Xiao Furen
d167c1f7c7
Introduce a robust coordinate transformation system to manage the relationship between original CT space and rotated/standardized segmentation spaces. This includes a new directory hierarchy to separate unrotated crops from rotated outputs and utility functions for geometric mapping. Key changes: - Implement `imaging/transforms.py` to handle bounding box metadata, affine standardization, and coordinate mapping between spaces. - Restructure dataset output: unrotated segmentation files (binary, SDF, ROI, etc.) are now stored in a `<vol>/crop/` subdirectory to distinguish them from `<vol>/rotated/` aligned versions. - Add `level_file_path` utility to abstract file discovery across legacy (top-level) and new (crop-based) directory structures. - Enhance `seg_bone` to capture and export bounding box metadata (`bbox2`, `nn_bbox`, `bbox_orig`) into `transform.json`. - Implement `xfr_cbt_native.py` for mapping screw positions back to original CT space. - Update preprocessing and visualization scripts to support the new directory layout and transformation metadata. - Improve TinyDB metadata migration logic to prevent accidental corruption of existing database structures.
99 lines
No EOL
3.9 KiB
Python
99 lines
No EOL
3.9 KiB
Python
#!/home/xfr/.conda/envs/cbt/bin/python
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"""
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為每個 (volume, level) 的骨頭遮罩（{level}_binary_sdf.nii.gz，缺則 _binary）繪製
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X-ray 四視角圖（不畫螺絲），存到 Output/{date}/{volume}/。
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輸出檔名：{volume} {level}_CBT.png
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（例：1.3.6.1.4.1.9328.50.4.0005 L1_CBT.png）
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Usage:
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python xfr_plot_level.py # 全部 volume 的 L1~L5
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python xfr_plot_level.py 0005 # 該 volume 的 L1~L5
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python xfr_plot_level.py 0005 L1 # 單一 (volume, level)
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python xfr_plot_level.py --dir <standardized_dir> --output <output_base>
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"""
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import argparse
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import os
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import sys
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from imaging.transforms import level_file_path
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from visualization.res_bone_figure import render_bone_figure
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standardized_dir = '/mnt/1248/open2/cyrou/CBT/Seg/Resample/standardized-xfr/'
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output_base = '/mnt/1248/open2/cyrou/Output'
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LEVELS = ('L1', 'L2', 'L3', 'L4', 'L5')
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USAGE = 'Usage: python xfr_plot_level.py [volume_id] [level] [--dir D] [--output O]'
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def parse_args(argv):
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parser = argparse.ArgumentParser(description='繪製各 lumbar level 的骨頭 X-ray 圖（不畫螺絲）')
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parser.add_argument('volume_id', nargs='?', default=None,
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help='volume ID（完整 UID 或末段，如 0005）；省略＝全部')
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parser.add_argument('level', nargs='?', default=None,
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help=f'level（{" / ".join(LEVELS)}）；省略＝全部')
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parser.add_argument('--dir', default=standardized_dir,
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help=f'standardized 資料夾（預設 {standardized_dir}）')
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parser.add_argument('--output', default=output_base,
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help=f'輸出根目錄（預設 {output_base}）')
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return parser.parse_args(argv)
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def main():
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args = parse_args(sys.argv[1:])
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if args.level is not None:
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level_key = args.level.upper()
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if level_key not in LEVELS:
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print(f'Invalid level: {args.level} (choose from {"/".join(LEVELS)})')
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sys.exit(1)
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if args.level is not None and args.volume_id is None:
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print(f'{USAGE}\nlevel requires volume_id')
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sys.exit(1)
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volumes = [d for d in sorted(os.listdir(args.dir))
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if os.path.isdir(os.path.join(args.dir, d))]
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if args.volume_id is not None:
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key = args.volume_id.lower()
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vols = [v for v in volumes
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if v.lower() == key or v.rsplit('.', 1)[-1] == key]
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if not vols:
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print(f'Volume not found: {args.volume_id}')
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sys.exit(1)
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volumes = vols
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levels = (args.level.upper(),) if args.level else LEVELS
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tasks = [(vid, lvl) for vid in volumes for lvl in levels]
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print(f'{len(volumes)} volume(s) x {len(levels)} level(s) = {len(tasks)} figure(s)',
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flush=True)
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ok, skip = [], []
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for i, (vid, lvl) in enumerate(tasks, 1):
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vol_dir = os.path.join(args.dir, vid)
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# _binary.nii.gz 現為原解析度；0.5mm 用 _binary_sdf（SDF 平滑遮罩）
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# 未旋轉檔：新世代在 crop/ 子資料夾、舊世代在頂層
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sdf_path = level_file_path(vol_dir, lvl, 'binary_sdf')
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binary_path = sdf_path if os.path.exists(sdf_path) \
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else level_file_path(vol_dir, lvl, 'binary')
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cortical_path = level_file_path(vol_dir, lvl, 'cortical')
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path = render_bone_figure(vid, lvl, binary_path, cortical_path,
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base_folder=args.output)
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if path is None:
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skip.append((vid, lvl))
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print(f'[{i}/{len(tasks)}] {vid} {lvl}: skipped', flush=True)
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else:
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ok.append((vid, lvl))
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print(f'[{i}/{len(tasks)}] {vid} {lvl}: saved {path}', flush=True)
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print('=' * 60)
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print(f'Done. saved={len(ok)} skipped={len(skip)}')
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if skip:
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print('Skipped (missing/empty mask):')
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for vid, lvl in skip:
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print(f' {vid} {lvl}')
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if __name__ == '__main__':
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main() |