Python preprocessing.preprocess_images() Examples

The following are 7 code examples of preprocessing.preprocess_images(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module preprocessing , or try the search function .
Example #1
Source File: model.py    From finetune_classification with Apache License 2.0 6 votes vote down vote up 
def preprocess(self, inputs):
        """preprocessing.
        
        Outputs of this function can be passed to loss or postprocess functions.
        
        Args:
            preprocessed_inputs: A float32 tensor with shape [batch_size,
                height, width, num_channels] representing a batch of images.
            
        Returns:
            prediction_dict: A dictionary holding prediction tensors to be
                passed to the Loss or Postprocess functions.
        """
        preprocessed_inputs = preprocessing.preprocess_images(
            inputs, self._default_image_size, self._default_image_size, 
            resize_side_min=self._fixed_resize_side,
            is_training=self._is_training,
            border_expand=True, normalize=False,
            preserving_aspect_ratio_resize=False)
        preprocessed_inputs = tf.cast(preprocessed_inputs, tf.float32)
        return preprocessed_inputs
Example #2
Source File: base_estimator.py    From yolo_v2 with Apache License 2.0 6 votes vote down vote up 
def preprocess_data(self, images, is_training):
    """Preprocesses raw images for either training or inference.

    Args:
      images: A 4-D float32 `Tensor` holding images to preprocess.
      is_training: Boolean, whether or not we're in training.

    Returns:
      data_preprocessed: data after the preprocessor.
    """
    config = self._config
    height = config.data.height
    width = config.data.width
    min_scale = config.data.augmentation.minscale
    max_scale = config.data.augmentation.maxscale
    p_scale_up = config.data.augmentation.proportion_scaled_up
    aug_color = config.data.augmentation.color
    fast_mode = config.data.augmentation.fast_mode
    crop_strategy = config.data.preprocessing.eval_cropping
    preprocessed_images = preprocessing.preprocess_images(
        images, is_training, height, width,
        min_scale, max_scale, p_scale_up,
        aug_color=aug_color, fast_mode=fast_mode,
        crop_strategy=crop_strategy)
    return preprocessed_images
Example #3
Source File: base_estimator.py    From Gun-Detector with Apache License 2.0 6 votes vote down vote up 
def preprocess_data(self, images, is_training):
    """Preprocesses raw images for either training or inference.

    Args:
      images: A 4-D float32 `Tensor` holding images to preprocess.
      is_training: Boolean, whether or not we're in training.

    Returns:
      data_preprocessed: data after the preprocessor.
    """
    config = self._config
    height = config.data.height
    width = config.data.width
    min_scale = config.data.augmentation.minscale
    max_scale = config.data.augmentation.maxscale
    p_scale_up = config.data.augmentation.proportion_scaled_up
    aug_color = config.data.augmentation.color
    fast_mode = config.data.augmentation.fast_mode
    crop_strategy = config.data.preprocessing.eval_cropping
    preprocessed_images = preprocessing.preprocess_images(
        images, is_training, height, width,
        min_scale, max_scale, p_scale_up,
        aug_color=aug_color, fast_mode=fast_mode,
        crop_strategy=crop_strategy)
    return preprocessed_images
Example #4
Source File: base_estimator.py    From object_detection_with_tensorflow with MIT License 6 votes vote down vote up 
def preprocess_data(self, images, is_training):
    """Preprocesses raw images for either training or inference.

    Args:
      images: A 4-D float32 `Tensor` holding images to preprocess.
      is_training: Boolean, whether or not we're in training.

    Returns:
      data_preprocessed: data after the preprocessor.
    """
    config = self._config
    height = config.data.height
    width = config.data.width
    min_scale = config.data.augmentation.minscale
    max_scale = config.data.augmentation.maxscale
    p_scale_up = config.data.augmentation.proportion_scaled_up
    aug_color = config.data.augmentation.color
    fast_mode = config.data.augmentation.fast_mode
    crop_strategy = config.data.preprocessing.eval_cropping
    preprocessed_images = preprocessing.preprocess_images(
        images, is_training, height, width,
        min_scale, max_scale, p_scale_up,
        aug_color=aug_color, fast_mode=fast_mode,
        crop_strategy=crop_strategy)
    return preprocessed_images
Example #5
Source File: base_estimator.py    From g-tensorflow-models with Apache License 2.0 6 votes vote down vote up 
def preprocess_data(self, images, is_training):
    """Preprocesses raw images for either training or inference.

    Args:
      images: A 4-D float32 `Tensor` holding images to preprocess.
      is_training: Boolean, whether or not we're in training.

    Returns:
      data_preprocessed: data after the preprocessor.
    """
    config = self._config
    height = config.data.height
    width = config.data.width
    min_scale = config.data.augmentation.minscale
    max_scale = config.data.augmentation.maxscale
    p_scale_up = config.data.augmentation.proportion_scaled_up
    aug_color = config.data.augmentation.color
    fast_mode = config.data.augmentation.fast_mode
    crop_strategy = config.data.preprocessing.eval_cropping
    preprocessed_images = preprocessing.preprocess_images(
        images, is_training, height, width,
        min_scale, max_scale, p_scale_up,
        aug_color=aug_color, fast_mode=fast_mode,
        crop_strategy=crop_strategy)
    return preprocessed_images
Example #6
Source File: base_estimator.py    From models with Apache License 2.0 6 votes vote down vote up 
def preprocess_data(self, images, is_training):
    """Preprocesses raw images for either training or inference.

    Args:
      images: A 4-D float32 `Tensor` holding images to preprocess.
      is_training: Boolean, whether or not we're in training.

    Returns:
      data_preprocessed: data after the preprocessor.
    """
    config = self._config
    height = config.data.height
    width = config.data.width
    min_scale = config.data.augmentation.minscale
    max_scale = config.data.augmentation.maxscale
    p_scale_up = config.data.augmentation.proportion_scaled_up
    aug_color = config.data.augmentation.color
    fast_mode = config.data.augmentation.fast_mode
    crop_strategy = config.data.preprocessing.eval_cropping
    preprocessed_images = preprocessing.preprocess_images(
        images, is_training, height, width,
        min_scale, max_scale, p_scale_up,
        aug_color=aug_color, fast_mode=fast_mode,
        crop_strategy=crop_strategy)
    return preprocessed_images
Example #7
Source File: base_estimator.py    From multilabel-image-classification-tensorflow with MIT License 6 votes vote down vote up 
def preprocess_data(self, images, is_training):
    """Preprocesses raw images for either training or inference.

    Args:
      images: A 4-D float32 `Tensor` holding images to preprocess.
      is_training: Boolean, whether or not we're in training.

    Returns:
      data_preprocessed: data after the preprocessor.
    """
    config = self._config
    height = config.data.height
    width = config.data.width
    min_scale = config.data.augmentation.minscale
    max_scale = config.data.augmentation.maxscale
    p_scale_up = config.data.augmentation.proportion_scaled_up
    aug_color = config.data.augmentation.color
    fast_mode = config.data.augmentation.fast_mode
    crop_strategy = config.data.preprocessing.eval_cropping
    preprocessed_images = preprocessing.preprocess_images(
        images, is_training, height, width,
        min_scale, max_scale, p_scale_up,
        aug_color=aug_color, fast_mode=fast_mode,
        crop_strategy=crop_strategy)
    return preprocessed_images