Xiao Furen
4f5be3d3e9
Refactor the optimization pipeline to eliminate module-level global variables, improving thread safety and modularity. Introduced `OptimizationContext` to explicitly manage shared state during cylinder evaluation. Key changes: - core: Replace global variables with `OptimizationContext` dataclass in `objective.py`. - core: Implement `refine_lateral_longer` in `optimizer.py` for deterministic local refinement of screw placement. - core: Update scoring logic in `scoring.py` to use higher penalties for out-of-bone voxels. - imaging: Add advanced symmetry detection including `best_symmetry_plane` and `best_symmetry_axis_angle` in `orientation.py`. - visualization: Enhance 3D plotting in `res_plot_3d.py` with volume absorption rendering (Beer-Lambert law) for an X-ray-like appearance. - xfr_debug: Implement a custom `_Tee` logger to support multi-process logging with volume and level-specific tags. - chore: Update `.gitignore` to include local logs and kilo directories.
534 lines
21 KiB
Python
534 lines
21 KiB
Python
import torch
|
||
import numpy as np
|
||
import matplotlib.pyplot as plt
|
||
from matplotlib.colors import to_rgba
|
||
from matplotlib.lines import Line2D
|
||
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
|
||
import os
|
||
from datetime import datetime
|
||
import csv
|
||
|
||
from core.cylinder import generate_cylinder_n_torch, generate_cylinder_o_torch, snap_to_discrete_values
|
||
from core.intersection import center_line_intersections_torch
|
||
from core.scoring import cl_score_torch, compute_overlap_ratio_from_cylinder_mask, cl_score_torch_xfr
|
||
from imaging.orientation import (azimuth_rotation, analyze_vertebral_tilt_contour,
|
||
best_symmetry_plane, best_upper_endplate_plane,
|
||
segment_spinous_process)
|
||
from utils.helpers import save_with_unique_name
|
||
|
||
# Volume absorption 渲染（Beer-Lambert）：每 voxel 不透明度 = 1 - exp(-mu * voxel_width)
|
||
# 骨頭核心厚度達 70-90 voxel，沿視線堆疊會使任何 per-voxel alpha 累積成不透明。
|
||
# 因此以「抽稀 (SUBSAMPLE) 降低堆疊數量」+「低 mu 控制每點吸收」兩項共同調出淡薄 X-ray 陰影，
|
||
# 同時保留皮質 / 鬆質的吸收入射差異（mu 比值）。
|
||
BONE_MU_CORTICAL = 0.02 # 1/mm → 每 voxel = 1-exp(-0.02*0.5) ~ 0.010
|
||
BONE_MU_TRABECULAR = 0.005 # 1/mm → 每 voxel = 1-exp(-0.005*0.5) ~ 0.0025
|
||
BONE_MARKER_SIZE = 3.0 # 骨骼散點點面積 (pt^2)；略大以補償抽稀後的顆粒感
|
||
BONE_SUBSAMPLE = 1 # 每 10 個骨 voxel 畫 1 個，降低堆疊不透明度（1=全畫）
|
||
|
||
def set_axes_equal_3d(ax):
|
||
"""
|
||
Make axes of 3D plot have equal scale so that spheres appear as spheres,
|
||
cubes as cubes, etc.
