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！")