official/projects/edgetpu/vision/dataloaders/classification_input.py
# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
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# http://www.apache.org/licenses/LICENSE-2.0
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"""Classification decoder and parser."""
# Import libraries
import tensorflow as tf, tf_keras
from official.vision.dataloaders import classification_input
from official.vision.ops import preprocess_ops
MEAN_RGB = (0.5 * 255, 0.5 * 255, 0.5 * 255)
STDDEV_RGB = (0.5 * 255, 0.5 * 255, 0.5 * 255)
def random_crop_image(image,
aspect_ratio_range=(0.75, 1.33),
area_range=(0.05, 1.0),
max_attempts=100):
"""Randomly crop an arbitrary shaped slice from the input image.
Args:
image: a Tensor of shape [height, width, 3] representing the input image.
aspect_ratio_range: a list of floats. The cropped area of the image must
have an aspect ratio = width / height within this range.
area_range: a list of floats. The cropped reas of the image must contain
a fraction of the input image within this range.
max_attempts: the number of attempts at generating a cropped region of the
image of the specified constraints. After max_attempts failures, return
the entire image.
Returns:
cropped_image: a Tensor representing the random cropped image. Can be the
original image if max_attempts is exhausted.
"""
with tf.name_scope('random_crop_image'):
crop_offset, crop_size, _ = tf.image.sample_distorted_bounding_box(
tf.shape(image),
tf.constant([0.0, 0.0, 1.0, 1.0], dtype=tf.float32, shape=[1, 1, 4]),
min_object_covered=0.1,
aspect_ratio_range=aspect_ratio_range,
area_range=area_range,
max_attempts=max_attempts,
use_image_if_no_bounding_boxes=True)
cropped_image = tf.slice(image, crop_offset, crop_size)
return cropped_image
def random_crop_image_v2(image_bytes,
image_shape,
aspect_ratio_range=(0.75, 1.33),
area_range=(0.05, 1.0),
max_attempts=100):
"""Randomly crop an arbitrary shaped slice from the input image.
This is a faster version of `random_crop_image` which takes the original
image bytes and image size as the inputs, and partially decode the JPEG
bytes according to the generated crop.
Args:
image_bytes: a Tensor of type string representing the raw image bytes.
image_shape: a Tensor specifying the shape of the raw image.
aspect_ratio_range: a list of floats. The cropped area of the image must
have an aspect ratio = width / height within this range.
area_range: a list of floats. The cropped reas of the image must contain
a fraction of the input image within this range.
max_attempts: the number of attempts at generating a cropped region of the
image of the specified constraints. After max_attempts failures, return
the entire image.
Returns:
cropped_image: a Tensor representing the random cropped image. Can be the
original image if max_attempts is exhausted.
"""
with tf.name_scope('random_crop_image_v2'):
crop_offset, crop_size, _ = tf.image.sample_distorted_bounding_box(
image_shape,
tf.constant([0.0, 0.0, 1.0, 1.0], dtype=tf.float32, shape=[1, 1, 4]),
min_object_covered=0.1,
aspect_ratio_range=aspect_ratio_range,
area_range=area_range,
max_attempts=max_attempts,
use_image_if_no_bounding_boxes=True)
offset_y, offset_x, _ = tf.unstack(crop_offset)
crop_height, crop_width, _ = tf.unstack(crop_size)
crop_window = tf.stack([offset_y, offset_x, crop_height, crop_width])
cropped_image = tf.image.decode_and_crop_jpeg(
image_bytes, crop_window, channels=3)
return cropped_image
class Decoder(classification_input.Decoder):
"""A tf.Example decoder for classification task."""
pass
class Parser(classification_input.Parser):
"""Parser to parse an image and its annotations into a dictionary of tensors."""
def _parse_train_image(self, decoded_tensors):
"""Parses image data for training."""
image_bytes = decoded_tensors[self._image_field_key]
if self._decode_jpeg_only:
image_shape = tf.image.extract_jpeg_shape(image_bytes)
# Crops image.
cropped_image = random_crop_image_v2(
image_bytes, image_shape)
image = tf.cond(
tf.reduce_all(tf.equal(tf.shape(cropped_image), image_shape)),
lambda: preprocess_ops.center_crop_image_v2(image_bytes, image_shape),
lambda: cropped_image)
else:
# Decodes image.
image = tf.io.decode_image(image_bytes, channels=3)
image.set_shape([None, None, 3])
# Crops image.
cropped_image = random_crop_image(image)
image = tf.cond(
tf.reduce_all(tf.equal(tf.shape(cropped_image), tf.shape(image))),
lambda: preprocess_ops.center_crop_image(image),
lambda: cropped_image)
if self._aug_rand_hflip:
image = tf.image.random_flip_left_right(image)
# Resizes image.
image = tf.image.resize(
image, self._output_size, method=tf.image.ResizeMethod.BILINEAR)
# Apply autoaug or randaug.
if self._augmenter is not None:
image = self._augmenter.distort(image)
# Normalizes image with mean and std pixel values.
image = preprocess_ops.normalize_image(image,
offset=MEAN_RGB,
scale=STDDEV_RGB)
# Convert image to self._dtype.
image = tf.image.convert_image_dtype(image, self._dtype)
return image
def _parse_eval_image(self, decoded_tensors):
"""Parses image data for evaluation."""
image_bytes = decoded_tensors[self._image_field_key]
if self._decode_jpeg_only:
image_shape = tf.image.extract_jpeg_shape(image_bytes)
# Center crops.
image = preprocess_ops.center_crop_image_v2(image_bytes, image_shape)
else:
# Decodes image.
image = tf.io.decode_image(image_bytes, channels=3)
image.set_shape([None, None, 3])
# Center crops.
image = preprocess_ops.center_crop_image(image)
image = tf.image.resize(
image, self._output_size, method=tf.image.ResizeMethod.BILINEAR)
# Normalizes image with mean and std pixel values.
image = preprocess_ops.normalize_image(image,
offset=MEAN_RGB,
scale=STDDEV_RGB)
# Convert image to self._dtype.
image = tf.image.convert_image_dtype(image, self._dtype)
return image