opencv-人脸识别-LBPH-Haar

#  sampleCollect.py



import cv2

cap = cv2.VideoCapture(0)  # 打开摄像头0 / 若为“xxxx.mp4”则为视频文件
isOpened = cap.isOpened()  # 判断是否打开/ 为后续提供条件语句
fps = cap.get(cv2.CAP_PROP_FPS)
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
print(fps, width, height)

i = 0  # 第二个人脸收集 i = 1001 i==2001 :break
while isOpened:
    if i == 1001:
        break
    else:
        i += 1
    (flag, frame) = cap.read()  # flag是否读取成功, frame为图片内容
    fileName = "image" + str(i) + ".jpg"
    if flag:
        cv2.imwrite(f"dataset\{fileName}", frame, [cv2.IMWRITE_JPEG_QUALITY, 100])

    cv2.imshow("Hello!", frame)
    if cv2.waitKey(1) == ord("q"):
        break

print("END")

# train.py


import cv2
import numpy as np

face_xml = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')


def samples_and_labels():
    faceData = []
    ids = []
    for i in range(1, 2001):
        path = "dataset/image" + str(i) + ".jpg"
        img = cv2.imread(path)
        imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        faces = face_xml.detectMultiScale(imgGray)

        for (x, y, w, h) in faces:
            if i <= 1000:
                ids.append(1)
            else:
                ids.append(2)
            faceData.append(imgGray[y:y+h, x:x+w])

    return faceData, ids




(faces, ids) = samples_and_labels()
print(faces, ids)
print("Training...")
jackData = cv2.face.LBPHFaceRecognizer_create()  # 创建LBPH

jackData.train(faces, np.array(ids))  # 参数1 为人脸像素数据  参数2 为对应人脸标签
jackData.save("wjhlxq.yml")
print("Finish")

# main.py

import cv2
import numpy as np


cap = cv2.VideoCapture(0)  # 摄像头
success, img = cap.read()  # 从摄像头读取照片


recog = cv2.face.LBPHFaceRecognizer_create()
recog.read("wjhlxq.yml")
face_xml = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")

names = ["wjh", "lxq"]

isOpened = cap.isOpened()

while(isOpened):
    (success, frame) = cap.read()
    frameGray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    face = face_xml.detectMultiScale(frameGray, 1.4, 3)

    for(x, y, w, h) in face:
        cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 255, 1), 2, cv2.LINE_AA)
        ids, confidence = recog.predict(frameGray[y:y+h, x:x+w])
        print(confidence)

        if confidence < 50:
            cv2.putText(frame, names[ids-1], (x, y - 8), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 2)

        else:
            cv2.putText(frame, "unknown", (x, y - 8), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 2)

    cv2.imshow("test", frame)
    if ord("q") == cv2.waitKey(1):
        break