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.
628 lines
24 KiB
Python
628 lines
24 KiB
Python
import numpy as np
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import SimpleITK as sitk
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from scipy.ndimage import center_of_mass, rotate
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import matplotlib.pyplot as plt
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def _best_vertical_split(mask2d):
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"""
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binary 2D 陣列（最後一軸 = x）：找讓 mask 與其鏡射重疊最大的垂直線 x = t。
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c[s] = Σ_u A[u]·A[s-u] 是每列自卷積；批次 FFT 一次算出所有 s。
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回傳 (score, s_best)；s = 2·t（t 可能為半整數）。
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"""
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n = mask2d.shape[-1]
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flat = np.asarray(mask2d).reshape(-1, n)
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rows = flat[flat.max(axis=1) > 0]
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if rows.size == 0:
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return 0.0, 0
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L = 1 << (2 * n - 1).bit_length()
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padded = np.zeros((rows.shape[0], L), dtype=np.float32)
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padded[:, :n] = rows.astype(np.float32)
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F = np.fft.rfft(padded, axis=1)
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conv = np.fft.irfft(F * F, axis=1)[:, :2 * n - 1]
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score = np.clip(conv.sum(axis=0), 0, None)
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s_best = int(np.argmax(score))
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return float(score[s_best]), s_best
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def best_symmetry_axis_angle(proj, coarse_step=10.0, fine_step=1.0):
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"""
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2D binary 投影（axis0=row y, axis1=col x）的左右鏡稱軸搜尋：
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將影像旋轉 θ 後，鏡稱軸變成垂直線（col = const），用 _best_vertical_split 評分；
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粗搜 0..180°（coarse_step）再在 winner 附近細搜（fine_step）。
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回傳 (theta_deg, score, s_best)。
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"""
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base = (np.asarray(proj) > 0).astype(np.float32)
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best_th, best, best_s = 0.0, -1.0, 0
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for th in np.arange(0.0, 180.0, coarse_step):
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sc, s = _best_vertical_split(rotate(base, th, reshape=False, order=0) > 0.5)
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if sc > best:
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best_th, best, best_s = float(th), sc, s
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for th in np.arange(best_th - coarse_step, best_th + coarse_step, fine_step):
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th2 = float(th) % 180.0
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sc, s = _best_vertical_split(rotate(base, th2, reshape=False, order=0) > 0.5)
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if sc > best:
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best_th, best, best_s = th2, sc, s
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return best_th, best, best_s
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def _mirror_hit_count(X, Y, Z, n, d, m, sz, sy, sx):
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"""每個骨 voxel 對平面 n·p=d 鏡射後四捨五入到最近 voxel，回傳命中骨 voxel 的數量"""
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nx, ny, nz = n
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dist = X * nx + Y * ny + Z * nz - d
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rx = (X - 2.0 * dist * nx).round().astype(np.int32)
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ry = (Y - 2.0 * dist * ny).round().astype(np.int32)
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rz = (Z - 2.0 * dist * nz).round().astype(np.int32)
