数据增强图像数据生成器Keras语义分割

2 回答

• 11

  有关扩展ImageDataGenerator的工作对于这些类型的情况更加灵活（参见this issue on Github中的示例） .

  此外，正如Mikael Rousson在评论中所提到的，您可以自己轻松创建自己的ImageDataGenerator版本，同时利用其许多内置函数使其更容易 . 下面是我用于图像去噪问题的示例代码，其中我使用随机作物加性噪声来动态生成干净且嘈杂的图像对 . 您可以轻松修改此选项以添加其他类型的扩充 . 之后，您可以使用Model.fit_generator来训练使用这些方法 .

  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
  

  然后您可以使用以上内容：

  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)
  

  回复于 2024-04-23T19:45:21+08:00
• 11

  是的你可以 . 这里是's an example from Keras'的文档 . 你将两个用相同种子播种的发生器和fit_generator压缩在一起 . https://keras.io/preprocessing/image/

  # we create two instances with the same arguments 
  data_gen_args = dict(featurewise_center=True,
                       featurewise_std_normalization=True,
                       rotation_range=90.,
                       width_shift_range=0.1,
                       height_shift_range=0.1,
                       zoom_range=0.2) 
  image_datagen = ImageDataGenerator(**data_gen_args) 
  mask_datagen = ImageDataGenerator(**data_gen_args)
  
  # Provide the same seed and keyword arguments to the fit and flow methods seed = 1 
  image_datagen.fit(images, augment=True, seed=seed) 
  mask_datagen.fit(masks, augment=True, seed=seed)
  
  image_generator = image_datagen.flow_from_directory(
      'data/images',
      class_mode=None,
      seed=seed)
  
  mask_generator = mask_datagen.flow_from_directory(
      'data/masks',
      class_mode=None,
      seed=seed)
  
  # combine generators into one which yields image and masks 
  train_generator = zip(image_generator, mask_generator)
  
  model.fit_generator(
      train_generator,
      samples_per_epoch=2000,
      nb_epoch=50)
  

  回复于 2024-04-23T19:45:21+08:00