File size: 7,135 Bytes
3050f1b | 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 | """Process a directory of images through NisabaRelief and save as PNG."""
import argparse
from pathlib import Path
from PIL import Image
from rich.console import Console
from rich.progress import (
BarColumn,
MofNCompleteColumn,
Progress,
ProgressColumn,
SpinnerColumn,
Task,
TextColumn,
TimeElapsedColumn,
)
from rich.text import Text
from nisaba_relief import NisabaRelief
from nisaba_relief.constants import MAX_TILE, MIN_IMAGE_DIMENSION
Image.MAX_IMAGE_PIXELS = None
IMAGE_EXTENSIONS = {".jpg", ".jpeg", ".png", ".tif", ".tiff", ".bmp", ".webp"}
class SimpleTimeRemainingColumn(ProgressColumn):
"""Estimates remaining time from the average duration of the last 10 iterations.
Only recomputes when a new step completes so the display is stable.
"""
def __init__(self, window: int = 10) -> None:
super().__init__()
self._last_completed: float = 0
self._last_elapsed: float = 0.0
self._durations: list[float] = []
self._window: int = window
self._cached: Text = Text("-:--:--", style="progress.remaining")
def render(self, task: Task) -> Text:
if task.completed <= self._last_completed:
return self._cached
elapsed = task.finished_time if task.finished else task.elapsed
if not elapsed or not task.completed:
self._last_completed = task.completed
self._cached = Text("-:--:--", style="progress.remaining")
return self._cached
step_duration = elapsed - self._last_elapsed
steps = task.completed - self._last_completed
if steps > 0 and self._last_completed > 0:
per_step = step_duration / steps
self._durations.append(per_step)
if len(self._durations) > self._window:
self._durations = self._durations[-self._window :]
self._last_completed = task.completed
self._last_elapsed = elapsed
if not self._durations:
self._cached = Text("-:--:--", style="progress.remaining")
return self._cached
avg = sum(self._durations) / len(self._durations)
remaining = task.total - task.completed
eta_seconds = avg * remaining
hours, rem = divmod(int(eta_seconds), 3600)
minutes, seconds = divmod(rem, 60)
if hours:
self._cached = Text(
f"{hours}:{minutes:02d}:{seconds:02d}", style="progress.remaining"
)
else:
self._cached = Text(f"{minutes}:{seconds:02d}", style="progress.remaining")
return self._cached
def main():
parser = argparse.ArgumentParser(
description="Process images through NisabaRelief and save as PNG."
)
parser.add_argument(
"--input-dir", type=Path, required=True, help="Source image directory"
)
parser.add_argument(
"--output-dir", type=Path, required=True, help="Destination directory (created if needed)"
)
parser.add_argument(
"--max-size", type=int, default=MAX_TILE * 5,
help="Downsample images larger than this before processing (default: %(default)s)",
)
parser.add_argument(
"--min-size", type=int, default=1536,
help="Skip images where max dimension < this (default: %(default)s)",
)
parser.add_argument("--seed", type=int, default=None, help="Reproducibility seed")
parser.add_argument("--weights-dir", type=Path, default=None, help="Local weights directory")
parser.add_argument("--batch-size", type=int, default=None, help="Tile batch size")
parser.add_argument("--num-steps", type=int, default=2, help="Solver steps (default: %(default)s)")
parser.add_argument("--device", default="cuda", help="Torch device (default: %(default)s)")
parser.add_argument(
"--overwrite", action="store_true", help="Re-process even if output file exists"
)
args = parser.parse_args()
console = Console()
input_dir: Path = args.input_dir
output_dir: Path = args.output_dir
if not input_dir.is_dir():
console.print(f"[red]Input directory not found:[/red] [cyan]{input_dir}[/cyan]")
return
input_images = sorted(
p for p in input_dir.iterdir() if p.suffix.lower() in IMAGE_EXTENSIONS
)
if not input_images:
console.print(f"[red]No images found in[/red] [cyan]{input_dir}[/cyan]")
return
output_dir.mkdir(parents=True, exist_ok=True)
to_process = []
skipped_existing = 0
skipped_small = 0
for src in input_images:
dst = output_dir / (src.stem + ".png")
if not args.overwrite and dst.exists():
skipped_existing += 1
continue
with Image.open(src) as img:
if max(img.size) < args.min_size or min(img.size) < MIN_IMAGE_DIMENSION:
skipped_small += 1
continue
to_process.append((src, dst))
if skipped_existing:
console.print(
f"[dim]Skipping {skipped_existing} already-processed image(s)[/dim]"
)
if skipped_small:
console.print(
f"[dim]Skipping {skipped_small} image(s) smaller than {args.min_size}px[/dim]"
)
if not to_process:
console.print("[green]All images already processed.[/green]")
return
console.print(
f"Processing [bold]{len(to_process)}[/bold] / {len(input_images)} images "
f"[dim]({input_dir} → {output_dir})[/dim]"
)
model_kwargs = dict(num_steps=args.num_steps, device=args.device)
if args.seed is not None:
model_kwargs["seed"] = args.seed
if args.weights_dir is not None:
model_kwargs["weights_dir"] = args.weights_dir
if args.batch_size is not None:
model_kwargs["batch_size"] = args.batch_size
model = NisabaRelief(**model_kwargs)
progress = Progress(
SpinnerColumn(),
TextColumn("[progress.description]{task.description}"),
BarColumn(),
MofNCompleteColumn(),
TimeElapsedColumn(),
TextColumn("eta"),
SimpleTimeRemainingColumn(),
)
with progress:
task = progress.add_task("Processing", total=len(to_process))
for src, dst in to_process:
progress.update(task, description=f"[cyan]{src.name}[/cyan]")
image = Image.open(src).convert("RGB")
original_size = image.size
if max(image.size) > args.max_size:
scale = args.max_size / max(image.size)
new_size = (
round(image.width * scale) // 16 * 16,
round(image.height * scale) // 16 * 16,
)
image = image.resize(new_size, Image.LANCZOS)
result = model.process(image, show_pbar=False)
if result.size != original_size:
result = result.resize(original_size, Image.LANCZOS)
result.save(dst)
progress.advance(task)
console.print(
f"[green]Done.[/green] {len(to_process)} image(s) saved to [cyan]{output_dir}[/cyan]"
)
if __name__ == "__main__":
main()
|