CBT_project/core/optimizer_ori.py

import time
from datetime import datetime
import SimpleITK as sitk
import torch
from imaging.orientation import azimuth_rotation, analyze_vertebral_tilt_contour
from config.constant import ALLOWED_DIAMETERS, ALLOWED_LENGTHS
from core.objective import objective_function
from pyswarm import pso 
import core.objective  # <--- 加入這行，讓我們可以直接操作 objective 模組
from core.cylinder import generate_cylinder_n_torch, snap_to_discrete_values, create_coordinate_grid
from core.scoring import compute_overlap_ratio_from_cylinder_mask, is_solution_ok
from config.constant import OVERLAP_THRESH
from visualization.res_plot_3d import res_plt_2_torch

def run_pso_torch(
    label_str: str,
    image1_path: str,
    image2_path: str,
    image3_path: str,
    folder: str,
    swarm_size: int,
    max_iter: int,
    spacing: list,
    CBT: bool,
    device: torch.device,
    optimize_size: bool = True,
    grid=None
):
    """
    Main function to run PSO. 
    如果 optimize_size=True，diameter 和 length 也會被最佳化
    如果 optimize_size=False，使用預設值（向後兼容）
    """
    start_time = time.time()

    # Use global references
    global image1_array, image2_array, image2_shape, image3_array
    global diameter, length  # 這些現在只用於非最佳化模式
    global spine_tensor, cortical_tensor, spine_roi_tensor

    # Load images
    image1 = sitk.ReadImage(image1_path)
    image2 = sitk.ReadImage(image2_path)
    image3 = sitk.ReadImage(image3_path)
    image1_array = sitk.GetArrayFromImage(image1)
    image2_array = sitk.GetArrayFromImage(image2)
    image3_array = sitk.GetArrayFromImage(image3)
    image2_shape = image2_array.shape
    image_shape = image2_shape

    # Move arrays to torch
    cortical_tensor = torch.from_numpy(image1_array).to(device=device, dtype=torch.uint8)
    spine_tensor = torch.from_numpy(image2_array).to(device=device, dtype=torch.uint8)
    spine_roi_tensor = torch.from_numpy(image3_array).to(device=device, dtype=torch.uint8)

    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']

    # 設定基本的 bounds
    if CBT == True:    
        z_bounds = (0, image_shape[0] - 1)
        y_bounds = (image_shape[1]/5, image_shape[1]/2 - 1)
        x_bounds_right = (image_shape[2]/2 + image_shape[2]/10, image_shape[2] - 1)
        x_bounds_left  = (0, image_shape[2]/2 - image_shape[2]/10 - 1)
        azimuth_bounds_l = ((95-azi), (145-azi))
        azimuth_bounds_r = ((50-azi), (85-azi))
        altitude_bounds = ((60-alt), (75-alt))
    else:      
        z_bounds = (0, image_shape[0] - 1)   
        y_bounds = (image_shape[1]/5, image_shape[1]/2 - 1)   
        x_bounds_left = (0, image_shape[2]/2 - image_shape[2]/10 - 1)   
        x_bounds_right = (image_shape[2]/2 + image_shape[2]/10, image_shape[2] - 1)
        azimuth_bounds_l = (60-azi, 90-azi) 
        azimuth_bounds_r = (90-azi, 120-azi) 
        altitude_bounds = (65-alt, 80-alt)

    def eval_overlap_from_position(pos, side: str, optimize_size: bool,
                               spine_tensor: torch.Tensor,
                               image_shape, spacing):
        """
        根據 PSO 給的 position 生成 cylinder mask，再算 overlap ratio
        side: "L" or "R" 只是方便 debug
        """
        if optimize_size:
            d, L = snap_to_discrete_values(pos[5], pos[6])
            params_5 = pos[:5]
        else:
            d, L = diameter, length
            params_5 = pos

        cyl_mask = generate_cylinder_n_torch(
            d, L,
            params_5[0], params_5[1], params_5[2],
            params_5[3], params_5[4],
            image_shape, spacing, device, grid
        )

        overlap = compute_overlap_ratio_from_cylinder_mask(cyl_mask, spine_tensor)
        return overlap, d, L

    if optimize_size:
        # 模式 1：優化 diameter 和 length
        print("=== 最佳化模式：最佳化位置、角度、直徑和長度 ===")
        
