feat(core): improve scoring logic and add X-ray projection rendering
Implement a more robust scoring mechanism for screw optimization and add functionality to generate synthetic X-ray projections (AP and lateral views) from CT data. Key changes: - core: add `generate_cylinder_butt_torch` to create a mask for the screw entrance (0.25mm) to exempt it from bone-breaching penalties. - core: update `cl_score_torch_xfr` to include a diameter preference bonus and utilize the entrance mask. - core: adjust optimizer bounds and scoring weights to favor larger diameter screws and improve convergence. - xfr_cbt_native: implement `render_xray_projections` to generate synthetic AP and lateral X-ray images for visualization. - visualization: enhance `render_bone_figure` with semi-transparent spinous process rendering and improved depth sorting for screws. - xfr_debug: improve level detection to support arbitrary lumbar levels (L1-L9) and add safe volume-level cleanup for CBT writing. - config: update allowed diameters and lengths constants.
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9 changed files with 631 additions and 51 deletions
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@ -19,17 +19,17 @@ ALLOWED_DIAMETERS = [
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5.0,
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5.5,
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6.0,
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6.5,
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# 6.5,
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# 7.0,
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# 7.5,
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]
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ALLOWED_LENGTHS = [
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# 25,
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30,
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# 30,
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35,
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40,
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45,
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# 50,
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50,
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# 60,
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# 70,
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# 80,
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@ -275,6 +275,61 @@ def generate_cylinder_numpy(diameter, length, position_z, position_y, position_x
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return cylinder_mask
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def generate_cylinder_butt_torch(
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diameter,
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position_z, position_y, position_x,
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azimuth, altitude,
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shape, spacing, device, grid=None,
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butt_mm=0.25 # 入口端最後 butt_mm（mm）
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) -> torch.Tensor:
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"""生成圓柱「最後端」mask：入口端（z_rot=0，遠離 VBODY 的一端）
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最靠近的 butt_mm 圓柱短柱（與 generate_cylinder_n_torch 同慣例、同直徑）。
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尖端在 z_rot=length（靠 VBODY 端），不在此 mask。
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此 mask 是完整圓柱的子集，可直接用於 not_in_bone 豁免。"""
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if grid is None:
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z_t, y_t, x_t = create_coordinate_grid(shape, device)
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else:
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z_t, y_t, x_t = grid
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azimuth_rad_t = torch.deg2rad(torch.tensor(azimuth, device=device, dtype=torch.float32))
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altitude_rad_t = torch.deg2rad(torch.tensor(altitude, device=device, dtype=torch.float32))
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z_t = z_t - position_z
