def firebase_add_user(userid,name,password,email):
url = URL_ROOT + "/users/%s" %(userid)
dict_user = {}
dict_user["name"] = name
dict_user["password"] = password
dict_user["email"] = email
firebase.patch(url,dict_user)
print "add_user with userid:%s,username:%s, password:%s, email:%s" %(userid,name,password,email)
def firebase_add_user(userid, name, password, email):
url = URL_ROOT + "/users/%s" % (userid)
dict_user = {}
dict_user["name"] = name
dict_user["password"] = password
dict_user["email"] = email
firebase.patch(url, dict_user)
print "add_user with userid:%s,username:%s, password:%s, email:%s" % (
userid, name, password, email)
def syncScreens():
global CONNECTIONS, prevResp, landscape
_screens = firebase.firebaseURL(APP_NAME + "/screens")
resp = firebase.get(_screens)
if resp is not None:
CONNECTIONS = len(resp) - 1
if len(resp) != len(prevResp):
print "Number of connections: ", CONNECTIONS
print resp
if CONNECTIONS >= 1:
if "innerWidth" in resp[-1].keys():
landscape = resp[-1]["innerWidth"] > resp[-1]["innerHeight"]
prevResp = resp
for idx, screen in enumerate(screens):
if idx > 0:
s = firebase.firebaseURL(APP_NAME + "/screens/" + str(idx))
center, dims, rotation = screen
firebase.patch(s, {u'center': {u'x':int(center[0]), u'y':int(center[1])}, u'dims': {u'h':int(dims[1]), u'w':int(dims[0])}, u'rotation': int(rotation)})
def firebase_add_device(userid, devicename):
url = URL_ROOT + "/devices/%s" % (userid)
dict_device = {}
dict_device[devicename] = 0
firebase.patch(url, dict_device)
print "add_device with userid:%s,devicename:%s" % (userid, devicename)
return -1, -1
return None, None
def drawScreens(frame):
#print screens
for idx, screen in enumerate(screens):
if idx > 0:
rect = (tuple(screen[0]), tuple(screen[1]), screen[2])
box = np.int0(boxPoints(rect))
fillConvexPoly(frame, box, colors[idx % len(colors)])
if __name__ == "__main__":
root = firebase.firebaseURL(APP_NAME)
firebase.patch(root, {u'FRAME_WIDTH': FRAME_WIDTH , u'FRAME_HEIGHT': FRAME_HEIGHT, u'RELOAD': -1, u'valid': "no"})
_screens = firebase.firebaseURL(APP_NAME + "/screens")
firebase.put(_screens, {u'0': 'none'})
capture = VideoCapture();
capture.open(0);
#set height and width of capture frame
capture.set(4,FRAME_WIDTH);
capture.set(5,FRAME_HEIGHT);
#start an infinite loop where webcam feed is copied to cameraFeed matrix
#all of our operations will be performed within this loop
firstFrame = None
while True:
if COUNTER == 0:
thread.start_new_thread(syncScreens, ())
#store image to matrix
def run(source=0, source2=0, source3=0):
# ---- Initializing DNN -----
from sklearn.preprocessing import normalize
caffe.set_mode_gpu()
# cap=cv2.VideoCapture('../../vd1.mp4')
#net = caffe.Net('../../segnet_inference.prototxt','../../segnet_iter_10000.caffemodel',caffe.TEST)
#net = caffe.Net('../../enet_deploy_final.prototxt','../../segnet_iter_32000.caffemodel',caffe.TEST)
net = caffe.Net('../../enet_deploy_final.prototxt','../../segnet_iter_55000.caffemodel',caffe.TEST)
transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
background = [255,0,0]
car = [0,255,0]
numberplate = [0,0,255]
label_colours = np.array([background, car, numberplate])
label_colour = np.array([2, 1, 0])
cropped=[]
resizeimg=[]
# ---- End of Initializing DNN -----
# ---- Initializing Tracker -----
# initializing yolo
options = {"model": "cfg/tiny-yolo-voc.cfg", "load": "tiny-yolo-voc.weights", "threshold": 0.5} # , "gpu":1.0
tfnet = TFNet(options)
#----violation detection intial data starts----
temp_count = 0
pre_plate=''
URL = "fypandroid-gpu"
slot1_violation = "no"
slot2_violation = "no"
slot1_occupancy = "free"