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ok = (rx >= 0) & (rx < sx) & (ry >= 0) & (ry < sy) & (rz >= 0) & (rz < sz)
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if not ok.any():
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return 0
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return int(m[rz[ok], ry[ok], rx[ok]].sum())
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def best_symmetry_plane(mask_zyx, phi_max=45.0, subsample=7,
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coarse_step=7.5, fine_step=1.0):
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"""
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3D bone mask (z, y, x) 的最佳鏡稱面，一般平面方程 a·x + b·y + c·z = d
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（voxel index 座標；(a,b,c) 為單位法線，方向任意，不限制平行 YZ 面）。
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以「每個 bone voxel 鏡射後四捨五入到最近 voxel 的命中率」為分數，
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參數化 n = R_y(phi)·R_z(theta)·(1,0,0)：
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theta : 法線在 axial (x,y) 平面內的旋轉
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phi : 法線出平面的傾斜，限制在 [-phi_max, +phi_max] 以確保仍切分左右
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搜尋：theta 由 2D axial 投影粗定位，再 3D 三段式（粗→細→微細）
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（粗/細階段用等距子樣 voxel 加速）。
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回傳 dict：
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plane : (a, b, c, d) -> a*x + b*y + c*z = d
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normal : (a, b, c)
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offset : d
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theta_deg / phi_deg : 法線參數
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ratio : 鏡射命中率（0..1）
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u, v : 平面內兩個正交方向（供繪製用）
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"""
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m = np.asarray(mask_zyx) > 0
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sz, sy, sx = m.shape
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zz, yy, xx = np.nonzero(m)
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if xx.size < 100:
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n = np.array([1.0, 0.0, 0.0])
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d = (sx - 1) / 2.0
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return {'plane': (1.0, 0.0, 0.0, float(d)), 'normal': (1.0, 0.0, 0.0),
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'offset': float(d), 'theta_deg': 0.0, 'phi_deg': 0.0,
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'ratio': 1.0, 'u': (0.0, 1.0, 0.0), 'v': (0.0, 0.0, 1.0)}
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xf = xx.astype(np.float32)
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yf = yy.astype(np.float32)
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zf = zz.astype(np.float32)
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N = xf.size
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c0 = np.array([(sx - 1) / 2.0, (sy - 1) / 2.0, (sz - 1) / 2.0])
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Xs, Ys, Zs = xf[::subsample], yf[::subsample], zf[::subsample]
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def n_of(theta_deg, phi_deg):
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t = np.deg2rad(theta_deg)
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p = np.deg2rad(phi_deg)
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return np.array([np.cos(t) * np.cos(p), np.sin(t), -np.cos(t) * np.sin(p)])
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# theta 初值：2D axial 投影搜尋（垂直面情形）
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proj = m.max(axis=0)
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theta0 = best_symmetry_axis_angle(proj.astype(np.float32))[0] if proj.sum() >= 10 else 0.0
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best = None
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def consider(theta, phi, d, full=False):
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nonlocal best
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n = n_of(theta, phi)
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X, Y, Z = (xf, yf, zf) if full else (Xs, Ys, Zs)
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s = _mirror_hit_count(X, Y, Z, n, d, m, sz, sy, sx)
