| """Minimal Gradio frontend for raw burst reconstruction.""" |
|
|
| from pathlib import Path |
| import shutil |
| import tempfile |
|
|
| import spaces |
| import gradio as gr |
| import numpy as np |
|
|
|
|
| @spaces.GPU(duration=120) |
| def reconstruct(files, scale): |
| |
| from handheld_super_resolution.config import Config |
| from handheld_super_resolution.monte_carlo import generate_noise_lut |
| from handheld_super_resolution.noise_lut import save_noise_lut |
| from handheld_super_resolution.utils_dng import read_dng_noise_profile |
| from handheld_super_resolution.super_resolution import process |
|
|
| with tempfile.TemporaryDirectory(prefix="handheld-") as directory: |
| burst = Path(directory) |
| for index, source in enumerate(sorted(files, key=lambda p: Path(p).name)): |
| shutil.copyfile(source, burst / f"{index:04d}.dng") |
|
|
| alpha, beta = read_dng_noise_profile(burst / "0000.dng") |
| lut = generate_noise_lut(alpha, beta, show_progress=False) |
| lut_path = burst / "noise.npz" |
| save_noise_lut(lut_path, lut, trials=10_000_000, seed=0) |
| config = Config(scale=float(scale)) |
| config.noise_model.lut_path = lut_path |
| config.robustness.save_mask = False |
| result, _ = process(burst, config) |
| return np.round(np.clip(np.nan_to_num(result), 0, 1) * 255).astype(np.uint8) |
|
|
|
|
| def run(files, scale): |
| if not files or len(files) < 2: |
| raise gr.Error("Upload at least two DNG images from the same burst.") |
| if any(Path(path).suffix.lower() != ".dng" for path in files): |
| raise gr.Error("This pipeline accepts raw DNG images.") |
| try: |
| return reconstruct(files, scale) |
| except Exception as error: |
| raise gr.Error(f"Reconstruction failed: {error}") from error |
|
|
|
|
| with gr.Blocks(delete_cache=(3600, 3600)) as demo: |
| gr.Markdown("# Handheld Super-Resolution\nUpload raw DNG images from one burst. " |
| "The first image alphabetically is the reference. " |
| "Noise calibration is automatic; the first run may take longer.") |
| images = gr.File(label="DNG burst", file_count="multiple", file_types=[".dng"], type="filepath") |
| scale = gr.Radio([1, 2], value=2, label="Output scale") |
| button = gr.Button("Reconstruct", variant="primary") |
| result = gr.Image(label="Result", format="png") |
| button.click(run, inputs=[images, scale], outputs=result, concurrency_limit=1) |
|
|
|
|
| if __name__ == "__main__": |
| demo.queue(max_size=8).launch() |
|
|