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y_t = y_t - position_y
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x_t = x_t - position_x
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x_rot = (
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x_t * torch.cos(azimuth_rad_t) * torch.cos(altitude_rad_t)
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+ y_t * torch.sin(azimuth_rad_t) * torch.cos(altitude_rad_t)
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- z_t * torch.sin(altitude_rad_t)
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)
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y_rot = -x_t * torch.sin(azimuth_rad_t) + y_t * torch.cos(azimuth_rad_t)
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z_rot = (
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x_t * torch.cos(azimuth_rad_t) * torch.sin(altitude_rad_t)
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+ y_t * torch.sin(azimuth_rad_t) * torch.sin(altitude_rad_t)
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+ z_t * torch.cos(altitude_rad_t)
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)
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# 與 generate_cylinder_n_torch 相同的 spacing/單位處理；
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# 長度固定為 butt_mm（mm）→ voxel
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if spacing == [1, 1, 1]:
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radius = diameter / 2.0
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butt_len = butt_mm
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elif spacing == [0.5, 0.5, 0.5]:
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radius = (diameter / 2.0) * 2
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butt_len = butt_mm * 2
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else:
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raise ValueError(f"Unsupported spacing: {spacing}")
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mask = (
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(x_rot**2 + y_rot**2 <= radius**2)
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& (z_rot >= 0)
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& (z_rot < butt_len)
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)
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return mask.to(torch.uint8)
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def generate_cylinder_tip_torch(
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diameter, length,
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position_z, position_y, position_x,
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@ -8,7 +8,7 @@ from scipy.ndimage import map_coordinates
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import numpy as np
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import torch
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from core.cylinder import generate_cylinder_n_torch, generate_cylinder_o_torch, snap_to_discrete_values, generate_cylinder_tip_torch, snap_to_discrete_values_xfr
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from core.cylinder import generate_cylinder_n_torch, generate_cylinder_o_torch, snap_to_discrete_values, generate_cylinder_tip_torch, generate_cylinder_butt_torch, snap_to_discrete_values_xfr
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from core.intersection import center_line_intersections_torch
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from core.scoring import cl_score_torch, cl_score_torch_xfr
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@ -120,12 +120,22 @@ def cylinder_circle_line_intersection_loss_deductions_torch(
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ctx.image2_shape, ctx.spacing, ctx.device, ctx.grid
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)
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# 最後端（入口端，遠離 VBODY 的一端）0.25mm 豁免 mask
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cyl_butt = generate_cylinder_butt_torch(
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diameter,
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position_z, position_y, position_x,
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float(azimuth), float(altitude),