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if best is None or s > best['score']:
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best = {'score': s, 'theta': float(theta), 'phi': float(phi), 'd': float(d)}
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# stage 1：粗搜尋（子樣）
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for th in np.arange(theta0 - coarse_step * 2, theta0 + coarse_step * 2 + 1e-9, coarse_step):
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for ph in np.arange(-phi_max, phi_max + 1e-9, coarse_step):
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n = n_of(th, ph)
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d0 = float(n @ c0)
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for dd in (-10.0, 0.0, 10.0):
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consider(th, ph, d0 + dd)
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# stage 2：細搜尋（子樣）
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for th in np.arange(best['theta'] - 2 * fine_step * 2, best['theta'] + 2 * fine_step * 2 + 1e-9, fine_step):
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for ph in np.arange(best['phi'] - 2 * fine_step * 2, best['phi'] + 2 * fine_step * 2 + 1e-9, fine_step):
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for dd in (-3.0, -1.0, 0.0, 1.0, 3.0):
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consider(th, ph, best['d'] + dd)
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# stage 3：微細搜尋（全分辨率）
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for th in np.arange(best['theta'] - fine_step, best['theta'] + fine_step + 1e-9, fine_step / 2):
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for ph in np.arange(best['phi'] - fine_step, best['phi'] + fine_step + 1e-9, fine_step / 2):
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for dd in (-1.0, -0.5, 0.0, 0.5, 1.0):
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consider(th, ph, best['d'] + dd, full=True)
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# stage 4：d 微調（全分辨率）
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b = best
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for dd in np.arange(-1.0, 1.0 + 1e-9, 0.25):
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consider(b['theta'], b['phi'], b['d'] + dd, full=True)
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n = n_of(best['theta'], best['phi'])
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theta = best['theta']
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d = best['d']
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if theta > 90.0 or theta < -90.0: # 正規化到 (-90, 90]，法線翻轉時 d 取反
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theta -= 180.0
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n = -n
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d = -d
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ratio = _mirror_hit_count(xf, yf, zf, n, d, m, sz, sy, sx) / N
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u = np.cross(n, [0.0, 0.0, 1.0])
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if np.linalg.norm(u) < 0.1:
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u = np.cross(n, [1.0, 0.0, 0.0])
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u = u / np.linalg.norm(u)
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v = np.cross(n, u)
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return {
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'plane': (float(n[0]), float(n[1]), float(n[2]), float(d)),
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'normal': (float(n[0]), float(n[1]), float(n[2])),
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'offset': float(d),
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'theta_deg': float(theta),
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'phi_deg': float(best['phi']),
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'ratio': float(ratio),
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'u': (float(u[0]), float(u[1]), float(u[2])),
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'v': (float(v[0]), float(v[1]), float(v[2])),
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}
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def segment_spinous_process(mask_zyx, sym, band_frac=0.06, min_band=6.0,
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min_mass_frac=0.05):
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"""
