how to pass continuous video stream to pyqt5 using Qthread correctly for face recognition? - pyqt5

Question

I want to pass continuous video stream to pyqt5 qlabel named label_cam to show up in ui using QThread but it keep failed showing the video stream on the qlabel. The video stream is later aimed to recognize people out.
I have been trying to connect the signal "change_Pixmap" from the VideoThread class to the "set_image" function in the "label_cam" object but i guess the flow of code or variable assign is wrong. Below is my code.
class FaceRecogScreen(QDialog):
def init(self):
super(FaceRecogScreen, self).init()
uic.loadUi("face_recog.ui", self)
self.update_date_time()
self.pushButton_back.clicked.connect(self.back_to_main3)
self.load_model()
def load_model(self):
self.prototxt = "deploy.prototxt.txt"
self.model = "res10_300x300_ssd_iter_140000.caffemodel"
print("[INFORMATION] Loading model....")
self.net = cv2.dnn.readNetFromCaffe(self.prototxt, self.model)
weight = "facenet_keras_weights.h5"
self.model2 = load_model('FaceNetModel.h5')
self.model2.load_weights(weight)
self.collected_encodings = pickle.loads(open('face_encoding.pickle', "rb").read())
infile = open('face_encoding', 'rb')
data = pickle.load(infile)
self.knownEncodings, self.knownNames = data['encodings'], data['names']
self.knownEncodings = np.array(self.knownEncodings)
self.knownNames = np.array(self.knownNames)
self.clf = svm.SVC(gamma="scale", probability=True, tol=0.01)
self.clf.fit(self.knownEncodings, self.knownNames)
# self.label_cam= VideoLabel()
self.thread = VideoThread(self)
self.thread.change_Pixmap.connect(self.set_image)
# call the run() function in VideoThread class
self.thread.start()
# self.thread.change_Pixmap.connect(self.label_cam.set_image)
# # call the run() function in VideoThread class
# self.thread.start()
# layout = self.layout()
# layout.addWidget(self.label_cam)
# self.thread.run.start()
def update_date_time(self):
# Get the current date and time
date_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
self.label_datetime.setText(date_time)
# Update the date and time in the table
def back_to_main3(self):
pushButton_back = WelcomeScreen()
widget.addWidget(pushButton_back)
widget.setCurrentIndex(widget.currentIndex()+1)
def set_image(self, frame):
self.setPixmap(QPixmap.fromImage(frame))
class VideoThread(QtCore.QThread):
change_Pixmap = QtCore.pyqtSignal(QtGui.QImage)
def run(self):
cap = cv2.VideoCapture(1)
while True:
ret, frame = cap.read()
if ret:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
h, w, ch = frame.shape
bytesPerLine = ch * w
convertToQtFormat = QtGui.QImage(frame.data, w, h, bytesPerLine, QtGui.QImage.Format_RGB888)
p = convertToQtFormat.scaled(640, 480, QtCore.Qt.KeepAspectRatio)
self.change_Pixmap.emit(p)
(h, w) = frame.shape[:2]
blob = cv2.dnn.blobFromImage(cv2.resize(
frame, (160, 160)), 1.0, (300, 300), (104, 177, 123))
self.net.setInput(blob)
detections = self.net.forward()
self.frame = frame
# self.frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
cv2.normalize(frame, None, 0, 1.0, cv2.NORM_MINMAX, dtype=cv2.CV_32F)
pixels = np.expand_dims(frame, axis=0)
encode = self.model2.predict(pixels)
face_name = []
for encoding in encode:
name = self.clf.predict([encoding])
face_name.extend(name)
for i in range(0, detections.shape[2]):
confidence = detections[0, 0, i, 2]
if confidence < 0.5:
continue
box = detections[0, 0, i, 3:7]*np.array([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
text = "{:.2f}%".format(confidence*100)
y = startY - 10 if startY - 10 > 10 else startY*10
if name == 'unknown':
cv2.rectangle(frame, (startX, y), (endX, endY), (0, 0, 255), 2)
cv2.putText(frame, name, (startX, startY),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
else:
cv2.rectangle(frame, (startX, y), (endX, endY), (0, 255, 0), 2)
cv2.putText(frame, name[0], (startX, startY),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)