slot2_occupancy = "free"
slot1_violation_new = "no"
slot2_violation_new = "no"
slot1_occupancy_new = "free"
slot2_occupancy_new = "free"
update_na = 0
slot1_num = "n/a"
slot2_num = "n/a"
updateonce = 0
firebase.patch(URL, {'Slot_01_vehicle_number': "n/a"})
firebase.patch(URL, {'Slot_02_vehicle_number': "n/a"})
imgavgback = cv2.imread(avgbackgrndpath, 1)
imgavgbackori = imgavgback.copy()
imgavgbackorigray = cv2.cvtColor(imgavgback,cv2.COLOR_BGR2GRAY)
imgavgback = imgavgback[80:400, 200:595]
imgrayavgback = cv2.cvtColor(imgavgback,cv2.COLOR_BGR2GRAY)
imgavgback -= 20
#emptyavg1 = np.mean(imgavgbackorigray[100:180,400:470])
#emptyavg2 = np.mean(imgavgbackorigray[205:300,400:470])
MidPoints = []
MidPoints.append([250,55]) #x,y slot 1
MidPoints.append([255,170]) #x,y slot 2
xyminmax = []
xyminmax.append([135,390,5,120]) #xmin,xmax,ymin,ymax slot 1
xyminmax.append([135,390,115,235]) #xmin,xmax,ymin,ymax slot 2
#cap = cv2.VideoCapture('cam1.mp4')
#----violation detection intial data ends----
# Create the VideoCapture object
cam = cv2.VideoCapture(source)
cam2 = cv2.VideoCapture(source2)
cap = cv2.VideoCapture(source3)
# If Camera Device is not opened, exit the program
if not cam.isOpened():
print "Video device 1 or file 1 couldn't be opened"
exit()
if not cam2.isOpened():
print "Video device 2 or file 2 couldn't be opened"
exit()
if not cap.isOpened():
print "Video device 3 or file 3 couldn't be opened"
exit()
retval, img = cam.read()
retval2, img2 = cam2.read()
ret,frame = cap.read()
if not retval:
print "Cannot capture frame device 1"
exit()
if not retval2:
print "Cannot capture frame device 2"
exit()
if not ret:
print "Cannot capture frame device 3"
exit()
cv2.namedWindow("Image", cv2.WINDOW_NORMAL)
cv2.namedWindow("Image2", cv2.WINDOW_NORMAL)
cv2.namedWindow("frame", cv2.WINDOW_NORMAL)
cv2.imshow("Image", img)
cv2.imshow("Image2", img2)
cv2.imshow("frame", frame)
# values for tracking
threshld = 10
height = 480 -threshld
width = 640 -threshld
limit = height - 20
tryframes = 8
points = []
points2 = []
# Create the tracker object
tracker = [dlib.correlation_tracker() for _ in xrange(0)]
tracker2 = [dlib.correlation_tracker() for _ in xrange(0)]
# Provide the tracker the initial position of the object
# [tracker[i].start_track(img, dlib.rectangle(*rect)) for i, rect in enumerate(points)]
for k, rect in enumerate(points):
tracker[k].start_track(img, dlib.rectangle(*rect))
for k2, rect in enumerate(points2):
tracker[k2].start_track(img2, dlib.rectangle(*rect))
temp_tracker = list(tracker) #to remove the deleted trackers from tracker without affecting for loop
temp_tracker2 = list(tracker2)
trigerYolo = 0
justdetected = 100
licenseplates = []
licenseplates2 = []
predetect = False
# time.sleep(5)
# ---- End Initializing Tracker -----
# count = 0 # facilitate to set the flag to start tracking (temporary) ****************
# refimg=cv2.imread("111.jpg",0)
# retref, refthresh = cv2.threshold(refimg, -1, 255,0)
# refimg, refcontours, refhierarchy = cv2.findContours(refthresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
# refcnt = refcontours[0]
while True:
# count += 1 # facilitate to set the flag to start tracking (temporary) ****************
# print count
# print "----- "+str(justdetected)+" --"
# ---- DNN starts -----
# time.sleep(.5)
start=time.time()
# Read frame from device or file
retval, img = cam.read()
retval2, img2 = cam2.read()
ret,frame=cap.read()
img = cv2.resize(img, (640,480))
img2 = cv2.resize(img2, (640,480))
s=cv2.resize(frame,(480,360))
img2ori = img2.copy()
if not retval:
print "Cannot capture frame device 1 | CODE TERMINATION....."
