唇语识别代码
时间: 2023-09-29 21:05:20 浏览: 73
唇语识别的代码需要综合运用图像处理、特征提取、机器学习和深度学习等多个方面的知识和技术。这里提供一个简单的唇语识别代码示例,供参考:
```
import cv2
import dlib
import numpy as np
import tensorflow as tf
# 加载唇语识别模型
model = tf.keras.models.load_model('lip_reading_model.h5')
# 提取唇部特征
def extract_lip_features(frame, detector, predictor):
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
rects = detector(gray, 0)
for rect in rects:
landmarks = predictor(gray, rect)
points = np.zeros((68, 2), dtype=np.int)
for i in range(68):
points[i] = (landmarks.part(i).x, landmarks.part(i).y)
mouth_points = points[48:68]
hull = cv2.convexHull(mouth_points)
features = np.zeros((40, 1))
for i in range(40):
features[i] = hull[i + 4][0][1] - hull[i][0][1]
return features
# 实时唇语识别
cap = cv2.VideoCapture(0)
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')
while True:
ret, frame = cap.read()
if not ret:
break
features = extract_lip_features(frame, detector, predictor)
if features is not None:
features = np.array(features).astype(np.float32)
features = np.expand_dims(features, axis=0)
result = model.predict(features)
label = np.argmax(result)
cv2.putText(frame, 'Lip Reading: ' + str(label), (30, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, cv2.LINE_AA)
cv2.imshow('frame', frame)
if cv2.waitKey(1) == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
```
这是一个简单的实时唇语识别代码示例,其中使用了dlib库提取唇部特征,使用tensorflow库加载训练好的模型进行识别。需要注意的是,这只是一个简单的示例,具体的实现可能需要根据实际情况进行修改和优化。