将相同的Keras Tensorflow预处理变换应用于多个图像

Question

我目前正在使用数据增强功能构建自己的自定义生成器。我必须对两个图像（火车一幅和目标一张）应用相同的变换，但是功能只能一张一张地工作。有什么想法如何在两个图像中应用确切的变换？ 这是我正在尝试的功能：

tf.keras.preprocessing.image.random_zoom

生成器的代码：

def generator(data, batch_size = 32, data_augmentation = False):
batch_number = 0
while True:
    original_train = []
    original_target = []
    train = []
    target = []
    if batch_number * batch_size >= data.shape[0]:
            batch_number = 0        
    for x in train_pd.values[batch_number * batch_size: (batch_number + 1) * batch_size ]:
        train_image = load_image_as_array(train_path+'images/'+x[0]+'.png').reshape(101,101,1)
        target_image = load_image_as_array(train_path+'masks/'+x[0]+'.png').reshape(101,101,1)
        original_train.append(train_image)
        original_target.append(target_image)
        if data_augmentation:
            train_image = random_zoom(train_image, (.25,.25))
            target_image = random_zoom(target_image, (.25,.25))
        train.append(train_image)
        target.append(target_image)            
    batch_number += 1
    yield np.array(train), np.array(target), np.array(original_train), np.array(original_target)

Answer 1

好的。除了您两次调用该函数外，它似乎还应用了精确的转换。