def __init__(self):
self._configs = {}
self.movidius = None
self.jumpwayClient = None
self.OpenCVCapture = None
self.graphfile = None
self.graph = None
self.CheckDevices()
self.Helpers = Helpers()
self.OpenCVHelpers = OpenCVHelpers()
self.FacenetHelpers = FacenetHelpers()
self._configs = self.Helpers.loadConfigs()
self.loadRequirements()
self.startMQTT()
self.detector = dlib.get_frontal_face_detector()
self.predictor = dlib.shape_predictor(
self._configs["ClassifierSettings"]["Dlib"])
print("")
print("-- Classifier Initiated")
print("")
def main():
global capture
global Classifier
global FacenetHelpers
Classifier = Classifier()
FacenetHelpers = FacenetHelpers()
try:
capture = WebcamVideoStream(
src=Classifier._configs["Cameras"][0]["URL"]).start()
print("-- CONNECTED TO WEBCAM")
except Exception as e:
print("-- FAILED TO CONNECT TO WEBCAM")
print(str(e))
sys.exit()
try:
server = ThreadedHTTPServer(
(Classifier._configs["Cameras"][0]["Stream"],
Classifier._configs["Cameras"][0]["StreamPort"]), CamHandler)
print("server started")
server.serve_forever()
except KeyboardInterrupt:
server.socket.close()
def main(argv):
humanStart = datetime.now()
clockStart = time.time()
print("-- FACENET TEST MODE STARTING ")
print("-- STARTED: ", humanStart)
print("")
validDir = Classifier._configs["ClassifierSettings"][
"NetworkPath"] + Classifier._configs["ClassifierSettings"]["ValidPath"]
testingDir = Classifier._configs["ClassifierSettings"][
"NetworkPath"] + Classifier._configs["ClassifierSettings"][
"TestingPath"]
files = 0
identified = 0
for test in os.listdir(testingDir):
if test.endswith('.jpg') or test.endswith('.jpeg') or test.endswith(
'.png') or test.endswith('.gif'):
#print(testingDir+test)
test_output = FacenetHelpers.infer(cv2.imread(testingDir + test),
Classifier.graph)
files = files + 1
for valid in os.listdir(validDir):
if valid.endswith('.jpg') or valid.endswith(
'.jpeg') or valid.endswith('.png') or valid.endswith(
'.gif'):
valid_output = FacenetHelpers.infer(
cv2.imread(validDir + valid), Classifier.graph)
if (FacenetHelpers.match(valid_output, test_output)):
identified = identified + 1
print("-- MATCH " + test)
print("")
Classifier.jumpwayClient.publishToDeviceChannel(
"Warnings", {
"WarningType":
"CCTV",
"WarningOrigin":
Classifier._configs["Cameras"][0]["ID"],
"WarningValue":
"RECOGNISED",
"WarningMessage":
test.rsplit(".", 1)[0] + " Detected"
})
break
else:
print("-- NO MATCH")
print("")
Classifier.jumpwayClient.publishToDeviceChannel(
"Warnings", {
"WarningType":
"CCTV",
"WarningOrigin":
Classifier._configs["Cameras"][0]["ID"],
"WarningValue":
"INTRUDER",
"WarningMessage":
"INTRUDER"
})
humanEnd = datetime.now()
clockEnd = time.time()
print("")
print("-- FACENET TEST MODE ENDING")
print("-- ENDED: ", humanEnd)
print("-- TESTED: ", files)
print("-- IDENTIFIED: ", identified)
print("-- TIME(secs): {0}".format(clockEnd - clockStart))
print("")
print("!! SHUTTING DOWN !!")