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ctx.image2_shape, ctx.spacing, ctx.device, ctx.grid
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)
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# loss_value = cl_score_torch(
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loss_value = cl_score_torch_xfr(
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ctx.cortical_tensor, ctx.spine_tensor,
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cyl_fwd, cyl_opp, intersections,
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diameter=diameter, length=length,
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cylinder_tip_torch=cyl_tip,
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vbody_tensor=ctx.vbody_tensor
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vbody_tensor=ctx.vbody_tensor,
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cylinder_butt_torch=cyl_butt
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)
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return loss_value
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@ -10,7 +10,7 @@ from imaging.orientation import (azimuth_rotation, analyze_vertebral_tilt_contou
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from config.constant import ALLOWED_DIAMETERS, ALLOWED_LENGTHS
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from core.objective import OptimizationContext, make_objective_function, make_objective_function_xfr
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from pyswarm import pso
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from core.cylinder import generate_cylinder_n_torch, generate_cylinder_o_torch, snap_to_discrete_values, create_coordinate_grid, snap_to_discrete_values_xfr
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from core.cylinder import generate_cylinder_n_torch, generate_cylinder_o_torch, snap_to_discrete_values, create_coordinate_grid, snap_to_discrete_values_xfr, generate_cylinder_butt_torch
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from core.intersection import center_line_intersections_torch
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from core.scoring import compute_overlap_ratio_from_cylinder_mask, is_solution_ok, cl_score_torch_xfr
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from config.constant import OVERLAP_THRESH
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@ -63,9 +63,13 @@ def refine_lateral_longer(
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if cyl.sum().item() == 0:
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return None
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inter, _ = center_line_intersections_torch(z_c, y_c, x_c, az_c, alt_c,
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L_c, spine_tensor, spacing, device)
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L_c, spine_tensor, spacing, device)
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cyl_butt = generate_cylinder_butt_torch(d_c, z_c, y_c, x_c, az_c, alt_c,
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image_shape, spacing, device, grid)
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loss = cl_score_torch_xfr(cortical_tensor, spine_tensor, cyl, cyl_o, inter,
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vbody_tensor=vbody_tensor)
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diameter=d_c,
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vbody_tensor=vbody_tensor,
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cylinder_butt_torch=cyl_butt)
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in_bone = ((spine_tensor == 1) & (cyl == 1)).sum().item() / cyl.sum().item()
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return {'pos': cand, 'loss': loss, 'in_bone': in_bone}
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@ -364,7 +368,7 @@ def run_pso_torch_xfr(
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# z_bounds = (0, image_shape[0]-1)
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# z_bounds = (z1, (z1+z2)/2)
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# z_bounds = (.1*image_shape[0], .8*image_shape[0])
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z_bounds = (0, z1+z_height*.8)
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z_bounds = (0, z1+z_height*.7)
