python实时打哈欠检测

效果

基本思路

在 OpenCV 中使用VideoCapture方法初始化视频渲染对象

创建灰度图像

导入预训练模型，识别脸部和人脸标志

计算上唇和下唇距离（其它类似）

创建唇边距离的If条件，满足则是打哈欠，不满足则只是简单的张嘴

显示帧/图像

部分源码

suc, frame = cam.read()
# 读取不到退出
if not suc:
break

# ---------FPS------------#
ctime = time.time()
fps = int(1 / (ctime - ptime))
ptime = ctime
cv2.putText(frame, f'FPS:{fps}', (frame.shape[1] - 120, frame.shape[0] - 20), cv2.FONT_HERSHEY_PLAIN, 2,
(0, 200, 0), 3)

# ------检测人脸------#
# 转为灰度
img_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = face_model(img_gray)
for face in faces:
# 检测人脸，框起来-#
x1 = face.left()
y1 = face.top()
x2 = face.right()
y2 = face.bottom()
# print(face.top())
cv2.rectangle(frame, (x1, y1), (x2, y2), (200, 0, 00), 2)

# ----------检测人脸标注-----------#
shapes = landmark_model(img_gray, face)
shape = face_utils.shape_to_np(shapes)

# -------检测上下唇--------#
lip = shape[48:60]
cv2.drawContours(frame, [lip], -1, (0, 165, 255), thickness=3)

# -------计算上下唇距离-----#
lip_dist = cal_yawn(shape)
# 打印距离
# print(lip_dist)
# 大于设定值，则认定是打哈欠
if lip_dist > yawn_thresh:
cv2.putText(frame, f'User Yawning!', (frame.shape[1] // 2 - 170, frame.shape[0] // 2),
cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 200), 2)

# 按字母q退出
cv2.imshow('Webcam', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break

完整项目

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