Detailed explanation of Python+OpenCV face recognition technology

We always see face recognition in science fiction movies, and now we can also implement it through programming. Haha~~
OpenCV is Intel® open source computer vision library. It consists of a series of C functions and a small number of C++ classes that implement many common algorithms in image processing and computer vision.
OpenCV has a cross-platform mid- and high-level API including more than 300 C functions. It has no dependencies on other external libraries - although some can be used. It also provides interfaces in Python, Ruby, MATLAB and other languages, and implements many common algorithms in image processing and computer vision.

So overall, OpenCV’s face detection function is very good.

The rendering is as follows:

Next we will use python + OpenCV to implement face recognition.

Development and running environment:
Centos5.5
OpenCV
python2.7
PIL

The code below:

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

# face_detect.py

# Face Detection using OpenCV. Based on sample code from:
# http://www.pythontab.com

# Usage: python face_detect.py

import sys, os
#Introducing the opencv library Corresponding components
from opencv.cv import *
from opencv.highgui import *
#Introduce the PIL library
from PIL import Image, ImageDraw

from math import sqrt

def detectObjects(image):
#First convert the image to gray degree mode to find the face position
grayscale = cvCreateImage(cvSize(image.width, image.height), 8, 1)
cvCvtColor(image, grayscale, CV_BGR2GRAY)

storage = cvCreateMemStorage(0)
cvClearMemStorage(s storage)
cvEqualizeHist(grayscale, grayscale)

cascade = cvLoadHaarClassifierCascade(
'/usr/share/opencv/haarcascades/haarcascade_frontalface_default.xml',
cvSize(1,1))
faces = cvHaarDetectObjects(grayscale, cascade, storage, 1.1, 2,
        CV_HAAR_DO_CANNY_PRUNING, cvSize(20,20))
 
   result = []
    for f in faces:
       result.append((f.x, f.y, f.x+f.width, f.y+f.height) )

return result

def grayscale(r, g, b):
return int(r * .3 + g * .59 + b * .11)

def process(infile, outfile):

image = cvLoadImage(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')

The code ends here. The above example cannot be understood. It doesn't matter, because we use a lot of functions and methods in the library. If If you don’t understand, we can check it online or use the manual. As long as you use these to understand this code, it will be ok. The important thing is to master the facial recognition implementation ideas