exit()
if not retval2:
print "Cannot capture frame device 2 | CODE TERMINATION....."
exit()
if not ret:
print "Cannot capture frame device 3 | CODE TERMINATION....."
exit()
justdetected += 1
if justdetected >100 :
justdetected =100
start_violation = 0
start_tracking = False
if justdetected > 4:
#ret=cap.set(cv2.CAP_PROP_FPS,4)
#ret=cap.set(3,360)
#ret=cap.set(4,480)
#frame = cv2.imread("myimage.png",1)
transformer.set_transpose('data', (2,0,1))
transformer.set_channel_swap('data', (2,1,0)) # if using RGB instead of BGR
transformer.set_raw_scale('data', 255.0)
net.blobs['data'].reshape(1,3,360,480)
net.blobs['data'].data[...] = transformer.preprocess('data', s)
output = net.forward()
# print("---%s in sec"%(time.time()-start))
predicted = net.blobs['prob'].data
output = np.squeeze(predicted[0,:,:,:])
ind = np.argmax(output, axis=0)
p = ind.copy()
d = ind.copy()
r = ind.copy()
g = ind.copy()
b = ind.copy()
for l in range(0,3):
p[ind==l] = label_colour[l]
cv2.imwrite("ind.png", p)
ind = cv2.imread("ind.png",0)
ctimage, contours, hierarchy = cv2.findContours(ind,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
# Vimgc = cv2.drawContours(s, contours, -1, (0,0,255), 3)
#label_colours = np.array([Sky, Building, Pole])
for l in range(0,3):
r[ind==l] = label_colours[l,0]
g[ind==l] = label_colours[l,1]
b[ind==l] = label_colours[l,2]
# cv2.imshow('framex',d)
rgb = np.zeros((ind.shape[0], ind.shape[1], 3))
rgb[:,:,0] = r/255.0
rgb[:,:,1] = g/255.0
rgb[:,:,2] = b/255.0
#plt.figure()
#plt.imshow(rgb,vmin=0, vmax=1)
#plt.show()
# im = cv2.imread(sys.argv[1])
# im_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY) / 255.
#f = numpy.load(sys.argv[2])
#param_vals = [f[n] for n in sorted(f.files, key=lambda s: int(s[4:]))]
# letter_probs = detect(im_gray, param_vals)
#code = letter_probs_to_code(letter_probs)
# print("Number Plate ->",code)
#pre_plate=''
detectnow = False
NoAllVContours = len(hierarchy[0])
# print NoAllVContours
if(NoAllVContours<6):
for i in range(NoAllVContours):
M = cv2.moments(contours[i])
# print M['m00']
# print "#############################################################################################################################"
# print cv2.matchShapes(refcnt,contours[i],1,0.0)
if (5000>M['m00']>3000):
if (int(M['m01']/M['m00'])>170):
# if ( cv2.matchShapes(refcnt,contours[i],1,0.0) < 0.7):
x,y,w,h=cv2.boundingRect(contours[i])
cropped = s[y:y+h, x:x+w]
resizeimg=cv2.resize(cropped,(256,32))
cv2.imshow('framerty',resizeimg)
im_gray = cv2.cvtColor(resizeimg, cv2.COLOR_BGR2GRAY) / 255.
