python人脸识别实时教程
python dlib人脸识别代码实例本文实例为大家分享了python dlib人脸识别的具体代码，供大家参考，具体内容如下
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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" alt="python人脸识别实时教程（python dlib人脸识别代码实例）" border="0" /> 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" alt="python人脸识别实时教程（python dlib人脸识别代码实例）" border="0" />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" alt="python人脸识别实时教程（python dlib人脸识别代码实例）" border="0" /> 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() |
以上所述是小编给大家介绍的python dlib人脸识别详解整合，希望对大家有所帮助，如果大家有任何疑问请给我留言，小编会及时回复大家的。在此也非常感谢大家对开心学习网网站的支持！
原文链接：https://blog.csdn.net/MAILLIBIN/article/details/88979691