        # 設定 diameter 和 length 的 bounds（連續範圍）
        diameter_bounds = (min(ALLOWED_DIAMETERS), max(ALLOWED_DIAMETERS))
        length_bounds = (min(ALLOWED_LENGTHS), max(ALLOWED_LENGTHS))
        
        # bounds 現在有 7 個參數
        lb_l = [z_bounds[0], y_bounds[0], x_bounds_left[0], azimuth_bounds_l[0], 
                altitude_bounds[0], diameter_bounds[0], length_bounds[0]]
        ub_l = [z_bounds[1], y_bounds[1], x_bounds_left[1], azimuth_bounds_l[1], 
                altitude_bounds[1], diameter_bounds[1], length_bounds[1]]

        lb_r = [z_bounds[0], y_bounds[0], x_bounds_right[0], azimuth_bounds_r[0], 
                altitude_bounds[0], diameter_bounds[0], length_bounds[0]]
        ub_r = [z_bounds[1], y_bounds[1], x_bounds_right[1], azimuth_bounds_r[1], 
                altitude_bounds[1], diameter_bounds[1], length_bounds[1]]
        
    else:
        # 模式 2：固定 diameter 和 length（向後兼容）
        print("=== 固定尺寸模式：最佳化位置和角度 ===")
        # 使用預設值（需要在調用時提供）
        diameter = 4.5  # 或從參數傳入
        length = 45     # 或從參數傳入
        
        lb_l = [z_bounds[0], y_bounds[0], x_bounds_left[0], azimuth_bounds_l[0], altitude_bounds[0]]
        ub_l = [z_bounds[1], y_bounds[1], x_bounds_left[1], azimuth_bounds_l[1], altitude_bounds[1]]

        lb_r = [z_bounds[0], y_bounds[0], x_bounds_right[0], azimuth_bounds_r[0], altitude_bounds[0]]
        ub_r = [z_bounds[1], y_bounds[1], x_bounds_right[1], azimuth_bounds_r[1], altitude_bounds[1]]

    best_loss_l = float('inf')
    best_loss_r = float('inf')
    best_position_l = None
    best_position_r = None

    # Left side optimization
    print("\n=== 左側 ===")
    position_l, loss_l = pso(objective_function, lb_l, ub_l, swarmsize=swarm_size, maxiter=max_iter)

    overlap_l, diameter_l, length_l = eval_overlap_from_position(
        position_l, "L", optimize_size, spine_tensor, image_shape, spacing
    )
    print(f"[LEFT] overlap: {overlap_l*100:.1f}%")

    if optimize_size:
        print(f"[LEFT] Position: {position_l[:5]}")
        print(f"[LEFT] Diameter: {diameter_l} mm (raw: {position_l[5]:.2f})")
        print(f"[LEFT] Length: {length_l} mm (raw: {position_l[6]:.2f})")
        best_position_l = list(position_l[:5]) + [diameter_l, length_l]
    else:
        print(f"[LEFT] Position: {position_l}")
        best_position_l = position_l

    best_loss_l = loss_l
    best_overlap_l = overlap_l   # 新增

    max_retries = 10
    retries = 0

    # 左側 retry：loss 要 <=0 且 overlap >= 0.5 才算過關
    while (best_loss_l > 0 or best_overlap_l < OVERLAP_THRESH) and retries < max_retries:
        position_l, loss_l = pso(objective_function, lb_l, ub_l, swarmsize=swarm_size, maxiter=max_iter)
        overlap_l, diameter_l, length_l = eval_overlap_from_position(
            position_l, "L", optimize_size, spine_tensor, image_shape, spacing
        )