start=time.time()
f = np.load("weights.npz")
param_vals = [f[n] for n in sorted(f.files, key=lambda s: int(s[4:]))]
letter_probs = detect(im_gray, param_vals)
code = letter_probs_to_code(letter_probs)
print("---%s in sec"%(time.time()-start))
#tempcode = code
if code[0] == "Z":
code = code[1:]
print("Number Plate ->",code)
detectnow = True
if (detectnow & predetect) : #((pre_plate == code) & (temp_count == 1)):
temp_count = 0
start_tracking = True
justdetected = 0
with open('GUI/VehicleNumber.csv','w') as f:
writer=csv.writer(f)
writer.writerow([code])
else:
if (pre_plate!=code):
temp_count = 1
pre_plate=code
# elif(cv2.matchShapes(refcnt,contours[i],1,0.0)==0.0):
# cv2.imshow('framex',rgb)
# cv2.imwrite("sd.png", rgb)
# print pre_plate
predetect = detectnow
# print licenseplates
#print cap.get(cv2.CAP_PROP_FPS)
#print frame.shape
cv2.imshow('frame2',rgb)
if cv2.waitKey(1) & 0xff== ord('q'):
break
# DNN sets the flag to start tracking
# if count == 36: # facilitate to set the flag to start tracking (temporary) ****************
# start_tracking = True
# print licenseplates
# ---- DNN stops -----
# ---- Tracker starts -----
# print temp_tracker
tracker = list(temp_tracker)
tracker2 = list(temp_tracker2)
new_points = []
if trigerYolo > 0: # if detection was not happened for the last frame
yoloresult = tfnet.return_predict(img)
for detectObj in yoloresult:
if (inRegion(detectObj)):
vehicleloc = (detectObj['topleft']['x'],detectObj['topleft']['y']+20,detectObj['bottomright']['x'],detectObj['bottomright']['y']+10)
new_points.append(vehicleloc)
licenseplates.append(code)
print ("Tracking started for the request")
trigerYolo = 0
break
print("Vehicle detection fails for the request for the next frame as well.")
trigerYolo -= 1
cv2.imwrite('track/im_'+str(trigerYolo)+'.png', img)
if(start_tracking): #new vehicle trying to add if "p" pressed
cv2.imwrite('track/im_'+str(trigerYolo)+'.png', img) # to save photos at tracking
# yolo detection
yoloresult = tfnet.return_predict(img)
for detectObj in yoloresult:
if (inRegion(detectObj)):
vehicleloc = (detectObj['topleft']['x'],detectObj['topleft']['y']+20,detectObj['bottomright']['x'],detectObj['bottomright']['y']+10)
new_points.append(vehicleloc)
licenseplates.append(code)
# print licenseplates
print ("Tracking started for the request")
break # only one vehicle will be added per one request *************** optimize this to reduce issues with multi vehicles trying to enter at the same time
if (len(new_points)<1):
print "no vehicle found in frame 1: trying few other frames triggered"
trigerYolo = tryframes # trying few frames to capture a vehicle if not detected from first few frames
if len(new_points) > 0: # if there is a new detected vehicled
# Create the tracker object
new_tracker = [dlib.correlation_tracker() for _ in xrange(len(new_points))]
# Provide the tracker the new positions of the object
for j, rect2 in enumerate(new_points):
new_tracker[j].start_track(img, dlib.rectangle(*rect2))
# [tracker[j].start_track(img, dlib.rectangle(*rect)) for j, rect in enumerate(points)]
tracker.extend(new_tracker)
print "Success: Tracking started for the newly entered vehicle."