|
||
"""
|
||
x_limits = ax.get_xlim3d()
|
||
y_limits = ax.get_ylim3d()
|
||
z_limits = ax.get_zlim3d()
|
||
|
||
x_range = abs(x_limits[1] - x_limits[0])
|
||
x_middle = np.mean(x_limits)
|
||
y_range = abs(y_limits[1] - y_limits[0])
|
||
y_middle = np.mean(y_limits)
|
||
z_range = abs(z_limits[1] - z_limits[0])
|
||
z_middle = np.mean(z_limits)
|
||
|
||
plot_radius = 0.5*max([x_range, y_range, z_range])
|
||
|
||
ax.set_xlim3d([x_middle - plot_radius, x_middle + plot_radius])
|
||
ax.set_ylim3d([y_middle - plot_radius, y_middle + plot_radius])
|
||
ax.set_zlim3d([z_middle - plot_radius, z_middle + plot_radius])
|
||
|
||
try:
|
||
ax.set_box_aspect([1, 1, 1])
|
||
except AttributeError:
|
||
pass
|
||
|
||
def res_plt_2_torch(
|
||
spine_tensor: torch.Tensor,
|
||
cortical_tensor: torch.Tensor,
|
||
image_shape: tuple[int, int, int],
|
||
image2_path: str,
|
||
base_folder: str,
|
||
label_str: str,
|
||
diameter_l: float,
|
||
length_l: float,
|
||
diameter_r: float,
|
||
length_r: float,
|
||
best_position_l: list[float],
|
||
best_position_r: list[float],
|
||
swarm_size: int,
|
||
max_iter: int,
|
||
total_time: float,
|
||
spacing: list[float],
|
||
CBT: bool,
|
||
device: torch.device,
|
||
grid=None
|
||
) -> None:
|
||
"""
|
||
Same plotting function as before, but it uses torch-based generation
|
||
and then moves data to CPU for matplotlib 3D scatter.
|
||
"""
|
||
cyl_l = generate_cylinder_n_torch(
|
||
diameter_l,
|
||
length_l,
|
||
best_position_l[0],
|
||
best_position_l[1],
|
||
best_position_l[2],
|
||
best_position_l[3],
|
||
best_position_l[4],
|
||
image_shape,
|
||
spacing,
|
||
device,
|
||
grid
|
||
)
|
||
|
||
cyl_lo = generate_cylinder_o_torch(
|
||
diameter_l,
|
||
length_l,
|
||
best_position_l[0],
|
||
best_position_l[1],
|
||
best_position_l[2],
|
||
best_position_l[3],
|
||
best_position_l[4],
|
||
image_shape,
|
||
spacing,
|
||
device,
|
||
grid
|
||
)
|
||
cyl_r = generate_cylinder_n_torch(
|
||
diameter_r,
|
||
length_r,
|
||
best_position_r[0],
|
||
best_position_r[1],
|
||
best_position_r[2],
|
||
best_position_r[3],
|
||
best_position_r[4],
|
||
image_shape,
|
||
spacing,
|
||
device,
|
||
grid
|
||
)
|
||
cyl_ro = generate_cylinder_o_torch(
|
||
diameter_r,
|
||
length_r,
|
||
best_position_r[0],
|
||
best_position_r[1],
|
||
best_position_r[2],
|
||
best_position_r[3],
|
||
best_position_r[4],
|
||
image_shape,
|
||
spacing,
|
||
device,
|
||
grid
|
||
)
|
||
|
||
intersections_l, line_mask_l = center_line_intersections_torch(
|
||
best_position_l[0],
|
||
best_position_l[1],
|
||
best_position_l[2],
|
||