print("")
Classifier.graph.DeallocateGraph()
Classifier.movidius.CloseDevice()
self._configs["IoTJumpWayMQTT"]["MQTTUsername"],
"password":
self._configs["IoTJumpWayMQTT"]["MQTTPassword"]
})
except Exception as e:
print(str(e))
sys.exit()
self.jumpwayClient.connectToDevice()
print("-- IoT JumpWay Initiated")
Classifier = Classifier()
FacenetHelpers = FacenetHelpers()
def main(argv):
humanStart = datetime.now()
clockStart = time.time()
print("-- FACENET TEST MODE STARTING ")
print("-- STARTED: ", humanStart)
print("")
validDir = Classifier._configs["ClassifierSettings"][
"NetworkPath"] + Classifier._configs["ClassifierSettings"]["ValidPath"]
testingDir = Classifier._configs["ClassifierSettings"][
"NetworkPath"] + Classifier._configs["ClassifierSettings"][
def TASSinference():
Server.CheckDevices()
Server.loadRequirements("TASS")
humanStart = datetime.now()
clockStart = time.time()
print("-- FACENET LIVE INFERENCE STARTED: ", humanStart)
r = request
nparr = np.fromstring(r.data, np.uint8)
print("-- Loading Face")
fileName = "data/captured/TASS/"+str(clockStart)+'.png'
img = cv2.imdecode(nparr, cv2.IMREAD_UNCHANGED)
cv2.imwrite(fileName,img)
img = cv2.imread(fileName).astype(np.float32)
print("-- Loaded Sample")
validDir = Server._configs["ClassifierSettings"]["NetworkPath"] + Server._configs["ClassifierSettings"]["ValidPath"]
testingDir = Server._configs["ClassifierSettings"]["NetworkPath"] + Server._configs["ClassifierSettings"]["TestingPath"]
files = 0
identified = 0
test_output = FacenetHelpers.infer(img, Server.fgraph)
files = files + 1
for valid in os.listdir(validDir):
if valid.endswith('.jpg') or valid.endswith('.jpeg') or valid.endswith('.png') or valid.endswith('.gif'):
valid_output = FacenetHelpers.infer(cv2.imread(validDir+valid), Server.fgraph)
known, confidence = FacenetHelpers.match(valid_output, test_output)
if (known=="True"):
identified = identified + 1
print("-- MATCH "+valid)
break
humanEnd = datetime.now()
clockEnd = time.time()
Server.fgraph.DeallocateGraph()
Server.movidius.CloseDevice()
print("")
print("-- FACENET LIVE INFERENCE ENDED: ", humanEnd)
print("-- TESTED: ", 1)
print("-- IDENTIFIED: ", identified)
print("-- TIME(secs): {0}".format(clockEnd - clockStart))
print("")
if identified:
validPerson = os.path.splitext(valid)[0]
message = validPerson +" Detected With Confidence " + str(confidence)
person = validPerson
else:
message = "Intruder Detected With Confidence " + str(confidence)
person = "Intruder"
response = {
'Response': 'OK',
'Results': identified,
'Person': person,
'Confidence': confidence,
'ResponseMessage': message
}
response_pickled = jsonpickle.encode(response)
return Response(response=response_pickled, status=200, mimetype="application/json")
def main(argv):
if argv[0] == "Facenet":
Classifier.loadRequirements(argv[0])
humanStart = datetime.now()
clockStart = time.time()
print("-- FACENET TEST MODE STARTING ")
print("-- STARTED: ", humanStart)
validDir = Classifier._configs["ClassifierSettings"][
"NetworkPath"] + Classifier._configs["ClassifierSettings"][
"ValidPath"]
testingDir = Classifier._configs["ClassifierSettings"][
"NetworkPath"] + Classifier._configs["ClassifierSettings"][
"TestingPath"]
files = 0
identified = 0
for test in os.listdir(testingDir):
if test.endswith('.jpg') or test.endswith(
'.jpeg') or test.endswith('.png') or test.endswith('.gif'):
#print(testingDir+test)
test_output = FacenetHelpers.infer(
cv2.imread(testingDir + test), Classifier.fgraph)
files = files + 1
for valid in os.listdir(validDir):
if valid.endswith('.jpg') or valid.endswith(
'.jpeg') or valid.endswith(
'.png') or valid.endswith('.gif'):
valid_output = FacenetHelpers.infer(
cv2.imread(validDir + valid), Classifier.fgraph)
if (FacenetHelpers.match(valid_output, test_output)):
identified = identified + 1
print("-- MATCH " + test)
Classifier.jumpwayClient.publishToDeviceChannel(
"Warnings", {
"WarningType":
"CCTV",
"WarningOrigin":
Classifier._configs["Cameras"][0]["ID"],
"WarningValue":
"RECOGNISED",
"WarningMessage":
test.rsplit(".", 1)[0] + " Detected"
})
break
else:
print("-- NO MATCH")
Classifier.jumpwayClient.publishToDeviceChannel(
"Warnings", {
"WarningType":
"CCTV",
"WarningOrigin":
Classifier._configs["Cameras"][0]["ID"],
"WarningValue":
"INTRUDER",
"WarningMessage":
"INTRUDER"
})
humanEnd = datetime.now()
clockEnd = time.time()
print("")
print("-- FACENET TEST MODE ENDING")
print("-- ENDED: ", humanEnd)
print("-- TESTED: ", files)
print("-- IDENTIFIED: ", identified)
print("-- TIME(secs): {0}".format(clockEnd - clockStart))
print("")
print("!! SHUTTING DOWN !!")
