File size: 2,289 Bytes
4ab0193 | 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 | import os
import numpy as np
from PIL import Image
data_root = './MRNet/MRNet-v1.0'
output_root = os.path.join(data_root, '{split}_slides')
splits = ['train', 'valid']
views = ['axial', 'coronal', 'sagittal']
num_slices_to_keep = 10
for split in splits:
for view in views:
input_dir = os.path.join(data_root, split, view)
output_dir = os.path.join(data_root, f'{split}_slides', view)
if not os.path.isdir(input_dir):
print(f"Directory not found, skipping: {input_dir}")
continue
os.makedirs(output_dir, exist_ok=True)
npy_files = sorted([f for f in os.listdir(input_dir) if f.endswith('.npy')])
print(f"[{split}/{view}] Found {len(npy_files)} .npy files, processing...")
for filename in npy_files:
file_path = os.path.join(input_dir, filename)
volume_data = np.load(file_path)
sample_name = os.path.splitext(filename)[0]
sample_output_dir = os.path.join(output_dir, sample_name)
os.makedirs(sample_output_dir, exist_ok=True)
total_slices = volume_data.shape[0]
if total_slices <= num_slices_to_keep:
selected_indices = np.arange(total_slices)
else:
selected_indices = np.linspace(0, total_slices - 1, num_slices_to_keep, dtype=int)
for save_idx, slice_idx in enumerate(selected_indices):
slice_2d = volume_data[slice_idx, :, :]
if slice_2d.dtype != np.uint8:
slice_min = slice_2d.min()
slice_max = slice_2d.max()
if slice_max > slice_min:
slice_2d = (slice_2d - slice_min) / (slice_max - slice_min) * 255.0
else:
slice_2d = np.zeros_like(slice_2d)
slice_2d = slice_2d.astype(np.uint8)
img = Image.fromarray(slice_2d)
save_path = os.path.join(
sample_output_dir,
f'slice_{save_idx:02d}_orig_{slice_idx:03d}.png'
)
img.save(save_path)
print(f" Converted: {sample_name} (original {total_slices} slices, saved {len(selected_indices)})")
print("\nAll done!")
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