best_position_l[3],
|
||
best_position_l[4],
|
||
int(length_l),
|
||
spine_tensor,
|
||
spacing,
|
||
device
|
||
)
|
||
loss_l = cl_score_torch(cortical_tensor, spine_tensor, cyl_l, cyl_lo, intersections_l)
|
||
|
||
intersections_r, line_mask_r = center_line_intersections_torch(
|
||
best_position_r[0],
|
||
best_position_r[1],
|
||
best_position_r[2],
|
||
best_position_r[3],
|
||
best_position_r[4],
|
||
int(length_r),
|
||
spine_tensor,
|
||
spacing,
|
||
device
|
||
)
|
||
# loss_r = cl_score_torch(cortical_tensor, spine_tensor, cyl_r, cyl_ro, intersections_r)
|
||
loss_r = cl_score_torch_xfr(cortical_tensor, spine_tensor, cyl_r, cyl_ro, intersections_r)
|
||
|
||
azi = azimuth_rotation(image2_path)
|
||
res = analyze_vertebral_tilt_contour(image2_path, edge_type='superior', show_plot=False, debug=False)
|
||
alt = res['superior']['tilt_angle_deg']
|
||
|
||
# Move data to CPU for plotting
|
||
line_mask_l_cpu = line_mask_l.cpu().numpy()
|
||
line_mask_r_cpu = line_mask_r.cpu().numpy()
|
||
cyl_l_cpu = cyl_l.cpu().numpy()
|
||
cyl_lo_cpu = cyl_lo.cpu().numpy()
|
||
cyl_r_cpu = cyl_r.cpu().numpy()
|
||
cyl_ro_cpu = cyl_ro.cpu().numpy()
|
||
spine_cpu = spine_tensor.cpu().numpy()
|
||
|
||
z_lin1, y_lin1, x_lin1 = np.where(line_mask_l_cpu == 1)
|
||
z_lin2, y_lin2, x_lin2 = np.where(line_mask_r_cpu == 1)
|
||
|
||
z_cyl_l1, y_cyl_l1, x_cyl_l1 = np.where(cyl_l_cpu == 1)
|
||
z_cyl_l2, y_cyl_l2, x_cyl_l2 = np.where(cyl_lo_cpu == 1)
|
||
z_cyl_r1, y_cyl_r1, x_cyl_r1 = np.where(cyl_r_cpu == 1)
|
||
z_cyl_r2, y_cyl_r2, x_cyl_r2 = np.where(cyl_ro_cpu == 1)
|
||
|
||
# 骨頭 voxel 依「體積吸收」分成兩組：皮質（高不透明度）與鬆質（低不透明度）
|
||
cortical_cpu = cortical_tensor.cpu().numpy()
|
||
voxel_mm = float(spacing[0])
|
||
alpha_cortical = 1.0 - np.exp(-BONE_MU_CORTICAL * voxel_mm)
|
||
alpha_trabecular = 1.0 - np.exp(-BONE_MU_TRABECULAR * voxel_mm)
|
||
z_corti, y_corti, x_corti = np.where((spine_cpu == 1) & (cortical_cpu == 1))
|
||
z_trab, y_trab, x_trab = np.where((spine_cpu == 1) & (cortical_cpu == 0))
|
||
|
||
# 中矢狀面：骨頭的最佳鏡稱面，一般平面 a·x + b·y + c·z = d（法線方向任意）
|
||
sym = best_symmetry_plane(spine_cpu)
|
||
|
||
# 棘突：鏡稱面中線帶（|s|<=w）且在 AP 谷底之後側的骨 voxel，換不同顏色標示
|
||
sp_mask, sp_th, sp_info = segment_spinous_process(spine_cpu, sym)
|
||
if sp_mask is not None and sp_mask.any():
|
||
sp_corti = sp_mask[z_corti, y_corti, x_corti]
|
||
sp_trab = sp_mask[z_trab, y_trab, x_trab]
|
||
sp_n_bone = max(int(spine_cpu.sum()), 1)
|
||
print(f"[SPINOUS] n={sp_info['n_sp']} "
|
||
f"({100.0 * sp_info['n_sp'] / sp_n_bone:.1f}% of bone) "
|
||
f"band=+/-{sp_info['band_w']:.1f} voxel AP>={sp_info['ap_thresh']:.1f} "
