import cv2
 
 
face_name = 'cjw'  # 該人臉的名字
 
 
# 加載OpenCV人臉檢測(cè)分類器
face_cascade = cv2.CascadeClassifier("D:/BaiduNetdiskDownload/python/opencv/opencv-4.5.1/"
                                     "data/haarcascades/haarcascade_frontalface_default.xml")
recognizer = cv2.face.LBPHFaceRecognizer_create()  # 準(zhǔn)備好識(shí)別方法LBPH方法
 
 
camera = cv2.VideoCapture(0)  # 0:開啟攝像頭
success, img = camera.read()  # 從攝像頭讀取照片
W_size = 0.1 * camera.get(3)  # 在視頻流的幀的寬度
H_size = 0.1 * camera.get(4)  # 在視頻流的幀的高度
 
 
def get_face():
    print("正在從攝像頭錄入新人臉信息 \n")
    picture_num = 0  # 設(shè)置錄入照片的初始值
    while True:  # 從攝像頭讀取圖片
        global success  # 設(shè)置全局變量
        global img  # 設(shè)置全局變量
        ret, frame = camera.read()  # 獲得攝像頭讀取到的數(shù)據(jù)(ret為返回值,frame為視頻中的每一幀)
        if ret is True:
            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)  # 轉(zhuǎn)為灰度圖片
        else:
            break
 
        face_detector = face_cascade  # 記錄攝像頭記錄的每一幀的數(shù)據(jù)，讓Classifier判斷人臉
        faces = face_detector.detectMultiScale(gray, 1.3, 5)  # gray是要灰度圖像，1.3為每次圖像尺寸減小的比例，5為minNeighbors
 
        for (x, y, w, h) in faces:  # 制造一個(gè)矩形框選人臉(xy為左上角的坐標(biāo),w為寬，h為高)
            cv2.rectangle(frame, (x, y), (x + w, y + w), (255, 0, 0))
            picture_num += 1  # 照片數(shù)加一
            t = face_name
            cv2.imwrite("./data/1." + str(t) + '.' + str(picture_num) + '.jpg', gray[y:y + h, x:x + w])
            # 保存圖像，將臉部的特征轉(zhuǎn)化為二維數(shù)組，保存在data文件夾內(nèi)
        maximums_picture = 13  # 設(shè)置攝像頭拍攝照片的數(shù)量的上限
        if picture_num > maximums_picture:
            break
        cv2.waitKey(1)
 
 
get_face()

import os
import cv2
from PIL import Image
import numpy as np
 
 
def getlable(path):
    facesamples = []  # 儲(chǔ)存人臉數(shù)據(jù)(該數(shù)據(jù)為二位數(shù)組)
    ids = []  # 儲(chǔ)存星門數(shù)據(jù)
    imagepaths = [os.path.join(path, f) for f in os.listdir(path)]  # 儲(chǔ)存圖片信息
    face_detector = cv2.CascadeClassifier('D:/BaiduNetdiskDownload/python/opencv/opencv-4.5.1/data/haarcascades/'
                                          'haarcascade_frontalface_alt2.xml')  # 加載分類器
    print('數(shù)據(jù)排列：', imagepaths)  # 打印數(shù)組imagepaths
    for imagePath in imagepaths:  # 遍歷列表中的圖片
        pil_img = Image.open(imagePath).convert('L')
        # 打開圖片，灰度化，PIL的兩種不同模式：
        # (1)1(黑白，有像素的地方為1，無(wú)像素的地方為0)
        # (2)L(灰度圖像，把每個(gè)像素點(diǎn)變成0~255的數(shù)值，顏色越深值越大)
        img_numpy = np.array(pil_img, 'uint8')  # 將圖像轉(zhuǎn)化為數(shù)組
        faces = face_detector.detectMultiScale(img_numpy)  # 獲取人臉特征
        id = int(os.path.split(imagePath)[1].split('.')[0])  # 獲取每張圖片的id和姓名
        for x, y, w, h in faces:  # 預(yù)防無(wú)面容照片
            ids.append(id)
            facesamples.append(img_numpy[y:y+h, x:x+w])
        # 打印臉部特征和id
        print('id:', id)
    print('fs:', facesamples)
    return facesamples, ids
 
 
if __name__ == '__main__':
    path = 'D:/BaiduNetdiskDownload/python/opencv/pythonProject/face1/data'  # 圖片路徑
    faces, ids = getlable(path)  # 獲取圖像數(shù)組和id標(biāo)簽數(shù)組和姓名
    recognizer = cv2.face.LBPHFaceRecognizer_create()  # 獲取訓(xùn)練對(duì)象
    recognizer.train(faces, np.array(ids))
    recognizer.write('trainer/trainer.yml')   # 保存生成的人臉特征數(shù)據(jù)文件
 

import cv2
import os
 
 
# 加載訓(xùn)練數(shù)據(jù)集文件
recogizer = cv2.face.LBPHFaceRecognizer_create()
recogizer.read('trainer/trainer.yml')  # 獲取臉部特征數(shù)據(jù)文件
names = []
warningtime = 0
 
 
def face_detect_demo(img):
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  # 轉(zhuǎn)換為灰度圖像
    face_detector = cv2.CascadeClassifier('D:/BaiduNetdiskDownload/python/opencv/opencv-4.5.1/'
                                          'data/haarcascades/haarcascade_frontalface_default.xml')  # 加載分類器
    face = face_detector.detectMultiScale(gray, 1.3, 5, cv2.CASCADE_SCALE_IMAGE, (100, 100), (300, 300))
    # 進(jìn)行識(shí)別，把整張人臉部分框起來(lái)
    for x, y, w, h in face:
        cv2.rectangle(img, (x, y), (x+w, y+h), color=(0, 0, 255), thickness=2)  # 矩形
        cv2.circle(img, center=(x+w//2, y+h//2), radius=w//2, color=(0, 255, 0), thickness=1)  # 圓形
        ids, confidence = recogizer.predict(gray[y:y + h, x:x + w])  # 進(jìn)行預(yù)測(cè)、評(píng)分
        if confidence > 80:
            global warningtime
            warningtime += 1
            if warningtime > 100:  # 警報(bào)達(dá)到一定次數(shù)，說明不是這個(gè)人
                warningtime = 0
            cv2.putText(img, 'unkonw', (x + 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 1)
        else:
            cv2.putText(img, str(names[ids-1]), (x + 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 1)
            # 把姓名打到人臉的框圖上
    cv2.imshow('result', img)
    # print('bug:',ids)
 
 
def name():
    path = 'D:/BaiduNetdiskDownload/python/opencv/pythonProject/face1/data'
    imagepaths = [os.path.join(path, f) for f in os.listdir(path)]
    for imagePath in imagepaths:
        name1 = str(os.path.split(imagePath)[1].split('.', 2)[1])
        names.append(name1)
 
 
cap = cv2.VideoCapture('3.mp4')
name()
while True:
    flag, frame = cap.read()  # 獲得攝像頭讀取到的數(shù)據(jù)(flag為返回值,frame為視頻中的每一幀)
    if not flag:
        break
    face_detect_demo(frame)
    if ord(' ') == cv2.waitKey(10):  # 按空格，退出
        break
cv2.destroyAllWindows()
cap.release()
# print(names)