仅调用摄像头
import cv2
import time
cv2.namedWindow("camera", 1)
video = 'http://192.168.123.29:4747/video'
capture = cv2.VideoCapture(video)
while True:
sucess, img = capture.read()
cv2.imshow("camera",img)
if cv2.waitKey(1) == 27:
break
capture.release()
cv2.destroyAllWindows()调用摄像头+人脸微笑检测
import cv2
import time
cv2.namedWindow("camera", 1)
video = 'http://192.168.123.29:4747/video'
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades+'haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades+'haarcascade_eye.xml')
smile_cascade = cv2.CascadeClassifier(cv2.data.haarcascades+'haarcascade_smile.xml')
# 调用摄像头摄像头
cap = cv2.VideoCapture(video)
while(True):
# 获取摄像头拍摄到的画面
ret, frame = cap.read()
faces = face_cascade.detectMultiScale(frame, 1.3, 2)
img = frame
for (x,y,w,h) in faces:
# 画出人脸框，蓝色，画笔宽度微
img = cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
# 框选出人脸区域，在人脸区域而不是全图中进行人眼检测，节省计算资源
face_area = img[y:y+h, x:x+w]
## 人眼检测
# 用人眼级联分类器引擎在人脸区域进行人眼识别，返回的eyes为眼睛坐标列表
eyes = eye_cascade.detectMultiScale(face_area,1.3,10)
for (ex,ey,ew,eh) in eyes:
#画出人眼框，绿色，画笔宽度为1
cv2.rectangle(face_area,(ex,ey),(ex+ew,ey+eh),(0,255,0),1)
## 微笑检测
# 用微笑级联分类器引擎在人脸区域进行人眼识别，返回的eyes为眼睛坐标列表
smiles = smile_cascade.detectMultiScale(face_area,scaleFactor= 2.16,minNeighbors=65,minSize=(25, 25),flags=cv2.CASCADE_SCALE_IMAGE)
for (ex,ey,ew,eh) in smiles:
#画出微笑框，红色（BGR色彩体系），画笔宽度为1
cv2.rectangle(face_area,(ex,ey),(ex+ew,ey+eh),(0,0,255),1)
cv2.putText(img,'Smile face',(x,y-7), 3, 1.2, (0, 0, 255), 2, cv2.LINE_AA)
# 实时展示效果画面
cv2.imshow('camera',img)
# 每5毫秒监听一次键盘动作
if cv2.waitKey(5) & 0xFF == ord('q'):
break
# 最后，关闭所有窗口
cap.release()
cv2.destroyAllWindows()
运行效果
参考资料：
www.programminghunter.com/article/1070783889/
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