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# x_bounds_right = (image_shape[2]/2 + image_shape[2]/10, image_shape[2] - 1)
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# x_bounds_left = (0, image_shape[2]/2 - image_shape[2]/10 - 1)
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@ -2,6 +2,11 @@ import torch
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from core.cylinder import generate_cylinder_n_torch, generate_cylinder_tip_torch
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from config.constant import OVERLAP_THRESH
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# 直徑偏好 bonus（每 mm）：ALLOWED_DIAMETERS 相鄰等級間隔 0.5 mm，
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# 20000/mm ⇒ 每級差 10000 分，約等於容許 1 個額外 out-of-bone voxel
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# （10000 分/voxel 懲罰）換 0.5 mm 直徑，讓結果在安全前提下偏好較粗螺絲。
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DIAMETER_BONUS_PER_MM = 20000.0
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def cl_score_torch_xfr(
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cortical_tensor: torch.Tensor,
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spine_tensor: torch.Tensor,
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@ -11,10 +16,13 @@ def cl_score_torch_xfr(
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diameter: float = None,
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length: float = None,
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cylinder_tip_torch: torch.Tensor = None, # 新增：尖端 mask
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vbody_tensor: torch.Tensor = None # VBODY（椎體）mask (z,y,x) 0/1，None = 不計 VBODY 獎勵
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vbody_tensor: torch.Tensor = None, # VBODY（椎體）mask (z,y,x) 0/1，None = 不計 VBODY 獎勵
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cylinder_butt_torch: torch.Tensor = None # 最後端（入口端，遠離 VBODY 的一端）0.25mm mask
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) -> float:
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"""
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漸進式評分：優先確保找到骨頭，再改善細節
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漸進式評分：優先確保找到骨頭，再改善細節。
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diameter 提供時附加直徑偏好 bonus（偏好較粗螺絲）。
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cylinder_butt_torch 提供時，該 mask 內 not_in_bone 的 voxel 免除 10000/voxel 扣分。
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"""
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cyl_total = cylinder_torch.sum().item()
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overlap = ((cortical_tensor == 1) & (cylinder_torch == 1)).sum().item() # in cortical
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@ -32,6 +40,11 @@ def cl_score_torch_xfr(
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in_bone= ((spine_tensor == 1) & (cylinder_torch == 1)).sum().item()
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not_in_bone= ((spine_tensor == 0) & (cylinder_torch == 1)).sum().item()
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# 最後端（入口端，遠離 VBODY 的一端）0.25mm 豁免：
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# 這些 voxel 即使 not_in_bone 也不計 10000/voxel 扣分
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if cylinder_butt_torch is not None:
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not_in_bone -= ((cylinder_butt_torch == 1) & (cylinder_torch == 1)
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& (spine_tensor == 0)).sum().item()
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if cyl_total == 0:
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return float(1e9) # 極差的情況
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@ -43,17 +56,24 @@ def cl_score_torch_xfr(
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score = cyl_total
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# 直徑偏好 bonus：相同幾何下偏好較大直徑（diameter 單位 mm）。
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# bonus 遠小於 breaching 懲罰（10000 分/voxel），不會把螺絲推出骨頭。
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if diameter is not None and diameter > 0:
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score += DIAMETER_BONUS_PER_MM * diameter
