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1d197a4 | 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 | #!/usr/bin/env python3
"""Generate a 4x4 augmentation preview grid without training.
Examples:
python -u scripts/finetune/shared/preview_augmentations.py \
--image-path data/roboflow/frame_01_0001_001_png.rf.19fb74b147f4e2ea7aeeeba9a8f9bb60.jpg
python -u scripts/finetune/shared/preview_augmentations.py \
--mode bifurcation --image-path data/roboflow/frame_01_0001_001_png.rf.19fb74b147f4e2ea7aeeeba9a8f9bb60.jpg
"""
from __future__ import annotations
import argparse
from pathlib import Path
import numpy as np
import tensorflow as tf
from PIL import Image
IMG_MEAN = tf.constant([60.3486], dtype=tf.float32)
def _load_gray(image_path: Path) -> np.ndarray:
img = Image.open(image_path).convert("L")
return np.asarray(img, dtype=np.uint8)
def _prepare_lumen_input(gray: np.ndarray) -> tf.Tensor:
x = tf.convert_to_tensor(gray[None, ...], dtype=tf.float32)
x = x - IMG_MEAN
x = tf.expand_dims(x, axis=-1)
x = tf.tile(x, [1, 1, 1, 3])
return x
def _prepare_bif_input(gray: np.ndarray, center_images: bool = False) -> tf.Tensor:
x = tf.convert_to_tensor(gray[None, ...], dtype=tf.float32)
if center_images:
x = x - IMG_MEAN
x = tf.expand_dims(x, axis=-1)
x = tf.tile(x, [1, 1, 1, 3])
return x
def _augment_seg_batch(images: tf.Tensor, seed: int) -> tf.Tensor:
stateless_seed = tf.convert_to_tensor([seed, 0], dtype=tf.int32)
flip_lr = tf.random.stateless_uniform([], seed=stateless_seed) > 0.5
images = tf.cond(flip_lr, lambda: tf.image.flip_left_right(images), lambda: images)
stateless_seed = tf.convert_to_tensor([seed, 1], dtype=tf.int32)
flip_ud = tf.random.stateless_uniform([], seed=stateless_seed) > 0.5
images = tf.cond(flip_ud, lambda: tf.image.flip_up_down(images), lambda: images)
stateless_seed = tf.convert_to_tensor([seed, 2], dtype=tf.int32)
k = tf.random.stateless_uniform([], seed=stateless_seed, minval=0, maxval=4, dtype=tf.int32)
images = tf.image.rot90(images, k=k)
shx = tf.random.stateless_uniform([], seed=tf.convert_to_tensor([seed, 30], dtype=tf.int32), minval=-0.08, maxval=0.08)
shy = tf.random.stateless_uniform([], seed=tf.convert_to_tensor([seed, 31], dtype=tf.int32), minval=-0.08, maxval=0.08)
p0 = tf.random.stateless_uniform([], seed=tf.convert_to_tensor([seed, 32], dtype=tf.int32), minval=-8e-4, maxval=8e-4)
p1 = tf.random.stateless_uniform([], seed=tf.convert_to_tensor([seed, 33], dtype=tf.int32), minval=-8e-4, maxval=8e-4)
base_t = tf.stack(
[
tf.constant(1.0, tf.float32),
shx,
tf.constant(0.0, tf.float32),
shy,
tf.constant(1.0, tf.float32),
tf.constant(0.0, tf.float32),
p0,
p1,
]
)
transforms = tf.tile(base_t[tf.newaxis, :], [tf.shape(images)[0], 1])
images = tf.raw_ops.ImageProjectiveTransformV3(
images=images,
transforms=transforms,
output_shape=tf.shape(images)[1:3],
interpolation="BILINEAR",
fill_mode="REFLECT",
fill_value=0.0,
)
stateless_seed = tf.convert_to_tensor([seed, 3], dtype=tf.int32)
images = tf.image.stateless_random_brightness(images, max_delta=10.0, seed=stateless_seed)
stateless_seed = tf.convert_to_tensor([seed, 4], dtype=tf.int32)
images = tf.image.stateless_random_contrast(images, lower=0.9, upper=1.1, seed=stateless_seed)
images = tf.clip_by_value(images, -255.0, 255.0)
return images
def _augment_cls_batch(images: tf.Tensor, seed: int) -> tf.Tensor:
stateless_seed = tf.convert_to_tensor([seed, 0], dtype=tf.int32)
flip_lr = tf.random.stateless_uniform([], seed=stateless_seed) > 0.5
images = tf.cond(flip_lr, lambda: tf.image.flip_left_right(images), lambda: images)
stateless_seed = tf.convert_to_tensor([seed, 1], dtype=tf.int32)
