Python基于OpenCV的人脸识别门禁系统源码.zip

import face_recognition import cv2 import os import time import threading from queue import Queue from PIL import Image,ImageDraw,ImageFont from scipy.spatial import distance from imutils import face_utils import dlib import numpy as np def load_image(path,q): print("正在加载已知人员图片.....") facelib = [] # 定义空列表，用来存储所有的面部编码信息 # 文件的路径，文件夹名字，图像名字 for dirpath,dirname,filenames in os.walk(path): print((dirpath,dirname,filenames)) # 把路径与名字拼接起来 for filename in filenames: # 将列表中的元素按照相对于平台的路径分隔符拼接起来 filepath = os.sep.join([dirpath,filename]) # print(filepath) # 加载所有员工的图片 face_image = face_recognition.load_image_file(filepath) # 获取对应的面部编码信息 face_encoding = face_recognition.face_encodings(face_image)[0] facelib.append(face_encoding) # return facelib,filenames while True: q.put((facelib,filenames)) if q.empty() == False: break def eye_aspect_ration(eye): # 计算欧式距离 A = distance.euclidean(eye[1],eye[5]) B = distance.euclidean(eye[2],eye[4]) C = distance.euclidean(eye[0],eye[3]) return (A + B) / (2.0 * C) def start_camera(q): facelib ,facenames = q.get() # print(facelib,facenames) #初始化摄像头参数0表示默认为笔记本的内置第一个摄像头，如果需要读取已有的视频则参数改为视频所在路径路径，例如：cap=cv2.VideoCapture(‘video.mp4’) detector = dlib.get_frontal_face_detector() print(detector) # 加载68个人脸特征数据 predictor = dlib.shape_predictor("data/shape_predictor_68_face_landmarks.dat") print(predictor) # 设置眼睛纵横比阈值 EAR = 0.3 # 假设连续3次帧以上EAR小于0.3 EAR_eye = 3 left_start = 37 - 1 left_end = 42 - 1 right_start = 43 - 1 right_end = 48 - 1 frame_count = 0 # 连续帧的计数 blink_count = 0 # 闭眼计数 video_cap = cv2.VideoCapture(0) # 调用摄像头 while True: # 捕获帧 ret,frame = video_cap.read() gray = cv2.cvtColor(frame,cv2.COLOR_BGR2RGB) rects = detector(gray,1) if len(rects) > 0: # rects[0] ：眼睛部分的特征点 shape = predictor(gray, rects[0]) # 检测特征点 points = face_utils.shape_to_np(shape) # 将特征点转换成numpy数组 # 取出左右眼的特征值 lefteye = points[left_start:left_end + 1] righteye = points[right_start:right_end + 1] leftear = eye_aspect_ration(lefteye) rightear = eye_aspect_ration(righteye) # 求左右眼的EAR平均值 ear = (leftear + rightear) / 2.0 # 寻找左右眼的轮廓 lefthull = cv2.convexHull(lefteye) righthull = cv2.convexHull(righteye) # 绘制轮廓 # image, contours: 需要绘制轮廓, contourIdx：绘制轮廓的索引，-1：自己匹配, color, thickness=None, cv2.drawContours(frame, [lefthull], -1, (0, 0, 255), 2) cv2.drawContours(frame, [righthull], -1, (0, 0, 255), 2) # 如果ear 小于阈值，开始计算连续帧 if ear < EAR: frame_count += 1 else: if frame_count >= EAR_eye: print("眨眼检测成功") blink_count += 1 # frame_count = 0 # 把图片缩小1/4 ,提高识别效率 ,参数 ：识别图片,输出图片的尺寸，fx,fy-> x轴y轴 small_image = cv2.resize(frame,(0,0),fx=0.25,fy=0.25) rbg_image = small_image[:,:,::-1] # BGR -> RBG # 获取摄像头人脸位置 face_locations = face_recognition.face_locations(rbg_image) # 获取人脸位置信息 face_encodings = face_recognition.face_encodings(rbg_image,face_locations) # 获取人脸面部编码 face_names = [] # 定义空列表存储所有的匹配成功人员名字 # 遍历多张人脸 for face_encoding in face_encodings: # 匹配人脸 ，matches true false matches = face_recognition.compare_faces(facelib,face_encoding,tolerance=0.39) name = '未知人员' if True in matches: #取出已知人员的未知索引 first_match_index = matches.index(True) name = facenames[first_match_index][:-4] face_names.append(name) for (top,right,bottom,left),name in zip(face_locations,face_names): top *= 4 right *= 4 bottom *= 4 left *= 4 # 绘制矩形 # img, pt1, pt2, color, thickness=None, lineType=None cv2.rectangle(img=frame,pt1=(left,top),pt2=(right,bottom),color=(0,0,255),thickness=2) # 转换类似于numpy 格式 img_PIL = Image.fromarray(cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)) # 设置字体 font = ImageFont.truetype('msyh.ttc',40) # 生成一张PIL图像 draw = ImageDraw.Draw(img_PIL) draw.text((left + 6 ,bottom -6),name,font=font,fill=(255,255,255)) frame = cv2.cvtColor(np.asarray(img_PIL),cv2.COLOR_RGB2BGR) cv2.imshow('video',frame) if cv2.waitKey(3) & 0xFF == ord('q'): break video_cap.release() cv2.destroyAllWindows() if __name__ == '__main__': start_time = time.time() # facelib,filenames = load_image('image') # print(facelib,filenames) q = Queue(4) t1 = threading.Thread(target=load_image,args=('image',q)) t2 = threading.Thread(target=start_camera,args=(q,)) t1.start() t2.start() end_time = time.time() print(end_time- start_time)