OpenCV + python 实现人脸检测（基于照片和视频进行检测）

Haar特征值反映了图像的灰度变化情况。例如：脸部的一些特征能由矩形特征简单的描述，如：眼睛要比脸颊颜色要深，鼻梁两侧比鼻梁颜色要深，嘴巴比周围颜色要深等。

import cv2
image = cv2.imread(imagepath)

import cv2
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)

opencv 的强大之处的一个体现就是其可以对图片进行任意编辑，处理。
下面的这个函数最后一个参数指定的就是画笔的大小。

import cv2
cv2.rectangle(image,(x,y),(x+w,y+w),(0,255,0),2)

import cv2
cv2.imshow("Image Title",image)

import cv2
face_cascade = cv2.CascadeClassifier(r'./haarcascade_frontalface_default.xml')

import cv2

# 探测图片中的人脸

faces = face_cascade.detectMultiScale(
gray,
scaleFactor = 1.15,
minNeighbors = 5,
minSize = (5,5),
flags = cv2.cv.CV_HAAR_SCALE_IMAGE
)

import cv2

print "发现{0}个人脸!".format(len(faces))

for(x,y,w,h) in faces:
cv2.rectangle(image,(x,y),(x+w,y+w),(0,255,0),2)

import cv2
import numpy as np

import sys,os,glob,numpy
from skimage import io

#指定图片的人脸识别然后存储
img = cv2.imread("test.jpg")
color = (0, 255, 0)

grey = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

classfier = cv2.CascadeClassifier("C:\\Users\\22291_000\\Anaconda3\\Lib\\site-packages\\cv2\\data\\haarcascade_frontalface_alt2.xml")

faceRects = classfier.detectMultiScale(grey, scaleFactor=1.2, minNeighbors=3, minSize=(32, 32))
if len(faceRects) > 0: # 大于0则检测到人脸
for faceRect in faceRects: # 单独框出每一张人脸
x, y, w, h = faceRect
cv2.rectangle(img, (x - 10, y - 10), (x + w + 10, y + h + 10), color, 3) #5控制绿色框的粗细

# 写入图像
cv2.imwrite('output.jpg',img)
cv2.imshow("Find Faces!",img)
cv2.waitKey(0)

import cv2
import sys
import logging as log
import datetime as dt
from time import sleep

cascPath = "C:\\Users\\22291_000\\Anaconda3\\Lib\\site-packages\\cv2\\data\\haarcascade_frontalface_alt2.xml"
faceCascade = cv2.CascadeClassifier(cascPath)

# 打开视频捕获设备
video_capture = cv2.VideoCapture(0)

while True:
if not video_capture.isOpened():
print('Unable to load camera.')
sleep(5)
pass

# 读视频帧
ret, frame = video_capture.read()

# 转为灰度图像
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

# 调用分类器进行检测
faces = faceCascade.detectMultiScale(
gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30),
#flags=cv2.cv.CV_HAAR_SCALE_IMAGE
)

# 画矩形框
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)

# 显示视频
cv2.imshow('Video', frame)

if cv2.waitKey(1) & 0xFF == ord('q'):
break

# 关闭摄像头设备
video_capture.release()

# 关闭所有窗口
cv2.destroyAllWindows()