python dlib人脸识别代码实例

本文实例为大家分享了python/' target='_blank'>python dlib人脸识别的具体代码，供大家参考，具体内容如下

import matplotlib.pyplot as pltimport dlibimport numpy as npimport globimport re #正脸检测器detector=dlib.get_frontal_face_detector()#脸部关键形态检测器sp=dlib.shape_predictor(r"D:\LB\JAVASCRIPT\shape_predictor_68_face_landmarks.dat")#人脸识别模型facerec = dlib.face_recognition_model_v1(r"D:\LB\JAVASCRIPT\dlib_face_recognition_resnet_model_v1.dat") #候选人脸部描述向量集descriptors=[] photo_locations=[] for photo in glob.glob(r'D:\LB\JAVASCRIPT\faces\*.jpg'):  photo_locations.append(photo)  img=plt.imread(photo)  img=np.array(img)    #开始检测人脸  dets=detector(img,1)    for k,d in enumerate(dets):    #检测每张照片中人脸的特征    shape=sp(img,d)    face_descriptor=facerec.compute_face_descriptor(img,shape)    v=np.array(face_descriptor)    descriptors.append(v)#输入的待识别的人脸处理方法相同img=plt.imread(r'D:\test_photo10.jpg')img=np.array(img)dets=detector(img,1)#计算输入人脸和已有人脸之间的差异程度（比如用欧式距离来衡量）differences=[]for k,d in enumerate(dets):  shape=sp(img,d)  face_descriptor=facerec.compute_face_descriptor(img,shape)  d_test=np.array(face_descriptor)    #计算输入人脸和所有已有人脸描述向量的欧氏距离  for i in descriptors:    distance=np.linalg.norm(i-d_test)    differences.append(distance) #按欧式距离排序 欧式距离最小的就是匹配的人脸candidate_count=len(photo_locations)candidates_dict=dict(zip(photo_locations,differences))candidates_dict_sorted=sorted(candidates_dict.items(),key=lambda x:x[1]) #matplotlib要正确显示中文需要设置plt.rcParams['font.family'] = ['sans-serif']plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['figure.figsize'] = (20.0, 70.0)  ax=plt.subplot(candidate_count+1,4,1)ax.set_title("输入的人脸")ax.imshow(img) for i,(photo,distance) in enumerate(candidates_dict_sorted):  img=plt.imread(photo)    face_name=""  photo_name=re.search(r'([^\\]*)\.jpg$',photo)  if photo_name:    face_name=photo_name[1]      ax=plt.subplot(candidate_count+1,4,i+2)  ax.set_xticks([])  ax.set_yticks([])  ax.spines['top'].set_visible(False)  ax.spines['right'].set_visible(False)  ax.spines['bottom'].set_visible(False)  ax.spines['left'].set_visible(False)    if i==0:    ax.set_title("最匹配的人脸\n\n"+face_name+"\n\n差异度："+str(distance))  else:    ax.set_title(face_name+"\n\n差异度："+str(distance))  ax.imshow(img) plt.show()

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