    # 只要找到更好的 loss（或你想用 loss+overlap 綜合排序也行）就更新 best
    # 安全版本：優先選「合格解」；沒有合格解時才用 loss 最小的當備案
        candidate_pos = (list(position_l[:5]) + [diameter_l, length_l]) if optimize_size else position_l

        candidate_ok = is_solution_ok(loss_l, overlap_l, OVERLAP_THRESH)
        best_ok = is_solution_ok(best_loss_l, best_overlap_l, OVERLAP_THRESH)

        if candidate_ok and (not best_ok or loss_l < best_loss_l):
            best_position_l = candidate_pos
            best_loss_l = loss_l
            best_overlap_l = overlap_l
            print(f"[LEFT][retry {retries+1}] ✅ ok | loss={loss_l:.4f}, overlap={overlap_l*100:.1f}%")
        elif (not best_ok) and (loss_l < best_loss_l):
            # best 還不合格時，先用更小 loss 的當暫存（至少越來越好）
            best_position_l = candidate_pos
            best_loss_l = loss_l
            best_overlap_l = overlap_l
            print(f"[LEFT][retry {retries+1}] ⚠️ not ok | loss improved={loss_l:.4f}, overlap={overlap_l*100:.1f}%")
        else:
            print(f"[LEFT][retry {retries+1}] ❌ no improve | loss={loss_l:.4f}, overlap={overlap_l*100:.1f}%")

        retries += 1

    # Right side optimization
    print("\n=== 右側 ===")
    position_r, loss_r = pso(objective_function, lb_r, ub_r, swarmsize=swarm_size, maxiter=max_iter)
    overlap_r, diameter_r, length_r = eval_overlap_from_position(
        position_r, "R", optimize_size, spine_tensor, image_shape, spacing
    )
    print(f"[RIGHT] overlap: {overlap_r*100:.1f}%")

    if optimize_size:
        diameter_r, length_r = snap_to_discrete_values(position_r[5], position_r[6])
        print(f"[RIGHT] Position: {position_r[:5]}")
        print(f"[RIGHT] Diameter: {diameter_r} mm (raw: {position_r[5]:.2f})")
        print(f"[RIGHT] Length: {length_r} mm (raw: {position_r[6]:.2f})")
        print(f"[RIGHT] Loss: {loss_r}\n")
        
        best_position_r = list(position_r[:5]) + [diameter_r, length_r]
    else:
        print(f"[RIGHT] Position: {position_r}")
        print(f"[RIGHT] Loss: {loss_r}\n")
        best_position_r = position_r
    
    best_loss_r = loss_r
    best_overlap_r = overlap_r 

    # 如果需要 retry（loss > 0）
    max_retries = 10
    retries = 0

    while (best_loss_r > 0 or best_overlap_r < OVERLAP_THRESH) and retries < max_retries:
        position_r, loss_r = pso(objective_function, lb_r, ub_r, swarmsize=swarm_size, maxiter=max_iter)
        overlap_r, diameter_r, length_r = eval_overlap_from_position(
            position_r, "R", optimize_size, spine_tensor, image_shape, spacing
        )

        # 只要找到更好的 loss（或你想用 loss+overlap 綜合排序也行）就更新 best
        # 這裡給你一個更安全的版本：優先選「合格解」；沒有合格解時才用 loss 最小的當備案
        candidate_pos = (list(position_r[:5]) + [diameter_r, length_r]) if optimize_size else position_r

        candidate_ok = is_solution_ok(loss_r, overlap_r, OVERLAP_THRESH)
        best_ok = is_solution_ok(best_loss_r, best_overlap_r, OVERLAP_THRESH)

