| import os
|
| import glob
|
| import numpy as np
|
| from PIL import Image
|
| import argparse
|
|
|
| def prepare_calibration_dataset(image_dir, output_path, size=(256, 256), num_samples=100):
|
| """
|
| Prepares a representative calibration dataset for INT8/W4A8 quantization.
|
| Extracts num_samples from the directory, resizes them, and saves as a .npy tensor.
|
| """
|
| all_files = []
|
| for ext in ['*.jpg', '*.png', '*.jpeg']:
|
| all_files.extend(glob.glob(os.path.join(image_dir, "**", ext), recursive=True))
|
|
|
| if len(all_files) == 0:
|
| print(f"Error: No images found in {image_dir}")
|
| return
|
|
|
|
|
| np.random.seed(42)
|
| np.random.shuffle(all_files)
|
| selected = all_files[:num_samples]
|
|
|
| print(f"Processing {len(selected)} samples...")
|
| images = []
|
| for f in selected:
|
| try:
|
| img = Image.open(f).convert('RGB')
|
| img = img.resize(size, Image.Resampling.LANCZOS)
|
| img_data = np.array(img).astype(np.float32) / 255.0
|
|
|
|
|
| img_data = np.transpose(img_data, (2, 0, 1))
|
| images.append(img_data)
|
| except Exception as e:
|
| print(f"Skipping {f}: {e}")
|
|
|
| if images:
|
| np.save(output_path, np.stack(images))
|
| print(f"Successfully saved {len(images)} samples to {output_path}")
|
|
|
| if __name__ == "__main__":
|
| parser = argparse.ArgumentParser(description="Lithotheque Calibration Preparator")
|
| parser.add_argument("--dir", type=str, required=True, help="Path to raw images")
|
| parser.add_argument("--out", type=str, required=True, help="Output .npy path")
|
| parser.add_argument("--size", type=int, default=256, help="Resize dimension (square)")
|
| args = parser.parse_args()
|
|
|
| prepare_calibration_dataset(args.dir, args.out, size=(args.size, args.size))
|
|
|
