与Django结合利用模型对上传图片预测的实例详解

def prepicture(picname):  img = Image.open('./media/pic/' + picname)  new_img = img.resize((100, 100), Image.BILINEAR)  new_img.save(os.path.join('./media/pic/', os.path.basename(picname)))

def read_image2(filename):  img = Image.open('./media/pic/'+filename).convert('RGB')  return np.array(img)

def testcat(picname):  # 预处理图片 变成100 x 100  prepicture(picname)  x_test = []  x_test.append(read_image2(picname))  x_test = np.array(x_test)  x_test = x_test.astype('float32')  x_test /= 255  keras.backend.clear_session() #清理session反复识别注意  model = Sequential()  model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(100, 100, 3)))  model.add(Conv2D(32, (3, 3), activation='relu'))  model.add(MaxPooling2D(pool_size=(2, 2)))  model.add(Dropout(0.25))  model.add(Conv2D(64, (3, 3), activation='relu'))  model.add(Conv2D(64, (3, 3), activation='relu'))  model.add(MaxPooling2D(pool_size=(2, 2)))  model.add(Dropout(0.25))  model.add(Flatten())  model.add(Dense(256, activation='relu'))  model.add(Dropout(0.5))  model.add(Dense(4, activation='softmax'))  sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)  model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])  model.load_weights('./cat/cat_weights.h5')  classes = model.predict_classes(x_test)[0]  # target = ['布偶猫', '孟买猫', '暹罗猫', '英国短毛猫']  # print(target[classes])  return classes

def catinfo(request):  if request.method == "POST":    f1 = request.FILES['pic1']    # 用于识别    fname = '%s/pic/%s' % (settings.MEDIA_ROOT, f1.name)    with open(fname, 'wb') as pic:      for c in f1.chunks():        pic.write(c)    # 用于显示    fname1 = './static/img/%s' % f1.name    with open(fname1, 'wb') as pic:      for c in f1.chunks():        pic.write(c)    num = testcat(f1.name)    # 有的数据库id从1开始这样就会报错    # 因此原本数据库中的id=0被系统改为id=4    # 遇到这样的问题就加上    # if(num == 0):    #  num = 4     # 通过id获取猫的信息    name = models.Catinfo.objects.get(id = num)    return render(request, 'info.html', {'nameinfo': name.nameinfo, 'feature': name.feature, 'livemethod': name.livemethod, 'feednn': name.feednn, 'feedmethod': name.feedmethod, 'picname': f1.name})  else:    return HttpResponse("上传失败！")