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allowed_error = cyl_total/100
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# if in_bone == 0:
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# return float(not_in_bone*200)
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score += 10 * in_bone # 10 實在太低
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score += 100 * overlap # in cortical
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score += 100 * in_corti_vb # (cortical + VBODY) 每 voxel 再加分
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score += 200 * in_corti_vb # (cortical + VBODY) 每 voxel 再加分
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score += 50 * in_vbody # VBODY 每 voxel 再加分
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# score -= 1000 * not_in_bone
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score -= 1000 * max(0, not_in_bone-10)
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# score -= 2000 * null_vox2
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score -= 2000 * max(0, null_vox2-10)
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score -= 10000 * not_in_bone
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# score -= 10000 * max(0, not_in_bone-allowed_error)
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score -= 20000 * null_vox2
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# score -= 2000 * max(0, null_vox2-10)
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return float(-score)
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@ -24,6 +24,8 @@ BONE_MU_CORTICAL = 0.02 # 1/mm
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BONE_MU_TRABECULAR = 0.005 # 1/mm
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BONE_MARKER_SIZE = 3.0 # 骨散點點面積 (pt^2)
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BONE_SUBSAMPLE = 1 # 抽稀（1 = 全畫）
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SPINOUS_MU = 0.075 # 1/mm，棘突吸收係數（半透明紫色層，密度約皮質骨 3 倍：
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# 看得見紫色、後方螺絲路徑仍透見）
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def set_axes_equal_3d(ax):
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@ -265,10 +267,13 @@ def render_bone_figure(volume_id, level, binary_path, cortical_path,
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"""統一骨頭 X-ray 四視角圖（合併原 render_bone_figure + res_plt_2_torch）。
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四視角：預設 / axial（俯視 XY）/ 冠狀（後視）/ 矢狀。
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內容：皮質 vs 鬆質吸收骨、椎體(gold)、棘突(purple，獨立層覆蓋椎體)、
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內容：皮質 vs 鬆質吸收骨、椎體(gold)、棘突(purple 半透明，獨立層覆蓋椎體)、
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中矢狀鏡稱面(orange)、上終板面(green)；螺絲模式另畫中心線(紅)
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+ 圓柱（L darkcyan / R blue，o 層粉）。
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繪製採固定分層（不依深度排序）：基底骨 < VBODY < 棘突 < 終板 < 鏡稱面 < 螺絲。
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繪製採固定分層（不依深度排序）：
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基底骨 < VBODY < 螺絲(棘突後方) < 棘突(半透明) < 終板 < 鏡稱面 < 螺絲(棘突前方)
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螺絲與棘突另依各視角相機深度拆分：比棘突中位深度深的螺絲畫在棘突之下，
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被半透明紫色正確遮擋（仍可透見螺絲路徑）；較淺的螺絲照舊畫在最上層。
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骨骼輸入：
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binary_path 骨頭遮罩：path (nifti) 或 (z,y,x) ndarray / torch tensor
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@ -339,6 +344,7 @@ def render_bone_figure(volume_id, level, binary_path, cortical_path,
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voxel_mm = float(spacing[0])
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alpha_cortical = 1.0 - np.exp(-BONE_MU_CORTICAL * voxel_mm)
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alpha_trabecular = 1.0 - np.exp(-BONE_MU_TRABECULAR * voxel_mm)
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alpha_spinous = 1.0 - np.exp(-SPINOUS_MU * voxel_mm)
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# 骨頭 voxel 拆成皮質 / 鬆質兩組（同 res_plt_2_torch）
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z_corti, y_corti, x_corti = np.where((spine) & (cortical))