        if candidate_ok and (not best_ok or loss_r < best_loss_r):
            best_position_r = candidate_pos
            best_loss_r = loss_r
            best_overlap_r = overlap_r
            print(f"[RIGHT][retry {retries+1}] ✅ ok | loss={loss_r:.4f}, overlap={overlap_r*100:.1f}%")
        elif (not best_ok) and (loss_r < best_loss_r):
            # best 還不合格時，先用更小 loss 的當暫存（至少越來越好）
            best_position_r = candidate_pos
            best_loss_r = loss_r
            best_overlap_r = overlap_r
            print(f"[RIGHT][retry {retries+1}] ⚠️ not ok | loss improved={loss_r:.4f}, overlap={overlap_r*100:.1f}%")
        else:
            print(f"[RIGHT][retry {retries+1}] ❌ no improve | loss={loss_r:.4f}, overlap={overlap_r*100:.1f}%")

        retries += 1
    end_time = time.time()
    total_time = end_time - start_time

    # 提取最終的 diameter 和 length
    if optimize_size:
        final_diameter_l = best_position_l[5]
        final_length_l = best_position_l[6]
        final_diameter_r = best_position_r[5]
        final_length_r = best_position_r[6]
        
        print(f"\n=== 最終結果 ===")
        print(f"Left - Diameter: {final_diameter_l} mm, Length: {final_length_l} mm")
        print(f"Right - Diameter: {final_diameter_r} mm, Length: {final_length_r} mm")
    else:
        final_diameter_l = diameter
        final_length_l = length
        final_diameter_r = diameter
        final_length_r = length

    res_plt_2_torch(
    spine_tensor,
    cortical_tensor,
    image_shape,
    image2_path,
    'Output',
    label_str,
    final_diameter_l,
    final_length_l,
    final_diameter_r,
    final_length_r,
    best_position_l,
    best_position_r,
    swarm_size,
    max_iter,
    total_time,
    spacing,
    CBT,
    device,
    grid)
    
    return best_position_l, best_loss_l, best_position_r, best_loss_r, total_time

import time
import numpy as np
import SimpleITK as sitk
import torch
from scipy.optimize import differential_evolution
from scipy.optimize import minimize
from imaging.orientation import azimuth_rotation, analyze_vertebral_tilt_contour
from config.constant import ALLOWED_DIAMETERS, ALLOWED_LENGTHS
from core.objective import objective_function
from core.cylinder import generate_cylinder_n_torch, snap_to_discrete_values, create_coordinate_grid
from core.scoring import compute_overlap_ratio_from_cylinder_mask, is_solution_ok
from config.constant import OVERLAP_THRESH
from visualization.res_plot_3d import res_plt_2_torch

def run_de_torch(
    label_str: str,
    image1_path: str,
    image2_path: str,
    image3_path: str,
    folder: str,
    swarm_size: int,
    max_iter: int,
    spacing: list,
    CBT: bool,
    device: torch.device,
    optimize_size: bool = True,
    grid=None
):
    """
    使用 Differential Evolution (DE) 進行最佳化
    """
    start_time = time.time()

    global image1_array, image2_array, image2_shape, image3_array
    global diameter, length  
    global spine_tensor, cortical_tensor, spine_roi_tensor

    image1 = sitk.ReadImage(image1_path)
    image2 = sitk.ReadImage(image2_path)
    image3 = sitk.ReadImage(image3_path)
    image1_array = sitk.GetArrayFromImage(image1)
    image2_array = sitk.GetArrayFromImage(image2)
    image3_array = sitk.GetArrayFromImage(image3)
    image2_shape = image2_array.shape
    image_shape = image2_shape

    cortical_tensor = torch.from_numpy(image1_array).to(device=device, dtype=torch.uint8)
    spine_tensor = torch.from_numpy(image2_array).to(device=device, dtype=torch.uint8)
    spine_roi_tensor = torch.from_numpy(image3_array).to(device=device, dtype=torch.uint8)

