opencv 简单的人脸识别

对于opencv 它提供了许多已经练习好的模型可供使用，我们需要通过他们来进行人脸识别

参考了网上许多资料 

假设你已经配好了开发环境 ，在我之前的博客中由开发环境的配置。

项目代码结构：

 

dataSet ： 存储训练用的图片,他由data_gen生成，当然也可以修改代码由其他方式生成

haarcascade_frontalface_alt.xml  、 haarcascade_frontalface_default.xml: 用于人脸检测的haar分类器，网上普遍说第一个效果更好，第二个运行速度更快

data_gen.py：生成我们所需的数据

trainer.py: 训练数据集

train.yml: 由train.py生成的人脸识别模型，供后面的人脸识别使用

recognize.py：视频中的人脸识别

 

 

data_gen.py

连续拍20张照片当作训练数据，每个人建立一组数据

import cv2

detector = cv2.CascadeClassifier('haarcascade_frontalface_alt.xml')
cap = cv2.VideoCapture(0)
sampleNum = 0
Id = input('enter your id: ')

while True:
    ret, img = cap.read()
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = detector.detectMultiScale(gray, 1.3, 5)
    for (x, y, w, h) in faces:
        cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)

        # incrementing sample number
        sampleNum = sampleNum + 1
        # saving the captured face in the dataset folder
        cv2.imwrite("dataSet/User." + str(Id) + '.' + str(sampleNum) + ".jpg", gray[y:y + h, x:x + w])  #

        cv2.imshow('frame', img)
    # wait for 100 miliseconds
    if cv2.waitKey(100) & 0xFF == ord('q'):
        break
    # break if the sample number is morethan 20
    elif sampleNum > 20:
        break

cap.release()
cv2.destroyAllWindows()

 

train.py

训练数据

import cv2
import os
import numpy as np
from PIL import Image

# recognizer = cv2.createLBPHFaceRecognizer()
detector = cv2.CascadeClassifier("/Users/qiuchenglin/PycharmProjects/face_recognize/haarcascade_frontalface_alt.xml")
recognizer = cv2.face.LBPHFaceRecognizer_create()


def get_images_and_labels(path):
    image_paths = [os.path.join(path, f) for f in os.listdir(path)]
    face_samples = []
    ids = []

    for image_path in image_paths:
        image = Image.open(image_path).convert('L')
        image_np = np.array(image, 'uint8')
        if os.path.split(image_path)[-1].split(".")[-1] != 'jpg':
            continue
        image_id = int(os.path.split(image_path)[-1].split(".")[1])
        faces = detector.detectMultiScale(image_np)
        for (x, y, w, h) in faces:
            face_samples.append(image_np[y:y + h, x:x + w])
            ids.append(image_id)

    return face_samples, ids


Faces, Ids = get_images_and_labels('dataSet')
recognizer.train(Faces, np.array(Ids))
recognizer.save('trainner.yml')

 

recognize.py

下面就是根据训练好的模型进行人脸识别，根据之前生成数据的编号，可以填入相对应的人名，例如以下示例我训练了三组人的数据

import cv2
import numpy as np

recognizer = cv2.face.LBPHFaceRecognizer_create()
# recognizer = cv2.createLBPHFaceRecognizer() # in OpenCV 2
recognizer.read('/Users/qiuchenglin/PycharmProjects/face_recognize/trainner.yml')
# recognizer.load('trainner/trainner.yml') # in OpenCV 2

cascade_path = "/Users/qiuchenglin/PycharmProjects/face_recognize/haarcascade_frontalface_alt.xml"
face_cascade = cv2.CascadeClassifier(cascade_path)
cam = cv2.VideoCapture(0)
# font = cv2.cv.InitFont(cv2.cv.CV_FONT_HERSHEY_SIMPLEX, 1, 1, 0, 1, 1) # in OpenCV 2
font = cv2.FONT_HERSHEY_SIMPLEX

while True:
    ret, im = cam.read()
    gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.2, 5)
    for (x, y, w, h) in faces:
        cv2.rectangle(im, (x - 50, y - 50), (x + w + 50, y + h + 50), (225, 0, 0), 2)
        img_id, conf = recognizer.predict(gray[y:y + h, x:x + w])
        if conf > 50:
            if img_id == 1:
                img_id = 'liuzb'
            elif img_id == 2:
                img_id = 'linqc'
            elif img_id == 3:
                img_id = 'keaibao'
        else:
            img_id = "Unknown"
        # cv2.cv.PutText(cv2.cv.fromarray(im), str(Id), (x, y + h), font, 255)
        cv2.putText(im, str(img_id), (x, y), font, 1, (0, 255, 0), 1)
    cv2.imshow('im', im)
    if cv2.waitKey(10) & 0xFF == ord('q'):
        break

cam.release()
cv2.destroyAllWindows()

 

简单的一个人脸识别就完成了，只能说准确率没有非常高 

之后想办法进行提高。