基于esp32-cam+opencv设计的人脸识别系统（皆可应用在毕设/课设/大作业/实训/竞赛/项目开发）

import cv2 import socket import io import numpy as np import os from PIL import Image import serial # coding=utf-8 import urllib import urllib.request import hashlib aa = 0 #加载训练数据集文件 recogizer=cv2.face.LBPHFaceRecognizer_create() recogizer.read('trainer/trainer.yml') names=[] s = socket.socket(socket.AF_INET,socket.SOCK_DGRAM,0) s.bind(("0.0.0.0",9090)) def face_detect_demo(img): global aa img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)#转换为灰度 face_detector = cv2.CascadeClassifier('C:/Users/16275/AppData/Local/Programs/Python/Python39/Lib/site-packages/cv2/data/haarcascade_frontalface_alt2.xml') face=face_detector.detectMultiScale(gray,1.1,5,cv2.CASCADE_SCALE_IMAGE,(100,100),(300,300)) for x,y,w,h in face: cv2.rectangle(img,(x,y),(x+w,y+h),color=(0,0,255),thickness=2) cv2.circle(img,center=(x+w//2,y+h//2),radius=w//2,color=(0,255,0),thickness=1) # 人脸识别 ids, confidence = recogizer.predict(gray[y:y + h, x:x + w]) #print('标签id:',ids,'置信评分：', confidence) 置信度评分用来衡量所识别人脸与原模型的差距，0 表示完全匹配。 if confidence > 50: cv2.putText(img, 'unkonw', (x + 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 1) print("FFFFFF") else: cv2.putText(img,str(names[ids-1]), (x + 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 1) s.sendto(b'3', ("192.168.43.228", 9090)) print("YYYYYYY") try: port = "COM7" bps = 9600 timeout = 5 # 打开串口，并返回串口对象 uart = serial.Serial(port, bps, timeout=timeout) # 要发送的数据（十六进制） hex_data = "ffabcdfe" # 将十六进制数据转换为字节数据 data = bytes.fromhex(hex_data) # 串口发送数据 length = uart.write(data) print("发送数据长度：", length) # 关闭串口 uart.close() except Exception as result: print("******错误******：", result) cv2.imshow('result',img) aa += 1 def name(): path = './img/' imagePaths=[os.path.join(path,f) for f in os.listdir(path)] for imagePath in imagePaths: name = str(os.path.split(imagePath)[1].split('.',2)[1]) names.append(name) name() while True: data, IP = s.recvfrom(100000) bytes_stream = io.BytesIO(data) image = Image.open(bytes_stream) img = np.asarray(image) face_detect_demo(img) if ord(' ') == cv2.waitKey(10): break cv2.destroyAllWindows()