|
||
f"mode={sp_info['mode']}")
|
||
else:
|
||
sp_corti = sp_trab = None
|
||
|
||
# X-ray 外觀：骨頭合成一個半透明體積吸收點雲（下方）；
|
||
# 螺絲（中心線 + 圓柱 + 入口軌跡延长）合成一個全不透明點雲，永遠畫在骨頭之上
|
||
def _rgba_block(n, color, a):
|
||
arr = np.empty((n, 4))
|
||
arr[:] = to_rgba(color)
|
||
arr[:, 3] = a
|
||
return arr
|
||
|
||
x_bone = np.concatenate([x_corti, x_trab])
|
||
y_bone = np.concatenate([y_corti, y_trab])
|
||
z_bone = np.concatenate([z_corti, z_trab])
|
||
bone_rgba = np.concatenate([
|
||
_rgba_block(len(x_corti), 'lightblue', float(alpha_cortical)),
|
||
_rgba_block(len(x_trab), 'lightblue', float(alpha_trabecular)),
|
||
])
|
||
bone_size = np.full(len(x_bone), BONE_MARKER_SIZE)
|
||
|
||
# 抽稀：降低堆疊不透明度以呈現淡薄 X-ray 陰影
|
||
if BONE_SUBSAMPLE > 1:
|
||
x_bone = x_bone[::BONE_SUBSAMPLE]
|
||
y_bone = y_bone[::BONE_SUBSAMPLE]
|
||
z_bone = z_bone[::BONE_SUBSAMPLE]
|
||
bone_rgba = bone_rgba[::BONE_SUBSAMPLE]
|
||
bone_size = bone_size[::BONE_SUBSAMPLE]
|
||
if sp_corti is not None:
|
||
sp_flag = np.concatenate([sp_corti, sp_trab])
|
||
if BONE_SUBSAMPLE > 1:
|
||
sp_flag = sp_flag[::BONE_SUBSAMPLE]
|
||
bone_rgba[sp_flag] = to_rgba('purple', 0.95)
|
||
|
||
x_screw = np.concatenate([x_lin1, x_lin2, x_cyl_l1, x_cyl_l2, x_cyl_r1, x_cyl_r2])
|
||
y_screw = np.concatenate([y_lin1, y_lin2, y_cyl_l1, y_cyl_l2, y_cyl_r1, y_cyl_r2])
|
||
z_screw = np.concatenate([z_lin1, z_lin2, z_cyl_l1, z_cyl_l2, z_cyl_r1, z_cyl_r2])
|
||
|
||
_a, _b, _c, _d = sym['plane']
|
||
_n = np.array([_a, _b, _c])
|
||
_u = np.array(sym['u'])
|
||
_v = np.array(sym['v'])
|
||
_p0 = _d * _n # 平面上最接近原點的點
|
||
xyz_bone = np.stack([x_bone - _p0[0], y_bone - _p0[1], z_bone - _p0[2]], axis=1)
|
||
_pu = xyz_bone @ _u
|
||
_pv = xyz_bone @ _v
|
||
_U_, _V_ = np.meshgrid(np.linspace(_pu.min(), _pu.max(), 8),
|
||
np.linspace(_pv.min(), _pv.max(), 8))
|
||
_Xp = _p0[0] + _U_ * _u[0] + _V_ * _v[0]
|
||
_Yp = _p0[1] + _U_ * _u[1] + _V_ * _v[1]
|
||
_Zp = _p0[2] + _U_ * _u[2] + _V_ * _v[2]
|
||
|
||
# 上終板平面：RANSAC 擬合骨頭頂面（前側）的最佳 a·x + b·y + c·z = d
|
||
symp = best_upper_endplate_plane(spine_cpu)
|
||
_EX = _EY = _EZ = None
|
||
if symp is not None:
|
||
_ea, _eb, _ec, _ed = symp['plane']
|
||
_en = np.array([_ea, _eb, _ec])
|
||
_eu = np.array(symp['u'])
|
||
_ev = np.array(symp['v'])
|
||
_ep0 = _ed * _en
|
||
xz_ep = np.stack([x_bone - _ep0[0], y_bone - _ep0[1], z_bone - _ep0[2]], axis=1)
|
||
_pu_ep = xz_ep @ _eu
|
||
_pv_ep = xz_ep @ _ev
|
||
_EU, _EV = np.meshgrid(np.linspace(_pu_ep.min(), _pu_ep.max(), 8),
|
||
np.linspace(_pv_ep.min(), _pv_ep.max(), 8))
|
||
