c#结合emgucv进行人脸检测_使用python3.7和opencv4.1来实现人脸识别和人脸特征比对以及模型训练...

pip install opencv-python
pip install opencv-contrib-python
pip install numpy
pip install pillow

[global]
 
trusted-host = mirrors.aliyun.com
 
index-url = http://mirrors.aliyun.com/pypi/simple

import numpy as np
import cv2
 
from settings import src
 
# 人脸识别
faceCascade = cv2.CascadeClassifier(src+'haarcascade_frontalface_default.xml')
 
# 识别眼睛
eyeCascade = cv2.CascadeClassifier(src+'haarcascade_eye.xml')
 
# 开启摄像头
cap = cv2.VideoCapture(0)
ok = True
 
result = []
 
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:    #按 'ESC' to quit
        break
 
cap.release()
cv2.destroyAllWindows()

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
from settings import src
 
cap = cv2.VideoCapture(0)
 
face_detector = cv2.CascadeClassifier(src+'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("./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
        break
 
    elif count >= 1200:  # 得到1000个样本后退出摄像
        break
 
# 关闭摄像头
cap.release()
cv2.destroyAllWindows()

import numpy as np
from PIL import Image
import os
import cv2
from settings import src
# 人脸数据路径
path = 'Facedata'
 
recognizer = cv2.face.LBPHFaceRecognizer_create()
detector = cv2.CascadeClassifier(src+"haarcascade_frontalface_default.xml")
 
def getImagesAndLabels(path):
    imagePaths = [os.path.join(path, f) for f in os.listdir(path)]  
    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('训练需要一定时间，请耐心等待....')
faces, ids = getImagesAndLabels(path)
recognizer.train(faces, np.array(ids))
 
recognizer.write(r'./face_trainer/trainer.yml')
print("{0} faces trained. Exiting Program".format(len(np.unique(ids))))

import cv2
from settings import src
 
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read('./face_trainer/trainer.yml')
cascadePath = src+"haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(cascadePath)
font = cv2.FONT_HERSHEY_SIMPLEX
 
idnum = 0
 
names = ['andonghui', 'admin']
 
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()