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以 best_symmetry_plane 的結果 sym 從 3D bone mask (z, y, x) 切出棘突。
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棘突是中線後側構造，利用鏡稱面 a·x+b·y+c·z=d 定義：
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1) 中線帶：骨 voxel 到平面的有號距離 |s| <= w，
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w = max(min_band, band_frac * s 全寬)
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2) 前後方向：平面內兩軸 (u, v) 中 |y| 分量大者，
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正規化成 +AP = 後側（本資料系 y 往前遞增，後側 = y 小側）
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3) 中線帶的 AP 分佈呈兩大叢（椎體在前、椎弓/棘突在後），
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以兩叢間的 AP 谷底為界，AP >= 谷底 的中線帶 voxel = 棘突（含中線椎弓）；
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無明顯谷底（如骨橋）fallback 取中線帶後側 15%。
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回傳 (sp_mask (z,y,x) bool, ap_thresh, info dict)；
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資料過少時 sp_mask = None（info['mode'] 說明原因）。
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"""
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m = np.asarray(mask_zyx) > 0
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zz, yy, xx = np.nonzero(m)
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info = {'n_bone': int(zz.size), 'n_sp': 0, 'ap_thresh': None,
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'band_w': None, 'mode': 'empty'}
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if zz.size < 50:
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return None, None, info
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a, b, c, d = sym['plane']
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X = xx.astype(np.float64)
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Y = yy.astype(np.float64)
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Z = zz.astype(np.float64)
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s = X * a + Y * b + Z * c - d
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u = np.array(sym['u'])
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v = np.array(sym['v'])
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u_ap = u if abs(u[1]) >= abs(v[1]) else v
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if u_ap[1] > 0:
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u_ap = -u_ap # +AP = 後側（y 小側）
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w = max(float(min_band), float(band_frac) * float(s.max() - s.min()))
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mid = np.abs(s) <= w
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ap = X * u_ap[0] + Y * u_ap[1] + Z * u_ap[2]
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aps = ap[mid]
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info['band_w'] = float(w)
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if aps.size < 50:
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info['mode'] = 'too_few_midline'
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return None, None, info
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lo = int(np.floor(aps.min()))
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hi = int(np.ceil(aps.max()))
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th = None
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mode = 'fallback'
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if hi - lo >= 10:
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hist, edges = np.histogram(aps, bins=range(lo, hi + 1))
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csum = np.concatenate([[0], np.cumsum(hist)])
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total = csum[-1]
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peak = hist.max()
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best_i, best_score = None, -1.0
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for i in range(len(hist)):
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if hist[i] >= 0.05 * peak:
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continue
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if csum[i] < min_mass_frac * total or (total - csum[i + 1]) < min_mass_frac * total:
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continue
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score = min(csum[i], total - csum[i + 1])
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if score > best_score:
