File size: 7,667 Bytes
1772f26 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 |
# 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.
# ==============================================================================
"""A wrapper class for running a frame interpolation TF2 saved model.
Usage:
model_path='/tmp/saved_model/'
it = Interpolator(model_path)
result_batch = it.interpolate(image_batch_0, image_batch_1, batch_dt)
Where image_batch_1 and image_batch_2 are numpy tensors with TF standard
(B,H,W,C) layout, batch_dt is the sub-frame time in range [0,1], (B,) layout.
"""
from typing import List, Optional
import numpy as np
import tensorflow as tf
def _pad_to_align(x, align):
"""Pad image batch x so width and height divide by align.
Args:
x: Image batch to align.
align: Number to align to.
Returns:
1) An image padded so width % align == 0 and height % align == 0.
2) A bounding box that can be fed readily to tf.image.crop_to_bounding_box
to undo the padding.
"""
# Input checking.
assert np.ndim(x) == 4
assert align > 0, 'align must be a positive number.'
height, width = x.shape[-3:-1]
height_to_pad = (align - height % align) if height % align != 0 else 0
width_to_pad = (align - width % align) if width % align != 0 else 0
bbox_to_pad = {
'offset_height': height_to_pad // 2,
'offset_width': width_to_pad // 2,
'target_height': height + height_to_pad,
'target_width': width + width_to_pad
}
padded_x = tf.image.pad_to_bounding_box(x, **bbox_to_pad)
bbox_to_crop = {
'offset_height': height_to_pad // 2,
'offset_width': width_to_pad // 2,
'target_height': height,
'target_width': width
}
return padded_x, bbox_to_crop
def image_to_patches(image: np.ndarray, block_shape: List[int]) -> np.ndarray:
"""Folds an image into patches and stacks along the batch dimension.
Args:
image: The input image of shape [B, H, W, C].
block_shape: The number of patches along the height and width to extract.
Each patch is shaped (H/block_shape[0], W/block_shape[1])
Returns:
The extracted patches shaped [num_blocks, patch_height, patch_width,...],
with num_blocks = block_shape[0] * block_shape[1].
"""
block_height, block_width = block_shape
num_blocks = block_height * block_width
height, width, channel = image.shape[-3:]
patch_height, patch_width = height//block_height, width//block_width
assert height == (
patch_height * block_height
), 'block_height=%d should evenly divide height=%d.'%(block_height, height)
assert width == (
patch_width * block_width
), 'block_width=%d should evenly divide width=%d.'%(block_width, width)
patch_size = patch_height * patch_width
paddings = 2*[[0, 0]]
patches = tf.space_to_batch(image, [patch_height, patch_width], paddings)
patches = tf.split(patches, patch_size, 0)
patches = tf.stack(patches, axis=3)
patches = tf.reshape(patches,
[num_blocks, patch_height, patch_width, channel])
return patches.numpy()
def patches_to_image(patches: np.ndarray, block_shape: List[int]) -> np.ndarray:
"""Unfolds patches (stacked along batch) into an image.
Args:
patches: The input patches, shaped [num_patches, patch_H, patch_W, C].
block_shape: The number of patches along the height and width to unfold.
Each patch assumed to be shaped (H/block_shape[0], W/block_shape[1]).
Returns:
The unfolded image shaped [B, H, W, C].
"""
block_height, block_width = block_shape
paddings = 2 * [[0, 0]]
patch_height, patch_width, channel = patches.shape[-3:]
patch_size = patch_height * patch_width
patches = tf.reshape(patches,
[1, block_height, block_width, patch_size, channel])
patches = tf.split(patches, patch_size, axis=3)
patches = tf.stack(patches, axis=0)
patches = tf.reshape(patches,
[patch_size, block_height, block_width, channel])
image = tf.batch_to_space(patches, [patch_height, patch_width], paddings)
return image.numpy()
class Interpolator:
"""A class for generating interpolated frames between two input frames.
Uses TF2 saved model format.
"""
def __init__(self, model_path: str,
align: Optional[int] = None,
block_shape: Optional[List[int]] = None) -> None:
"""Loads a saved model.
Args:
model_path: Path to the saved model. If none are provided, uses the
default model.
align: 'If >1, pad the input size so it divides with this before
inference.'
block_shape: Number of patches along the (height, width) to sid-divide
input images.
"""
self._model = tf.compat.v2.saved_model.load(model_path)
self._align = align or None
self._block_shape = block_shape or None
def interpolate(self, x0: np.ndarray, x1: np.ndarray,
dt: np.ndarray) -> np.ndarray:
"""Generates an interpolated frame between given two batches of frames.
All input tensors should be np.float32 datatype.
Args:
x0: First image batch. Dimensions: (batch_size, height, width, channels)
x1: Second image batch. Dimensions: (batch_size, height, width, channels)
dt: Sub-frame time. Range [0,1]. Dimensions: (batch_size,)
Returns:
The result with dimensions (batch_size, height, width, channels).
"""
if self._align is not None:
x0, bbox_to_crop = _pad_to_align(x0, self._align)
x1, _ = _pad_to_align(x1, self._align)
inputs = {'x0': x0, 'x1': x1, 'time': dt[..., np.newaxis]}
result = self._model(inputs, training=False)
image = result['image']
if self._align is not None:
image = tf.image.crop_to_bounding_box(image, **bbox_to_crop)
return image.numpy()
def __call__(self, x0: np.ndarray, x1: np.ndarray,
dt: np.ndarray) -> np.ndarray:
"""Generates an interpolated frame between given two batches of frames.
All input tensors should be np.float32 datatype.
Args:
x0: First image batch. Dimensions: (batch_size, height, width, channels)
x1: Second image batch. Dimensions: (batch_size, height, width, channels)
dt: Sub-frame time. Range [0,1]. Dimensions: (batch_size,)
Returns:
The result with dimensions (batch_size, height, width, channels).
"""
if self._block_shape is not None and np.prod(self._block_shape) > 1:
# Subdivide high-res images into managable non-overlapping patches.
x0_patches = image_to_patches(x0, self._block_shape)
x1_patches = image_to_patches(x1, self._block_shape)
# Run the interpolator on each patch pair.
output_patches = []
for image_0, image_1 in zip(x0_patches, x1_patches):
mid_patch = self.interpolate(image_0[np.newaxis, ...],
image_1[np.newaxis, ...], dt)
output_patches.append(mid_patch)
# Reconstruct interpolated image by stitching interpolated patches.
output_patches = np.concatenate(output_patches, axis=0)
return patches_to_image(output_patches, self._block_shape)
else:
# Invoke the interpolator once.
return self.interpolate(x0, x1, dt)
|