_EX = _ep0[0] + _EU * _eu[0] + _EV * _ev[0]
|
||
_EY = _ep0[1] + _EU * _eu[1] + _EV * _ev[1]
|
||
_EZ = _ep0[2] + _EU * _eu[2] + _EV * _ev[2]
|
||
screw_rgba = np.concatenate([
|
||
_rgba_block(len(x_lin1), 'r', 1.0),
|
||
_rgba_block(len(x_lin2), 'r', 1.0),
|
||
_rgba_block(len(x_cyl_l1), 'darkcyan', 1.0),
|
||
_rgba_block(len(x_cyl_l2), 'pink', 1.0),
|
||
_rgba_block(len(x_cyl_r1), 'blue', 1.0),
|
||
_rgba_block(len(x_cyl_r2), 'pink', 1.0),
|
||
])
|
||
screw_size = np.concatenate([
|
||
np.full(len(x_lin1), 3), np.full(len(x_lin2), 3),
|
||
np.full(len(x_cyl_l1), 36), np.full(len(x_cyl_l2), 36),
|
||
np.full(len(x_cyl_r1), 36), np.full(len(x_cyl_r2), 36),
|
||
])
|
||
|
||
fig = plt.figure(figsize=(12, 12))
|
||
|
||
# 圖例色塊提高到可讀不透明度（實際渲染仍用真實吸收 alpha）
|
||
_leg_alpha_c = max(float(alpha_cortical), 0.35)
|
||
_leg_alpha_t = max(float(alpha_trabecular), 0.2)
|
||
legend_handles = [
|
||
Line2D([], [], marker='o', ls='', ms=5, color=to_rgba('lightblue', _leg_alpha_c), label='Spine (cortical)'),
|
||
Line2D([], [], marker='o', ls='', ms=5, color=to_rgba('lightblue', _leg_alpha_t), label='Spine (trabecular)'),
|
||
Line2D([], [], marker='o', ls='', ms=2, color='r', label='Centerline'),
|
||
Line2D([], [], marker='o', ls='', ms=6, color='darkcyan', label='Cylinder(L)'),
|
||
Line2D([], [], marker='o', ls='', ms=6, color='blue', label='Cylinder(R)'),
|
||
Line2D([], [], marker='o', ls='', ms=6, color='pink', label='Entry track (outer)'),
|
||
Line2D([], [], color='orange', lw=2, alpha=0.6,
|
||
label=f"Mirror plane {sym['plane'][0]:+.2f}x {sym['plane'][1]:+.2f}y {sym['plane'][2]:+.2f}z = {sym['plane'][3]:.1f}"),
|
||
]
|
||
if sp_corti is not None:
|
||
legend_handles.append(Line2D([], [], marker='o', ls='', ms=6, color='purple',
|
||
label=f"Spinous process (mirror-plane midline, {sp_info['n_sp']} vox)"))
|
||
if symp is not None:
|
||
legend_handles.append(Line2D([], [], color='green', lw=2, alpha=0.7,
|
||
label=f"Upper endplate plane tilt {symp['tilt_deg']:.1f} deg"))
|
||
|
||
def _fill_ax(ax):
|
||
# X-ray 外觀：關閉 mplot3d 依深度自動排序 zorder（否則半透明骨頭會被重繪到
|
||
# 螺絲上方）；改為固定分層：吸收骨頭 zorder=5，全不透明螺絲 zorder=10
|
||
ax.computed_zorder = False
|
||
sc_bone = ax.scatter(x_bone, y_bone, z_bone, c=bone_rgba, s=bone_size, marker='o')
|
||
sc_bone.set_zorder(5)
|
||
sc_screw = ax.scatter(x_screw, y_screw, z_screw, c=screw_rgba, s=screw_size, marker='o')
|
||
sc_screw.set_zorder(10)
|
||
# 中矢狀面（理論左右對稱切分面）：半透明橘色平面 x = x_mid
|
||
# 平面邊緣畫橘色線，讓 axial / 正視（側看時）也能清楚看到切分線
|
||
plane = ax.plot_surface(_Xp, _Yp, _Zp, color='orange', alpha=0.30,
|
||
linewidth=1.0, edgecolor='orange', rstride=1, cstride=1)