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best_score, best_i = score, i
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if best_i is not None:
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th = float(0.5 * (edges[best_i] + edges[best_i + 1]))
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mode = 'gap'
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if th is None:
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th = float(np.quantile(aps, 0.85))
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sp_mask = np.zeros(m.shape, dtype=bool)
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sel = mid & (ap >= th)
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sp_mask[zz[sel], yy[sel], xx[sel]] = True
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info.update(n_sp=int(sel.sum()), ap_thresh=th, mode=mode)
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return sp_mask, th, info
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def best_upper_endplate_plane(mask_zyx, angle_max=45.0, thresh=3.0,
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n_iter=500, seed=42):
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"""
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3D bone mask (z, y, x) 的最佳「上終板」近似平面 a·x + b·y + c·z = d
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（voxel index 座標；(a,b,c) 為朝上的單位法線）。
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1) 每個 (y, x) 欄位取最上方 bone voxel 作為頂面點（僅前側半邊，
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y >= COM_y，避開後方元素，與 2D superior endplate 定義一致）
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2) RANSAC 三點擬平面：法線限制在與 +z 軸 ≤ angle_max° 內，
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計數 ±thresh voxel 內的頂面點為 inlier，取 inlier 最多者
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3) SVD 最小二乘微調
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回傳 dict（資料不足時回傳 None）：
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plane : (a, b, c, d)
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normal / offset
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tilt_deg : 法線與 +z 軸的夾角（上終板傾斜）
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inlier_ratio : 頂面點落在平面 ±thresh 的比例
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n_points / n_inliers
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u, v : 平面內正交方向（供繪製用）
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"""
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m = np.asarray(mask_zyx) > 0
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nz, ny, nx = m.shape
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idx = np.where(m, np.arange(nz)[:, None, None], -1)
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ztop = idx.max(axis=0) # (y, x) 每欄最上 z
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y_split = int(round(center_of_mass(m)[1])) if m.sum() else 0
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# ztop 是 (y, x)：條件作用在 y 軸（axis 0）
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sel = (ztop >= 0) & (np.arange(ny)[:, None] >= y_split)
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yy, xx = np.where(sel)
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if xx.size < 8:
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return None
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P = np.stack((xx, yy, ztop[yy, xx]), axis=1).astype(np.float64)
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n = P.shape[0]
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rng = np.random.default_rng(seed)
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cos_min = np.cos(np.deg2rad(angle_max))
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best_cnt, best_nv, best_d = -1, None, 0.0
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for _ in range(n_iter):
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i, j, k = rng.choice(n, 3, replace=False)
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cr = np.cross(P[j] - P[i], P[k] - P[i])
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ln = np.linalg.norm(cr)
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if ln < 1e-6:
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continue
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nv = cr / ln
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if nv[2] < 0:
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nv = -nv
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if nv[2] < cos_min: # 法線必須朝上
