official/legacy/detection/utils/input_utils.py
# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
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# 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
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# See the License for the specific language governing permissions and
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"""Utility functions for input processing."""
import math
import tensorflow as tf, tf_keras
from official.legacy.detection.utils import box_utils
from official.vision.utils.object_detection import preprocessor
def pad_to_fixed_size(input_tensor, size, constant_values=0):
"""Pads data to a fixed length at the first dimension.
Args:
input_tensor: `Tensor` with any dimension.
size: `int` number for the first dimension of output Tensor.
constant_values: `int` value assigned to the paddings.
Returns:
`Tensor` with the first dimension padded to `size`.
"""
input_shape = input_tensor.get_shape().as_list()
padding_shape = []
# Computes the padding length on the first dimension.
padding_length = tf.maximum(0, size - tf.shape(input_tensor)[0])
assert_length = tf.Assert(
tf.greater_equal(padding_length, 0), [padding_length])
with tf.control_dependencies([assert_length]):
padding_shape.append(padding_length)
# Copies shapes of the rest of input shape dimensions.
for i in range(1, len(input_shape)):
padding_shape.append(tf.shape(input=input_tensor)[i])
# Pads input tensor to the fixed first dimension.
paddings = tf.cast(constant_values * tf.ones(padding_shape),
input_tensor.dtype)
padded_tensor = tf.concat([input_tensor, paddings], axis=0)
output_shape = input_shape
output_shape[0] = size
padded_tensor.set_shape(output_shape)
return padded_tensor
def normalize_image(image,
offset=(0.485, 0.456, 0.406),
scale=(0.229, 0.224, 0.225)):
"""Normalizes the image to zero mean and unit variance."""
image = tf.image.convert_image_dtype(image, dtype=tf.float32)
offset = tf.constant(offset)
offset = tf.expand_dims(offset, axis=0)
offset = tf.expand_dims(offset, axis=0)
image -= offset
scale = tf.constant(scale)
scale = tf.expand_dims(scale, axis=0)
scale = tf.expand_dims(scale, axis=0)
image /= scale
return image
def compute_padded_size(desired_size, stride):
"""Compute the padded size given the desired size and the stride.
The padded size will be the smallest rectangle, such that each dimension is
the smallest multiple of the stride which is larger than the desired
dimension. For example, if desired_size = (100, 200) and stride = 32,
the output padded_size = (128, 224).
Args:
desired_size: a `Tensor` or `int` list/tuple of two elements representing
[height, width] of the target output image size.
stride: an integer, the stride of the backbone network.
Returns:
padded_size: a `Tensor` or `int` list/tuple of two elements representing
[height, width] of the padded output image size.
"""
if isinstance(desired_size, list) or isinstance(desired_size, tuple):
padded_size = [
int(math.ceil(d * 1.0 / stride) * stride) for d in desired_size
]
else:
padded_size = tf.cast(
tf.math.ceil(tf.cast(desired_size, dtype=tf.float32) / stride) * stride,
tf.int32)
return padded_size
def resize_and_crop_image(image,
desired_size,
padded_size,
aug_scale_min=1.0,
aug_scale_max=1.0,
seed=1,
method=tf.image.ResizeMethod.BILINEAR):
"""Resizes the input image to output size.
Resize and pad images given the desired output size of the image and
stride size.
Here are the preprocessing steps.
1. For a given image, keep its aspect ratio and rescale the image to make it
the largest rectangle to be bounded by the rectangle specified by the
`desired_size`.
2. Pad the rescaled image to the padded_size.
Args:
image: a `Tensor` of shape [height, width, 3] representing an image.
desired_size: a `Tensor` or `int` list/tuple of two elements representing
[height, width] of the desired actual output image size.
padded_size: a `Tensor` or `int` list/tuple of two elements representing
[height, width] of the padded output image size. Padding will be applied
after scaling the image to the desired_size.
aug_scale_min: a `float` with range between [0, 1.0] representing minimum
random scale applied to desired_size for training scale jittering.
aug_scale_max: a `float` with range between [1.0, inf] representing maximum
random scale applied to desired_size for training scale jittering.
seed: seed for random scale jittering.
method: function to resize input image to scaled image.
