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
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:
imdepth = np.array(fe.get_depth())
if not detectedSign:
cv2.putText(
imbgr, labels[rand_sign], (5, 50), cv2.FONT_HERSHEY_COMPLEX, 2, (255, 255, 255), 5
)
v = f.addPoint(time.time(), imbgr, imdepth)
obs = np.nan_to_num(f.getFeatures())
detected = models.detect(obs, rand_sign)
if detected:
detectedSign = 1
# print feedback
if detectedSign and detectedSign < 30:
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')
#plt.scatter(feature[16],480 - feature[17],c='g')
