linux2':
facedetector=cv2.CascadeClassifier("/home/pi/opencv-2.4.9/data/haarcascades/"+cascade)
else:
print "something went wrong detecting the system"
stream=io.BytesIO()
with PiCamera() as cam:
cam.resolution=(width,height)
cam.start_preview()
time.sleep(2)
cam.capture(stream,"rgb")
cam.stop_preview()
stream.seek(0)
fwidth = (width + 31) // 32 * 32
fheight = (height + 15) // 16 * 16
img=np.fromstring(stream.getvalue(), dtype=np.uint8).reshape((fheight, fwidth, 3))[:height, :width, :]
print "got first image"
print "generating time distribution for image"
gen.dumptimes(img,scale=scale)
times=gen.loadtimes()
#scale=1.1
dview["scale"]=scale
for i in ippids:
c[i].execute('if sys.platform=="darwin": haarface=cv2.CascadeClassifier("/usr/local/Cellar/opencv/2.4.9/share/OpenCV/haarcascades/haarcascade_frontalface_alt2.xml")')
c[i].execute('if sys.platform=="linux2": haarface=cv2.CascadeClassifier("/home/pi/opencv-2.4.9/data/haarcascades/haarcascade_frontalface_alt2.xml")')
numsizes=len(times)
trans=[None]*numsizes
for i in range(numsizes):
trans[i]=int(20*(scale**i))
mintime=min(times)
times=[thistime-mintime for thistime in times]
print "predicting even distribution..."
def predictdist(numnodes,times,numsizes,c):
timepernode=sum(times)/numnodes
thresholds=[None]*(numnodes+1)
thresholds[0]=0
for i in range(1,numnodes+1):
j=thresholds[i-1]+1
while sum(times[thresholds[i-1]:j])<timepernode and j<numsizes:
j=j+1
#print j
#time.sleep(3)
if sum(times[thresholds[i-1]:j])-timepernode>timepernode-sum(times[thresholds[i-1]:j-1]):
j=j-1 #makes more even, but last few take longest...otherwise even but last is really short
thresholds[i]=j
thresholds[numnodes]=numsizes-1
for i in range(numnodes):
c[ippids[i]]['thismin']=tuple([trans[thresholds[i]]-1]*2)
c[ippids[i]]['thismax']=tuple([trans[thresholds[i+1]]+1]*2)
predictdist(numnodes,times,numsizes,c)
def findfaces(img,scale,serial):
starttime=time.time()
numnodes=len(ippids)
img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
dview['img']=img
dview.execute('img=img.copy();mypid=getmypid()')
#dview.execute("mypid=getmypid()")
runstate=open('runstate','w')
cPickle.dump(1,runstate)
runstate.close()
dview.execute("state=getrunstate()") #may be redundant but avoids a lock
#for i in range(numnodes):
# print c[c.ids[i]]["state"]
dview.execute("if mypid and not state: os.kill(mypid,signal.SIGTSTP)")
dview.execute("exectime=time.time();myfaces=haarface.detectMultiScale(img,scale,4,1,thismin,thismax);exectime=time.time()-exectime;endtime=time.time()",block=False)
faces=set()
thistimes=[None]*numnodes
thistimesremote=[None]*numnodes
for i in range(numnodes):
thistimes[i]=time.time()
print "checking engine "+str(ippids[i])
thisfaces=c[ippids[i]]["myfaces"]
thistimesremote[i]=c[ippids[i]]["exectime"]
print len(thisfaces)
if len(thisfaces)>0:
for face in thisfaces:
faces.add(tuple(face))
thistimes[i]=time.time()-thistimes[i]
print thistimes
print thistimesremote
duptime=time.time()
facelist=list(faces)
donttest=[]
finallist=[]
testlen=len(facelist)
origlist=facelist
testlennew=0
print "faces found: "
print len(origlist)
while not testlennew==testlen:
testlen=len(facelist)
for first in range(len(facelist)):
if first not in donttest:
for second in range(first+1,len(facelist)):
sub=facelist[first]
j=facelist[second]
var=abs(j[3]-sub[3])/2
if ((j[0]+j[3]/2-var<sub[0]+sub[3]/2) and (j[0]+j[3]/2+var>sub[0]+sub[3]/2)) and ((j[1]+j[2]/2-var<sub[1]+sub[2]/2) and (j[1]+j[2]/2+var>sub[1]+sub[2]/2)) and min([float(j[3])/sub[3],float(sub[3])/j[3]])>1.0/2.0:
donttest.append(first)
donttest.append(second)
#facelist.append(tuple([(j[0]+sub[0])/2,(j[1]+sub[1])/2,(j[2]+sub[2])/2,(j[3]+sub[3])/2]))
inter=tuple([(j[0]+sub[0])/2,(j[1]+sub[1])/2,(j[2]+sub[2])/2,(j[3]+sub[3])/2])
finallist.append(inter)
break
if first not in donttest:
finallist.append(facelist[first])
testlennew=len(finallist)
facelist=finallist
finallist=[]
donttest=[]
finallist=facelist
duptime=time.time()-duptime
print "faces found after removing likely duplicates: "
print len(finallist)
starttime=time.time()-starttime
print "min/avg/max/regtime/tottime: "+str(min(thistimesremote))+"/"+str(sum(thistimesremote)/numnodes)+"/"+str(max(thistimesremote))+"/"+str(starttime)+"/"+str(sum(thistimesremote))
if serial=='yes':
serialtime=time.time()
facedetector.detectMultiScale(img,scale,4,1)
serialtime=time.time()-serialtime