|
||
plane.set_zorder(8)
|
||
# 上終板平面：半透明綠色平面（邊緣綠線）
|
||
if _EX is not None:
|
||
ep = ax.plot_surface(_EX, _EY, _EZ, color='green', alpha=0.35,
|
||
linewidth=1.0, edgecolor='green', rstride=1, cstride=1)
|
||
ep.set_zorder(7)
|
||
|
||
ax1 = fig.add_subplot(221, projection='3d')
|
||
_fill_ax(ax1)
|
||
ax1.set_xlabel('X-axis'); ax1.set_ylabel('Y-axis'); ax1.set_zlabel('Z-axis')
|
||
set_axes_equal_3d(ax1)
|
||
|
||
ax2 = fig.add_subplot(222, projection='3d')
|
||
ax2.view_init(elev=90, azim=-90, roll=0)
|
||
_fill_ax(ax2)
|
||
ax2.set_xlabel('X-axis'); ax2.set_ylabel('Y-axis'); ax2.set_zlabel('Z-axis')
|
||
set_axes_equal_3d(ax2)
|
||
ax2.legend(handles=legend_handles)
|
||
|
||
ax3 = fig.add_subplot(223, projection='3d')
|
||
ax3.view_init(elev=0, azim=90, roll=0)
|
||
_fill_ax(ax3)
|
||
ax3.set_xlabel('X-axis'); ax3.set_ylabel('Y-axis'); ax3.set_zlabel('Z-axis')
|
||
set_axes_equal_3d(ax3)
|
||
|
||
ax4 = fig.add_subplot(224, projection='3d')
|
||
ax4.view_init(elev=0, azim=0, roll=0)
|
||
_fill_ax(ax4)
|
||
ax4.set_xlabel('X-axis'); ax4.set_ylabel('Y-axis'); ax4.set_zlabel('Z-axis')
|
||
set_axes_equal_3d(ax4)
|
||
|
||
cyl_points_l = torch.sum(cyl_l).item()
|
||
cyl_points_r = torch.sum(cyl_r).item()
|
||
|
||
overlap_l = ((cortical_tensor == 1) & (cyl_l == 1)).sum().item()
|
||
overlap_r = ((cortical_tensor == 1) & (cyl_r == 1)).sum().item()
|
||
overlap_b_l = ((spine_tensor == 1) & (cyl_l == 1)).sum().item()
|
||
overlap_b_r = ((spine_tensor == 1) & (cyl_r == 1)).sum().item()
|
||
|
||
overlap_cortical_l = (overlap_l / cyl_points_l) * 100 if cyl_points_l else 0.0
|
||
overlap_cortical_r = (overlap_r / cyl_points_r) * 100 if cyl_points_r else 0.0
|
||
overlap_vertebral_l = (overlap_b_l / cyl_points_l) * 100 if cyl_points_l else 0.0
|
||
overlap_vertebral_r = (overlap_b_r / cyl_points_r) * 100 if cyl_points_r else 0.0
|
||
cb_ratio_l = overlap_cortical_l/overlap_vertebral_l if overlap_vertebral_l else 0.0
|
||
cb_ratio_r = overlap_cortical_r/overlap_vertebral_r if overlap_vertebral_r else 0.0
|
||
user_altitude_l = 90 - best_position_l[4] - alt
|
||
user_altitude_r = 90 - best_position_r[4] - alt
|
||
user_azimuth_l = 90 - best_position_l[3] - azi
|
||
user_azimuth_r = 90 - best_position_r[3] - azi
|
||
|
||
date_str = datetime.now().strftime("%Y%m%d")
|
||
patient_id = os.path.basename(os.path.dirname(image2_path))
|
||
output_folder = os.path.join(base_folder, date_str, patient_id)
|
||
os.makedirs(output_folder, exist_ok=True)
|
||
csv_path = os.path.join(output_folder, 'output.csv')
|
||
|
||
# 檢查檔案是否存在 (決定是否寫入標題)
|
||
