def setup_app(app):
print('Connecting to a DLI PowerSwitch at lpc.digital-loggers.com')
global controller
controller = MainController()
manager = DeviceManager(controller)
manager.start()
global pSuccess
if not controller.pSuccess:
pSuccess = 'not established'
global pList
pList = controller.get_list()
def main():
application = MainController()
application.geometry("700x500")
application.title("Finit State Machine")
frame = Frame(application, bg='#0555ff')
application.mainloop()
def __init__(self):
self.root = Tk()
self.root.resizable(width=False, height=False)
self.root.geometry("300x500")
self.root.title("What is Title")
self.root.configure(background="black")
self.mc = MainController.MainController()
frameList = {
'sign': SignUpFrame.SignUpFrame(self, self.root),
'main': MainFrame.MainFrame(self, self.root),
'selling': SellingFrame.SellingFrame(self, self.root),
'setting': SettingFrame.SettingFrame(self, self.root),
'login': LoginFrame.LoginFrame(self, self.root),
'room': RoomFrame.RoomFrame(self, self.root),
'watch': WatchlistFrame.WatchlistFrame(self, self.root)
}
self.mc.createHandler(frameList)
# self.mc.eventHandler['room'].clock.sem.acquire()
# Set the Frame for LoginFrame
self.mc.eventHandler.changeFrame(self.mc.frameList['login'].loginFrame)
self.frameList = frameList
self.root.mainloop()
self.quit()
def __init__(self, port, clientList):
threading.Thread.__init__(self)
self.port = port
self.clientList = clientList
self.mc = MainController.MainController(self.userList, self.threadList)
self.db = DBConnection.DBConnection()
self.threadList = []
def handle(sck, addr):
dataBuf = ""
startTime = time.time()
while True:
dataBuf += sck.recv(1024)
nowTime = time.time()
# 若达到包头长度则处理
if len(dataBuf) >= sCon.HEADER_SIZE:
# 读包头
startTime = time.time()
header = struct.unpack('>I4s', dataBuf[:sCon.HEADER_SIZE])
#print header
# 若达到body长度则处理
if len(dataBuf) >= sCon.HEADER_SIZE + header[0]:
body = dataBuf[sCon.HEADER_SIZE:sCon.HEADER_SIZE + header[0]]
# 分割body
# 获取body中各参数
bDic = body.split()
print "bDic:", bDic
bMap = {}
for value in bDic:
tDic = value.split(":")
if len(tDic) > 1:
bMap[tDic[0]] = tDic[1]
print "bMap:", bMap
# 数据包字典
data = {"Header": header, "Body": bMap}
MainController.GetInstance().Handle(sck, addr, data)
# 抛弃已经处理完的数据包
dataBuf = dataBuf[sCon.HEADER_SIZE + header[0]:]
else:
#raise NameError, "body's len is:%s not enough" % (len(dataBuf) - sCon.HEADER_SIZE)
continue
else:
if nowTime - startTime > sCon.ALIVE:
sck.close()
removeSocket(addr)
return
else:
#raise NameError, "data'len is:%s not enough" % len(dataBuf)
continue
from Controller.MainController import *
if __name__ == "__main__":
controller = MainController()
controller.run()
def detect(save_img):
out, source, weights, view_img, save_txt, imgsz = \
opt.output, opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
webcam = source.isnumeric() or source.startswith(
('rtsp://', 'rtmp://', 'http://')) or source.endswith('.txt')
# Initialize
set_logging()
cars = MainController.getLatestVehicleAmount('cars', 'carId')
motors = MainController.getLatestVehicleAmount('motorcycles',
'motorcycleId')
trucks = MainController.getLatestVehicleAmount('trucks', 'truckId')
totalCarAmount = cars + motors + trucks
totalCars = cars
totalTrucks = trucks
totalMotors = motors
displayTotalAmount = totalCarAmount
displayCarAmount = totalCars
displayTruckAmount = totalTrucks
displayMotorAmount = totalMotors
oldCombinedAmount = 0
combinedAmount = 0
tempAmount = 0
# Video = False, Webcam = True
control = False
elapsed = 0
device = select_device(opt.device)
if os.path.exists(out):
