def applyFilter(self):
imycrcb = cv2.cvtColor(self.img,cv.CV_BGR2YCrCb)
lowx = min(self.pt1[0],self.pt2[0])
highx = max(self.pt1[0],self.pt2[0])
lowy = min(self.pt1[1],self.pt2[1])
highy = max(self.pt1[1],self.pt2[1])
y = []
cr = []
cb = []
for i in range(lowx,highx+1):
for j in range(lowy,highy+1):
y.append(imycrcb[i,j,0])
cr.append(imycrcb[i,j,1])
cb.append(imycrcb[i,j,2])
ymean = np.mean(y)
crmean = np.mean(cr)
cbmean = np.mean(cb)
ystd = np.std(y)
crstd = np.std(cr)
cbstd = np.std(cb)
print (ymean,crmean,cbmean)
print (ystd,crstd,cbstd)
self.filter = fe.colourFilter((ymean-ystd*2*delta,crmean-crstd*delta,cbmean-cbstd*delta),(ymean+ystd*2*delta,crmean+crstd*delta,cbmean+cbstd*delta))
hull = self.filter.getColourHull(self.img)
print self.filter.low
print self.filter.high
imgray = cv2.cvtColor(self.img,cv.CV_BGR2GRAY)
cv2.drawContours(imgray,[hull],-1,(255,0,0),2)
self.displayImage = imgray
def applyFilter(self):
imycrcb = cv2.cvtColor(self.img,cv.CV_BGR2YCrCb)
lowx = min(self.pt1[0],self.pt2[0])
highx = max(self.pt1[0],self.pt2[0])
lowy = min(self.pt1[1],self.pt2[1])
highy = max(self.pt1[1],self.pt2[1])
y = []
cr = []
cb = []
for i in range(lowx,highx+1):
for j in range(lowy,highy+1):
y.append(imycrcb[i,j,0])
cr.append(imycrcb[i,j,1])
cb.append(imycrcb[i,j,2])
ymean = np.mean(y)
crmean = np.mean(cr)
cbmean = np.mean(cb)
ystd = np.std(y)
crstd = np.std(cr)
cbstd = np.std(cb)
print (ymean,crmean,cbmean)
print (ystd,crstd,cbstd)
self.filter = fe.colourFilter((ymean-ystd*2*delta,crmean-crstd*delta,cbmean-cbstd*delta),(ymean+ystd*2*delta,crmean+crstd*delta,cbmean+cbstd*delta))
hull = self.filter.getColourHull(self.img)
print self.filter.low
print self.filter.high
imgray = cv2.cvtColor(self.img,cv.CV_BGR2GRAY)
cv2.drawContours(imgray,[hull],-1,(255,0,0),2)
self.displayImage = imgray
def getFeatures(folder):
features = []
with open(os.path.join(folder,"calibration.csv")) as csvfile:
reader = csv.reader(csvfile)
low = [ float(x) for x in reader.next()]
high = [ float(x) for x in reader.next()]
green = fe.colourFilter(tuple(low),tuple(high))
low = [ float(x) for x in reader.next()]
high = [ float(x) for x in reader.next()]
blue = fe.colourFilter(tuple(low),tuple(high))
for f in os.listdir(folder):
if os.path.splitext(f)[1] == ".ppm":
imbgr = cv2.imread(os.path.join(folder,f))
hull = green.getColourHull(imbgr)
features.append(fe.getFeatureVector(hull,['central']))
hull = blue.getColourHull(imbgr)
features.append(fe.getFeatureVector(hull,['central']))
return features
def get_error(self, imbgr,low,high,ideal,blue=False):
filt = fe.colourFilter(low,high)
if blue:
cnt = filt.getColourContours(imbgr)
feature = self.blue_contours(cnt)
else:
if len(filt.getColourContours(imbgr)) != 5:
return 20
hull = filt.getColourHull(imbgr)
if len(hull):
m = cv2.moments(hull)
feature = np.array([m['nu20'],m['nu11'],m['nu02'],m['nu30'],m['nu21'],m['nu12'],m['nu03']])
else:
feature = np.array([0,0,0,0,0,0,0])
dist = np.sqrt(np.sum(np.square(ideal-feature)))
return dist
if __name__ == "__main__":
modelfile = open(sys.argv[2])
model = pickle.load(modelfile)
labels = pickle.load(modelfile)
running = deque([])
with open(sys.argv[1]) as csvfile:
reader = csv.reader(csvfile)
low = [ float(x) for x in reader.next()]
high = [ float(x) for x in reader.next()]
green = fe.colourFilter(tuple(low),tuple(high))
while 1:
try:
imbgr = fe.get_video()
hull = green.getColourHull(imbgr)
if len(hull):
features = fe.getFeatureVector(hull,['central'])
imbgrclass = model.predict([features])
imbgrclass = imbgrclass[0]
if len(running) < 10:
sign = imbgrclass
running.append(sign)
else:
def autocalibration(self, calibration_file, calibration_folder):
g_m,r_m,b_m,c = self.get_features(calibration_folder)
got_green = False
got_blue = False
got_red = False
gcount = 0
bcount = 0
rcount = 0
frame_count = 0
glow,ghigh = self.get_start(c,'glow','ghigh')
blow,bhigh = self.get_start(c,'blow','bhigh')
rlow,rhigh = self.get_start(c,'rlow','rhigh')
while 1:
try:
imbgr = np.array(fe.get_video())
#print "green"
got_green,gcount,glow,ghigh = self.optimize(imbgr,got_green,gcount,glow,ghigh,g_m,c,"glow","ghigh",0.05)
#print "red"
got_red,rcount,rlow,rhigh = self.optimize(imbgr,got_red,rcount,rlow,rhigh,r_m,c,"rlow","rhigh",0.05)
#print "blue"
got_blue,bcount,blow,bhigh = self.optimize(imbgr,got_blue,bcount,blow,bhigh,b_m,c,"blow","bhigh",0.05,blue=True)
green = fe.colourFilter(glow,ghigh)
hull = green.getColourHull(imbgr)
if len(hull):
cv2.drawContours(imbgr,[hull],-1,(0,255,0),2)
red = fe.colourFilter(rlow,rhigh)
hull = red.getColourHull(imbgr)
if len(hull):
cv2.drawContours(imbgr,[hull],-1,(0,0,255),2)
blue = fe.colourFilter(blow,bhigh)
cnt = blue.getColourContours(imbgr)
if len(cnt) >= 2:
cnt_sorted = sorted(cnt,key=cv2.contourArea)
cv2.drawContours(imbgr,[cnt_sorted[-1]],-1,(255,0,0),2)
cv2.drawContours(imbgr,[cnt_sorted[-2]],-1,(255,0,0),2)
cv2.imshow("Demo",imbgr)
if got_green and got_red and got_blue:
frame_count += 1
except KeyboardInterrupt:
break
if cv.WaitKey(10) == 32:
break
if frame_count >= 30:
break
with open(calibration_file,'w') as csvfile:
writer = csv.writer(csvfile)
writer.writerow(glow)
writer.writerow(ghigh)
writer.writerow(blow)
writer.writerow(bhigh)
writer.writerow(rlow)
writer.writerow(rhigh)
return glow,ghigh,blow,bhigh,rlow,rhigh
