用OpenCv来做人脸识别

参考这篇文章： http://tech.idv2.com/2012/01/20/face-detection-with-python-opencv/

python比较简单，只需安装 python-opencv 就行：

$ sudo apt-get install python-opencv

python的实现也很简单，参考：http://opencv.willowgarage.com/documentation/python/objdetect_cascade_classification.html

代码：

#!/usr/bin/python
# -*- coding: UTF-8 -*-

# face_detect.py

# Face Detection using OpenCV. Based on sample code from:
# http://python.pastebin.com/m76db1d6b

# Usage: python face_detect.py <image_file>

import sys, os
import cv
from PIL import Image, ImageDraw

def detectObjects(image):
    """Converts an image to grayscale and prints the locations of any faces found"""
    storage = cv.CreateMemStorage()

    cascade = cv.Load('haarcascade_frontalface_alt.xml')
    faces = cv.HaarDetectObjects(image, cascade, storage)

    result = []
    for (x,y,w,h),n in faces:
        result.append((x, y, x+w, y+h))

    return result

def process(infile, outfile):

    image = cv.LoadImage(infile);
    if image:
        faces = detectObjects(image)

    im = Image.open(infile)

    if faces:
        draw = ImageDraw.Draw(im)
        for f in faces:
            draw.rectangle(f, outline=(255, 0, 255))

        im.save(outfile, "JPEG", quality=100)
    else:
        print "Error: cannot detect faces on %s" % infile

if __name__ == "__main__":
    process('input.jpg', 'output.jpg')

注：haarcascade_frontalface_alt.xml 可以在 https://github.com/talvarez/Face.js/tree/master/cascades 找到

Node.js的话，有一个叫Face.js的项目，对OpenCv做了简单封装，地址是： https://github.com/talvarez/Face.js
这个需要先安装OpenCv库，目前版本是2.3.1，按照可以参考： UBUNTU 下编译安装opencv 2.3.1

Face.js的具体的示例代码可以在Face.js里面的example里面找到，这里贴个简单的：

var Face = require('../build/default/face.node'),
  detector = new Face.init();

detector.img = './samples/frame1.png';
detector.maxsize = 20;
detector.pathto = '../cascades/'

detector.oncomplete = function(faces){
  console.log("I found " + faces.length + " faces");
    for(var i = 0; i < faces.length; i++) {
    console.log(faces[i].x, faces[i].y, faces[i].width, faces[i].height);
  }
};

detector.run();