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@ -451,7 +457,8 @@ def render_bone_figure(volume_id, level, binary_path, cortical_path,
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# x_bone 保留完整點雲（含 VBODY / SP）供下方平面 patch 算範圍
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#（VBODY 前側是整顆骨最前緣，剔除後綠色終板 patch 會縮小）；
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# mpl 3D scatter 在同一 collection 內依深度排序 markers，
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# 「棘突覆蓋椎體、螺絲覆蓋骨頭」改以固定 zorder 分層達成（見 _fill_ax）
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# 「棘突覆蓋椎體」以固定 zorder 分層達成；螺絲與棘突的遮擋則依各視角
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# 相機深度拆分（半透明棘突正確遮擋其後方的螺絲，見 _fill_ax）
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base_idx = ~(vb_flag | sp_flag)
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x_base, y_base, z_base = x_bone[base_idx], y_bone[base_idx], z_bone[base_idx]
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rgba_base, size_base = bone_rgba[base_idx], bone_size[base_idx]
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@ -473,7 +480,8 @@ def render_bone_figure(volume_id, level, binary_path, cortical_path,
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spacing = list(spacing) # core.cylinder 以 list 比對 spacing
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import torch
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from core.cylinder import (generate_cylinder_n_torch,
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generate_cylinder_o_torch)
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generate_cylinder_o_torch,
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generate_cylinder_butt_torch)
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from core.intersection import center_line_intersections_torch
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from core.scoring import cl_score_torch, cl_score_torch_xfr
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@ -538,9 +546,14 @@ def render_bone_figure(volume_id, level, binary_path, cortical_path,
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loss = cl_score_torch(cortical_tensor, spine_tensor,
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cyl_n, cyl_o, inter)
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else:
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cyl_butt = generate_cylinder_butt_torch(
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d, pos[0], pos[1], pos[2], pos[3], pos[4],
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image_shape, spacing, device, grid)
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loss = cl_score_torch_xfr(cortical_tensor, spine_tensor,
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cyl_n, cyl_o, inter,
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vbody_tensor=vbody_tensor)
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diameter=d,
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vbody_tensor=vbody_tensor,
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cylinder_butt_torch=cyl_butt)
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cyl_points = int(torch.sum(cyl_n).item())
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ovc = (100.0 * int(((cortical_tensor == 1) & (cyl_n == 1)).sum().item())
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/ cyl_points) if cyl_points else 0.0
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@ -633,9 +646,21 @@ def render_bone_figure(volume_id, level, binary_path, cortical_path,
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if sp_corti is not None:
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legend_handles.append(Line2D([], [], marker="o", ls="", ms=6, color="purple", label="SpinousProcess"))
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def _fill_ax(ax):
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def _view_dir(elev_d, azim_d):
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"""本視角相機位置方向（從場景中心指向相機），(x, y, z) 資料座標系。
|
||||
view_init(elev, azim) 的球座標慣例：azim 繞 +z、自 +x 起算。"""
|
||||
e, a = np.radians(elev_d), np.radians(azim_d)
|
||||
return np.array([np.cos(e) * np.cos(a),
|
||||
np.cos(e) * np.sin(a),
|
||||
np.sin(e)])
|
||||
|
||||
def _fill_ax(ax, elev, azim):
|
||||
# 固定分層（關 depth zorder，否則半透明骨頭會被重繪到螺絲上方）：
|
||||
