69 lines
2.1 KiB
Python
69 lines
2.1 KiB
Python
#!/usr/bin/env python3
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"""
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Quickly preview training pairs (original, transformed, H) from ICLayoutTrainingDataset.
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Saves a grid image for visual inspection.
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"""
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from __future__ import annotations
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import argparse
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from pathlib import Path
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import numpy as np
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import torch
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from PIL import Image
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from torchvision.utils import make_grid, save_image
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from data.ic_dataset import ICLayoutTrainingDataset
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from utils.data_utils import get_transform
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def to_pil(t: torch.Tensor) -> Image.Image:
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# input normalized to [-1,1] for 3-channels; invert normalization
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x = t.clone()
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if x.dim() == 3 and x.size(0) == 3:
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x = (x * 0.5) + 0.5 # unnormalize
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x = (x * 255.0).clamp(0, 255).byte()
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if x.dim() == 3 and x.size(0) == 3:
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x = x
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elif x.dim() == 3 and x.size(0) == 1:
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x = x.repeat(3, 1, 1)
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else:
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raise ValueError("Unexpected tensor shape")
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np_img = x.permute(1, 2, 0).cpu().numpy()
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return Image.fromarray(np_img)
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def main():
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parser = argparse.ArgumentParser(description="Preview dataset samples")
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parser.add_argument("--dir", dest="image_dir", type=str, required=True, help="PNG images directory")
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parser.add_argument("--out", dest="out_path", type=str, default="preview.png")
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parser.add_argument("--n", dest="num", type=int, default=8)
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parser.add_argument("--patch", dest="patch_size", type=int, default=256)
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parser.add_argument("--elastic", dest="use_elastic", action="store_true")
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args = parser.parse_args()
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transform = get_transform()
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ds = ICLayoutTrainingDataset(
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args.image_dir,
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patch_size=args.patch_size,
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transform=transform,
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scale_range=(1.0, 1.0),
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use_albu=args.use_elastic,
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albu_params={"prob": 0.5},
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)
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images = []
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for i in range(min(args.num, len(ds))):
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orig, rot, H = ds[i]
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# Stack orig and rot side-by-side for each sample
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images.append(orig)
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images.append(rot)
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grid = make_grid(torch.stack(images, dim=0), nrow=2, padding=2)
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save_image(grid, args.out_path)
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print(f"Saved preview to {args.out_path}")
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if __name__ == "__main__":
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main()
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