def image_preprocess(image, image_size: int): input_processor = dataloader.DetectionInputProcessor(image, image_size) input_processor.normalize_image() input_processor.set_scale_factors_to_output_size() image = input_processor.resize_and_crop_image() image_scale = input_processor.image_scale_to_original return image, image_scale
def map_fn(image): input_processor = dataloader.DetectionInputProcessor( image, image_size) input_processor.normalize_image(mean_rgb, stddev_rgb) input_processor.set_scale_factors_to_output_size() image = input_processor.resize_and_crop_image() image_scale = input_processor.image_scale_to_original return image, image_scale
def image_preprocess(image, image_size: Union[int, Tuple[int, int]]): """Preprocess image for inference. Args: image: input image, can be a tensor or a numpy arary. image_size: single integer of image size for square image or tuple of two integers, in the format of (image_height, image_width). Returns: (image, scale): a tuple of processed image and its scale. """ input_processor = dataloader.DetectionInputProcessor(image, image_size) input_processor.normalize_image() input_processor.set_scale_factors_to_output_size() image = input_processor.resize_and_crop_image() image_scale = input_processor.image_scale_to_original return image, image_scale
def image_preprocess(image, image_size: int): """Preprocess image for inference. Args: image: input image, can be a tensor or a numpy arary. image_size: integer of image size. Returns: (image, scale): a tuple of processed image and its scale. """ input_processor = dataloader.DetectionInputProcessor(image, image_size) input_processor.normalize_image() input_processor.set_scale_factors_to_output_size() image = input_processor.resize_and_crop_image() image_scale = input_processor.image_scale_to_original return image, image_scale
def image_preprocess(image, image_size, mean_rgb, stddev_rgb): """Preprocess image for inference. Args: image: input image, can be a tensor or a numpy arary. image_size: single integer of image size for square image or tuple of two integers, in the format of (image_height, image_width). mean_rgb: Mean value of RGB, can be a list of float or a float value. stddev_rgb: Standard deviation of RGB, can be a list of float or a float value. Returns: (image, scale): a tuple of processed image and its scale. """ input_processor = dataloader.DetectionInputProcessor(image, image_size) input_processor.normalize_image(mean_rgb, stddev_rgb) input_processor.set_scale_factors_to_output_size() image = input_processor.resize_and_crop_image() image_scale = input_processor.image_scale_to_original return image, image_scale
def _preprocessing(self, raw_images, image_size, mode=None): """Preprocess images before feeding to the network.""" if not mode: return raw_images, None image_size = utils.parse_image_size(image_size) if mode != 'infer': # We only support inference for now. raise ValueError('preprocessing must be infer or empty') scales, images = [], [] if isinstance(raw_images, tf.Tensor): batch_size = raw_images.shape[0] else: batch_size = len(raw_images) for i in range(batch_size): input_processor = dataloader.DetectionInputProcessor( raw_images[i], image_size) input_processor.normalize_image() input_processor.set_scale_factors_to_output_size() images.append(input_processor.resize_and_crop_image()) scales.append(input_processor.image_scale_to_original) return tf.stack(images), tf.stack(scales)