# 基底骨(5) < VBODY gold(6) < 棘突 purple(6.5) < 終板(7) < 鏡稱面(8) < 螺絲(10)
|
||||
# 基底骨(5) < VBODY gold(6) < 螺絲「棘突後方」(6.2)
|
||||
# < 棘突 purple(6.5, 半透明) < 終板(7) < 鏡稱面(8) < 螺絲「棘突前方」(10)
|
||||
# 螺絲與棘突按本視角相機深度拆分：depth = P·cam（大 = 靠近相機）。
|
||||
# 比棘突中位深度深（farther）的螺絲先畫、被半透明紫色擋住但仍透見
|
||||
# （正確遮蔽螺絲路徑）；較淺的螺絲照舊畫在最上層。無棘突時不拆分。
|
||||
ax.computed_zorder = False
|
||||
sc_bone = ax.scatter(x_base, y_base, z_base, c=rgba_base, s=size_base, marker="o")
|
||||
sc_bone.set_zorder(5)
|
||||
|
|
@ -644,8 +669,28 @@ def render_bone_figure(volume_id, level, binary_path, cortical_path,
|
|||
sc_vb = ax.scatter(x_vb, y_vb, z_vb, c=np.array([to_rgba("gold", 0.95)]),
|
||||
s=BONE_MARKER_SIZE, marker="o")
|
||||
sc_vb.set_zorder(6)
|
||||
|
||||
# 螺絲拆「棘突前方 / 後方」（front = 本視角較淺或等深）：無螺絲時
|
||||
# None；無棘突時全 True（維持舊行為）；有棘突時依相機深度 vs 棘突
|
||||
# 中位深度拆分
|
||||
if x_screw is None:
|
||||
front = None
|
||||
elif x_sp.size:
|
||||
cam = _view_dir(elev, azim)
|
||||
d_screw = x_screw * cam[0] + y_screw * cam[1] + z_screw * cam[2]
|
||||
d_sp = x_sp * cam[0] + y_sp * cam[1] + z_sp * cam[2]
|
||||
front = d_screw >= np.median(d_sp)
|
||||
else:
|
||||
front = np.ones(len(x_screw), dtype=bool)
|
||||
|
||||
if front is not None and (~front).any():
|
||||
m = ~front
|
||||
sc_bh = ax.scatter(x_screw[m], y_screw[m], z_screw[m],
|
||||
c=screw_rgba[m], s=screw_size[m], marker="o")
|
||||
sc_bh.set_zorder(6.2)
|
||||
if x_sp.size:
|
||||
sc_sp = ax.scatter(x_sp, y_sp, z_sp, c=np.array([to_rgba("purple", 0.95)]),
|
||||
sc_sp = ax.scatter(x_sp, y_sp, z_sp,
|
||||
c=np.array([to_rgba("purple", float(alpha_spinous))]),
|
||||
s=BONE_MARKER_SIZE, marker="o")
|
||||
sc_sp.set_zorder(6.5)
|
||||
if _EX is not None:
|
||||
|
|
@ -655,19 +700,19 @@ def render_bone_figure(volume_id, level, binary_path, cortical_path,
|
|||
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)
|
||||
if x_screw is not None:
|
||||
sc_screw = ax.scatter(x_screw, y_screw, z_screw,
|
||||
c=screw_rgba, s=screw_size, marker="o")
|
||||
if front is not None and front.any():
|
||||
sc_screw = ax.scatter(x_screw[front], y_screw[front], z_screw[front],
|
||||
c=screw_rgba[front], s=screw_size[front], marker="o")
|
||||
sc_screw.set_zorder(10)
|
||||
|
||||
ax1 = fig.add_subplot(221, projection="3d")
|
||||
_fill_ax(ax1)
|
||||
_fill_ax(ax1, 30, -60) # matplotlib 3D 預設視角
|
||||
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)
|
||||
_fill_ax(ax2, 90, -90)
|
||||
ax2.set_xlabel("X-axis"); ax2.set_ylabel("Y-axis"); ax2.set_zlabel("Z-axis")
|
||||
set_axes_equal_3d(ax2)
|
||||
if legend_handles:
|
||||
|
|
@ -676,13 +721,13 @@ def render_bone_figure(volume_id, level, binary_path, cortical_path,
|
|||
ax3 = fig.add_subplot(223, projection="3d")
|
||||
# 後視圖：相機在 −y 後側，x 軸畫面左小右大
|
||||
ax3.view_init(elev=0, azim=-90, roll=0)
|
||||
_fill_ax(ax3)
|
||||
_fill_ax(ax3, 0, -90)
|
||||
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)
|
||||
_fill_ax(ax4, 0, 0)
|
||||
ax4.set_xlabel("X-axis"); ax4.set_ylabel("Y-axis"); ax4.set_zlabel("Z-axis")
|
||||
set_axes_equal_3d(ax4)
|
||||
|
||||
|
|
|
|||
|
|
@ -4,6 +4,10 @@
|
|||
（L1-L5 x L/R，最多 10 支）存成單一 label 體積：
|
||||
|
||||
Output_dir/<run_date>/<volume_id>/cbt.nii.gz
|
||||
Output_dir/<run_date>/<volume_id>/x-ap.jpg 合成前後(AP) X 光投影视圖
|
||||
Output_dir/<run_date>/<volume_id>/x-lat.jpg 合成側位 X 光投影视圖
|
||||
（只投影脊椎骨 + 螺絲、不含軟組織；
|
||||
見 render_xray_projections）
|
||||
|
||||
label 值：L1L=1 L1R=2 L2L=3 L2R=4 L3L=5 L3R=6 L4L=7 L4R=8 L5L=9 L5R=10
|
||||
（0 = 背景）。
|
||||
|
|
@ -74,6 +78,7 @@ META_DB = os.path.join(_PROJ_DIR, 'xfr_image_metadata.json')
|
|||
|
||||
LEVELS = ('L1', 'L2', 'L3', 'L4', 'L5')
|
||||
LEVEL_LABEL_VAL = {v: int(k) for k, v in LABEL_MAP.items() if v in LEVELS} # {'L1': 20, ...}
|
||||
PROJ_FILENAME = {'ap': 'x-ap.jpg', 'lateral': 'x-lat.jpg'}
|
||||
|
||||
logger = logging.getLogger('xfr_cbt_native')
|
||||
|
||||
|
|
@ -258,9 +263,129 @@ def write_volume_cbt(volume_id, run_id, output_root=Output_dir, date=None):
|
|||
logger.warning(f'{volume_id}: failed sides: {", ".join(skipped)}')
|
||||
logger.info(f'{volume_id}: {n_screws}/10 screws -> {out_path} '
|
||||
f'(transform.json={n_tf}, re-est={n_fb}, ap_flip={ap_flip})')
|
||||
|
||||
# 收尾：原 CT + 螺絲 -> 合成 AP / Lateral X 光投影视圖 (x-ap.jpg / x-lat.jpg)
|
||||
try:
|
||||