    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']

    if CBT == True:    
        z_bounds = (0, image_shape[0] - 1)
        y_bounds = (image_shape[1]/5, image_shape[1]/2 - 1)
        x_bounds_right = (image_shape[2]/2 + image_shape[2]/10, image_shape[2] - 1)
        x_bounds_left  = (0, image_shape[2]/2 - image_shape[2]/10 - 1)
        azimuth_bounds_l = ((95-azi), (145-azi))
        azimuth_bounds_r = ((50-azi), (85-azi))
        altitude_bounds = ((60-alt), (75-alt))
    else:      
        z_bounds = (0, image_shape[0] - 1)   
        y_bounds = (image_shape[1]/5, image_shape[1]/2 - 1)   
        x_bounds_left = (0, image_shape[2]/2 - image_shape[2]/10 - 1)   
        x_bounds_right = (image_shape[2]/2 + image_shape[2]/10, image_shape[2] - 1)
        azimuth_bounds_l = (60-azi, 90-azi) 
        azimuth_bounds_r = (90-azi, 120-azi) 
        altitude_bounds = (65-alt, 80-alt)

    def eval_overlap_from_position(pos, side: str, optimize_size: bool, spine_tensor: torch.Tensor, image_shape, spacing):
        if optimize_size:
            d, L = snap_to_discrete_values(pos[5], pos[6])
            params_5 = pos[:5]
        else:
            d, L = diameter, length
            params_5 = pos

        cyl_mask = generate_cylinder_n_torch(
            d, L, params_5[0], params_5[1], params_5[2], params_5[3], params_5[4],
            image_shape, spacing, device, grid
        )
        overlap = compute_overlap_ratio_from_cylinder_mask(cyl_mask, spine_tensor)
        return overlap, d, L

    if optimize_size:
        print("=== DE 最佳化模式：最佳化位置、角度、直徑和長度 ===")
        diameter_bounds = (min(ALLOWED_DIAMETERS), max(ALLOWED_DIAMETERS))
        length_bounds = (min(ALLOWED_LENGTHS), max(ALLOWED_LENGTHS))
        
        bounds_l = [z_bounds, y_bounds, x_bounds_left, azimuth_bounds_l, altitude_bounds, diameter_bounds, length_bounds]
        bounds_r = [z_bounds, y_bounds, x_bounds_right, azimuth_bounds_r, altitude_bounds, diameter_bounds, length_bounds]
    else:
        print("=== DE 固定尺寸模式：最佳化位置和角度 ===")
        diameter = 4.5  
        length = 45     
        
        bounds_l = [z_bounds, y_bounds, x_bounds_left, azimuth_bounds_l, altitude_bounds]
        bounds_r = [z_bounds, y_bounds, x_bounds_right, azimuth_bounds_r, altitude_bounds]

    # DE 的 popsize 實際粒子數 = popsize * len(bounds)
    # 為了跟 PSO 公平比較，我們讓它轉換一下
    de_popsize = max(1, swarm_size // len(bounds_l))

    # --- 左側最佳化 ---
    print("\n=== 左側 (DE) ===")
    res_l = differential_evolution(objective_function, bounds_l, popsize=de_popsize, maxiter=max_iter)
    position_l, loss_l = res_l.x, res_l.fun

    overlap_l, diameter_l, length_l = eval_overlap_from_position(position_l, "L", optimize_size, spine_tensor, image_shape, spacing)
    best_position_l = list(position_l[:5]) + [diameter_l, length_l] if optimize_size else list(position_l)
    best_loss_l, best_overlap_l = loss_l, overlap_l
    
    retries = 0
    while (best_loss_l > 0 or best_overlap_l < OVERLAP_THRESH) and retries < 10:
        res_l = differential_evolution(objective_function, bounds_l, popsize=de_popsize, maxiter=max_iter)
        position_l, loss_l = res_l.x, res_l.fun
        overlap_l, diameter_l, length_l = eval_overlap_from_position(position_l, "L", optimize_size, spine_tensor, image_shape, spacing)
        
