人脸识别(2)--Python3.6+OpenCV3.2识别实例
这里提供两个简单实例：
-
- 调用摄像头获取图片进行实时检测
- 本地获取图片进行人脸检测并保存图片
前提条件：
确保python+opencv环境已经搭建成功。搭建步骤可以参考上一篇博客：http://blog.csdn.net/u012842255/article/details/70171617
调用摄像头获取图片进行实时检测
调用摄像头，可以看到头像用方框框起来，并实时根据情况调整位置。
import cv2
import numpy as np
cv2.namedWindow(“test”)
cap=cv2.VideoCapture(0)
success, frame = cap.read()
color = (0,0,0)
classfier=cv2.CascadeClassifier(“/Users/liuqi/opencv/data/haarcascades/haarcascade_frontalface_alt.xml”)
while success:
success, frame = cap.read()
size=frame.shape[:2]
image=np.zeros(size,dtype=np.float16)
image = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cv2.equalizeHist(image, image)
divisor=8
h, w = size
minSize =(w//divisor, h//divisor)
faceRects = classfier.detectMultiScale(image, 1.2, 2, cv2.CASCADE_SCALE_IMAGE,minSize)
if len(faceRects)>0:
for faceRect in faceRects:
x, y, w, h = faceRect
cv2.rectangle(frame, (x, y), (x+w, y+h), color)
cv2.imshow(“test”, frame)
key=cv2.waitKey(10)
c = chr(key & 255)
if c in [‘q’, ‘Q’, chr(27)]:
break
cv2.destroyWindow(“test”)
识别结果实例：
本地获取图片进行人脸检测并保存图片
从本地路径获取图片，识别人脸后将结果图片再保存在本地。
#!/usr/bin/env python
#coding=utf-8
import os
import sys
from PIL import Image, ImageDraw
import numpy as np
import cv2
#import dlib
def detect_object(infile, save_path):
image = cv2.imread(infile)
”’检测图片，获取人脸在图片中的坐标”’
size=image.shape[:2]#获得当前桢彩色图像的大小
#image_set=np.zeros(size,dtype=np.float16)#定义一个与当前桢图像大小相同的的灰度图像矩阵
image_grey = np.zeros(size, np.uint8)#创建一个空白图片
#image_grey = Image.new(mode= “RGBA”,size = size, color = (117,255,0))
#image_grey = cv2.imread(img_grey)
grey = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)#将当前桢图像转换成灰度图像
#img_binary = cv2.threshold(image,127,255,0)#将灰度图片转化为二进制图片
color = (135,206,250) #设置人脸框的颜色
#eye_cascade = cv2.CascadeClassifier(‘/Users/liuqi/opencv/data/haarcascades/haarcascade_eye.xml’)
classfier=cv2.CascadeClassifier(“/Users/liuqi/opencv/data/haarcascades/haarcascade_frontalface_alt.xml”)
#人脸检测，1.2和2分别为图片缩放比例和需要检测的有效点数
faceRects = classfier.detectMultiScale(grey, scaleFactor = 1.3, minNeighbors = 4, minSize = (32, 32))
result = []
im = Image.open(infile)
if len(faceRects) > 0: #大于0则检测到人脸
draw = ImageDraw.Draw(im)
num = 0
for faceRect in faceRects:
num += 1
#单独框出每一张人脸
x, y, w, h = faceRect
#画出矩形框
#cv2.rectangle(image_grey, (x - 10, y - 10), (x + w + 10, y + h + 10), color, 2)
cv2.rectangle(grey, (x - 10, y - 10), (x + w + 10, y + h + 10), color, 2)
a = im.crop(faceRect)
file_name = os.path.join(save_path,str(num)+”.jpg”)
#a.save(file_name)
”’保存新生成的图片”’
#将当前帧保存为图片
#cv2.imwrite(file_name,image_grey)
cv2.imwrite(file_name,grey)
drow_save_path = os.path.join(save_path,”out.jpg”)
im.save(drow_save_path, “JPEG”, quality=80)
else:
print (“Error: cannot detect faces on %s” % infile)
return 0
def process(infile):
#获取图片，进行检测
#image = cv2.imread(infile)
”’在原图上框出头像并且截取每个头像到单独文件夹”’
#创建输出图片的路径
#im = Image.open(infile)
path = os.path.abspath(infile)
save_path = os.path.splitext(path)[0]+”_face”
try:
os.mkdir(save_path)
except:
pass
faces = detect_object(infile, save_path)
if name == “main“:
process(“/Users/liuqi/Desktop/2.jpg”)
实现的效果如下：
选取的图片与识别后的图片： 
