how to fit a windowed output to a pyqt widget

I am trying to fit a separate dialog window into my PyQT GUI. I have the camera feed in a separate window, but the stats pop up in another separate window. I wish to get it into my application next to the camera feed so it is all in one. It is the section labeled emotional probabilities.

here is the GUI application main window:

Here is my code that I have tried thus far with no success (ImgWidget_3 is the Emotional probability group box/container in the pyqt designer .ui file ):

from keras.preprocessing.image import img_to_array
from keras.models import load_model

# parameters for loading data and images
detection_model_path = '/xxxxxxx/haarcascade_frontalface_default.xml'
emotion_model_path = '/xxxxxxx/_mini_XCEPTION.102-0.66.hdf5'

# hyper-parameters for bounding boxes shape
# loading models
face_detection = cv2.CascadeClassifier(detection_model_path)
emotion_classifier = load_model(emotion_model_path, compile=False)
EMOTIONS = ["angry" ,"disgust","scared", "happy", "sad", "surprised",
 "neutral"]


running = False
capture_thread = None
form_class = uic.loadUiType("simple.ui")[0]
q = Queue.Queue()


def grab(cam, queue, width, height, fps):
    global running
    capture = cv2.VideoCapture(cam)
    capture.set(cv2.CAP_PROP_FRAME_WIDTH, width)
    capture.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
    capture.set(cv2.CAP_PROP_FPS, fps)

    while(running):
        frame = {}        
        capture.grab()
        retval, img = capture.retrieve(0)
        frame["img"] = img

        if queue.qsize() < 10:
            queue.put(frame)
        else:
            print queue.qsize()

class OwnImageWidget(QtGui.QWidget):
    def __init__(self, parent=None):
        super(OwnImageWidget, self).__init__(parent)
        self.image = None

    def setImage(self, image):
        self.image = image
        sz = image.size()
        self.setMinimumSize(sz)
        self.update()

    def paintEvent(self, event):
        qp = QtGui.QPainter()
        qp.begin(self)
        if self.image:
            qp.drawImage(QtCore.QPoint(0, 0), self.image)
        qp.end()

class StatImageWidget(QtGui.QWidget):
    def __init__(self, parent=None):
        super(StatImageWidget, self).__init__(parent)
        self.image = None

    def setImage(self, image):
        self.image = image
        sz = image.size()
        self.setMinimumSize(sz)
        self.update()

    def paintEvent(self, event):
        qp = QtGui.QPainter()
        qp.begin(self)
        if self.image:
            qp.drawImage(QtCore.QPoint(0, 0), self.image)
        qp.end()



class MyWindowClass(QtGui.QMainWindow, form_class):
    def __init__(self, parent=None):
        QtGui.QMainWindow.__init__(self, parent)
        self.setupUi(self)

        self.startButton.clicked.connect(self.start_clicked)

        self.window_width = self.ImgWidget.frameSize().width()
        self.window_height = self.ImgWidget.frameSize().height()
        self.ImgWidget = OwnImageWidget(self.ImgWidget)

        self.ImgWidget_3 = StatImageWidget(self.ImgWidget_3)


        self.timer = QtCore.QTimer(self)
        self.timer.timeout.connect(self.update_frame)
        self.timer.start(1)


    def start_clicked(self):
        global running
        running = True
        capture_thread.start()
        self.startButton.setEnabled(False)
        self.startButton.setText('Starting...')


    def update_frame(self):
        if not q.empty():
            self.startButton.setText('Camera is live')
            frame = q.get()
            img = frame["img"]

            img_height, img_width, img_colors = img.shape
            scale_w = float(self.window_width) / float(img_width)
            scale_h = float(self.window_height) / float(img_height)
            scale = min([scale_w, scale_h])

            if scale == 0:
                scale = 1

            img = cv2.resize(img, None, fx=scale, fy=scale, interpolation = cv2.INTER_CUBIC)
            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
            height, width, bpc = img.shape
            bpl = bpc * width
            image = QtGui.QImage(img.data, width, height, bpl, QtGui.QImage.Format_RGB888)
            self.ImgWidget.setImage(image)

            gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
            faces = face_detection.detectMultiScale(gray,scaleFactor=1.1,minNeighbors=5,minSize=(30,30),flags=cv2.CASCADE_SCALE_IMAGE)

            canvas = np.zeros((250, 300, 3), dtype="uint8")
            frameClone = frame.copy()
            if len(faces) > 0:
                faces = sorted(faces, reverse=True,
                key=lambda x: (x[2] - x[0]) * (x[3] - x[1]))[0]
                (fX, fY, fW, fH) = faces
                            # Extract the ROI of the face from the grayscale image, resize it to a fixed 28x28 pixels, and then prepare
                    # the ROI for classification via the CNN
                roi = gray[fY:fY + fH, fX:fX + fW]
                roi = cv2.resize(roi, (64, 64))
                roi = roi.astype("float") / 255.0
                roi = img_to_array(roi)
                roi = np.expand_dims(roi, axis=0)


                preds = emotion_classifier.predict(roi)[0]
                emotion_probability = np.max(preds)
                label = EMOTIONS[preds.argmax()]


                for (i, (emotion, prob)) in enumerate(zip(EMOTIONS, preds)):
                        # construct the label text
                        text = "{}: {:.2f}%".format(emotion, prob * 100)

                        # draw the label + probability bar on the canvas
                       # emoji_face = feelings_faces[np.argmax(preds)]


                        w = int(prob * 300)
                        cv2.rectangle(canvas, (7, (i * 35) + 5),
                        (w, (i * 35) + 35), (0, 0, 255), -1)
                        cv2.putText(canvas, text, (10, (i * 35) + 23),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.45,
                        (255, 255, 255), 2)
                        cv2.putText(img, label, (fX, fY - 10),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)
                        cv2.rectangle(img, (fX, fY), (fX + fW, fY + fH),
                                      (0, 0, 255), 2)


        cv2.imshow("Emotional Probabilities", canvas)
        cv2.waitKey(1) & 0xFF == ord('q')
        self.ImgWidget_3.canvas(imshow)


    def closeEvent(self, event):
        global running
        running = False


capture_thread = threading.Thread(target=grab, args = (0, q, 1920, 1080, 30))

app = QtGui.QApplication(sys.argv)
w = MyWindowClass(None)
w.setWindowTitle('Test app')
w.show()
app.exec_()

How can I make this successfully work?

from how to fit a windowed output to a pyqt widget