opencv python训练人脸识别

总计分为三个步骤

　　一、捕获人脸照片

　　二、对捕获的照片进行训练

　　三、加载训练的数据，识别

使用python3.6.8，opencv，numpy，pil

第一步：通过笔记本前置摄像头捕获脸部图片

　　将捕获的照片存在picData文件夹中，并格式为user.id.num.jpg。id在识别时和人名数组一一对应。

import numpy as np
import cv2

cap = cv2.VideoCapture0)
face_cascade = cv2.CascadeClassifier"data/haarcascade_frontalface_default.xml")
sampleNum = 0
Id = input'请输入id：')

while True:
    ret, img = cap.read)
    gray = cv2.cvtColorimg, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScalegray, 1.3, 5)
    for x, y, w, h) in faces:
        img = cv2.rectangleimg, x, y), x + w, y + h), 255, 0, 0), 2)
        # 增加例子数
        sampleNum = sampleNum + 1
       
        # 把照片保存到数据集文件夹
        cv2.imwrite
            "picData/user." + strId) + "." + strsampleNum) + ".jpg",
            gray[y : y + h, x : x + w],
        )
    cv2.imshow"img", img)
    if cv2.waitKey1) & 0xFF == ord"q"):
        break
    if sampleNum == 3000:
        break

cap.release)
cv2.destroyAllWindows)

第二步：训练数据

　　将训练好的数据存储在data/trainner.yml中

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

recognizer = cv2.face.LBPHFaceRecognizer_create)
detector = cv2.CascadeClassifier"data/haarcascade_frontalface_default.xml")


def get_images_and_labelspath):
    image_paths = [os.path.joinpath, f) for f in os.listdirpath)]
    face_samples = []
    ids = []

    for image_path in image_paths:
        image = Image.openimage_path).convert"L")
        image_np = np.arrayimage, "uint8")
        if os.path.splitimage_path)[-1].split".")[-1] != "jpg":
            continue
        image_id = intos.path.splitimage_path)[-1].split".")[1])
        faces = detector.detectMultiScaleimage_np)
        for x, y, w, h) in faces:
            face_samples.appendimage_np[y : y + h, x : x + w])
            ids.appendimage_id)

    return face_samples, ids


faces, Ids = get_images_and_labels"picData")
recognizer.trainfaces, np.arrayIds))
recognizer.save"data/trainner.yml")

第三步：人脸识别

 　　加载第二步训练的数据，定义需要识别的人名。

import cv2

recognizer = cv2.face.LBPHFaceRecognizer_create)
recognizer.read'data/trainner.yml')
faceCascade = cv2.CascadeClassifier"data/haarcascade_frontalface_default.xml")
font = cv2.FONT_HERSHEY_SIMPLEX

idnum = 0

names = ['kAng']

cam = cv2.VideoCapture0)
minW = 0.1*cam.get3)
minH = 0.1*cam.get4)

while True:
    ret, img = cam.read)
    gray = cv2.cvtColorimg, cv2.COLOR_BGR2GRAY)

    faces = faceCascade.detectMultiScale
        gray,
        scaleFactor=1.2,
        minNeighbors=5,
        minSize=intminW), intminH))
    )

    for x, y, w, h) in faces:
        cv2.rectangleimg, x, y), x+w, y+h), 0, 255, 0), 2)
        idnum, confidence = recognizer.predictgray[y:y+h, x:x+w])

        if confidence >50:
            idnum = names[idnum]
            confidence = "{0}%".formatroundconfidence))
        else:
            idnum = "unknown"
            confidence = "{0}%".formatroundconfidence))

        cv2.putTextimg, stridnum), x+5, y-5), font, 1, 0, 0, 255), 1)
        cv2.putTextimg, strconfidence), x+5, y+h-5), font, 1, 255, 255, 0), 1)

    cv2.imshow'camera', img)
    k = cv2.waitKey10)
    if k == 27:
        break

cam.release)
cv2.destroyAllWindows)

　　

　效果图：

 

参考：https://segmentfault.com/a/1190000014943784（详细解析）