SimCLR_data_util.py

# coding=utf-8
# Copyright 2020 The SimCLR Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific simclr governing permissions and
# limitations under the License.
# ==============================================================================
# Minor changes have been performed to allow for use without other scripts from
# repo: https://github.com/google-research/simclr
# ==============================================================================
"""Data preprocessing and augmentation."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import functools

import tensorflow.compat.v1 as tf


CROP_PROPORTION = 0.875  # Standard for ImageNet.


def random_apply(func, p, x):
    """Randomly apply function func to x with probability p."""
    return tf.cond(
      tf.less(tf.random_uniform([], minval=0, maxval=1, dtype=tf.float32),
              tf.cast(p, tf.float32)),
      lambda: func(x),
      lambda: x)


def random_brightness(image, max_delta, impl='simclrv2'):
    """A multiplicative vs additive change of brightness."""
    if impl == 'simclrv2':
        factor = tf.random_uniform(
            [], tf.maximum(1.0 - max_delta, 0), 1.0 + max_delta)
        image = image * factor
    elif impl == 'simclrv1':
        image = random_brightness(image, max_delta=max_delta)
    else:
        raise ValueError('Unknown impl {} for random brightness.'.format(impl))
    return image


def to_grayscale(image, keep_channels=True):
    image = tf.image.rgb_to_grayscale(image)
    if keep_channels:
        image = tf.tile(image, [1, 1, 3])
    return image


def color_jitter(image,
                 strength,
                 random_order=True):
    """Distorts the color of the image.
    Args:
    image: The input image tensor.
    strength: the floating number for the strength of the color augmentation.
    random_order: A bool, specifying whether to randomize the jittering order.
    Returns:
    The distorted image tensor.
    """
    brightness = 0.8 * strength
    contrast = 0.8 * strength
    saturation = 0.8 * strength
    hue = 0.2 * strength
    if random_order:
        return color_jitter_rand(image, brightness, contrast, saturation, hue)
    else:
        return color_jitter_nonrand(image, brightness, contrast, saturation, hue)


def color_jitter_nonrand(image, brightness=0, contrast=0, saturation=0, hue=0):
    """Distorts the color of the image (jittering order is fixed).
    Args:
    image: The input image tensor.
    brightness: A float, specifying the brightness for color jitter.
    contrast: A float, specifying the contrast for color jitter.
    saturation: A float, specifying the saturation for color jitter.
    hue: A float, specifying the hue for color jitter.
    Returns:
    The distorted image tensor.
    """
    with tf.name_scope('distort_color'):
        def apply_transform(i, x, brightness, contrast, saturation, hue):
            """Apply the i-th transformation."""
            if brightness != 0 and i == 0:
                x = random_brightness(x, max_delta=brightness)
            elif contrast != 0 and i == 1:
                x = tf.image.random_contrast(
                    x, lower=1-contrast, upper=1+contrast)
            elif saturation != 0 and i == 2:
                x = tf.image.random_saturation(
                    x, lower=1-saturation, upper=1+saturation)
            elif hue != 0:
                x = tf.image.random_hue(x, max_delta=hue)
            return x

        for i in range(4):
            image = apply_transform(i, image, brightness, contrast, saturation, hue)
            image = tf.clip_by_value(image, 0., 1.)
        return image


def color_jitter_rand(image, brightness=0, contrast=0, saturation=0, hue=0):
    """Distorts the color of the image (jittering order is random).
    Args:
    image: The input image tensor.
    brightness: A float, specifying the brightness for color jitter.
    contrast: A float, specifying the contrast for color jitter.
    saturation: A float, specifying the saturation for color jitter.
    hue: A float, specifying the hue for color jitter.
    Returns:
    The distorted image tensor.
    """
    with tf.name_scope('distort_color'):
        def apply_transform(i, x):
            """Apply the i-th transformation."""
            def brightness_foo():
                if brightness == 0:
                    return x
                else:
                    return random_brightness(x, max_delta=brightness)
            def contrast_foo():
                if contrast == 0:
                    return x
                else:
                    return tf.image.random_contrast(x, lower=1-contrast, upper=1+contrast)
            def saturation_foo():
                if saturation == 0:
                    return x
                else:
                    return tf.image.random_saturation(
                      x, lower=1-saturation, upper=1+saturation)
            def hue_foo():
                if hue == 0:
                    return x
                else:
                    return tf.image.random_hue(x, max_delta=hue)
            x = tf.cond(tf.less(i, 2),
                  lambda: tf.cond(tf.less(i, 1), brightness_foo, contrast_foo),
                  lambda: tf.cond(tf.less(i, 3), saturation_foo, hue_foo))
            return x