file_exists = os.path.isfile(csv_path)
|
||
|
||
# 欄位標題 (Header)
|
||
headers = [
|
||
'Label', 'Side', 'Diameter', 'Length', 'Swarm_Size', 'Max_Iter',
|
||
'Position_XYZ', 'Raw_Azimuth', 'Azimuth_Diff', 'Raw_Altitude', 'Altitude_Diff',
|
||
'Intersections', 'Best_Loss', 'cyl_points', 'Overlap_Cortical', 'Overlap_Bone',
|
||
'Cortical_Bone_Ratio', 'User_Azimuth', 'User_Altitude', 'Total_Time'
|
||
]
|
||
|
||
try:
|
||
with open(csv_path, 'a', newline='') as csvfile:
|
||
writer = csv.writer(csvfile)
|
||
|
||
# 如果是新檔案，寫入 Header
|
||
if not file_exists:
|
||
writer.writerow(headers)
|
||
|
||
# 寫入 Left 數據
|
||
writer.writerow([
|
||
label_str,
|
||
'L',
|
||
diameter_l,
|
||
length_l,
|
||
swarm_size,
|
||
max_iter,
|
||
# f"({best_position_l[0]:.2f}, {best_position_l[1]:.2f}, {best_position_l[2]:.2f})",
|
||
f"({best_position_l[2]:.2f}, {best_position_l[1]:.2f}, {best_position_l[0]:.2f})",
|
||
f"{best_position_l[3]:.2f}",
|
||
f"{best_position_l[3]-azi:.2f}",
|
||
f"{best_position_l[4]:.2f}",
|
||
f"{best_position_l[4]-alt:.2f}",
|
||
intersections_l,
|
||
f"{loss_l:.2f}",
|
||
cyl_points_l,
|
||
f"{overlap_cortical_l:.2f}",
|
||
f"{overlap_vertebral_l:.2f}",
|
||
f"{(overlap_cortical_l/overlap_vertebral_l if overlap_vertebral_l!=0 else 0):.2f}",
|
||
f"{user_azimuth_l:.2f}",
|
||
f"{user_altitude_l:.2f}",
|
||
f"{total_time:.2f}"
|
||
])
|
||
|
||
# 寫入 Right 數據
|
||
writer.writerow([
|
||
label_str,
|
||
'R',
|
||
diameter_r,
|
||
length_r,
|
||
swarm_size,
|
||
max_iter,
|
||
# f"({best_position_r[0]:.2f}, {best_position_r[1]:.2f}, {best_position_r[2]:.2f})",
|
||
f"({best_position_r[2]:.2f}, {best_position_r[1]:.2f}, {best_position_r[0]:.2f})",
|
||
f"{best_position_r[3]:.2f}",
|
||
f"{best_position_r[3]-azi:.2f}",
|
||
f"{best_position_r[4]:.2f}",
|
||
f"{best_position_r[4]-alt:.2f}",
|
||
intersections_r,
|
||
f"{loss_r:.2f}",
|
||
cyl_points_r,
|
||
f"{overlap_cortical_r:.2f}",
|
||
f"{overlap_vertebral_r:.2f}",
|
||
f"{(overlap_cortical_r/overlap_vertebral_r if overlap_vertebral_r!=0 else 0):.2f}",
|
||
f"{user_azimuth_r:.2f}",
|
||
f"{user_altitude_r:.2f}",
|
||
f"{total_time:.2f}"
|
||
])
|
||
print(f"[CSV Saved] {csv_path}")
|
||
|
||
except Exception as e:
|
||
print(f"[Error] Failed to write CSV: {e}")
|
||
|
||
fig.text(0.5, 0.98, f'{label_str} Best Position', ha='center', fontsize=15)
|
||
fig.text(
|
||
0.5, 0.44,
|
||
f'L: Diameter = {diameter_l} mm, {length_l} mm, '
|
||
f'R: Diameter = {diameter_r} mm, {length_r} mm, '
|
||
f'Swarm size = {swarm_size}, Iteration = {max_iter}, Total time = {total_time:.2f} s',
|
||
ha='center', fontsize=12