temp_tracker = list(tracker)
# Update the tracker
for i in xrange(len(tracker)): #for number of objects to track
confdnc = tracker[i].update(img)
print confdnc
# Get the position of th object, draw a
# bounding box around it and display it.
rect = tracker[i].get_position()
pt1 = (int(rect.left()), int(rect.top()))
pt2 = (int(rect.right()), int(rect.bottom()))
if (pt1[0]< threshld or pt1[1]< threshld or pt2[0]>=width):
del licenseplates[i]
del temp_tracker[i]
print "Object removed: out of the frame/ unexpected behavior"
continue
if (pt2[1]>= limit):
licenseplates2.append(licenseplates[i])
del licenseplates[i]
del temp_tracker[i]
print "Object removed: vehicle enters next view"
new_points2 = [(10, 150, 120, 260)]
# new_tracker2 = dlib.correlation_tracker()
# # Provide the tracker the new positions of the object
# new_tracker2.start_track(img2, dlib.rectangle(*new_points2[0]))
# # [tracker[j].start_track(img, dlib.rectangle(*rect)) for j, rect in enumerate(points)]
# tracker2.append(new_tracker2)
new_tracker2 = [dlib.correlation_tracker() for _ in xrange(len(new_points2))]
# Provide the tracker the new positions of the object
for j2, rect2_2 in enumerate(new_points2):
new_tracker2[j2].start_track(img2, dlib.rectangle(*rect2_2))
# [tracker[j].start_track(img, dlib.rectangle(*rect)) for j, rect in enumerate(points)]
tracker2.extend(new_tracker2)
print "Success: Tracking started from view 2."
continue
# if (confdnc < 3.5):
# print "Low Confidence"
# # continue
cv2.rectangle(img, pt1, pt2, (255, 255, 255), 3)
# print "Object {} tracked at [{}, {}] \r".format(i, pt1, pt2),
# show location of box if mentionedd
loc = (int(rect.left()), int(rect.top()-20))
txt = "Vehicle : [{}]".format(licenseplates[i])
cv2.putText(img, txt, loc , cv2.FONT_HERSHEY_SIMPLEX, .5, (255,255,255), 1)
temp_tracker2 = list(tracker2)
for i2 in xrange(len(tracker2)): #for number of objects to track
confdnc2 = tracker2[i2].update(img2)
rect_2 = tracker2[i2].get_position()
pt1_2 = (int(rect_2.left()), int(rect_2.top()))
pt2_2 = (int(rect_2.right()), int(rect_2.bottom()))
cv2.rectangle(img2, pt1_2, pt2_2, (255, 255, 255), 3)
loc = (int(rect_2.left()), int(rect_2.top()-20))
txt = "Vehicle : [{}]".format(licenseplates2[i2])
cv2.putText(img2, txt, loc , cv2.FONT_HERSHEY_SIMPLEX, .5, (255,255,255), 1)
if rect_2.right() >200:
dist1 = ((MidPoints[0][0]- ((pt1_2[0]+pt2_2[0])/2))**2+(MidPoints[0][1]-((pt1_2[1]+pt2_2[1])/2))**2)
dist2 = ((MidPoints[1][0]- ((pt1_2[0]+pt2_2[0])/2))**2+(MidPoints[1][1]-((pt1_2[1]+pt2_2[1])/2))**2)
if dist1<dist2:
if slot1_num != txt:
firebase.patch(URL, {'Slot_01_vehicle_number': licenseplates2[i2]})
slot1_num = txt
if len(tracker2) == 1:
firebase.patch(URL, {'Slot_02_vehicle_number': "n/a"})
else:
if slot2_num != txt:
firebase.patch(URL, {'Slot_02_vehicle_number': licenseplates2[i2]})
slot2_num = txt
if len(tracker2) == 1:
firebase.patch(URL, {'Slot_01_vehicle_number': "n/a"})
start_violation = 1
update_na = 1
else:
start_violation = 0
if update_na == 1:
firebase.patch(URL, {'Slot_01_vehicle_number': "n/a"})
firebase.patch(URL, {'Slot_02_vehicle_number': "n/a"})
update_na = 0
cv2.namedWindow("Image", cv2.WINDOW_NORMAL) # ***** commented *******
