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face_recognition.py
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face_recognition.py
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import dlib,os,glob,time
import cv2
import numpy as np
import csv
import pandas as pd
# @author 许翔
# @function 利用opencv和dlib实现人脸识别
# @time 2020-2-13
# 声明各个资源路径
resources_path = os.path.abspath(".")+"\Resources\\"
predictor_path = resources_path + "shape_predictor_68_face_landmarks.dat"
model_path = resources_path + "dlib_face_recognition_resnet_model_v1.dat"
video_path =resources_path + "face_recognition.mp4"
resources_vResult=resources_path+"video\\"
faceDB_path="Resources/featureMean/"
# 加载视频,加载失败则退出
video = cv2.VideoCapture(video_path)
# 获得视频的fps
fps = video.get(cv2.CAP_PROP_FPS)
if not video.isOpened():
print("video is not opened successfully!")
exit(0)
# # 加载模型
#人脸特征提取器
detector = dlib.get_frontal_face_detector()
#人脸关键点标记
predictor= dlib.shape_predictor(predictor_path)
#生成面部识别器
facerec = dlib.face_recognition_model_v1(model_path)
#定义视频创建器,用于输出视频
video_writer = cv2.VideoWriter(resources_vResult+"result1.avi",
cv2.VideoWriter_fourcc(*'XVID'), int(fps),
(int(video.get(cv2.CAP_PROP_FRAME_WIDTH)), int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))))
#读取本地人脸库
head = []
for i in range(128):
fe = "feature_" + str(i + 1)
head.append(fe)
face_path=faceDB_path+"feature_all.csv"
face_feature=pd.read_csv(face_path,names=head)
print(face_feature.shape)
face_feature_array=np.array(face_feature)
print(face_feature_array.shape)
face_list=["Chandler","Joey","Monica","phoebe","Rachel","Ross"]
# 创建窗口
cv2.namedWindow("Face Recognition", cv2.WINDOW_KEEPRATIO)
cv2.resizeWindow("Face Recognition", 720,576)
#计算128D描述符的欧式距离
def compute_dst(feature_1,feature_2):
feature_1 = np.array(feature_1)
feature_2 = np.array(feature_2)
dist = np.linalg.norm(feature_1 - feature_2)
return dist
descriptors = []
faces = []
# 处理视频，按帧处理
ret,frame = video.read()
flag = True # 标记是否是第一次迭代
i = 0 # 记录当前迭代到的帧位置
while ret:
if i % 6== 0: # 每6帧截取一帧
# 转为灰度图像处理
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
dets = detector(gray, 1) # 检测帧图像中的人脸
# for i in range(len(dets)):
# landmarks = np.matrix([[p.x, p.y] for p in predictor(gray,dets[i]).parts()])
# 处理检测到的每一张人脸
if len(dets)>0:
for index,value in enumerate(dets):
#获取面部关键点
shape = predictor(gray,value)
#pos = (value[0, 0], value[0, 1])
#标记人脸
cv2.rectangle(frame, (value.left(), value.top()), (value.right(), value.bottom()), (0, 255, 0), 2)
#进行人脸识别并打上姓名标签
# 提取特征-图像中的68个关键点转换为128D面部描述符，其中同一人的图片被映射到彼此附近，并且不同人的图片被远离地映射。
face_descriptor = facerec.compute_face_descriptor(frame, shape)
v = np.array(face_descriptor)
print(v.shape)
l = len(descriptors)
Flen=len(face_list)
flag=0
for j in range(Flen):
# 人脸匹配，距离小于阈值，表示识别成功，打上标签
if(compute_dst(v,face_feature_array[j])<0.56):
flag=1
cv2.putText(frame,face_list[j],(value.left(), value.top()),cv2.FONT_HERSHEY_COMPLEX,0.8, (0, 255, 255), 1, cv2.LINE_AA)
break
if(flag==0):
cv2.putText(frame,"Unknonw", (value.left(), value.top()), cv2.FONT_HERSHEY_COMPLEX, 0.8, (0, 255, 255), 1,
cv2.LINE_AA)
#标记关键点
for pti,pt in enumerate(shape.parts()):
pos=(pt.x,pt.y)
cv2.circle(frame, pos, 1, color=(0, 255, 0))
#faces.append(frame)
# 将第一张人脸照片直接保存
if flag:
descriptors.append(v)
faces.append(frame)
flag = False
else:
sign = True # 用来标记当前人脸是否为新的
for i in range(l):
distance = compute_dst(descriptors[i] , v) # 计算两张脸的欧式距离，判断是否是一张脸
# 取阈值0.5，距离小于0.5则认为人脸已出现过
if distance < 0.4:
# print(faces[i].shape)
face_gray = cv2.cvtColor(faces[i], cv2.COLOR_BGR2GRAY)
# 比较两张人脸的清晰度，保存更清晰的人脸
if cv2.Laplacian(gray, cv2.CV_64F).var() > cv2.Laplacian(face_gray, cv2.CV_64F).var():
faces[i] = frame
sign = False
break
# 如果是新的人脸则保存
if sign:
descriptors.append(v)
faces.append(frame)
cv2.imshow("Face Recognition", frame) # 在窗口中显示
exitKey= cv2.waitKey(1)
if exitKey == 27:
video.release()
video_writer.release()
cv2.destroyWindow("Face Recognition")
break
video_writer.write(frame)
ret,frame = video.read()
i += 1
print(len(descriptors)) # 输出不同的人脸数
print(len(faces)) #输出的照片数
# 将不同的比较清晰的人脸照片输出到本地
j = 1
for fc in faces:
cv2.imwrite(resources_path + "\pictures\\" + str(j) +".jpg", fc)
j += 1