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continue
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d = float(nv @ P[i])
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cnt = int(np.count_nonzero(np.abs(P @ nv - d) <= thresh))
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if cnt > best_cnt:
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best_cnt, best_nv, best_d = cnt, nv, d
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if best_nv is None:
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return None
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# SVD 微調
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inl = P[np.abs(P @ best_nv - best_d) <= thresh]
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if inl.shape[0] < 3:
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return None
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mean = inl.mean(axis=0)
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_, _, Vt = np.linalg.svd(inl - mean, full_matrices=False)
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nv = Vt[2]
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if nv[2] < 0:
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nv = -nv
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d = float(nv @ mean)
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dist = np.abs(P @ nv - d)
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inl = P[dist <= thresh]
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u = np.cross(nv, [1.0, 0.0, 0.0])
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u = u / np.linalg.norm(u)
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v = np.cross(nv, u)
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return {
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'plane': (float(nv[0]), float(nv[1]), float(nv[2]), d),
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'normal': (float(nv[0]), float(nv[1]), float(nv[2])),
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'offset': d,
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'tilt_deg': float(np.degrees(np.arccos(np.clip(nv[2], -1.0, 1.0)))),
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'inlier_ratio': float(inl.shape[0] / n),
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'n_points': int(n),
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'n_inliers': int(inl.shape[0]),
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'u': (float(u[0]), float(u[1]), float(u[2])),
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'v': (float(v[0]), float(v[1]), float(v[2])),
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}
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def azimuth_rotation(image, show_plt=False, save_plt=False, output_path=None):
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img = sitk.ReadImage(image, sitk.sitkUInt8)
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arr_zyx = sitk.GetArrayFromImage(img) # (z, y, x)
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max_proj = np.max(arr_zyx, axis=0) # -> (y, x)
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binary_proj = (max_proj > 0).astype(np.uint8)
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ys, xs = np.where(binary_proj > 0)
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if len(xs) < 10:
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raise ValueError("Not enough foreground points")
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# 2) centroid ←←← 這裡一定會定義 cx, cy
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cy = ys.mean()
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cx = xs.mean()
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centroid = np.array([cy, cx])
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cy = ys.mean()
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cx = xs.mean()
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centroid = np.array([cy, cx])
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y_min = ys.min()
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top_row_mask = (ys == y_min)
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xs_top_row = xs[top_row_mask]
|
||
|
||
# 取這一排的中位數或平均值
|
||
x_center = int(np.median(xs_top_row)) # 或用 np.mean()
|
||
top_point = (y_min, x_center)
|
||
# print(f"最上排中心點: {top_point}")
|
||
|
||
# 計算從 top_point 到 centroid 的向量
|
||
dy = cy - y_min # y 方向的變化
|
||
dx = cx - x_center # x 方向的變化
|
