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# Copyright 2022 Google LLC
# 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
# https://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 language governing permissions and
# limitations under the License.
# ==============================================================================
"""Utility functions for creating a tf.train.Example proto of image triplets."""
import io
import os
from typing import Any, List, Mapping, Optional
from absl import logging
import apache_beam as beam
import numpy as np
import PIL.Image
import six
from skimage import transform
import tensorflow as tf
_UINT8_MAX_F = float(np.iinfo(np.uint8).max)
_GAMMA = 2.2
def _resample_image(image: np.ndarray, resample_image_width: int,
resample_image_height: int) -> np.ndarray:
"""Re-samples and returns an `image` to be `resample_image_size`."""
# Convert image from uint8 gamma [0..255] to float linear [0..1].
image = image.astype(np.float32) / _UINT8_MAX_F
image = np.power(np.clip(image, 0, 1), _GAMMA)
# Re-size the image
resample_image_size = (resample_image_height, resample_image_width)
image = transform.resize_local_mean(image, resample_image_size)
# Convert back from float linear [0..1] to uint8 gamma [0..255].
image = np.power(np.clip(image, 0, 1), 1.0 / _GAMMA)
image = np.clip(image * _UINT8_MAX_F + 0.5, 0.0,
_UINT8_MAX_F).astype(np.uint8)
return image
def generate_image_triplet_example(
triplet_dict: Mapping[str, str],
scale_factor: int = 1,
center_crop_factor: int = 1) -> Optional[tf.train.Example]:
"""Generates and serializes a tf.train.Example proto from an image triplet.
Default setting creates a triplet Example with the input images unchanged.
Images are processed in the order of center-crop then downscale.
Args:
triplet_dict: A dict of image key to filepath of the triplet images.
scale_factor: An integer scale factor to isotropically downsample images.
center_crop_factor: An integer cropping factor to center crop images with
the original resolution but isotropically downsized by the factor.
Returns:
tf.train.Example proto, or None upon error.
Raises:
ValueError if triplet_dict length is different from three or the scale input
arguments are non-positive.
"""
if len(triplet_dict) != 3:
raise ValueError(
f'Length of triplet_dict must be exactly 3, not {len(triplet_dict)}.')
if scale_factor <= 0 or center_crop_factor <= 0:
raise ValueError(f'(scale_factor, center_crop_factor) must be positive, '
f'Not ({scale_factor}, {center_crop_factor}).')
feature = {}
# Keep track of the path where the images came from for debugging purposes.
mid_frame_path = os.path.dirname(triplet_dict['frame_1'])
feature['path'] = tf.train.Feature(
bytes_list=tf.train.BytesList(value=[six.ensure_binary(mid_frame_path)]))
for image_key, image_path in triplet_dict.items():
if not tf.io.gfile.exists(image_path):
logging.error('File not found: %s', image_path)
return None
# Note: we need both the raw bytes and the image size.
# PIL.Image does not expose a method to grab the original bytes.
# (Also it is not aware of non-local file systems.)
# So we read with tf.io.gfile.GFile to get the bytes, and then wrap the
# bytes in BytesIO to let PIL.Image open the image.
try:
byte_array = tf.io.gfile.GFile(image_path, 'rb').read()
except tf.errors.InvalidArgumentError:
logging.exception('Cannot read image file: %s', image_path)
return None
try:
pil_image = PIL.Image.open(io.BytesIO(byte_array))
except PIL.UnidentifiedImageError:
logging.exception('Cannot decode image file: %s', image_path)
return None
width, height = pil_image.size
pil_image_format = pil_image.format
# Optionally center-crop images and downsize images
# by `center_crop_factor`.
if center_crop_factor > 1:
image = np.array(pil_image)
quarter_height = image.shape[0] // (2 * center_crop_factor)
quarter_width = image.shape[1] // (2 * center_crop_factor)
image = image[quarter_height:-quarter_height,
quarter_width:-quarter_width, :]
pil_image = PIL.Image.fromarray(image)
# Update image properties.
height, width, _ = image.shape
buffer = io.BytesIO()
try:
pil_image.save(buffer, format='PNG')
except OSError:
logging.exception('Cannot encode image file: %s', image_path)
return None
byte_array = buffer.getvalue()
# Optionally downsample images by `scale_factor`.
if scale_factor > 1:
image = np.array(pil_image)
image = _resample_image(image, image.shape[1] // scale_factor,
image.shape[0] // scale_factor)
pil_image = PIL.Image.fromarray(image)
# Update image properties.
height, width, _ = image.shape
buffer = io.BytesIO()
try:
pil_image.save(buffer, format='PNG')
except OSError:
logging.exception('Cannot encode image file: %s', image_path)
return None
byte_array = buffer.getvalue()
# Create tf Features.
image_feature = tf.train.Feature(
bytes_list=tf.train.BytesList(value=[byte_array]))
height_feature = tf.train.Feature(
int64_list=tf.train.Int64List(value=[height]))
width_feature = tf.train.Feature(
int64_list=tf.train.Int64List(value=[width]))
encoding = tf.train.Feature(
bytes_list=tf.train.BytesList(
value=[six.ensure_binary(pil_image_format.lower())]))
# Update feature map.
feature[f'{image_key}/encoded'] = image_feature
feature[f'{image_key}/format'] = encoding
feature[f'{image_key}/height'] = height_feature
feature[f'{image_key}/width'] = width_feature
# Create tf Example.
features = tf.train.Features(feature=feature)
example = tf.train.Example(features=features)
return example
class ExampleGenerator(beam.DoFn):
"""Generate a tf.train.Example per input image triplet filepaths."""
def __init__(self,
images_map: Mapping[str, Any],
scale_factor: int = 1,
center_crop_factor: int = 1):
"""Initializes the map of 3 images to add to each tf.train.Example.
Args:
images_map: Map from image key to image filepath.
scale_factor: A scale factor to downsample frames.
center_crop_factor: A factor to centercrop and downsize frames.
"""
super().__init__()
self._images_map = images_map
self._scale_factor = scale_factor
self._center_crop_factor = center_crop_factor
def process(self, triplet_dict: Mapping[str, str]) -> List[bytes]:
"""Generates a serialized tf.train.Example for a triplet of images.
Args:
triplet_dict: A dict of image key to filepath of the triplet images.
Returns:
A serialized tf.train.Example proto. No shuffling is applied.
"""
example = generate_image_triplet_example(triplet_dict, self._scale_factor,
self._center_crop_factor)
if example:
return [example.SerializeToString()]
else:
return []
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