# cv2.namedWindow("Image2", cv2.WINDOW_NORMAL)
cv2.imshow("Image", img)
cv2.imshow("Image2", img2)
cv2.imshow('frame',s)
# ---- end of tracker -----
# ---- Violation detection starts ----
#print "qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq" + str(start_violation)
if start_violation == 1:
#retpark, carpark = cam2.read()
carpark = img2ori
# carpark = cv2.resize(carpark, (640,480)) # Width,Height
#carparkori = carpark.copy()
#carparkgrayori = cv2.cvtColor(carpark,cv2.COLOR_BGR2GRAY)
#vehavg1 = np.mean(carparkgrayori[100:180,400:470])
#vehavg2 = np.mean(carparkgrayori[205:300,400:470])
updateonce = 0
carpark = carpark[80:400, 200:595]
carparkgray = cv2.cvtColor(carpark,cv2.COLOR_BGR2GRAY)
#cv2.imshow('frame',frame)
#if cv2.waitKey(1) & 0xFF == ord('q'):
#break
vehicles = cv2.subtract(carpark, imgavgback)
#vehicles = carpark - imgavgback
#vehiclesgray = imgavgbackgray - imgrayavgback
vehiclesgray = cv2.cvtColor(vehicles,cv2.COLOR_BGR2GRAY)
#retv,vehiclesgray = cv2.threshold(imgrayavgback,50,255,cv2.THRESH_BINARY)
blur = cv2.GaussianBlur(vehiclesgray,(5,5),0)
ret3,vehiclesgray = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
ret1,vehiclesgray = cv2.threshold(blur,ret3-25,255,cv2.THRESH_BINARY)
#ret2,vehiclesgray = cv2.threshold(vehiclesgray,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
#vehiclesgray = cv2.adaptiveThreshold(vehiclesgray,255,cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY,11,2)
#vehiclesgray = cv2.adaptiveThreshold(vehiclesgray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,11,2)
vcontimage, vehicontours, vehihierarchy = cv2.findContours(vehiclesgray,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
NoAllContours = len(vehihierarchy[0])
#print(NoAllContours)
#print(vehicles)
#Getting the parent Vcontour numbers
VehicleNo = []
#Mid points of vehicles
VehicleMid = []
for i in range(NoAllContours):
#if (hierarchy[0,i,3]==-1):
M = cv2.moments(vehicontours[i])
if ((M['m00']>3000)):
VehicleNo.append(i)
cx = int(M['m10']/M['m00'])
cy = int(M['m01']/M['m00'])
VehicleMid.append([cx,cy])
#Number of all child countours
NumOfVehicles = len(VehicleNo)
#for i in range(NumOfVehicles):
#Vimgc = cv2.drawContours(Pimg, contours, VehicleNo[i] , (0,0,255), 3)
if (NumOfVehicles==0):
imgavgbackori[100:180,400:470] = (0, 255, 0)
imgavgbackori[205:300,400:470] = (0, 255, 0)
if slot1_occupancy != slot1_occupancy_new:
firebase.patch(URL, {'Slot_01_occupancy_status': 'free'})
slot1_occupancy = slot1_occupancy_new
if slot2_occupancy != slot2_occupancy_new:
firebase.patch(URL, {'Slot_02_occupancy_status': 'free'})
slot2_occupancy = slot2_occupancy_new
if slot1_violation != slot1_violation_new:
firebase.patch(URL, {'Slot_01_violations': 'no'})
slot1_violation = slot1_violation_new
if slot2_violation != slot2_violation_new:
firebase.patch(URL, {'Slot_02_violations': 'no'})
slot2_violation = slot2_violation_new
#if ((abs(vehavg1-emptyavg1))>30):
#img[100:180,400:470] = (0, 0, 255)
#else:
#img[100:180,400:470] = (0, 255, 0)
#if ((abs(vehavg2-emptyavg2))>30):
#img[205:300,400:470] = (0, 0, 255)
#else:
#img[205:300,400:470] = (0, 255, 0)
#if (NumOfVehicles>0):
else:
counter = 0
#error = 0
for i in VehicleNo:
