基于OpenCv的人脸识别

训练模型

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


FACEIDEN = "F:/Program Files/openCV/opencv/sources/data/haarcascades/haarcascade_frontalface_default.xml"
PATH = './trains/'


def get_image_path_id(path):
    facesSample = []
    ids = []
    imagePath = [os.path.join(path, f) for f in os.listdir(path)]
    faceDetector = cv2.CascadeClassifier(FACEIDEN)
    for itemPath in imagePath:
        PLI_img = Image.open(itemPath).convert('L')
        ima_numpy = np.array(PLI_img)
        faces = faceDetector.detectMultiScale(ima_numpy)
        id = int(os.path.split(itemPath)[1].split('.')[0])
        for x, y, w, h in faces:
            facesSample.append(ima_numpy[y:y + h, x:x + w])
            ids.append(id)

    return facesSample, ids


if __name__ == '__main__':
    path = PATH
    faces, ids = get_image_path_id(path)
    recognizer = cv2.face.LBPHFaceRecognizer_create()
    recognizer.train(faces, np.array(ids))
    recognizer.write("./yml/train.yml")
    print("训练完成")

识别模型

import cv2

FACEIDEN = "" # 填写opencv的面部识别 yml文件

recogizer = cv2.face.LBPHFaceRecognizer_create()
recogizer.read('yml/train.yml')
img = cv2.imread('1.jpg') #20200721134535.jpg  IMG_20200721_134537
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faceDetector = cv2.CascadeClassifier(FACEIDEN)
faces = faceDetector.detectMultiScale(gray)
for x, y, w, h in faces:
    cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
    id, confidence = recogizer.predict(gray[y:y + h, x:x + w])
    print(id, confidence, "评分")
resize = cv2.resize(img, (300, 400))
cv2.imshow('result', resize)
cv2.waitKey(0)
cv2.destroyAllWindows()