print serialtime
dview.execute("if mypid: os.kill(mypid,signal.SIGCONT)")
#for i in range(numnodes):
# print c[c.ids[i]]['mypid']
runstate=open('runstate','w')
cPickle.dump(0,runstate)
runstate.close()
return finallist
def stdtrigger(img,framenum=1,threshold=threshold,message=False):
try:
global avg
global std
global xstart
xstart=width/4
global ystart
ystart=height/4
global sumlist
except:
sys.exit("error getting globals in stdtrigger")
if message:
if message=="update":
sumlist[0]=img[xstart:(xstart*3),ystart:(ystart*3),:].sum()
std=stats.tstd(sumlist)
avg=sum(sumlist)/n
return
sumlist=[None]*n
def calibcam(n,sumlist):
stream=io.BytesIO()
for i in range(n):
yield stream
stream.seek(0)
fwidth = (width + 31) // 32 * 32
fheight = (height + 15) // 16 * 16
#starttime=time.time()
img=np.fromstring(stream.getvalue(), dtype=np.uint8).reshape((fheight, fwidth, 3))[:height, :width, :]
#print str((time.time()-starttime)*1000)+" ms for reading the image"
#xstart=img.shape[0]/4
#ystart=img.shape[1]/4
#starttime=time.time()
sumlist[i]=img[xstart:(xstart*3),ystart:(ystart*3),:].sum()
#print str((time.time()-starttime)*1000)+" ms"
stream.seek(0)
stream.truncate()
sys.stdout.write("\r"+str(float(i+1)*100/n)+"% ")
sys.stdout.flush()
print "calibrating stdtrigger"
with PiCamera() as cam:
cam.resolution=(width,height)
cam.framerate=80
time.sleep(.3)
cam.capture_sequence(calibcam(n,sumlist),"rgb",use_video_port=True)
std=stats.tstd(sumlist)
avg=sum(sumlist)/n
return
thisz=(img[xstart:(xstart*3),ystart:(ystart*3),:].sum()-avg)/std
#print thisz
if abs(thisz)>threshold:
print "\nsomething weird, zscore="+str(thisz)+" at "+time.strftime("%a, %d %b %H:%M:%S", time.localtime())
return True
if framenum%10==0:
sumlist[0]=img[xstart:(xstart*3),ystart:(ystart*3),:].sum()
std=stats.tstd(sumlist)
avg=sum(sumlist)/n
return False
framenum=0
oldids=ippids
print "calibrating chosen trigger..."
stdtrigger(None,message="init")
print "\nstarting detection"
with PiCamera() as cam:
cam.resolution=(width,height)
cam.framerate=80
time.sleep(.3)
print "creating stream"
stream=io.BytesIO()
print "created stream, entering loop"
imgstarttime=time.time()
for foo in cam.capture_continuous(stream,"rgb",use_video_port=True):
#print str((time.time()-imgstarttime)*1000)+" ms to get image"
imgtotaltime=(time.time()-imgstarttime)*1000
loopstarttime=time.time()
stream.seek(0)
fwidth = (width + 31) // 32 * 32
fheight = (height + 15) // 16 * 16
#print "getting image"
img = np.fromstring(stream.getvalue(), dtype=np.uint8).reshape((fheight, fwidth, 3))[:height, :width, :]
#print "got the image, analyzing it"
if stdtrigger(img,framenum,threshold):
print "searching for faces..."
newimg=img.copy()
faces=findfaces(newimg,scale,serial)
if len(faces)>0:
print "FOUND A FACEZ!!!!!11!"
if show==True:
for (x,y,h,w) in faces:
cv2.rectangle(newimg,(x,y),(x+w,y+h),(0,255,255),1)
cv2.imshow("obj found",newimg)
else:
print "no facez :("
if show==True:
cv2.imshow("obj not found",newimg)
if show==True:
cv2.waitKey(1)
stdtrigger(img,message="update")
framenum=framenum+1
if framenum%(10*numnodes)==0:
if not len(c.ids)-len(oldids)==rejects:
print "cluster changed"
numnodes=len(oldids)
dview=c[:]
print "repredicting even distribution..."
for id in c.ids:
if id not in oldids:
c[id].execute('import cv2,sys,time,math,os,cPickle,signal;myfaces=[]')
c[id]['getrunstate']=getrunstate
c[id]['getmypid']=getmypid
c[id]['scale']=scale
c[id].execute('haarface=cv2.CascadeClassifier("/home/pi/opencv-2.4.9/data/haarcascades/haarcascade_frontalface_alt2.xml")')
#else:
# c[id].execute('myfaces=[]')
#oldids=c.ids
#c.spin()
#trying reseting the client
c=Client(profile=profile)
dview=c[:]
myippid=-1
for proc in psutil.process_iter():
if proc.name()=='ipengine':
myippid=proc.pid
ippids=[]
dview.execute("import os;enginepid=os.getpid()")
for i in range(len(c.ids)):
if not myippid==c[c.ids[i]]['enginepid']:
ippids.append(c.ids[i])
dview=c.activate(ippids)
numnodes=len(ippids)
oldids=ippids
rejects=len(c.ids)-numnodes
predictdist(numnodes,times,numsizes,c)
stream.seek(0)
stream.truncate(0)
looptotaltime=(time.time()-loopstarttime)*1000
detectfreq=round(1000/(looptotaltime+imgtotaltime),2)
sys.stdout.write("\r"+str(int(looptotaltime))+" ms for detection, "+str(int(imgtotaltime))+" ms for image, running at "+str(detectfreq)+" hz ")
sys.stdout.flush()
imgstarttime=time.time()