NoIndices = len(vehicontours[i])
vehix = []
vehiy = []
for j in range(NoIndices):
vehix.append(vehicontours[i][j][0,0])
vehiy.append(vehicontours[i][j][0,1])
#Vehicle Contour min max coordinate values
xmin = min(vehix)
xmax = max(vehix)
ymin = min(vehiy)
ymax = max(vehiy)
distance = []
for k in range(2):
vehid = ((MidPoints[k][0]-VehicleMid[counter][0])**2+(MidPoints[k][1]-VehicleMid[counter][1])**2)
distance.append(vehid)
parkingslot = distance.index(min(distance))
drawcon = 0
if (xmin < xyminmax[parkingslot][0]):
#error += 1
drawcon = 1
elif (xmax > xyminmax[parkingslot][1]):
#error += 1
drawcon = 1
elif (ymin < xyminmax[parkingslot][2]):
#error += 1
drawcon = 1
elif (ymax > xyminmax[parkingslot][3]):
#error += 1
drawcon = 1
if ((parkingslot == 0)&(drawcon == 1)):
veimgc = cv2.drawContours(carpark, vehicontours, i, (0,0,255), 3)
imgavgbackori[100:180,400:470] = (255, 0, 0)
slot1_violation_new = "yes"
slot1_occupancy_new = "occupied"
if slot1_occupancy != slot1_occupancy_new:
firebase.patch(URL, {'Slot_01_occupancy_status': 'occupied'})
slot1_occupancy = slot1_occupancy_new
if slot1_violation != slot1_violation_new:
firebase.patch(URL, {'Slot_01_violations': 'yes'})
slot1_violation = slot1_violation_new
if (NumOfVehicles == 1):
imgavgbackori[205:300,400:470] = (0, 255, 0)
slot2_violation_new = "no"
slot2_occupancy_new = "free"
if slot2_occupancy != slot2_occupancy_new:
firebase.patch(URL, {'Slot_02_occupancy_status': 'free'})
slot2_occupancy = slot2_occupancy_new
if slot2_violation != slot2_violation_new:
firebase.patch(URL, {'Slot_02_violations': 'no'})
slot2_violation = slot2_violation_new
elif((parkingslot == 0)&(drawcon == 0)):
veimgc = cv2.drawContours(carpark, vehicontours, i, (0,255,0), 3)
imgavgbackori[100:180,400:470] = (0, 0, 255)
slot1_violation_new = "no"
slot1_occupancy_new = "occupied"
if slot1_occupancy != slot1_occupancy_new:
firebase.patch(URL, {'Slot_01_occupancy_status': 'occupied'})
slot1_occupancy = slot1_occupancy_new
if slot1_violation != slot1_violation_new:
firebase.patch(URL, {'Slot_01_violations': 'no'})
slot1_violation = slot1_violation_new
if (NumOfVehicles == 1):
imgavgbackori[205:300,400:470] = (0, 255, 0)
slot2_violation_new = "no"
slot2_occupancy_new = "free"
if slot2_occupancy != slot2_occupancy_new:
firebase.patch(URL, {'Slot_02_occupancy_status': 'free'})
slot2_occupancy = slot2_occupancy_new
if slot2_violation != slot2_violation_new:
firebase.patch(URL, {'Slot_02_violations': 'no'})
slot2_violation = slot2_violation_new
elif ((parkingslot == 1)&(drawcon == 1)):
veimgc = cv2.drawContours(carpark, vehicontours, i, (0,0,255), 3)
imgavgbackori[205:300,400:470] = (255, 0, 0)
slot2_violation_new = "yes"
slot2_occupancy_new = "occupied"
if slot2_occupancy != slot2_occupancy_new:
firebase.patch(URL, {'Slot_02_occupancy_status': 'occupied'})
slot2_occupancy = slot2_occupancy_new
if slot2_violation != slot2_violation_new:
firebase.patch(URL, {'Slot_02_violations': 'yes'})
slot2_violation = slot2_violation_new
if (NumOfVehicles == 1):
imgavgbackori[100:180,400:470] = (0, 255, 0)
slot1_violation_new = "no"
slot1_occupancy_new = "free"
if slot1_occupancy != slot1_occupancy_new:
