def main():
modelfile = open(sys.argv[2])
model = pickle.load(modelfile)
f = fe.FeatureExtractor(sys.argv[1])
f.setStartPoint()
detected = False
labels = model.labels
while 1:
try:
imbgr = np.array(fe.get_video())
imdepth = np.array(fe.get_depth())
f.addPoint(time.time(),imbgr,imdepth)
obs = np.nan_to_num(f.getFeatures())
prediction = model.detect(obs)
if prediction is not None:
cv2.putText(imbgr,labels[prediction],(5,50),cv2.FONT_HERSHEY_COMPLEX,2,(0,0,255),5)
print labels[prediction]
cv2.imshow("Demo",imbgr)
except KeyboardInterrupt:
break
if cv.WaitKey(10) == 32:
break
def main():
#autocalibrate(sys.argv[1])
f = extract.FeatureExtractor(sys.argv[1])
f.setStartPoint()
imbgr = np.array(extract.get_video())
mean = get_mean(imbgr,f)
while 1:
try:
imbgr = np.array(extract.get_video())
mask = get_mask(imbgr,mean)
print np.max(mask)
print np.min(mask)
mask = mask.astype('uint8')
print np.max(mask)
print np.min(mask)
mask = f.markers['right'].blobSmoothing(mask)
cv2.imshow('Demo', mask)
except KeyboardInterrupt:
break
if cv.WaitKey(10) == 27:
break
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:
_ = running.popleft()
running.append(imbgrclass)
sign = median(running)
f = fe.FeatureExtractor("calibration.csv")
else:
f = fe.FeatureExtractor(sys.argv[1])
# for i in range(10):
for i, rand_sign in enumerate([1, 5, 8, 7, 9, 0, 2, 6]):
try:
# rand_sign = np.random.randint(0,len(labels))
f.setStartPoint()
detectedSign = 0
countDown = 0
while detectedSign < 30:
try:
imbgr = np.array(fe.get_video())
img = np.copy(imbgr)
if i == 0 and countDown < 90:
imbgr = np.zeros((480, 640, 3))
if countDown < 30:
cv2.putText(imbgr, "3", (250, 250), cv2.FONT_HERSHEY_COMPLEX, 5, (255, 255, 255), 5)
elif countDown < 60:
cv2.putText(imbgr, "2", (250, 250), cv2.FONT_HERSHEY_COMPLEX, 5, (255, 255, 255), 5)
elif countDown < 90:
cv2.putText(imbgr, "1", (250, 250), cv2.FONT_HERSHEY_COMPLEX, 5, (255, 255, 255), 5)
cv2.imshow("Demo", imbgr)
countDown += 1
else:
if not detectedSign:
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
rightPast = deque([])
leftPast = deque([])
f = extract.FeatureExtractor(sys.argv[1])
f.setStartPoint()
#plt.ion()
#fig = plt.figure()
#plt.xlim([0,640])
#plt.ylim([0,255])
while 1:
try:
#cv.ShowImage('Depth', get_depth())
imbgr = np.array(extract.get_video())
imdepth = extract.get_depth()
greenmoments,greenhull = f.getCentralMoments(imbgr,'right')
redmoments,redhull = f.getCentralMoments(imbgr,'left')
cv2.drawContours(imbgr,[greenhull],-1,(0,255,0),2)
cv2.drawContours(imbgr,[redhull],-1,(0,0,255),2)
feature = np.nan_to_num(f.addPoint(time.time(),imbgr,imdepth))
print feature
if feature.shape:
cv2.circle(imbgr,(int(feature[14]),int(feature[15])),3,(0,0,255),4)
cv2.circle(imbgr,(int(feature[16]),int(feature[17])),3,(0,255,0),4)
#plt.scatter(feature[14],480 - feature[15],c='r')
