我正在对某些图像数据拟合全卷积网络，以使用Keras进行语义分割。但是，我遇到了一些过拟合的问题。我没有那么多数据，我想进行数据扩充。但是，由于我想按像素进行分类，因此我需要进行诸如翻转，旋转和平移之类的增强，以同时应用于特征图像和标签图像。理想情况下，我想使用Keras ImageDataGenerator进行即时转换。但是，据我所知，您不能同时对要素数据和标签数据进行等效转换。
有谁知道是否是这种情况，如果没有，是否有人有任何想法？否则，我将使用其他工具来创建更大的数据集，然后一次全部输入。
谢谢！
有一些关于扩展ImageDataGenerator的工作，以针对这些类型的案例提供更大的灵活性（有关示例，请参阅本期在Github上的案例）。
Additionally, as mentioned by Mikael Rousson in the comments, you can easily create your own version of ImageDataGenerator yourself, while leveraging many of its built-in functions to make it easier. Here is an example code I've used for an image denoising problem, where I use random crops + additive noise to generate clean and noisy image pairs on the fly. You could easily modify this to add other types of augmentations. After which, you can use Model.fit_generator to train using these methods.
from keras.preprocessing.image import load_img, img_to_array, list_pictures
def random_crop(image, crop_size):
height, width = image.shape[1:]
dy, dx = crop_size
if width < dx or height < dy:
return None
x = np.random.randint(0, width - dx + 1)
y = np.random.randint(0, height - dy + 1)
return image[:, y:(y+dy), x:(x+dx)]
def image_generator(list_of_files, crop_size, to_grayscale=True, scale=1, shift=0):
while True:
filename = np.random.choice(list_of_files)
try:
img = img_to_array(load_img(filename, to_grayscale))
except:
return
cropped_img = random_crop(img, crop_size)
if cropped_img is None:
continue
yield scale * cropped_img - shift
def corrupted_training_pair(images, sigma):
for img in images:
target = img
if sigma > 0:
source = img + np.random.normal(0, sigma, img.shape)/255.0
else:
source = img
yield (source, target)
def group_by_batch(dataset, batch_size):
while True:
try:
sources, targets = zip(*[next(dataset) for i in xrange(batch_size)])
batch = (np.stack(sources), np.stack(targets))
yield batch
except:
return
def load_dataset(directory, crop_size, sigma, batch_size):
files = list_pictures(directory)
generator = image_generator(files, crop_size, scale=1/255.0, shift=0.5)
generator = corrupted_training_pair(generator, sigma)
generator = group_by_batch(generator, batch_size)
return generator
You can then use the above like so:
train_set = load_dataset('images/train', (patch_height, patch_width), noise_sigma, batch_size)
val_set = load_dataset('images/val', (patch_height, patch_width), noise_sigma, batch_size)
model.fit_generator(train_set, samples_per_epoch=batch_size * 1000, nb_epoch=nb_epoch, validation_data=val_set, nb_val_samples=1000)
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