python - 如何在python的框架（tkinter）内的标签中显示我的覆盆子凸轮？

Question

我正在尝试在框架（tkinter）内的标签内显示我的覆盆子相机，我有一种识别加载视频捕获的面孔的方法，但它没有在标签中显示相机。

这是我创建gui的代码

def create_ui(self):
        self.window = tk.Tk()
        self.window.title("App Faces")
        self.window.geometry('860x720')
        self.window.bind('<Escape>', lambda e: self.window.quit())
        self.tab_control = ttk.Notebook(self.window)

        self.tab2 = ttk.Frame(self.tab_control)
        self.tab_control.add(self.tab2, text="Real Time Face Recognition")
        self.label2 = ttk.Label(self.tab2, text="Real Time Face Recognition")
        self.label2.place(x=12, y=50)
        self.lmain2 = ttk.Label(self.tab2)
        self.lmain2.place(x=12, y=70)

        self.boton2 = ttk.Button(self.tab2, text="START", command=self.recognizing_faces_video)
        self.boton2.pack()
        self.boton2.place(x=50, y=20)

        self.tab_control.pack(expand=1, fill='both')
        self.window.mainloop()

在这个方法中，我调用了 self.recognizing_faces_video 方法，但它不起作用

def recognizing_faces_video(self):
    self.recognizer = pickle.loads(open(self.recognizer, "rb").read())
    self.le = pickle.loads(open(self.labelEncoder, "rb").read())

    self.cap = cv2.VideoCapture("http://192.168.1.71:8000/stream.mjpg")
    # print(self.cap)
    # cap = cv2.VideoCapture("http://raspberrypi.mshome.net:8000/stream.mjpg")
    self.cap.set(cv2.CAP_PROP_FRAME_WIDTH, self.width)
    self.cap.set(cv2.CAP_PROP_FRAME_HEIGHT, self.width)
    print(self.cap.isOpened())

    # grab the frame from the threaded video stream
    _, frame = self.cap.read()
    #cv2image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGBA)

    # resize the frame to have a width of 600 pixels (while
    # maintaining the aspect ratio), and then grab the image
    # dimensions
    frame = imutils.resize(frame, width=600)

    (h, w) = frame.shape[:2]

    # construct a blob from the image
    imageBlob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)), 1.0, (300, 300), (104.0, 177.0, 123.0),
                                      swapRB=False, crop=False)

    # apply OpenCV's deep learning-based face detector to localize
    # faces in the input image
    self.model.setInput(imageBlob)
    detections = self.model.forward()

    # loop over the detections
    for i in range(0, detections.shape[2]):
        # extract the confidence (i.e., probability) associated with
        # the prediction
        confidence = detections[0, 0, i, 2]

        # filter out weak detections
        if confidence > 0.5:
            # compute the (x, y)-coordinates of the bounding box for
            # the face
            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
            (startX, startY, endX, endY) = box.astype("int")

            # extract the face ROI
            face = frame[startY:endY, startX:endX]
            (fH, fW) = face.shape[:2]

            # ensure the face width and height are sufficiently large
            if fW < 20 or fH < 20:
                continue

            # construct a blob for the face ROI, then pass the blob
            # through our face embedding model to obtain the 128-d
            # quantification of the face
            faceBlob = cv2.dnn.blobFromImage(face, 1.0 / 255,
                                             (96, 96), (0, 0, 0), swapRB=True, crop=False)
            self.embedder.setInput(faceBlob)
            vec = self.embedder.forward()

            # perform classification to recognize the face
            preds = self.recognizer.predict_proba(vec)[0]
            j = np.argmax(preds)
            proba = preds[j]
            name = self.le.classes_[j]

            # draw the bounding box of the face along with the
            # associated probability
            text = "{}: {:.2f}%".format(name, proba * 100)
            y = startY - 10 if startY - 10 > 10 else startY + 10
            cv2.rectangle(frame, (startX, startY), (endX, endY),
                          (0, 0, 255), 2)
            cv2.putText(frame, text, (startX, y),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)
    self.boton2['state'] = "disabled"
    img = Image.fromArray(frame)
    imgTk = ImageTk.PhotoImage(image = img)
    self.lmain2.imgtk = imgTk
    self.lmain2.configure(image=imgTk)
    self.lmain2.after(10, self.recognizing_faces_video)

谁能帮帮我？

标签： pythonpython-3.xtkinterraspberry-pilabel