shutil.rmtree(out) # delete output folder
os.makedirs(out) # make new output folder
start = time.time()
half = device.type != 'cpu' # half precision only supported on CUDA
# Load model
model = attempt_load(weights, map_location=device) # load FP32 model
imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size
if half:
model.half() # to FP16
# Second-stage classifier
classify = False
if classify:
modelc = load_classifier(name='resnet101', n=2) # initialize
modelc.load_state_dict(
torch.load('weights/resnet101.pt',
map_location=device)['model']) # load weights
modelc.to(device).eval()
# Set Dataloader
vid_path, vid_writer = None, None
if webcam:
view_img = True
cudnn.benchmark = True # set True to speed up constant image size inference
dataset = LoadStreams(source, img_size=imgsz)
else:
save_img = True
dataset = LoadImages(source, img_size=imgsz)
# Get names and colors
names = model.module.names if hasattr(model, 'module') else model.names
# colors = [[np.randint(0, 255) for _ in range(3)] for _ in range(len(names))]
# Run inference
t0 = time.time()
ct = CentroidTracker()
listDet = ['car', 'motorcycle', 'truck']
totalDownCar = 0
totalDownMotor = 0
totalDownTruck = 0
totalUpCar = 0
totalUpMotor = 0
totalUpTruck = 0
trackableObjects = {}
img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img
_ = model(img.half() if half else img
) if device.type != 'cpu' else None # run once
for path, img, im0s, vid_cap in dataset:
elapsed = time.time() - start
img = torch.from_numpy(img).to(device)
img = img.half() if half else img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
# Inference
t1 = time_synchronized()
pred = model(img, augment=opt.augment)[0]
# Apply NMS
pred = non_max_suppression(pred,
opt.conf_thres,
opt.iou_thres,
classes=opt.classes,
agnostic=opt.agnostic_nms)
t2 = time_synchronized()
# Apply Classifier
if classify:
pred = apply_classifier(pred, modelc, img, im0s)
rects = []
labelObj = []
arrCentroid = []
# Process detections
for i, det in enumerate(pred): # detections per image
if webcam: # batch_size >= 1
p, s, im0 = path[i], '%g: ' % i, im0s[i].copy()
# cv2.resize(im0, (2560, 1440))
else:
p, s, im0 = path, '', im0s
# cv2.resize(im0, (2560, 1440))
height, width, channels = im0.shape
cv2.line(im0, (0, int(height / 1.5)),
(int(width), int(height / 1.5)), (255, 0, 0),
thickness=3)
if not control:
cv2.putText(im0,
'Totale koeretoejer: ' + str(displayTotalAmount),
(int(width * 0.02), int(height * 0.5)),
cv2.FONT_HERSHEY_SIMPLEX, 1, (50, 255, 255), 2)
cv2.putText(im0, 'Bil: ' + str(displayCarAmount),
(int(width * 0.02), int(height * 0.55)),
cv2.FONT_HERSHEY_SIMPLEX, .75, (50, 255, 255), 2)
cv2.putText(im0, 'Motorcykel: ' + str(displayMotorAmount),
(int(width * 0.02), int(height * 0.60)),
cv2.FONT_HERSHEY_SIMPLEX, .75, (50, 255, 255), 2)
cv2.putText(im0, 'Lastbil: ' + str(displayTruckAmount),
(int(width * 0.02), int(height * 0.65)),
cv2.FONT_HERSHEY_SIMPLEX, .75, (50, 255, 255), 2)
else:
cv2.putText(im0,
'Totale koeretoejer: ' + str(displayTotalAmount),
(int(width * 0.02), int(height * 0.5)),
cv2.FONT_HERSHEY_SIMPLEX, 3, (50, 255, 255), 3)
# cv2.line(im0, (int(width / 1.8), int(height / 1.5)), (int(width), int(height / 1.5)), (255, 127, 0), thickness=3)
save_path = str(Path(out) / Path(p).name)
txt_path = str(Path(out) / Path(p).stem) + (
'_%g' % dataset.frame if dataset.mode == 'video' else '')
s += '%gx%g ' % img.shape[2:] # print string
gn = torch.tensor(im0.shape)[[1, 0, 1,
0]] # normalization gain whwh
if det is not None and len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(img.shape[2:], det[:, :4],