|
||
)
|
||
fig.text(
|
||
0.5, 0.03,
|
||
# f'Left : Position = ({best_position_l[0]:.2f}, {best_position_l[1]:.2f}, {best_position_l[2]:.2f}), '
|
||
f'Left : Position = ({best_position_l[2]:.2f}, {best_position_l[1]:.2f}, {best_position_l[0]:.2f}), '
|
||
f'Azimuth = {user_azimuth_l:.2f}, Altitude = {user_altitude_l:.2f}, '
|
||
f'Intersection = {intersections_l}, Score = {overlap_cortical_l:.2f} / {overlap_vertebral_l:.2f} / {cb_ratio_l:.2f}',
|
||
ha='center', fontsize=9
|
||
)
|
||
fig.text(
|
||
0.5, 0.01,
|
||
# f'Right : Position = ({best_position_r[0]:.2f}, {best_position_r[1]:.2f}, {best_position_r[2]:.2f}), '
|
||
f'Right : Position = ({best_position_r[2]:.2f}, {best_position_r[1]:.2f}, {best_position_r[0]:.2f}), '
|
||
f'Azimuth = {user_azimuth_r:.2f}, Altitude = {user_altitude_r:.2f}, '
|
||
f'Intersection = {intersections_r}, Score = {overlap_cortical_r:.2f} / {overlap_vertebral_r:.2f} / {cb_ratio_r:.2f}',
|
||
ha='center', fontsize=9
|
||
)
|
||
|
||
fig.tight_layout()
|
||
|
||
date_str = datetime.now().strftime("%Y%m%d")
|
||
file_name = os.path.basename(image2_path)
|
||
level = file_name.split('_')[0]
|
||
output_folder = os.path.join(base_folder, date_str, patient_id)
|
||
os.makedirs(output_folder, exist_ok=True)
|
||
|
||
if CBT == True:
|
||
way = 'CBT'
|
||
|
||
else:
|
||
way = 'TPS'
|
||
|
||
path = save_with_unique_name(output_folder, label_str, way,
|
||
diameter_l, length_l, diameter_r, length_r,
|
||
swarm_size, max_iter)
|
||
|
||
fig.savefig(path, dpi=200, bbox_inches="tight")
|
||
print("[Saved figure]", path)
|
||
plt.close(fig)
|
||
|
||
|
||
def eval_overlap_from_position(
|
||
pos,
|
||
optimize_size: bool,
|
||
spine_tensor: torch.Tensor,
|
||
image_shape,
|
||
spacing,
|
||
device: torch.device,
|
||
grid=None,
|
||
fixed_diameter: float | None = None,
|
||
fixed_length: float | None = None,
|
||
):
|
||
"""
|
||
根據 position 生成 cylinder mask，再算 overlap ratio
|
||
"""
|
||
|
||
if optimize_size:
|
||
d, L = snap_to_discrete_values(pos[5], pos[6])
|
||
params_5 = pos[:5]
|
||
else:
|
||
if fixed_diameter is None or fixed_length is None:
|
||
raise ValueError("fixed_diameter and fixed_length must be provided when optimize_size=False")
|
||
d, L = fixed_diameter, fixed_length
|
||
params_5 = pos
|
||
|
||
z, y, x, az, alt = params_5
|
||
|
||
cyl_mask = generate_cylinder_n_torch(
|
||
d, L,
|
||
z, y, x,
|
||
az, alt,
|
||
image_shape, spacing,
|
||
device=device,
|
||
grid=grid
|
||
)
|
||
|
||
overlap = compute_overlap_ratio_from_cylinder_mask(cyl_mask, spine_tensor)
|
||
return overlap, d, L
|
||
|
||
|