        perm = tf.random_shuffle(tf.range(4))
        for i in range(4):
            image = apply_transform(perm[i], image)
            image = tf.clip_by_value(image, 0., 1.)
        return image

def _compute_crop_shape(
    image_height, image_width, aspect_ratio, crop_proportion):
    """Compute aspect ratio-preserving shape for central crop.
    The resulting shape retains `crop_proportion` along one side and a proportion
    less than or equal to `crop_proportion` along the other side.
    Args:
    image_height: Height of image to be cropped.
    image_width: Width of image to be cropped.
    aspect_ratio: Desired aspect ratio (width / height) of output.
    crop_proportion: Proportion of image to retain along the less-cropped side.
    Returns:
    crop_height: Height of image after cropping.
    crop_width: Width of image after cropping.
    """
    image_width_float = tf.cast(image_width, tf.float32)
    image_height_float = tf.cast(image_height, tf.float32)

    def _requested_aspect_ratio_wider_than_image():
        crop_height = tf.cast(tf.rint(
            crop_proportion / aspect_ratio * image_width_float), tf.int32)
        crop_width = tf.cast(tf.rint(
            crop_proportion * image_width_float), tf.int32)
        return crop_height, crop_width

    def _image_wider_than_requested_aspect_ratio():
        crop_height = tf.cast(
            tf.rint(crop_proportion * image_height_float), tf.int32)
        crop_width = tf.cast(tf.rint(
            crop_proportion * aspect_ratio *
            image_height_float), tf.int32)
        return crop_height, crop_width

    return tf.cond(
      aspect_ratio > image_width_float / image_height_float,
      _requested_aspect_ratio_wider_than_image,
      _image_wider_than_requested_aspect_ratio)


def center_crop(image, height, width, crop_proportion):
    """Crops to center of image and rescales to desired size.
    Args:
    image: Image Tensor to crop.
    height: Height of image to be cropped.
    width: Width of image to be cropped.
    crop_proportion: Proportion of image to retain along the less-cropped side.
    Returns:
    A `height` x `width` x channels Tensor holding a central crop of `image`.
    """
    shape = tf.shape(image)
    image_height = shape[0]
    image_width = shape[1]
    crop_height, crop_width = _compute_crop_shape(
      image_height, image_width, height / width, crop_proportion)
    offset_height = ((image_height - crop_height) + 1) // 2
    offset_width = ((image_width - crop_width) + 1) // 2
    image = tf.image.crop_to_bounding_box(
      image, offset_height, offset_width, crop_height, crop_width)

    image = tf.image.resize_bicubic([image], [height, width])[0]

    return image


def distorted_bounding_box_crop(image,
                                bbox,
                                min_object_covered=0.1,
                                aspect_ratio_range=(0.75, 1.33),
                                area_range=(0.05, 1.0),
                                max_attempts=100,
                                scope=None):
    """Generates cropped_image using one of the bboxes randomly distorted.
    See `tf.image.sample_distorted_bounding_box` for more documentation.
    Args:
    image: `Tensor` of image data.
    bbox: `Tensor` of bounding boxes arranged `[1, num_boxes, coords]`
        where each coordinate is [0, 1) and the coordinates are arranged
        as `[ymin, xmin, ymax, xmax]`. If num_boxes is 0 then use the whole
        image.
    min_object_covered: An optional `float`. Defaults to `0.1`. The cropped
        area of the image must contain at least this fraction of any bounding
        box supplied.
    aspect_ratio_range: An optional list of `float`s. The cropped area of the
        image must have an aspect ratio = width / height within this range.
    area_range: An optional list of `float`s. The cropped area of the image
        must contain a fraction of the supplied image within in this range.
    max_attempts: An optional `int`. Number of attempts at generating a cropped
        region of the image of the specified constraints. After `max_attempts`
        failures, return the entire image.
    scope: Optional `str` for name scope.
    Returns:
    (cropped image `Tensor`, distorted bbox `Tensor`).
    """
    with tf.name_scope(scope, 'distorted_bounding_box_crop', [image, bbox]):
        shape = tf.shape(image)
        sample_distorted_bounding_box = tf.image.sample_distorted_bounding_box(
            shape,
            bounding_boxes=bbox,
            min_object_covered=min_object_covered,
            aspect_ratio_range=aspect_ratio_range,
            area_range=area_range,
            max_attempts=max_attempts,
            use_image_if_no_bounding_boxes=True)
        bbox_begin, bbox_size, _ = sample_distorted_bounding_box