im0.shape).round()
# Print results
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum() # detections per class
s += '%g %ss, ' % (n, names[int(c)]) # add to string
# Write results
for *xyxy, conf, cls in reversed(det):
label = '%s %.2f' % (names[int(cls)], conf)
# print(xyxy)
x = xyxy
tl = None or round(0.002 * (im0.shape[0] + im0.shape[1]) /
2) + 1 # line/font thickness
c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
label1 = label.split(' ')
if save_txt: # Write to file
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) /
gn).view(-1).tolist() # normalized xywh
with open(txt_path + '.txt', 'a') as f:
f.write(('%g ' * 5 + '\n') %
(cls, *xywh)) # label format
if label1[0] in listDet:
cv2.rectangle(im0,
c1,
c2, (0, 0, 0),
thickness=tl,
lineType=cv2.LINE_AA)
box = (int(x[0]), int(x[1]), int(x[2]), int(x[3]))
rects.append(box)
labelObj.append(label1[0])
tf = max(tl - 1, 1)
t_size = cv2.getTextSize(label,
0,
fontScale=tl / 3,
thickness=tf)[0]
c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
cv2.rectangle(im0, c1, c2, (0, 100, 0), -1,
cv2.LINE_AA)
cv2.putText(im0,
label, (c1[0], c1[1] - 2),
0,
tl / 3, [225, 255, 255],
thickness=tf,
lineType=cv2.LINE_AA)
detCentroid = generateCentroid(rects)
objects = ct.update(rects)
for (objectID, centroid) in objects.items():
arrCentroid.append(centroid[1])
for (objectID, centroid) in objects.items():
# print(idxDict)
to = trackableObjects.get(objectID, None)
if to is None:
to = TrackableObject(objectID, centroid)
else:
y = [c[1] for c in to.centroids]
direction = centroid[1] - np.mean(y)
to.centroids.append(centroid)
if not to.counted: # arah up
if direction < 0 and centroid[
1] < height / 1.5 and centroid[
1] > height / 1.7: ##up truble when at distant car counted twice because bbox reappear
idx = detCentroid.tolist().index(
centroid.tolist())
if (labelObj[idx] == 'car'):
totalUpCar += 1
to.counted = True
elif (labelObj[idx] == 'motorcycle'):
totalUpMotor += 1
to.counted = True
elif (labelObj[idx] == 'truck'):
totalUpTruck += 1
to.counted = True
elif direction > 0 and centroid[
1] > height / 1.5: # arah down
idx = detCentroid.tolist().index(
centroid.tolist())
if (labelObj[idx] == 'car'):
totalDownCar += 1
to.counted = True
elif (labelObj[idx] == 'motorcycle'):
totalDownMotor += 1
to.counted = True
elif (labelObj[idx] == 'truck'):
totalDownTruck += 1
to.counted = True
trackableObjects[objectID] = to
oldCarAmount = totalCarAmount
oldTotalCars = totalCars
oldTotalTrucks = totalTrucks
oldTotalMotors = totalMotors
combinedAmount = totalDownCar + totalDownTruck + totalDownMotor + \
totalUpCar + totalUpMotor + totalUpTruck
totalCars = totalDownCar + totalUpCar
totalTrucks = totalDownTruck + totalUpTruck
totalMotors = totalDownMotor + totalUpMotor
if not oldCombinedAmount == combinedAmount:
tempAmount = totalCarAmount + combinedAmount
oldCombinedAmount = combinedAmount
if oldCarAmount < tempAmount:
totalCarAmount = tempAmount
if not oldCarAmount == totalCarAmount:
displayTotalAmount += 1
if not oldTotalCars == totalCars:
dbInsOrUpdCar(totalCars)
displayCarAmount += 1
if not oldTotalTrucks == totalTrucks:
dbInsOrUpdTruck(totalTrucks)
displayTruckAmount += 1
if not oldTotalMotors == totalMotors:
dbInsOrUpdMotorcycle(totalMotors)
displayMotorAmount += 1
if not control:
cv2.putText(im0, 'Frakoerende: ',
(int(width * 0.6), int(height * 0.10)),
cv2.FONT_HERSHEY_SIMPLEX, 1, (50, 255, 255), 2)
cv2.putText(im0, 'Bil: ' + str(totalUpCar),
(int(width * 0.6), int(height * 0.15)),
cv2.FONT_HERSHEY_SIMPLEX, .75, (50, 255, 255),
2)
