File size: 3,237 Bytes
c4c5273 | 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 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 | import os, json, random, glob
import numpy as np
import yaml
def load_config(path="configs/default.yaml"):
with open(path, "r") as f:
cfg = yaml.safe_load(f)
return cfg
def set_seed(seed=42):
random.seed(seed)
np.random.seed(seed)
try:
import torch
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
except Exception:
pass
def ensure_dir(p):
os.makedirs(p, exist_ok=True)
return p
# ----------------------------- NIfTI IO -----------------------------
def load_nii(path):
"""Return (array[z,y,x] -> we keep numpy in (x,y,z)? ) We use SimpleITK and keep
arrays in numpy index order [x, y, z] together with spacing/origin/direction."""
import SimpleITK as sitk
img = sitk.ReadImage(path)
arr = sitk.GetArrayFromImage(img) # [z, y, x]
arr = np.transpose(arr, (2, 1, 0)) # -> [x, y, z]
meta = dict(
spacing=np.array(img.GetSpacing(), dtype=np.float64), # (sx, sy, sz)
origin=np.array(img.GetOrigin(), dtype=np.float64),
direction=np.array(img.GetDirection(), dtype=np.float64),
)
return arr, meta
def save_nii(arr_xyz, meta, path):
import SimpleITK as sitk
arr = np.transpose(arr_xyz, (2, 1, 0)) # back to [z, y, x]
img = sitk.GetImageFromArray(arr)
img.SetSpacing(tuple(float(s) for s in meta["spacing"]))
img.SetOrigin(tuple(float(o) for o in meta["origin"]))
img.SetDirection(tuple(float(d) for d in meta["direction"]))
sitk.WriteImage(img, path)
def resample_to_spacing(arr_xyz, meta, new_spacing, is_label=False):
"""Resample a volume (numpy [x,y,z]) to an isotropic-ish new_spacing (list of 3)."""
import SimpleITK as sitk
arr = np.transpose(arr_xyz, (2, 1, 0))
img = sitk.GetImageFromArray(arr)
img.SetSpacing(tuple(float(s) for s in meta["spacing"]))
img.SetOrigin(tuple(float(o) for o in meta["origin"]))
img.SetDirection(tuple(float(d) for d in meta["direction"]))
old_spacing = np.array(img.GetSpacing())
old_size = np.array(img.GetSize())
new_spacing = np.array(new_spacing, dtype=np.float64)
new_size = np.round(old_size * (old_spacing / new_spacing)).astype(int).tolist()
rs = sitk.ResampleImageFilter()
rs.SetOutputSpacing(tuple(float(s) for s in new_spacing))
rs.SetSize([int(s) for s in new_size])
rs.SetOutputOrigin(img.GetOrigin())
rs.SetOutputDirection(img.GetDirection())
rs.SetInterpolator(sitk.sitkNearestNeighbor if is_label else sitk.sitkLinear)
out = rs.Execute(img)
o_arr = sitk.GetArrayFromImage(out)
o_arr = np.transpose(o_arr, (2, 1, 0))
o_meta = dict(spacing=new_spacing,
origin=np.array(out.GetOrigin()),
direction=np.array(out.GetDirection()))
return o_arr, o_meta
def numeric_id(case_id):
"""Extract leading number from a case folder name like '031-修牙髓...' -> 31."""
import re
m = re.match(r"\s*0*(\d+)", str(case_id))
return int(m.group(1)) if m else -1
def write_json(obj, path):
with open(path, "w") as f:
json.dump(obj, f, ensure_ascii=False, indent=2)
def read_json(path):
with open(path, "r") as f:
return json.load(f)
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