        # Crop the image to the specified bounding box.
        offset_y, offset_x, _ = tf.unstack(bbox_begin)
        target_height, target_width, _ = tf.unstack(bbox_size)
        image = tf.image.crop_to_bounding_box(
            image, offset_y, offset_x, target_height, target_width)

        return image


def crop_and_resize(image, height, width):
    """Make a random crop and resize it to height `height` and width `width`.
    Args:
    image: Tensor representing the image.
    height: Desired image height.
    width: Desired image width.
    Returns:
    A `height` x `width` x channels Tensor holding a random crop of `image`.
    """
    bbox = tf.constant([0.0, 0.0, 1.0, 1.0], dtype=tf.float32, shape=[1, 1, 4])
    aspect_ratio = width / height
    image = distorted_bounding_box_crop(
      image,
      bbox,
      min_object_covered=0.7,
      aspect_ratio_range=(3. / 4 * aspect_ratio, 4. / 3. * aspect_ratio),
      area_range=(0.7, 1.0),
      max_attempts=100,
      scope=None)
    return tf.image.resize_bicubic([image], [height, width])[0]


def gaussian_blur(image, kernel_size, sigma, padding='SAME'):
    """Blurs the given image with separable convolution.
    Args:
    image: Tensor of shape [height, width, channels] and dtype float to blur.
    kernel_size: Integer Tensor for the size of the blur kernel. This is should
      be an odd number. If it is an even number, the actual kernel size will be
      size + 1.
    sigma: Sigma value for gaussian operator.
    padding: Padding to use for the convolution. Typically 'SAME' or 'VALID'.
    Returns:
    A Tensor representing the blurred image.
    """
    radius = tf.to_int32(kernel_size / 2)
    kernel_size = radius * 2 + 1
    x = tf.to_float(tf.range(-radius, radius + 1))
    blur_filter = tf.exp(
      -tf.pow(x, 2.0) / (2.0 * tf.pow(tf.to_float(sigma), 2.0)))
    blur_filter /= tf.reduce_sum(blur_filter)
    # One vertical and one horizontal filter.
    blur_v = tf.reshape(blur_filter, [kernel_size, 1, 1, 1])
    blur_h = tf.reshape(blur_filter, [1, kernel_size, 1, 1])
    num_channels = tf.shape(image)[-1]
    blur_h = tf.tile(blur_h, [1, 1, num_channels, 1])
    blur_v = tf.tile(blur_v, [1, 1, num_channels, 1])
    expand_batch_dim = image.shape.ndims == 3
    if expand_batch_dim:
        # Tensorflow requires batched input to convolutions, which we can fake with
        # an extra dimension.
        image = tf.expand_dims(image, axis=0)
    blurred = tf.nn.depthwise_conv2d(
      image, blur_h, strides=[1, 1, 1, 1], padding=padding)
    blurred = tf.nn.depthwise_conv2d(
      blurred, blur_v, strides=[1, 1, 1, 1], padding=padding)
    if expand_batch_dim:
        blurred = tf.squeeze(blurred, axis=0)
    return blurred


def random_crop_with_resize(image, height, width, p=1.0):
    """Randomly crop and resize an image.
    Args:
    image: `Tensor` representing an image of arbitrary size.
    height: Height of output image.
    width: Width of output image.
    p: Probability of applying this transformation.
    Returns:
    A preprocessed image `Tensor`.
    """
    def _transform(image):  # pylint: disable=missing-docstring
        image = crop_and_resize(image, height, width)
        return image
    return random_apply(_transform, p=p, x=image)