        candidate_pos = (list(position_l[:5]) + [diameter_l, length_l]) if optimize_size else list(position_l)
        if is_solution_ok(loss_l, overlap_l, OVERLAP_THRESH) and (not is_solution_ok(best_loss_l, best_overlap_l, OVERLAP_THRESH) or loss_l < best_loss_l):
            best_position_l, best_loss_l, best_overlap_l = candidate_pos, loss_l, overlap_l
        elif (not is_solution_ok(best_loss_l, best_overlap_l, OVERLAP_THRESH)) and (loss_l < best_loss_l):
            best_position_l, best_loss_l, best_overlap_l = candidate_pos, loss_l, overlap_l
        retries += 1

    # --- 右側最佳化 ---
    print("\n=== 右側 (DE) ===")
    res_r = differential_evolution(objective_function, bounds_r, popsize=de_popsize, maxiter=max_iter)
    position_r, loss_r = res_r.x, res_r.fun

    overlap_r, diameter_r, length_r = eval_overlap_from_position(position_r, "R", optimize_size, spine_tensor, image_shape, spacing)
    best_position_r = list(position_r[:5]) + [diameter_r, length_r] if optimize_size else list(position_r)
    best_loss_r, best_overlap_r = loss_r, overlap_r

    retries = 0
    while (best_loss_r > 0 or best_overlap_r < OVERLAP_THRESH) and retries < 10:
        res_r = differential_evolution(objective_function, bounds_r, popsize=de_popsize, maxiter=max_iter)
        position_r, loss_r = res_r.x, res_r.fun
        overlap_r, diameter_r, length_r = eval_overlap_from_position(position_r, "R", optimize_size, spine_tensor, image_shape, spacing)
        
        candidate_pos = (list(position_r[:5]) + [diameter_r, length_r]) if optimize_size else list(position_r)
        if is_solution_ok(loss_r, overlap_r, OVERLAP_THRESH) and (not is_solution_ok(best_loss_r, best_overlap_r, OVERLAP_THRESH) or loss_r < best_loss_r):
            best_position_r, best_loss_r, best_overlap_r = candidate_pos, loss_r, overlap_r
        elif (not is_solution_ok(best_loss_r, best_overlap_r, OVERLAP_THRESH)) and (loss_r < best_loss_r):
            best_position_r, best_loss_r, best_overlap_r = candidate_pos, loss_r, overlap_r
        retries += 1

    total_time = time.time() - start_time
    
    final_diameter_l = best_position_l[5] if optimize_size else diameter
    final_length_l = best_position_l[6] if optimize_size else length
    final_diameter_r = best_position_r[5] if optimize_size else diameter
    final_length_r = best_position_r[6] if optimize_size else length

    res_plt_2_torch(
        spine_tensor, cortical_tensor, image_shape, image2_path, 'Output', label_str,
        final_diameter_l, final_length_l, final_diameter_r, final_length_r,
        best_position_l, best_position_r, swarm_size, max_iter, total_time, spacing, CBT, device, grid
    )
    
    return best_position_l, best_loss_l, best_position_r, best_loss_r, total_time

def run_nm_torch(
    label_str: str,
    image1_path: str,
    image2_path: str,
    image3_path: str,
    folder: str,
    swarm_size: int, # NM 不用 swarm_size，但保留參數以維持介面統一
    max_iter: int,
    spacing: list,
    CBT: bool,
    device: torch.device,
    optimize_size: bool = True,
    grid=None
):
    """
    使用 Nelder-Mead 進行最佳化
    """
    start_time = time.time()

    global image1_array, image2_array, image2_shape, image3_array
    global diameter, length  
    global spine_tensor, cortical_tensor, spine_roi_tensor

    image1 = sitk.ReadImage(image1_path)
    image2 = sitk.ReadImage(image2_path)
    image3 = sitk.ReadImage(image3_path)
    image1_array = sitk.GetArrayFromImage(image1)
    image2_array = sitk.GetArrayFromImage(image2)
    image3_array = sitk.GetArrayFromImage(image3)
    image2_shape = image2_array.shape
    image_shape = image2_shape

    cortical_tensor = torch.from_numpy(image1_array).to(device=device, dtype=torch.uint8)
    spine_tensor = torch.from_numpy(image2_array).to(device=device, dtype=torch.uint8)
    spine_roi_tensor = torch.from_numpy(image3_array).to(device=device, dtype=torch.uint8)