||
|
||
# 計算與 y 軸的夾角
|
||
# 注意：影像座標系中 y 軸向下，所以要特別處理
|
||
angle_rad = np.arctan2(dx, dy) # 弧度
|
||
angle_deg = np.degrees(angle_rad) # 轉成角度
|
||
|
||
if show_plt:
|
||
|
||
fig = plt.figure(figsize=(12, 12))
|
||
|
||
# 視覺化時加上角度資訊
|
||
plt.imshow(binary_proj, cmap='gray')
|
||
|
||
plt.scatter(x_center, y_min, c='red', s=60, label='Top')
|
||
plt.scatter(cx, cy, c='yellow', s=60, label='Centroid')
|
||
plt.plot([x_center, cx], [y_min, cy], 'c-', lw=2,
|
||
label=f'Angle with y-axis: {angle_deg:.1f}°')
|
||
|
||
# 中矢狀面：最佳鏡稱面 a·x + b·y + c·z = d（法線方向任意，不平行 YZ 面）
|
||
# 畫該平面在體積中央 z 切片上的截線
|
||
sym3 = best_symmetry_plane(arr_zyx)
|
||
a3, b3, c3, d3 = sym3['plane']
|
||
zc_ = (arr_zyx.shape[0] - 1) / 2.0
|
||
rhs_ = d3 - c3 * zc_ # a*x + b*y = rhs
|
||
n2d = np.hypot(a3, b3)
|
||
x0_ = a3 * rhs_ / (n2d * n2d)
|
||
y0_ = b3 * rhs_ / (n2d * n2d)
|
||
u2_ = np.array([b3, -a3]) / n2d
|
||
tfg_ = (xs - x0_) * u2_[0] + (ys - y0_) * u2_[1]
|
||
L_ = float(np.abs(tfg_).max())
|
||
plt.plot([x0_ - L_ * u2_[0], x0_ + L_ * u2_[0]],
|
||
[y0_ - L_ * u2_[1], y0_ + L_ * u2_[1]],
|
||
color='magenta', lw=2, linestyle='--',
|
||
label=f'Mirror plane: {a3:+.2f}x {b3:+.2f}y {c3:+.2f}z = {d3:.1f}')
|
||
|
||
plt.legend()
|
||
plt.title("Top point + centroid-directed line")
|
||
plt.axis("off")
|
||
|
||
if save_plt:
|
||
if output_path is None:
|
||
output_path = "azimuth_rotation.png"
|
||
fig.savefig(output_path, dpi=200, bbox_inches="tight")
|
||
|
||
plt.show()
|
||
plt.close(fig)
|
||
|
||
return angle_deg
|
||
|
||
def split_spine_anterior_posterior(image_path, center_mode='com'):
|
||
"""
|
||
從 sagittal view 看，沿著 y 軸（前後方向）將脊椎切成前半部和後半部
|
||
|
||
Parameters:
|
||
image_path (str): 影像路徑
|
||
center_mode (str): 'com' 使用質心的 y 座標，'image' 使用圖片中心的 y 座標
|
||
|
||
Returns:
|
||
anterior, posterior: 前半部和後半部的 binary mask
|
||
"""
|
||
|
||
# Sagittal projection: 沿著 x 軸投影 -> (z, y)
|
||
img = sitk.ReadImage(image_path, sitk.sitkUInt8)
|
||
arr_zyx = sitk.GetArrayFromImage(img)
|
||
|
||
# Sagittal projection
|
||
max_proj_sagittal = np.max(arr_zyx, axis=2)
|
||
binary_proj = (max_proj_sagittal > 0).astype(np.uint8)
|
||
|
||
# 決定切割的 y 座標
|
||
if center_mode == 'com':
|
||
cz, cy = center_of_mass(binary_proj)
|
||
split_y = int(round(cy))
|
||
label = f'Center of Mass (y={split_y})'
|
||
|
||
elif center_mode == 'image':
|
||
split_y = binary_proj.shape[1] // 2
|
||
label = f'Image Center (y={split_y})'
|
||
|
||
# 檢查是否為數字，且範圍在 0 到 1 之間 (不含邊界)
|
||
elif isinstance(center_mode, (int, float)) and 0 < center_mode < 1:
|
||
ys = np.where(binary_proj > 0)[1]
|
||
|
||
if ys.size == 0: # 額外保險：如果投影是空的
|
||
split_y = binary_proj.shape[1] // 2
|
||
else:
|
||
y_min, y_max = ys.min(), ys.max()
|
||
split_y = int(round(y_min + center_mode * (y_max - y_min)))
|
||
|
||
label = f'Custom Ratio {center_mode} (y={split_y})'
|
||
|
||
else:
|
||
raise ValueError("center_mode 必須是 'com'、'image' 或介於 0 到 1 之間的浮點數 (例如 0.8)")
|
||
|
||
# 切割：anterior (y < split_y) 和 posterior (y >= split_y)
|
||
anterior = binary_proj.copy()
|
||
posterior = binary_proj.copy()
|
||
|
||
anterior[:, :split_y] = 0 # 保留spine前半部(image後半部)（y >= split_y）
|
||
posterior[:, split_y:] = 0 # 保留spine後半部(image前半部)（y < split_y）
|
||
|
||
return anterior, posterior, binary_proj
|
||
|
||
def analyze_vertebral_tilt_contour(image_path, edge_type='superior', show_plot=False, debug=False, save_plt=False, output_path=None):
|
||
"""
|
||
通過椎體前緣輪廓分析傾斜（可選上或下終板）
|
||
|
||
Parameters:
|
||
edge_type: 'superior' 上終板, 'inferior' 下終板, 'both' 兩者都分析
|
||
"""
|
||
|
||
# 切割出 anterior 部分
|
||
anterior, posterior, binary_proj = split_spine_anterior_posterior(image_path, center_mode='com')
|
||
|
||
zs, ys = np.where(anterior > 0)
|
||
|
||
if len(zs) == 0:
|
||
return None
|
||
|
||
from sklearn.linear_model import RANSACRegressor
|
||
|
||
results = {}
|
||
|
||
# === 根據 edge_type 決定要分析哪些邊 ===
|
||
edges_to_analyze = []
|
||
if edge_type == 'superior' or edge_type == 'both':
|
||
edges_to_analyze.append('superior')
|
||
if edge_type == 'inferior' or edge_type == 'both':
|
||
edges_to_analyze.append('inferior')
|
||
|
||
all_edge_points = {}
|
||
all_inliers = {}
|
||
all_outliers = {}
|
||
all_slopes = {}
|
||
all_intercepts = {}
|
||
all_angles = {}
|
||
|
||
for edge in edges_to_analyze:
|
||
# 對每個 y，找對應的邊緣點
|
||
edge_points = []
|
||
unique_ys = np.unique(ys)
|
||
|
||
for y in unique_ys:
|
||
z_at_y = zs[ys == y]
|
||
|
||
if edge == 'superior':
|
||
z_edge = z_at_y.max() # 最上面的點（z 最小）
|
||
else: # inferior