cv2.putText(im0, 'Motorcykel: ' + str(totalUpMotor),
(int(width * 0.6), int(height * 0.2)),
cv2.FONT_HERSHEY_SIMPLEX, .75, (50, 255, 255),
2)
cv2.putText(im0, 'Lastbil: ' + str(totalUpTruck),
(int(width * 0.6), int(height * 0.25)),
cv2.FONT_HERSHEY_SIMPLEX, .75, (50, 255, 255),
2)
cv2.putText(im0, 'Modkoerende: ',
(int(width * 0.02), int(height * 0.10)),
cv2.FONT_HERSHEY_SIMPLEX, 1, (50, 255, 255), 2)
cv2.putText(im0, 'Bil: ' + str(totalDownCar),
(int(width * 0.02), int(height * 0.15)),
cv2.FONT_HERSHEY_SIMPLEX, .75, (50, 255, 255),
2)
cv2.putText(im0, 'Motorcykel: ' + str(totalDownMotor),
(int(width * 0.02), int(height * 0.2)),
cv2.FONT_HERSHEY_SIMPLEX, .75, (50, 255, 255),
2)
cv2.putText(im0, 'Lastbil: ' + str(totalDownTruck),
(int(width * 0.02), int(height * 0.25)),
cv2.FONT_HERSHEY_SIMPLEX, .75, (50, 255, 255),
2)
else:
cv2.putText(im0, 'Frakoerende: ',
(int(width * 0.6), int(height * 0.10)),
cv2.FONT_HERSHEY_SIMPLEX, 4, (50, 255, 255), 3)
cv2.putText(im0, 'Bil: ' + str(totalUpCar),
(int(width * 0.6), int(height * 0.15)),
cv2.FONT_HERSHEY_SIMPLEX, 3, (50, 255, 255), 3)
cv2.putText(im0, 'Motorcykel: ' + str(totalUpMotor),
(int(width * 0.6), int(height * 0.2)),
cv2.FONT_HERSHEY_SIMPLEX, 3, (50, 255, 255), 3)
cv2.putText(im0, 'Lastbil: ' + str(totalUpTruck),
(int(width * 0.6), int(height * 0.25)),
cv2.FONT_HERSHEY_SIMPLEX, 3, (50, 255, 255), 3)
cv2.putText(im0, 'Modkoerende: ',
(int(width * 0.02), int(height * 0.10)),
cv2.FONT_HERSHEY_SIMPLEX, 4, (50, 255, 255), 3)
cv2.putText(im0, 'Bil: ' + str(totalDownCar),
(int(width * 0.02), int(height * 0.15)),
cv2.FONT_HERSHEY_SIMPLEX, 3, (50, 255, 255), 3)
cv2.putText(im0, 'Motorcykel: ' + str(totalDownMotor),
(int(width * 0.02), int(height * 0.2)),
cv2.FONT_HERSHEY_SIMPLEX, 3, (50, 255, 255), 3)
cv2.putText(im0, 'Lastbil: ' + str(totalDownTruck),
(int(width * 0.02), int(height * 0.25)),
cv2.FONT_HERSHEY_SIMPLEX, 3, (50, 255, 255), 3)
# Print time (inference + NMS)
print('%sDone. (%.3fs)' % (s, t2 - t1))
# Stream results
if view_img:
cv2.namedWindow('Main', cv2.WINDOW_NORMAL)
cv2.resizeWindow('Main', 1920, 1080)
cv2.imshow("Main", im0)
if cv2.waitKey(1) == ord('q'): # q to quit
raise StopIteration
# Save results (image with detections)
if save_img:
if dataset.mode == 'images':
cv2.imwrite(save_path, im0)
else:
if vid_path != save_path: # new video
vid_path = save_path
if isinstance(vid_writer, cv2.VideoWriter):
vid_writer.release(
) # release previous video writer
fourcc = 'mp4v' # output video codec
fps = vid_cap.get(cv2.CAP_PROP_FPS)
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
vid_writer = cv2.VideoWriter(
save_path, cv2.VideoWriter_fourcc(*fourcc), fps,
(w, h))
vid_writer.write(im0)
if save_txt or save_img:
print('Results saved to %s' % Path(out))
print('Done. (%.3fs)' % (time.time() - t0))
def OnInit(self):
self.frame = MainController()
self.frame.show()
return True
class App(wx.App):
def OnInit(self):
self.frame = MainController()
self.frame.show()
return True
from Utility.Info.InitializeArisuRecord import *
import sys
from PyQt5.QtWidgets import QApplication
from Controller.MainController import *
if __name__ == '__main__':
try:
# Execute App
app = QApplication(sys.argv)
ExecuteLogger.printLog(str(sys.argv)) # todo 제거하기
view = MainView()
c = MainController(view)
c.start()
if len(sys.argv) > 1:
for idx in range(1, len(sys.argv)):
c.openRecordFile(sys.argv[idx]) # todo 오늘꺼를 또 열수도 있음
app.exec_()
except Exception as e:
ExecuteLogger.printLog(str(e) + 'TopLevel Quit')
ErrorLogger.reportError('Unexpected TopLevel Quit', e)
finally:
ExecuteLogger.printLog('@@Finish program@@\n')