Returns:
output_image: `Tensor` of shape [height, width, 3] where [height, width]
equals to `output_size`.
image_info: a 2D `Tensor` that encodes the information of the image and the
applied preprocessing. It is in the format of
[[original_height, original_width], [desired_height, desired_width],
[y_scale, x_scale], [y_offset, x_offset]], where [desired_height,
desireed_width] is the actual scaled image size, and [y_scale, x_scale] is
the scaling factory, which is the ratio of
scaled dimension / original dimension.
"""
with tf.name_scope('resize_and_crop_image'):
image_size = tf.cast(tf.shape(input=image)[0:2], tf.float32)
random_jittering = (aug_scale_min != 1.0 or aug_scale_max != 1.0)
if random_jittering:
random_scale = tf.random.uniform([],
aug_scale_min,
aug_scale_max,
seed=seed)
scaled_size = tf.round(random_scale * desired_size)
else:
scaled_size = desired_size
scale = tf.minimum(scaled_size[0] / image_size[0],
scaled_size[1] / image_size[1])
scaled_size = tf.round(image_size * scale)
# Computes 2D image_scale.
image_scale = scaled_size / image_size
# Selects non-zero random offset (x, y) if scaled image is larger than
# desired_size.
if random_jittering:
max_offset = scaled_size - desired_size
max_offset = tf.where(
tf.less(max_offset, 0), tf.zeros_like(max_offset), max_offset)
offset = max_offset * tf.random.uniform([
2,
], 0, 1, seed=seed)
offset = tf.cast(offset, tf.int32)
else:
offset = tf.zeros((2,), tf.int32)
scaled_image = tf.image.resize(
image, tf.cast(scaled_size, tf.int32), method=method)
if random_jittering:
scaled_image = scaled_image[offset[0]:offset[0] + desired_size[0],
offset[1]:offset[1] + desired_size[1], :]
output_image = tf.image.pad_to_bounding_box(scaled_image, 0, 0,
padded_size[0], padded_size[1])
image_info = tf.stack([
image_size,
tf.cast(desired_size, dtype=tf.float32), image_scale,
tf.cast(offset, tf.float32)
])
return output_image, image_info
def resize_and_crop_image_v2(image,
short_side,
long_side,
padded_size,
aug_scale_min=1.0,
aug_scale_max=1.0,
seed=1,
method=tf.image.ResizeMethod.BILINEAR):
"""Resizes the input image to output size (Faster R-CNN style).
Resize and pad images given the specified short / long side length and the
stride size.
Here are the preprocessing steps.
1. For a given image, keep its aspect ratio and first try to rescale the short
side of the original image to `short_side`.
2. If the scaled image after 1 has a long side that exceeds `long_side`, keep
the aspect ratio and rescal the long side of the image to `long_side`.
2. Pad the rescaled image to the padded_size.
Args:
image: a `Tensor` of shape [height, width, 3] representing an image.
short_side: a scalar `Tensor` or `int` representing the desired short side
to be rescaled to.
long_side: a scalar `Tensor` or `int` representing the desired long side to
be rescaled to.
padded_size: a `Tensor` or `int` list/tuple of two elements representing
[height, width] of the padded output image size. Padding will be applied
after scaling the image to the desired_size.
aug_scale_min: a `float` with range between [0, 1.0] representing minimum
random scale applied to desired_size for training scale jittering.
aug_scale_max: a `float` with range between [1.0, inf] representing maximum
random scale applied to desired_size for training scale jittering.
seed: seed for random scale jittering.
method: function to resize input image to scaled image.
Returns:
output_image: `Tensor` of shape [height, width, 3] where [height, width]
equals to `output_size`.
image_info: a 2D `Tensor` that encodes the information of the image and the
applied preprocessing. It is in the format of
[[original_height, original_width], [desired_height, desired_width],
[y_scale, x_scale], [y_offset, x_offset]], where [desired_height,
desired_width] is the actual scaled image size, and [y_scale, x_scale] is
the scaling factor, which is the ratio of
scaled dimension / original dimension.