|
||
z_edge = z_at_y.min() # 最下面的點（z 最大）
|
||
|
||
edge_points.append([y, z_edge])
|
||
|
||
edge_points = np.array(edge_points)
|
||
all_edge_points[edge] = edge_points
|
||
|
||
if len(edge_points) < 10:
|
||
continue
|
||
|
||
# RANSAC 擬合
|
||
X = edge_points[:, 0].reshape(-1, 1)
|
||
y_data = edge_points[:, 1]
|
||
|
||
ransac = RANSACRegressor(
|
||
residual_threshold=5.0,
|
||
random_state=42
|
||
)
|
||
ransac.fit(X, y_data)
|
||
|
||
inlier_mask = ransac.inlier_mask_
|
||
outlier_mask = ~inlier_mask
|
||
|
||
edge_points_inliers = edge_points[inlier_mask]
|
||
edge_points_outliers = edge_points[outlier_mask]
|
||
|
||
all_inliers[edge] = edge_points_inliers
|
||
all_outliers[edge] = edge_points_outliers
|
||
|
||
# 獲取擬合結果
|
||
slope = ransac.estimator_.coef_[0]
|
||
intercept = ransac.estimator_.intercept_
|
||
|
||
all_slopes[edge] = slope
|
||
all_intercepts[edge] = intercept
|
||
|
||
# 計算 R²
|
||
y_pred = ransac.predict(edge_points_inliers[:, 0].reshape(-1, 1))
|
||
ss_res = np.sum((edge_points_inliers[:, 1] - y_pred) ** 2)
|
||
ss_tot = np.sum((edge_points_inliers[:, 1] - np.mean(edge_points_inliers[:, 1])) ** 2)
|
||
r_squared = 1 - (ss_res / ss_tot) if ss_tot > 0 else 0
|
||
|
||
# 計算傾斜角度
|
||
tilt_angle = np.degrees(np.arctan(slope))
|
||
all_angles[edge] = tilt_angle
|
||
|
||
if debug:
|
||
print(f"\n=== {edge.upper()} ENDPLATE ===")
|
||
print(f"Total points: {len(edge_points)}")
|
||
print(f"Inliers: {len(edge_points_inliers)}")
|
||
print(f"Outliers: {len(edge_points_outliers)}")
|
||
|
||
print(f"{edge.capitalize()} 終板傾斜角度: {tilt_angle:.2f}°")
|
||
print(f"斜率: {slope:.4f}, R²: {r_squared:.4f}")
|
||
|
||
results[edge] = {
|
||
'tilt_angle_deg': tilt_angle,
|
||
'slope': slope,
|
||
'intercept': intercept,
|
||
'r_squared': r_squared,
|
||
'n_inliers': len(edge_points_inliers),
|
||
'n_outliers': len(edge_points_outliers)
|
||
}
|
||
|
||
# Visualization
|
||
if show_plot:
|
||
n_edges = len(edges_to_analyze)
|
||
fig, axes = plt.subplots(n_edges, 2, figsize=(16, 6*n_edges))
|
||
|
||
if n_edges == 1:
|
||
axes = axes.reshape(1, -1)
|
||
|
||
colors = {'superior': 'red', 'inferior': 'cyan'}
|
||
|
||
for idx, edge in enumerate(edges_to_analyze):
|
||
edge_points = all_edge_points[edge]
|
||
edge_points_inliers = all_inliers[edge]
|
||
edge_points_outliers = all_outliers[edge]
|
||
slope = all_slopes[edge]
|
||
intercept = all_intercepts[edge]
|
||
tilt_angle = all_angles[edge]
|
||
color = colors[edge]
|
||
|
||
# 左圖：scatter plot
|
||
ax_left = axes[idx, 0]
|
||
|
||
if len(edge_points_outliers) > 0:
|
||
ax_left.scatter(edge_points_outliers[:, 0], edge_points_outliers[:, 1],
|
||
c='lightcoral', s=30, alpha=0.6, marker='x',
|
||
label=f'Outliers ({len(edge_points_outliers)})', zorder=3)
|
||
|
||
ax_left.scatter(edge_points_inliers[:, 0], edge_points_inliers[:, 1],
|
||
c=color, s=20, alpha=0.7,
|
||
label=f'Inliers ({len(edge_points_inliers)})', zorder=4)
|
||
|
||
# 擬合線
|
||
y_line = np.array([edge_points[:, 0].min(), edge_points[:, 0].max()])
|
||
z_line = slope * y_line + intercept
|
||
ax_left.plot(y_line, z_line, 'lime', linewidth=3,
|
||
label=f'Angle: {tilt_angle:.1f}°\nR²: {results[edge]["r_squared"]:.3f}',
|
||
zorder=5)
|
||
|
||
ax_left.set_xlabel('y')
|
||
ax_left.set_ylabel('z')
|
||
ax_left.set_title(f'{edge.capitalize()} Endplate Analysis\nTilt: {tilt_angle:.1f}°')
|
||
ax_left.invert_yaxis()
|
||
ax_left.legend()
|
||
ax_left.grid(True, alpha=0.3)
|
||
|
||
# 右圖：原始影像
|
||
ax_right = axes[idx, 1]
|
||
ax_right.imshow(anterior, cmap='gray', aspect='equal')
|
||
|
||
if len(edge_points_outliers) > 0:
|
||
ax_right.scatter(edge_points_outliers[:, 0], edge_points_outliers[:, 1],
|
||
c='red', s=40, alpha=0.7, marker='x', label='Outliers', zorder=4)
|
||
|
||
ax_right.scatter(edge_points_inliers[:, 0], edge_points_inliers[:, 1],
|
||
c=color, s=25, alpha=0.8, label='Inliers', zorder=3)
|
||
|
||
ax_right.plot(y_line, z_line, 'lime', linewidth=3, linestyle='--',
|
||
label=f'{edge.capitalize()}: {tilt_angle:.1f}°', zorder=5)
|
||
|
||
ax_right.set_title(f'Anterior Half - {edge.capitalize()} Edge')
|
||
ax_right.set_xlabel('y')
|
||
ax_right.set_ylabel('z')
|
||
ax_right.invert_yaxis()
|
||
ax_right.legend()
|
||
|
||
plt.tight_layout()
|
||
|
||
if save_plt:
|
||
if output_path is None:
|
||
output_path = "analyze_vertebral_tilt_contour.png"
|
||
fig.savefig(output_path, dpi=200, bbox_inches="tight")
|
||
|
||
plt.show()
|
||
plt.close(fig)
|
||
|
||
return results
|