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| """ | |
| prepare_examples.py | |
| ------------------- | |
| Utility script: extracts 5 sample CT scan images from the IBM dataset | |
| and saves them as PNG files in the examples/ folder for Gradio demos. | |
| Run once before pushing to Hugging Face: | |
| python prepare_examples.py | |
| python prepare_examples.py --data_dir "C:/path/to/2d_images" | |
| """ | |
| import argparse | |
| import os | |
| import glob | |
| import numpy as np | |
| from skimage.io import imread | |
| from skimage.transform import resize | |
| from PIL import Image | |
| def prepare_examples(data_dir=".", n=5, output_dir="examples"): | |
| """ | |
| Load n CT scan .tif files, convert to PNG, save to output_dir. | |
| Args: | |
| data_dir : directory containing 2d_images/**/*.tif | |
| n : number of example images to extract | |
| output_dir : folder to save PNG files into | |
| """ | |
| os.makedirs(output_dir, exist_ok=True) | |
| # Search for .tif files | |
| pattern = os.path.join(data_dir, "2d_images", "**", "*.tif") | |
| img_list = sorted(glob.glob(pattern, recursive=True)) | |
| if not img_list: | |
| # Try the IBMDATA desktop path as fallback | |
| fallback = r"C:\Users\Shind\OneDrive\Desktop\IBMDATA\2d_images" | |
| pattern = os.path.join(fallback, "**", "*.tif") | |
| img_list = sorted(glob.glob(pattern, recursive=True)) | |
| if not img_list: | |
| print( | |
| "ERROR: No .tif images found.\n" | |
| "Run from the directory that contains 2d_images/, or pass --data_dir." | |
| ) | |
| return | |
| # Pick n evenly-spaced images | |
| indices = np.linspace(0, len(img_list) - 1, min(n, len(img_list)), dtype=int) | |
| chosen = [img_list[i] for i in indices] | |
| print(f"Extracting {len(chosen)} example images to '{output_dir}/'...") | |
| for k, path in enumerate(chosen, start=1): | |
| img = imread(path).astype(np.float32) | |
| # Resize to 256x256 (matches model input) | |
| img_resized = resize(img, (256, 256), preserve_range=True, anti_aliasing=True) | |
| # Normalise to uint8 for display | |
| lo, hi = img_resized.min(), img_resized.max() | |
| if hi > lo: | |
| img_uint8 = ((img_resized - lo) / (hi - lo) * 255).astype(np.uint8) | |
| else: | |
| img_uint8 = np.zeros((256, 256), dtype=np.uint8) | |
| out_path = os.path.join(output_dir, f"ct_sample_{k:02d}.png") | |
| Image.fromarray(img_uint8).save(out_path) | |
| print(f" [{k}/{len(chosen)}] Saved: {out_path}") | |
| print("Done. Example images are ready in", output_dir) | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser(description="Prepare example CT scan PNGs for Gradio") | |
| parser.add_argument("--data_dir", type=str, default=".", help="Directory with 2d_images/") | |
| parser.add_argument("--n", type=int, default=5, help="Number of examples to extract") | |
| parser.add_argument("--output_dir", type=str, default="examples", help="Output folder for PNGs") | |
| args = parser.parse_args() | |
| prepare_examples(data_dir=args.data_dir, n=args.n, output_dir=args.output_dir) | |