print("")
Classifier.fgraph.DeallocateGraph()
Classifier.movidius.CloseDevice()
elif argv[0] == "Inception":
Classifier.loadRequirements("IDC")
humanStart = datetime.now()
clockStart = time.time()
print("-- INCEPTION V3 TEST MODE STARTED: : ", humanStart)
rootdir = Classifier._configs["ClassifierSettings"][
"NetworkPath"] + Classifier._configs["ClassifierSettings"][
"InceptionImagePath"]
files = 0
identified = 0
for file in os.listdir(rootdir):
if file.endswith('.jpg') or file.endswith(
'.jpeg') or file.endswith('.png') or file.endswith('.gif'):
files = files + 1
fileName = rootdir + file
print("-- Loaded Test Image", fileName)
img = cv2.imread(fileName).astype(np.float32)
dx, dy, dz = img.shape
delta = float(abs(dy - dx))
if dx > dy:
img = img[int(0.5 * delta):dx - int(0.5 * delta), 0:dy]
else:
img = img[0:dx, int(0.5 * delta):dy - int(0.5 * delta)]
img = cv2.resize(img, (Classifier.reqsize, Classifier.reqsize))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
for i in range(3):
img[:, :,
i] = (img[:, :, i] - Classifier.mean) * Classifier.std
detectionStart = datetime.now()
detectionClockStart = time.time()
print("-- DETECTION STARTING ")
print("-- STARTED: : ", detectionStart)
Classifier.graph.LoadTensor(img.astype(np.float16),
'user object')
output, userobj = Classifier.graph.GetResult()
top_inds = output.argsort()[::-1][:5]
detectionEnd = datetime.now()
detectionClockEnd = time.time()
print("-- DETECTION ENDING")
print("-- ENDED: ", detectionEnd)
print("-- TIME: {0}".format(detectionClockEnd -
detectionClockStart))
if output[top_inds[0]] > Classifier._configs[
"ClassifierSettings"][
"InceptionThreshold"] and Classifier.categories[
top_inds[0]] == "1":
identified = identified + 1
print("")
print("!! TASS Identified IDC with a confidence of",
str(output[top_inds[0]]))
print("")
Classifier.jumpwayClient.publishToDeviceChannel(
"Warnings", {
"WarningType":
"CCTV",
"WarningOrigin":
Classifier._configs["Cameras"][0]["ID"],
"WarningValue":
"RECOGNISED",
"WarningMessage":
"IDC Detected"
})
print("")
else:
print("")
print("!! TASS Did Not Identify IDC")
print("")
Classifier.jumpwayClient.publishToDeviceChannel(
"Warnings", {
"WarningType":
"CCTV",
"WarningOrigin":
Classifier._configs["Cameras"][0]["ID"],
"WarningValue":
"NOT RECOGNISED",
"WarningMessage":
"IDC Not Detected"
})
print("")
Classifier.jumpwayClient.publishToDeviceChannel(
"Sensors", {
"Sensor":
"CCTV",
"SensorID":
Classifier._configs["Cameras"][0]["ID"],
"SensorValue":
"IDC: " + Classifier.categories[top_inds[0]] +
" (Confidence: " + str(output[top_inds[0]]) + ")"
})
humanEnd = datetime.now()
clockEnd = time.time()
print("")
print("-- INCEPTION V3 TEST MODE ENDING")
print("-- ENDED: ", humanEnd)
print("-- TESTED: ", files)
print("-- IDENTIFIED: ", identified)
print("-- TIME(secs): {0}".format(clockEnd - clockStart))
print("")
print("!! SHUTTING DOWN !!")