flip_ud = tf.random.stateless_uniform([], seed=stateless_seed) > 0.5
images = tf.cond(flip_ud, lambda: tf.image.flip_up_down(images), lambda: images)
stateless_seed = tf.convert_to_tensor([seed, 2], dtype=tf.int32)
k = tf.random.stateless_uniform([], seed=stateless_seed, minval=0, maxval=4, dtype=tf.int32)
images = tf.image.rot90(images, k=k)
shx = tf.random.stateless_uniform([], seed=tf.convert_to_tensor([seed, 30], dtype=tf.int32), minval=-0.08, maxval=0.08)
shy = tf.random.stateless_uniform([], seed=tf.convert_to_tensor([seed, 31], dtype=tf.int32), minval=-0.08, maxval=0.08)
p0 = tf.random.stateless_uniform([], seed=tf.convert_to_tensor([seed, 32], dtype=tf.int32), minval=-8e-4, maxval=8e-4)
p1 = tf.random.stateless_uniform([], seed=tf.convert_to_tensor([seed, 33], dtype=tf.int32), minval=-8e-4, maxval=8e-4)
base_t = tf.stack(
[
tf.constant(1.0, tf.float32),
shx,
tf.constant(0.0, tf.float32),
shy,
tf.constant(1.0, tf.float32),
tf.constant(0.0, tf.float32),
p0,
p1,
]
)
transforms = tf.tile(base_t[tf.newaxis, :], [tf.shape(images)[0], 1])
images = tf.raw_ops.ImageProjectiveTransformV3(
images=images,
transforms=transforms,
output_shape=tf.shape(images)[1:3],
interpolation="BILINEAR",
fill_mode="REFLECT",
fill_value=0.0,
)
stateless_seed = tf.convert_to_tensor([seed, 3], dtype=tf.int32)
images = tf.image.stateless_random_brightness(images, max_delta=10.0, seed=stateless_seed)
stateless_seed = tf.convert_to_tensor([seed, 4], dtype=tf.int32)
images = tf.image.stateless_random_contrast(images, lower=0.9, upper=1.1, seed=stateless_seed)
images = tf.clip_by_value(images, -255.0, 255.0)
return images
def _tile_to_uint8(tile: np.ndarray) -> np.ndarray:
x = np.asarray(tile, dtype=np.float32)
if x.ndim == 3 and x.shape[-1] > 1:
x = x[..., 0]
if not np.isfinite(x).any():
return np.zeros_like(x, dtype=np.uint8)
mn = float(np.nanmin(x))
mx = float(np.nanmax(x))
if mx <= mn + 1e-6:
return np.clip(x, 0, 255).astype(np.uint8)
x = (x - mn) / (mx - mn)
return (x * 255.0).clip(0, 255).astype(np.uint8)
def _build_grid(tiles: list[np.ndarray], h: int, w: int) -> Image.Image:
grid = Image.new("L", (4 * w, 4 * h), color=0)
for i, tile in enumerate(tiles[:16]):
r = i // 4
c = i % 4
grid.paste(Image.fromarray(_tile_to_uint8(tile), mode="L"), (c * w, r * h))
return grid
def main() -> None:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--image-path", type=Path, required=True)
parser.add_argument("--mode", type=str, default="lumen", choices=["lumen", "bifurcation"])
parser.add_argument("--seed", type=int, default=7)
parser.add_argument(
"--output-path",
type=Path,
default=Path("output/augment_preview/augment_preview_4x4.png"),
)
parser.add_argument(
"--center-images",
action="store_true",
help="For bifurcation custom-model style preview (subtract IMG_MEAN before aug).",
)
args = parser.parse_args()
if not args.image_path.exists():
raise FileNotFoundError(f"Image not found: {args.image_path}")
gray = _load_gray(args.image_path)
h, w = int(gray.shape[0]), int(gray.shape[1])
if args.mode == "lumen":
base = _prepare_lumen_input(gray)
aug_fn = _augment_seg_batch
else:
base = _prepare_bif_input(gray, center_images=args.center_images)
aug_fn = _augment_cls_batch
tiles: list[np.ndarray] = []
for i in range(16):
out = aug_fn(base, seed=args.seed + i).numpy()[0]
tiles.append(out)
grid = _build_grid(tiles, h=h, w=w)
args.output_path.parent.mkdir(parents=True, exist_ok=True)
grid.save(args.output_path)
print(f"Saved augmentation grid: {args.output_path}")
if __name__ == "__main__":
main()
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