Python + Django + face |
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前段时间，公司人工智能平台关于人脸识别授权到期，与某公司续签续期合同，私有化部署居然需要二十多万，不禁咂舌。 以此为背景想到，前段时间学习Python+人工智能，了解到有很多的第三方开源项目，可以不必亲自写高深莫测的算法，直接使用既能实现人脸识别。 于是，选择face_recognition人脸识别开源库，实现一个强大、简单、易上手的人脸识别系统案例； 废话不多说，先上设计思路： 1.设计思路 总体按照人脸识别、人脸比对以及识别记录，最后实现在线识别API接口。 2.模块设计 models 首先简单设计一下数据库模块； 两个表IMG、faceImg，分别保存原始图片和识别到的人脸图片；from dja 123456789101112from django.db import models # Create your models here. class IMG(models.Model): img = models.ImageField(upload_to='img') name = models.CharField(max_length=20) class faceImg(models.Model): parentid = models.ForeignKey(IMG,on_delete=models.CASCADE,verbose_name='来源',default=0) img = models.CharField(max_length=200,verbose_name='文件名',default='') name = models.CharField(max_length=20)3.功能代码 views 核心代码 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149#获取上传照片信息，切取人脸 def uploadImg(request): """ 图片上传 :param request: :return: """ if requesthod == 'POST': mdl = request.POST.get('mdl','hog') new_img = getImageFromRequest(request,img='img') if new_img != None : new_img.save() print(new_img.id) start = datetime.now() face_locations = recognition(new_img.img,mdl) end = datetime.now() print(end - start) # 遍历每个人脸，并切出单独人脸保存 faceNum = len(face_locations) urls = [] for i in range(0, faceNum): top = face_locations[i][0] right = face_locations[i][1] bottom = face_locations[i][2] left = face_locations[i][3] box = (left, top,right,bottom) im = Image.open(new_img.img) face_img = im.crop(box) f = datetime.now().strftime('%Y%m%d%H%M%S%f') + str(i) + '.jpg' print(f) face_img.save( os.path.join(settings.MEDIA_ROOT, 'face/') + f,'jpeg') face1 = faceImg( parentid = new_img, img = os.path.join(settings.MEDIA_URL, 'face/') + f , name= f ) face1.save() urls.append(os.path.join(settings.MEDIA_URL, 'face/') + f) print(faceNum) return render(request, 'uploading.html',{ 'source':new_img.img.url,'faceNum':faceNum,'urls': urls,'useTime':end - start }) else: return render(request, 'uploading.html',{'faceNum':'请选择图片'}) else: return render(request, 'uploading.html') #调用人脸识别，获取人脸位置 def recognition(object,modal): img = face_recognition.load_image_file(object) face_locations = face_recognition.face_locations(img,1,modal) print(face_locations) return face_locations #通过request获取image file def getImageFromRequest(request,img): try: return IMG( img=request.FILES.get(img), name=request.FILES.get(img).name ) except Exception as e: return None #两个照片比对 def image_compare_image(img1,img2,threshold): im1 = face_recognition.load_image_file(img1.img) im2 = face_recognition.load_image_file(img2.img) try: img1_encoding = face_recognition.face_encodings(im1)[0] img2_encoding = face_recognition.face_encodings(im2)[0] known_faces = [img1_encoding] sort = face_recognition.face_distance(known_faces, img2_encoding) # sort = 1 - sort print('sort = %f' % sort) result = face_recognition.compare_faces(known_faces, img2_encoding, tolerance=float(threshold)) print(result) return sort, result except Exception as e: print(e) return -1,False #人脸比对 def compare(request): if requesthod == 'POST': urls = [] sort = 0 result = False t = request.POST.get('t',0.6) print('t = %s'%t) img1 = getImageFromRequest(request,img='img1') img2 = getImageFromRequest(request, img='img2') start = datetime.now() if img1 != None and img2 != None: sort,result = image_compare_image(img1,img2,t) end = datetime.now() print('use time : %s' %str(end - start)) return render(request, 'compare.html',{'urls':urls,'sort':sort ,'result':result,'useTime':end-start}) else: return render(request, 'compare.html',{}) #Web api 接口，返回人脸位置json @csrf_exempt def getFaceLocation(request): if requesthod == 'POST': new_img = getImageFromRequest(request,img='img') if new_img != None: mdl = request.POST.get('mdl', 'hog') start = datetime.now() face_locations = recognition(new_img.img,mdl) end = datetime.now() print('use time : %s' % str(end - start)) # 遍历每个人脸，并标注 faceNum = len(face_locations) return JsonResponse({'result':faceNum,'locations':face_locations,'useTime':end-start}) return JsonResponse({'result':0,'locations':[]}) #Web api 接口，返回人脸比对分数 @csrf_exempt def getCompare(request): if requesthod == 'POST': sort = 0 result = False t = request.POST.get('t',0.6) img1 = getImageFromRequest(request,img='img1') img2 = getImageFromRequest(request, img='img2') start = datetime.now() if img1 != None and img2 != None: sort, result = image_compare_image(img1, img2, t) end = datetime.now() print('use time : %s' %str(end - start)) return JsonResponse({'sort':sort,'result':result}) return None4.测试报告，识别率说明 本项目，有2中模式，CPU和GPU，两种模式都要求高质量的照片； 经过测试N多的照片，单人多人的都有的情况下： cpu模式中，速度快，但受到戴帽子、图片质量差的、背景太亮、逆光、人脸相对较小、人脸角度较大等因素的影响，识别率可达到90%左右； gpu模式中，速度慢，对戴帽子的、人脸相对较小、人脸角度较大、识别率较高；但背景太亮、逆光、图片质量差的，识别较差，识别率可达98%； 亲，如果您觉得本文不错，愿意给我一些动力的话，请用手机扫描二维码即可向我打赏 |
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