    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']

    if CBT == True:    
        z_bounds = (0, image_shape[0] - 1)
        y_bounds = (image_shape[1]/5, image_shape[1]/2 - 1)
        x_bounds_right = (image_shape[2]/2 + image_shape[2]/10, image_shape[2] - 1)
        x_bounds_left  = (0, image_shape[2]/2 - image_shape[2]/10 - 1)
        azimuth_bounds_l = ((95-azi), (145-azi))
        azimuth_bounds_r = ((50-azi), (85-azi))
        altitude_bounds = ((60-alt), (75-alt))
    else:      
        z_bounds = (0, image_shape[0] - 1)   
        y_bounds = (image_shape[1]/5, image_shape[1]/2 - 1)   
        x_bounds_left = (0, image_shape[2]/2 - image_shape[2]/10 - 1)   
        x_bounds_right = (image_shape[2]/2 + image_shape[2]/10, image_shape[2] - 1)
        azimuth_bounds_l = (60-azi, 90-azi) 
        azimuth_bounds_r = (90-azi, 120-azi) 
        altitude_bounds = (65-alt, 80-alt)

    def eval_overlap_from_position(pos, side: str, optimize_size: bool, spine_tensor: torch.Tensor, image_shape, spacing):
        if optimize_size:
            d, L = snap_to_discrete_values(pos[5], pos[6])
            params_5 = pos[:5]
        else:
            d, L = diameter, length
            params_5 = pos

        cyl_mask = generate_cylinder_n_torch(
            d, L, params_5[0], params_5[1], params_5[2], params_5[3], params_5[4],
            image_shape, spacing, device, grid
        )
        overlap = compute_overlap_ratio_from_cylinder_mask(cyl_mask, spine_tensor)
        return overlap, d, L

    if optimize_size:
        print("=== NM 最佳化模式 ===")
        bounds_l = [z_bounds, y_bounds, x_bounds_left, azimuth_bounds_l, altitude_bounds, 
                    (min(ALLOWED_DIAMETERS), max(ALLOWED_DIAMETERS)), (min(ALLOWED_LENGTHS), max(ALLOWED_LENGTHS))]
        bounds_r = [z_bounds, y_bounds, x_bounds_right, azimuth_bounds_r, altitude_bounds, 
                    (min(ALLOWED_DIAMETERS), max(ALLOWED_DIAMETERS)), (min(ALLOWED_LENGTHS), max(ALLOWED_LENGTHS))]
    else:
        print("=== NM 固定尺寸模式 ===")
        diameter, length = 4.5, 45     
        bounds_l = [z_bounds, y_bounds, x_bounds_left, azimuth_bounds_l, altitude_bounds]
        bounds_r = [z_bounds, y_bounds, x_bounds_right, azimuth_bounds_r, altitude_bounds]

    def get_random_x0(bounds):
        # 產生在 Bounds 內的隨機起始點
        return [np.random.uniform(b[0], b[1]) for b in bounds]

