前序准备

1.建立文件夹

2.在下述代码中文件路径改为自己的文件路径

FaceDetection

import numpy as np
import cv2

# 人脸识别分类器
faceCascade = cv2.CascadeClassifier(r'D:/Python/face/Cascades/haarcascade_frontalface_default.xml')

# 识别眼睛的分类器
eyeCascade = cv2.CascadeClassifier(r'D:/Python/face/Cascades/haarcascade_eye.xml')

# 开启摄像头
cap = cv2.VideoCapture(0)
ok = True

while ok:
    # 读取摄像头中的图像，ok为是否读取成功的判断参数
    ok, img = cap.read()
    # 转换成灰度图像
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    # 人脸检测
    faces = faceCascade.detectMultiScale(
        gray,
        scaleFactor=1.2,
        minNeighbors=5,
        minSize=(32, 32)
    )

    # 在检测人脸的基础上检测眼睛
    for (x, y, w, h) in faces:
        fac_gray = gray[y: (y+h), x: (x+w)]
        result = []
        eyes = eyeCascade.detectMultiScale(fac_gray, 1.3, 2)

        # 眼睛坐标的换算，将相对位置换成绝对位置
        for (ex, ey, ew, eh) in eyes:
            result.append((x+ex, y+ey, ew, eh))

    # 画矩形
    for (x, y, w, h) in faces:
        cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)

    for (ex, ey, ew, eh) in result:
        cv2.rectangle(img, (ex, ey), (ex+ew, ey+eh), (0, 255, 0), 2)

    cv2.imshow('video', img)

    k = cv2.waitKey(1)
    if k == 27:    # press 'ESC' to quit
        break

cap.release()
cv2.destroyAllWindows()

安装numpy包：pip install numpy

安装cv2包：利用清华源
pip install opencv-contrib-python -i pypi.tuna.tsinghua.edu.cn/simple

pip install opencv-python -i pypi.tuna.tsinghua.edu.cn/simple

安装pillow包：pip install pillow

FaceDataCollect

import cv2
import os
# 调用笔记本内置摄像头，所以参数为0，如果有其他的摄像头可以调整参数为1，2

cap = cv2.VideoCapture(0)

face_detector = cv2.CascadeClassifier('D:/Python/face/Cascades/haarcascade_frontalface_default.xml')

face_id = input('\n enter user id:')

print('\n Initializing face capture. Look at the camera and wait ...')

count = 0

while True:

    # 从摄像头读取图片

    sucess, img = cap.read()

    # 转为灰度图片

    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    # 检测人脸

    faces = face_detector.detectMultiScale(gray, 1.3, 5)

    for (x, y, w, h) in faces:
        cv2.rectangle(img, (x, y), (x+w, y+w), (255, 0, 0))
        count += 1

        # 保存图像
        cv2.imwrite("D:/Python/face/Facedata/User." + str(face_id) + '.' + str(count) + '.jpg', gray[y: y + h, x: x + w])

        cv2.imshow('image', img)

    # 保持画面的持续。

    k = cv2.waitKey(1)

    if k == 27:   # 通过esc键退出摄像1
        break

    elif count >= 10:  # 得到1000个样本后退出摄像
        break

# 关闭摄像头
cap.release()
cv2.destroyAllWindows()

face_training

import numpy as np
from PIL import Image
import os
import cv2
# 人脸数据路径
path = 'D:/Python/face/Facedata'

recognizer = cv2.face.LBPHFaceRecognizer_create()
detector = cv2.CascadeClassifier("D:/Python/face/Cascades/haarcascade_frontalface_default.xml")

def getImagesAndLabels(path):
    imagePaths = [os.path.join(path, f) for f in os.listdir(path)]  # join函数的作用？
    faceSamples = []
    ids = []
    for imagePath in imagePaths:
        PIL_img = Image.open(imagePath).convert('L')   # convert it to grayscale
        img_numpy = np.array(PIL_img, 'uint8')
        id = int(os.path.split(imagePath)[-1].split(".")[1])
        faces = detector.detectMultiScale(img_numpy)
        for (x, y, w, h) in faces:
            faceSamples.append(img_numpy[y:y + h, x: x + w])
            ids.append(id)
    return faceSamples, ids


print('Training faces. It will take a few seconds. Wait ...')
faces, ids = getImagesAndLabels(path)
recognizer.train(faces, np.array(ids))

recognizer.write(r'D:/Python/face/face_training/trainer.yml')
print("{0} faces trained. Exiting Program".format(len(np.unique(ids))))

face_recognition

import cv2

recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read('D:/Python/face/face_training/trainer.yml')
cascadePath = "D:/Python/face/Cascades/haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(cascadePath)
font = cv2.FONT_HERSHEY_SIMPLEX

idnum = 0

names = ['Allen', 'Bob']

cam = cv2.VideoCapture(0)
minW = 0.1*cam.get(3)
minH = 0.1*cam.get(4)

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

    faces = faceCascade.detectMultiScale(
        gray,
        scaleFactor=1.2,
        minNeighbors=5,
        minSize=(int(minW), int(minH))
    )

    for (x, y, w, h) in faces:
        cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2)
        idnum, confidence = recognizer.predict(gray[y:y+h, x:x+w])

        if confidence < 100:
            idnum = names[idnum]
            confidence = "{0}%".format(round(100 - confidence))
        else:
            idnum = "unknown"
            confidence = "{0}%".format(round(100 - confidence))

        cv2.putText(img, str(idnum), (x+5, y-5), font, 1, (0, 0, 255), 1)
        cv2.putText(img, str(confidence), (x+5, y+h-5), font, 1, (0, 0, 0), 1)

    cv2.imshow('camera', img)
    k = cv2.waitKey(10)
    if k == 27:
        break

cam.release()
cv2.destroyAllWindows()