print("")
Classifier.graph.DeallocateGraph()
Classifier.movidius.CloseDevice()
else:
print("**ERROR** Check Your Commandline Arguments")
print("")
def do_GET(self):
if self.path.endswith('.mjpg'):
self.send_response(200)
self.send_header(
'Content-type',
'multipart/x-mixed-replace; boundary=--jpgboundary')
self.end_headers()
frameWait = 0
fps = FPS().start()
try:
while True:
# grab the frame from the threaded video stream and resize it
# to have a maximum width of 400 pixels
frame = capture.read()
frame = imutils.resize(frame, width=640)
rawFrame = frame.copy()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
rects = Classifier.detector(gray, 0)
for (i, rect) in enumerate(rects):
# determine the facial landmarks for the face region, then
# convert the facial landmark (x, y)-coordinates to a NumPy
# array
shape = Classifier.predictor(gray, rect)
shape = face_utils.shape_to_np(shape)
# convert dlib's rectangle to a OpenCV-style bounding box
# [i.e., (x, y, w, h)], then draw the face bounding box
(x, y, w, h) = face_utils.rect_to_bb(rect)
cv2.rectangle(frame, (x, y), (x + w, y + h),
(0, 255, 0), 2)
# loop over the (x, y)-coordinates for the facial landmarks
# and draw them on the image
for (x, y) in shape:
cv2.circle(frame, (x, y), 1, (0, 255, 0), -1)
frameWait = 0
currentFace = rawFrame[
max(0,
rect.top() - 100):min(rect.bottom() +
100, 480),
max(0,
rect.left() - 100):min(rect.right() +
100, 640)]
cv2.imwrite("test.jpg", currentFace)
validDir = Classifier._configs["ClassifierSettings"][
"NetworkPath"] + Classifier._configs[
"ClassifierSettings"]["ValidPath"]
for valid in os.listdir(validDir):
if valid.endswith('.jpg') or valid.endswith(
'.jpeg') or valid.endswith(
'.png') or valid.endswith('.gif'):
if (FacenetHelpers.match(
FacenetHelpers.infer(
cv2.imread(validDir + valid),
Classifier.graph),
FacenetHelpers.infer(
currentFace, Classifier.graph))):
name = valid.rsplit('.', 1)[0]
print("-- MATCH " + name)
print("")
Classifier.jumpwayClient.publishToDeviceChannel(
"Warnings", {
"WarningType":
"CCTV",
"WarningOrigin":
Classifier._configs["Cameras"][0]
["ID"],
"WarningValue":
"RECOGNISED",
"WarningMessage":
name + " Detected"
})
break
else:
print("-- NO MATCH")
print("")
cv2.rectangle(frame, (x, y),
(x + w, y + h), (255, 0, 0),
2)
Classifier.jumpwayClient.publishToDeviceChannel(
"Warnings", {
"WarningType":
"CCTV",
"WarningOrigin":
Classifier._configs["Cameras"][0]
["ID"],
"WarningValue":
"INTRUDER",
"WarningMessage":
"INTRUDER"
})
else:
print("-- NO VALID ID")
print("")
imgRGB = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
imgRGB = cv2.flip(imgRGB, 1)
jpg = Image.fromarray(imgRGB)
tmpFile = BytesIO()
jpg.save(tmpFile, 'JPEG')
self.wfile.write("--jpgboundary".encode())
self.send_header('Content-type', 'image/jpeg')
self.send_header('Content-length',
str(tmpFile.getbuffer().nbytes))
self.end_headers()
self.wfile.write(tmpFile.getvalue())
#time.sleep(0.05)
fps.update()
frameWait = frameWait + 1
except KeyboardInterrupt:
exit
return
if self.path.endswith('.html'):
src = '<img src="http://' + Classifier._configs["Cameras"][0][
"Stream"] + ':' + str(Classifier._configs["Cameras"][0]
["StreamPort"]) + '/cam.mjpg" />'
self.send_response(200)
self.send_header('Content-type', 'text/html')
self.end_headers()
self.wfile.write('<html><head></head><body>'.encode())
self.wfile.write(src.encode())
self.wfile.write('</body></html>'.encode())
return