firebase.patch(URL, {'Slot_01_occupancy_status': 'free'})
slot1_occupancy = slot1_occupancy_new
if slot1_violation != slot1_violation_new:
firebase.patch(URL, {'Slot_01_violations': 'no'})
slot1_violation = slot1_violation_new
elif((parkingslot == 1)&(drawcon == 0)):
veimgc = cv2.drawContours(carpark, vehicontours, i, (0,255,0), 3)
imgavgbackori[205:300,400:470] = (0, 0, 255)
slot2_violation_new = "no"
slot2_occupancy_new = "occupied"
if slot2_occupancy != slot2_occupancy_new:
firebase.patch(URL, {'Slot_02_occupancy_status': 'occupied'})
slot2_occupancy = slot2_occupancy_new
if slot2_violation != slot2_violation_new:
firebase.patch(URL, {'Slot_02_violations': 'no'})
slot2_violation = slot2_violation_new
if (NumOfVehicles == 1):
imgavgbackori[100:180,400:470] = (0, 255, 0)
slot1_violation_new = "no"
slot1_occupancy_new = "free"
if slot1_occupancy != slot1_occupancy_new:
firebase.patch(URL, {'Slot_01_occupancy_status': 'free'})
slot1_occupancy = slot1_occupancy_new
if slot1_violation != slot1_violation_new:
firebase.patch(URL, {'Slot_01_violations': 'no'})
slot1_violation = slot1_violation_new
counter += 1
cv2.imshow('thresh',veimgc)
else:
imgavgbackori[100:180,400:470] = (0, 255, 0)
imgavgbackori[205:300,400:470] = (0, 255, 0)
if updateonce == 0:
firebase.patch(URL, {'Slot_01_occupancy_status': 'free'})
firebase.patch(URL, {'Slot_02_occupancy_status': 'free'})
firebase.patch(URL, {'Slot_01_violations': 'no'})
firebase.patch(URL, {'Slot_02_violations': 'no'})
slot1_violation_new = 'no'
slot2_violation_new = 'no'
slot1_occupancy_new = 'free'
slot2_occupancy_new = 'free'
slot1_occupancy = slot1_occupancy_new
slot2_occupancy = slot2_occupancy_new
slot1_violation = slot1_violation_new
slot2_violation = slot2_violation_new
updateonce = 1
# if slot1_occupancy != slot1_occupancy_new:
# firebase.patch(URL, {'Slot_01_occupancy_status': 'free'})
# slot1_occupancy = slot1_occupancy_new
# if slot2_occupancy != slot2_occupancy_new:
# firebase.patch(URL, {'Slot_02_occupancy_status': 'free'})
# slot2_occupancy = slot2_occupancy_new
# if slot1_violation != slot1_violation_new:
# firebase.patch(URL, {'Slot_01_violations': 'no'})
# slot1_violation = slot1_violation_new
# if slot2_violation != slot2_violation_new:
# firebase.patch(URL, {'Slot_02_violations': 'no'})
cv2.imshow('image',imgavgbackori)
#cv2.imshow('imgrayneg',vehiclesgray)
# ---- Violation detection ends ----
# ---- Empty parking space detection starts ----
#ret, carpark = cam2.read()
#carparkgray = cv2.cvtColor(carpark, cv2.COLOR_BGR2GRAY)
##cv2.imshow('frame',frame)
#if cv2.waitKey(1) & 0xFF == ord('q'):
#break
#vehavg = np.mean(carparkgray[250:300,400:470])
#if abs(vehavg-emptyavg)>30:
#imgavgback[250:300,400:470] = (0, 0, 255)
#else:
#imgavgback[250:300,400:470] = (0, 255, 0)
#cv2.imshow('park',carpark)
#cv2.imshow('empty',imgavgback)
# ---- Empty parking space detection ends ----
# Relase the VideoCapture object
cam.release()
cam2.release()
cap.release()
cv2.destroyAllwindows()
def firebase_add_device(userid, devicename):
url = URL_ROOT + "/devices/%s" %(userid)
dict_device = {}
dict_device[devicename] = 0
firebase.patch(url,dict_device)
print "add_device with userid:%s,devicename:%s" %(userid,devicename)