def random_color_jitter(image, p=1.0):

    def _transform(image):
        """
        flags.DEFINE_float(
        'color_jitter_strength', 1.0,
        'The strength of color jittering.')
        """
        color_jitter_t = functools.partial(
            color_jitter, strength=0.5)
        image = random_apply(color_jitter_t, p=0.8, x=image)
        return random_apply(to_grayscale, p=0.2, x=image)
    return random_apply(_transform, p=p, x=image)


def random_blur(image, height, width, p=0.5):
    """Randomly blur an image.
    Args:
    image: `Tensor` representing an image of arbitrary size.
    height: Height of output image.
    width: Width of output image.
    p: probability of applying this transformation.
    Returns:
    A preprocessed image `Tensor`.
    """
    del width
    def _transform(image):
        sigma = tf.random.uniform([], 0.1, 2.0, dtype=tf.float32)
        return gaussian_blur(
            image, kernel_size=height//10, sigma=sigma, padding='SAME')
    return random_apply(_transform, p=p, x=image)


def batch_random_blur(images_list, height, width, blur_probability=0.5):
    """Apply efficient batch data transformations.
    Args:
    images_list: a list of image tensors.
    height: the height of image.
    width: the width of image.
    blur_probability: the probaility to apply the blur operator.
    Returns:
    Preprocessed feature list.
    """
    def generate_selector(p, bsz):
        shape = [bsz, 1, 1, 1]
        selector = tf.cast(
            tf.less(tf.random_uniform(shape, 0, 1, dtype=tf.float32), p),
            tf.float32)
        return selector

    new_images_list = []
    for images in images_list:
        images_new = random_blur(images, height, width, p=1.)
        selector = generate_selector(blur_probability, tf.shape(images)[0])
        images = images_new * selector + images * (1 - selector)
        images = tf.clip_by_value(images, 0., 1.)
        new_images_list.append(images)

    return new_images_list


def preprocess_for_train(image, height, width,
                         color_distort=True, crop=True, flip=True, blur = True):
    """Preprocesses the given image for training.
    Args:
    image: `Tensor` representing an image of arbitrary size.
    height: Height of output image.
    width: Width of output image.
    color_distort: Whether to apply the color distortion.
    crop: Whether to crop the image.
    flip: Whether or not to flip left and right of an image.
    Returns:
    A preprocessed image `Tensor`.
    """
    if crop:
        image = random_crop_with_resize(image, height, width)
    if flip:
        image = tf.image.random_flip_left_right(image)
    if color_distort:
        image = random_color_jitter(image)
    if blur:
        image = random_blur(image, height, width)
    image = tf.reshape(image, [height, width, 3])
    image = tf.clip_by_value(image, 0., 1.)
    return image


def preprocess_for_eval(image, height, width, crop=True):
    """Preprocesses the given image for evaluation.
    Args:
    image: `Tensor` representing an image of arbitrary size.
    height: Height of output image.
    width: Width of output image.
    crop: Whether or not to (center) crop the test images.
    Returns:
    A preprocessed image `Tensor`.
    """
    if crop:
        image = center_crop(image, height, width, crop_proportion=CROP_PROPORTION)
    image = tf.reshape(image, [height, width, 3])
    image = tf.clip_by_value(image, 0., 1.)
    return image


def preprocess_image(image, height, width, is_training=False,
                     color_distort=True, test_crop=True):
    """Preprocesses the given image.
    Args:
    image: `Tensor` representing an image of arbitrary size.
    height: Height of output image.
    width: Width of output image.
    is_training: `bool` for whether the preprocessing is for training.
    color_distort: whether to apply the color distortion.
    test_crop: whether or not to extract a central crop of the images
        (as for standard ImageNet evaluation) during the evaluation.
    Returns:
    A preprocessed image `Tensor` of range [0, 1].
    """
    image = tf.image.convert_image_dtype(image, dtype=tf.float32)
    if is_training:
        return preprocess_for_train(image, height, width, color_distort)
    else:
        return preprocess_for_eval(image, height, width, test_crop)