"""
with tf.name_scope('resize_and_crop_image_v2'):
image_size = tf.cast(tf.shape(image)[0:2], tf.float32)
scale_using_short_side = (
short_side / tf.math.minimum(image_size[0], image_size[1]))
scale_using_long_side = (
long_side / tf.math.maximum(image_size[0], image_size[1]))
scaled_size = tf.math.round(image_size * scale_using_short_side)
scaled_size = tf.where(
tf.math.greater(
tf.math.maximum(scaled_size[0], scaled_size[1]), long_side),
tf.math.round(image_size * scale_using_long_side), scaled_size)
desired_size = scaled_size
random_jittering = (aug_scale_min != 1.0 or aug_scale_max != 1.0)
if random_jittering:
random_scale = tf.random.uniform([],
aug_scale_min,
aug_scale_max,
seed=seed)
scaled_size = tf.math.round(random_scale * scaled_size)
# Computes 2D image_scale.
image_scale = scaled_size / image_size
# Selects non-zero random offset (x, y) if scaled image is larger than
# desired_size.
if random_jittering:
max_offset = scaled_size - desired_size
max_offset = tf.where(
tf.math.less(max_offset, 0), tf.zeros_like(max_offset), max_offset)
offset = max_offset * tf.random.uniform([
2,
], 0, 1, seed=seed)
offset = tf.cast(offset, tf.int32)
else:
offset = tf.zeros((2,), tf.int32)
scaled_image = tf.image.resize(
image, tf.cast(scaled_size, tf.int32), method=method)
if random_jittering:
scaled_image = scaled_image[offset[0]:offset[0] + desired_size[0],
offset[1]:offset[1] + desired_size[1], :]
output_image = tf.image.pad_to_bounding_box(scaled_image, 0, 0,
padded_size[0], padded_size[1])
image_info = tf.stack([
image_size,
tf.cast(desired_size, dtype=tf.float32), image_scale,
tf.cast(offset, tf.float32)
])
return output_image, image_info
def resize_and_crop_boxes(boxes, image_scale, output_size, offset):
"""Resizes boxes to output size with scale and offset.
Args:
boxes: `Tensor` of shape [N, 4] representing ground truth boxes.
image_scale: 2D float `Tensor` representing scale factors that apply to
[height, width] of input image.
output_size: 2D `Tensor` or `int` representing [height, width] of target
output image size.
offset: 2D `Tensor` representing top-left corner [y0, x0] to crop scaled
boxes.
Returns:
boxes: `Tensor` of shape [N, 4] representing the scaled boxes.
"""
# Adjusts box coordinates based on image_scale and offset.
boxes *= tf.tile(tf.expand_dims(image_scale, axis=0), [1, 2])
boxes -= tf.tile(tf.expand_dims(offset, axis=0), [1, 2])
# Clips the boxes.
boxes = box_utils.clip_boxes(boxes, output_size)
return boxes
def resize_and_crop_masks(masks, image_scale, output_size, offset):
"""Resizes boxes to output size with scale and offset.
Args:
masks: `Tensor` of shape [N, H, W, 1] representing ground truth masks.
image_scale: 2D float `Tensor` representing scale factors that apply to
[height, width] of input image.
output_size: 2D `Tensor` or `int` representing [height, width] of target
output image size.
offset: 2D `Tensor` representing top-left corner [y0, x0] to crop scaled
boxes.
Returns:
masks: `Tensor` of shape [N, H, W, 1] representing the scaled masks.
"""
mask_size = tf.shape(input=masks)[1:3]
scaled_size = tf.cast(image_scale * tf.cast(mask_size, image_scale.dtype),
tf.int32)
scaled_masks = tf.image.resize(
masks, scaled_size, method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
offset = tf.cast(offset, tf.int32)
scaled_masks = scaled_masks[:, offset[0]:offset[0] + output_size[0],
offset[1]:offset[1] + output_size[1], :]
output_masks = tf.image.pad_to_bounding_box(scaled_masks, 0, 0,
output_size[0], output_size[1])
return output_masks
def random_horizontal_flip(image, boxes=None, masks=None):
"""Randomly flips input image and bounding boxes."""
return preprocessor.random_horizontal_flip(image, boxes, masks)