python-OpenCV-人脸、眼睛,微笑检测 Fu Xianjun. All Rights Reserved.
这里写目录标题
- 一.人脸识别
-
- 二.案例
一.人脸识别
人脸识别(face recognition system),是基于人的脸部特征信息进行身份识别的一种生物识别技术。用摄像机或摄像头采集含有人脸的图像或视频流,并自动在图像中检测和跟踪人脸,进而对检测到的人脸进行脸部识别的一系列相关技术,通常也叫做人像识别、面部识别。
二.案例
import cv2
import numpy as np
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')
smile_cascade = cv2.CascadeClassifier(cv2.data.haarcascades+'haarcascade_smile.xml')
cap = cv2.VideoCapture(0)
width=1280
height=960
cap.set(cv2.CAP_PROP_FRAME_WIDTH,width)#设置图像宽度
cap.set(cv2.CAP_PROP_FRAME_HEIGHT,height)#设置图像高度
fgbg = cv2.createBackgroundSubtractorMOG2(
history=500, varThreshold=100, detectShadows=False)#基于自适应混合高斯背景建模的背景减除法,去除干扰
cnt=1
while(1):
# get a frame
ret, frame = cap.read()
# show a frame
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5,0) #把灰度图片传给haar进行灰度处理,返回值是人脸左上角坐标,宽度和高度
#1.3为缩放比例,默认为1.1即每次搜索窗口依次扩大10%
#5为构成检测目标的相邻矩形的最小个数
#0为flag,表示使用表认知,会使用Canny边缘检测来排除边缘过多或过少的区域
for(x, y, w, h) in faces:
img = cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
roi_gray = gray[y:y+h, x:x+w]
eyes = eye_cascade.detectMultiScale(roi_gray, 1.8, 5,0)
roi_color = img[y:y + h, x:x + w]
for(ex, ey, ew, eh) in eyes:
cv2.rectangle(roi_color, (ex, ey), (ex+ew, ey+eh), (0, 255, 0), 2)
smiles = smile_cascade.detectMultiScale(roi_gray, scaleFactor = 1.16,\
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(roi_gray, (ex,ey), (ex+ew,ey+eh), (0,0,255), 1)
cv2.putText(img, "smile", (x,y-7), 3, 1.2, (0,0,225), 2, cv2.LINE_AA)
#cv2.LINE_AA 为抗锯齿,这样看起来会非常平滑
#cv2.imwrite(f"img{cnt}.png",img)
cnt+=1
cv2.imshow("camera", frame)
if cv2.waitKey(5) == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
import cv2
import numpy as np
cap = cv2.VideoCapture("redone.mp4")
while(cap.isOpened()):
ret, frame = cap.read()
HSV = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)#把BGR图像转换为HSV格式
Lower = np.array([156, 43, 46])#要识别颜色的下限
Upper = np.array([180, 255, 255])#要识别的颜色的上限
#mask是把HSV图片中在颜色范围内的区域变成白色,其他区域变成黑色
mask = cv2.inRange(HSV, Lower, Upper)
yu = cv2.bitwise_and(frame,frame,mask=mask)
cv2.imshow("frame",yu)
if cv2.waitKey(25) == ord("q"):
break
cap.release()#释放摄像头的资源
cv2.destroyAllWindows()
![](https://img.laitimes.com/img/_0nNw4CM6IyYiwiM6ICdiwiIyVGduV2YfNWawNCM38FdsYkRGZkRG9lcvx2bjxiNx8VZ6l2cs0TPR9EeBpXT4FkeOFDOsJGcohVYsR2MMBjVtJWd0ckW65UbM5WOHJWa5kHT20ESjBjUIF2X0hXZ0xCMx81dvRWYoNHLrdEZwZ1Rh5WNXp1bwNjW1ZUba9VZwlHdssmch1mclRXY39CXldWYtlWPzNXZj9mcw1ycz9WL49zZuBnL3MTO2EDN0ATMzAjNwEjMwIzLc52YucWbp5GZzNmLn9Gbi1yZtl2Lc9CX6MHc0RHaiojIsJye.png)