    # --- 左側最佳化 ---
    print("\n=== 左側 (Nelder-Mead) ===")
    x0_l = get_random_x0(bounds_l)
    res_l = minimize(objective_function, x0_l, method='Nelder-Mead', bounds=bounds_l, options={'maxiter': max_iter})
    position_l, loss_l = res_l.x, res_l.fun

    overlap_l, diameter_l, length_l = eval_overlap_from_position(position_l, "L", optimize_size, spine_tensor, image_shape, spacing)
    best_position_l = list(position_l[:5]) + [diameter_l, length_l] if optimize_size else list(position_l)
    best_loss_l, best_overlap_l = loss_l, overlap_l
    
    retries = 0
    while (best_loss_l > 0 or best_overlap_l < OVERLAP_THRESH) and retries < 10:
        x0_l = get_random_x0(bounds_l) # 每次 retry 都換一個隨機起始點
        res_l = minimize(objective_function, x0_l, method='Nelder-Mead', bounds=bounds_l, options={'maxiter': max_iter})
        position_l, loss_l = res_l.x, res_l.fun
        overlap_l, diameter_l, length_l = eval_overlap_from_position(position_l, "L", optimize_size, spine_tensor, image_shape, spacing)
        
        candidate_pos = (list(position_l[:5]) + [diameter_l, length_l]) if optimize_size else list(position_l)
        if is_solution_ok(loss_l, overlap_l, OVERLAP_THRESH) and (not is_solution_ok(best_loss_l, best_overlap_l, OVERLAP_THRESH) or loss_l < best_loss_l):
            best_position_l, best_loss_l, best_overlap_l = candidate_pos, loss_l, overlap_l
        elif (not is_solution_ok(best_loss_l, best_overlap_l, OVERLAP_THRESH)) and (loss_l < best_loss_l):
            best_position_l, best_loss_l, best_overlap_l = candidate_pos, loss_l, overlap_l
        retries += 1

    # --- 右側最佳化 ---
    print("\n=== 右側 (Nelder-Mead) ===")
    x0_r = get_random_x0(bounds_r)
    res_r = minimize(objective_function, x0_r, method='Nelder-Mead', bounds=bounds_r, options={'maxiter': max_iter})
    position_r, loss_r = res_r.x, res_r.fun

    overlap_r, diameter_r, length_r = eval_overlap_from_position(position_r, "R", optimize_size, spine_tensor, image_shape, spacing)
    best_position_r = list(position_r[:5]) + [diameter_r, length_r] if optimize_size else list(position_r)
    best_loss_r, best_overlap_r = loss_r, overlap_r

    retries = 0
    while (best_loss_r > 0 or best_overlap_r < OVERLAP_THRESH) and retries < 10:
        x0_r = get_random_x0(bounds_r)
        res_r = minimize(objective_function, x0_r, method='Nelder-Mead', bounds=bounds_r, options={'maxiter': max_iter})
        position_r, loss_r = res_r.x, res_r.fun
        overlap_r, diameter_r, length_r = eval_overlap_from_position(position_r, "R", optimize_size, spine_tensor, image_shape, spacing)
        
        candidate_pos = (list(position_r[:5]) + [diameter_r, length_r]) if optimize_size else list(position_r)
        if is_solution_ok(loss_r, overlap_r, OVERLAP_THRESH) and (not is_solution_ok(best_loss_r, best_overlap_r, OVERLAP_THRESH) or loss_r < best_loss_r):
            best_position_r, best_loss_r, best_overlap_r = candidate_pos, loss_r, overlap_r
        elif (not is_solution_ok(best_loss_r, best_overlap_r, OVERLAP_THRESH)) and (loss_r < best_loss_r):
            best_position_r, best_loss_r, best_overlap_r = candidate_pos, loss_r, overlap_r
        retries += 1

    total_time = time.time() - start_time
    
    final_diameter_l = best_position_l[5] if optimize_size else diameter
    final_length_l = best_position_l[6] if optimize_size else length
    final_diameter_r = best_position_r[5] if optimize_size else diameter
    final_length_r = best_position_r[6] if optimize_size else length

    res_plt_2_torch(
        spine_tensor, cortical_tensor, image_shape, image2_path, 'Output', label_str,
        final_diameter_l, final_length_l, final_diameter_r, final_length_r,
        best_position_l, best_position_r, swarm_size, max_iter, total_time, spacing, CBT, device, grid
    )
    
    return best_position_l, best_loss_l, best_position_r, best_loss_r, total_time