人脸表情摄像头的实时识别怎么转变为图片识别呢？

from statistics import mode
import cv2
from tensorflow.python.keras.models import load_model
import numpy as np
from utils import preprocess_input

parameters for loading data and images

detection_model_path = 'trained_models/facemodel/haarcascade_frontalface_default.xml'
emotion_model_path = 'trained_models/float_models/fer2013_mini_XCEPTION.33-0.65.hdf5'
emotion_labels = {0:'angry',1:'disgust',2:'fear',3:'happy',
4:'sad',5:'surprise',6:'neutral'}

face_detection = cv2.CascadeClassifier(detection_model_path)
emotion_classifier = load_model(emotion_model_path, compile=False)
emotion_target_size = emotion_classifier.input_shape[1:3]
emotion_window = []
frame_window = 10

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 = face_detection.detectMultiScale(gray_image, 1.3, 5)
for face_coordinates in faces:

    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_text = mode(emotion_window)
    except:
        continue
    color = (0,0,255)
   
bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
cv2.imshow('window_frame', bgr_image)
if cv2.waitKey(1) & 0xFF == ord('q'):
    break

while true和video_capture = cv2.VideoCapture(0)去掉，然后bgr_image =imread('你的图片路径')，下面的缩进改好就行

img