如何再python中输出实时表情识别的数据到excel表格中？

Question

在运行实时表情识别的时候发现运行关闭后数据无法保存！

下面是主要代码

from statistics import mode

import cv2
from keras.models import load_model
import numpy as np

from utils.datasets import get_labels
from utils.inference import detect_faces
from utils.inference import draw_text
from utils.inference import draw_bounding_box
from utils.inference import apply_offsets
from utils.inference import load_detection_model
from utils.preprocessor import preprocess_input

parameters for loading data and images

detection_model_path = 'C:\Users\11396\AppData\Roaming\Python\Python39\site-packages\cv2\data\haarcascade_frontalface_default.xml'
emotion_model_path = 'C:\Users\11396\Desktop\face_classification-master\trained_models\emotion_models\fer2013_mini_XCEPTION.102-0.66.hdf5'
emotion_labels = get_labels('fer2013')

hyper-parameters for bounding boxes shape

frame_window = 10
emotion_offsets = (20, 40)

loading models

face_detection = load_detection_model(detection_model_path)
emotion_classifier = load_model(emotion_model_path, compile=False)

getting input model shapes for inference

emotion_target_size = emotion_classifier.input_shape[1:3]

starting lists for calculating modes

emotion_window = []

starting video streaming

cv2.namedWindow('window_frame')
video_capture = cv2.VideoCapture(0)
while True:
bgr_image = video_capture.read()[1]
gray_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2GRAY)
rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)
faces = detect_faces(face_detection, gray_image)

for face_coordinates in faces:

    x1, x2, y1, y2 = apply_offsets(face_coordinates, emotion_offsets)
    gray_face = gray_image[y1:y2, x1:x2]
    try:
        gray_face = cv2.resize(gray_face, (emotion_target_size))
    except:
        continue

    gray_face = preprocess_input(gray_face, True)
    gray_face = np.expand_dims(gray_face, 0)
    gray_face = np.expand_dims(gray_face, -1)
    emotion_prediction = emotion_classifier.predict(gray_face)
    emotion_probability = np.max(emotion_prediction)
    emotion_label_arg = np.argmax(emotion_prediction)
    emotion_text = emotion_labels[emotion_label_arg]
    emotion_window.append(emotion_text)

    if len(emotion_window) > frame_window:
        emotion_window.pop(0)
    try:
        emotion_mode = mode(emotion_window)
    except:
        continue

    if emotion_text == 'angry':
        color = emotion_probability * np.asarray((255, 0, 0))
    elif emotion_text == 'sad':
        color = emotion_probability * np.asarray((0, 0, 255))
    elif emotion_text == 'happy':
        color = emotion_probability * np.asarray((255, 255, 0))
    elif emotion_text == 'surprise':
        color = emotion_probability * np.asarray((0, 255, 255))
    else:
        color = emotion_probability * np.asarray((0, 255, 0))

    color = color.astype(int)
    color = color.tolist()


    draw_bounding_box(face_coordinates, rgb_image, color)
    draw_text(face_coordinates, rgb_image, emotion_mode,
              color, 0, -45, 1, 1)

bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
cv2.imshow('window_frame', bgr_image)
if cv2.waitKey(1) & 0xFF == ord('q'):
    break

video_capture.release()
cv2.destroyAllWindows()