render_xray_projections(out_path, ct_path, out_dir)
|
||||
except Exception as e:
|
||||
logger.warning(f'{volume_id}: X-ray projection render failed: {e}')
|
||||
return out_path, n_screws
|
||||
|
||||
|
||||
def render_xray_projections(cbt_path, ct_path, out_dir,
|
||||
views=('ap', 'lateral'), margin_mm=50.0):
|
||||
"""原 CT + cbt.nii.gz（native 同一 grid 的螺絲 label）-> 合成「只有骨頭」X 光投影视圖。
|
||||
|
||||
只投影脊椎骨 + 螺絲，不含軟組織：
|
||||
- spine mask：native 分割 label（1-24 = C1..L5，見 config.constant.LABEL_MAP）；
|
||||
label 缺 / grid 不符時退回 HU 300-3000 閾值。
|
||||
- 骨 μ = clip(HU, 0, 2000)/400（松質骨~0.2-1、皮質/終板~2-5）。
|
||||
- 螺絲 voxel μ = 60（金屬等效，最亮白）。
|
||||
投影视圖 = 沿中心射線 μ 的線積分（AP 沿 y、Lateral 沿 x）；裁到螺絲 bbox 外
|
||||
margin_mm 的 spine 區域；骨用 percentile(1,99) 獨立視窗，螺絲再疊加裁白。
|
||||
方向：SimpleITK LPS（x→右、y→後、z→上）；AP 頂=上、病人右在畫面左（R 標記）；
|
||||
Lateral 頂=上、前位在左、後位在右（A/P 標記）。
|
||||
spacing 各軸不等時先重取樣到 min(spacing) 各向同性格，維持投影 aspect ratio。
|
||||
輸出 x-ap.jpg / x-lat.jpg，回傳 {view: jpg_path}。"""
|
||||
import matplotlib
|
||||
matplotlib.use('Agg')
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
vid = os.path.basename(os.path.normpath(out_dir))
|
||||
ct = sitk.ReadImage(ct_path)
|
||||
cbt = sitk.ReadImage(cbt_path)
|
||||
if ct.GetSize() != cbt.GetSize():
|
||||
raise ValueError(f'cbt/CT grid 尺寸不符 {cbt.GetSize()} vs {ct.GetSize()}')
|
||||
sp = np.array(ct.GetSpacing(), float) # (x,y,z)
|
||||
native_size = ct.GetSize()
|
||||
# spacing 各軸不等：先重取樣 CT/螺絲（與後面的分割 label）到
|
||||
# min(spacing) 的各向同性格，維持投影视圖正確的 aspect ratio
|
||||
min_sp = float(sp.min())
|
||||
isosp = isosize = None
|
||||
if not np.allclose(sp, min_sp):
|
||||
isosp = [min_sp, min_sp, min_sp]
|
||||
isosize = [max(1, int(round(s * v / min_sp))) for s, v in zip(sp, ct.GetSize())]
|
||||
o, d = ct.GetOrigin(), ct.GetDirection()
|
||||
ct = sitk.Resample(sitk.Cast(ct, sitk.sitkFloat32), isosize, sitk.Transform(),
|
||||
sitk.sitkLinear, o, isosp, d, 0.0)
|
||||
cbt = sitk.Resample(cbt, isosize, sitk.Transform(),
|
||||
sitk.sitkNearestNeighbor, o, isosp, d, 0)
|
||||
ct_arr = sitk.GetArrayFromImage(ct).astype(np.float32) # (z,y,x)
|
||||
cbt_arr = sitk.GetArrayFromImage(cbt)
|
||||
sp = np.array(ct.GetSpacing(), float)
|
||||
|
||||
metal = cbt_arr > 0
|
||||
try:
|
||||
_, lb_path, _ = find_native_paths(vid)
|
||||
lb_img = sitk.ReadImage(lb_path)
|
||||
# 比對「重取樣前」的 native grid（label 與原 CT 同格）
|
||||
if lb_img.GetSize() != native_size:
|
||||
raise ValueError(f'label/CT grid 尺寸不符 {lb_img.GetSize()} vs {native_size}')
|
||||
if isosp is not None:
|
||||
lb_img = sitk.Resample(lb_img, isosize, sitk.Transform(),
|
||||
sitk.sitkNearestNeighbor,
|
||||
ct.GetOrigin(), isosp, ct.GetDirection(), 0)
|
||||
lb = sitk.GetArrayFromImage(lb_img)
|
||||
spine = (lb >= 1) & (lb <= 24) # LABEL_MAP: C1..L5 全為脊椎
|
||||
except Exception as e:
|
||||
logger.warning(f'{vid}: native 分割 label 不可用（{e}）；'
|
||||
f'退回 HU 300-3000 閾值當脊椎骨')
|
||||
spine = (ct_arr >= 300) & (ct_arr <= 3000)
|
||||
|
||||
bone_mu = np.where(spine & ~metal, np.clip(ct_arr, 0, 2000) / 400.0, 0.0)
|
||||
metal_mu = 60.0 * metal
|
||||
|
||||
z, y, x = np.where(metal)
|
||||
if z.size == 0:
|
||||
raise ValueError(f'{vid}: cbt 無螺絲 voxel')
|
||||
mz, my, mx = (int(margin_mm / s) for s in (sp[2], sp[1], sp[0]))
|
||||
zs = slice(max(0, z.min() - mz), min(ct_arr.shape[0], z.max() + mz + 1))
|
||||
ys = slice(max(0, y.min() - my), min(ct_arr.shape[1], y.max() + my + 1))
|
||||
xs = slice(max(0, x.min() - mx), min(ct_arr.shape[2], x.max() + mx + 1))
|
||||
|
||||
proj = {
|
||||
'ap': (bone_mu[zs, :, xs].sum(axis=1), metal_mu[zs, :, xs].sum(axis=1)), # 沿 y 積分
|
||||
'lateral': (bone_mu[zs, ys, :].sum(axis=2), metal_mu[zs, ys, :].sum(axis=2)) # 沿 x 積分
|
||||
}
|
||||
flip_x = {'ap': True, 'lateral': False}
|
||||
markers = {'ap': ('R', 'L'), 'lateral': ('A', 'P')} # 畫面左=前位(A)、右=後位(P)
|
||||
os.makedirs(out_dir, exist_ok=True)
|
||||
paths = {}
|
||||
for view in views:
|
||||
if view not in proj:
|
||||
continue
|
||||
b, m = proj[view]
|
||||
b = b[::-1, ::-1] if flip_x[view] else b[::-1, :] # 頂=頭端
|
||||
m = m[::-1, ::-1] if flip_x[view] else m[::-1, :]
|
||||
bnz = b[b > 0]
|
||||
if bnz.size:
|
||||
lo, hi = np.percentile(bnz, 1), np.percentile(bnz, 99)
|
||||
bone = np.clip((b - lo) / (hi - lo + 1e-9), 0, 1) ** 0.7
|
||||
else:
|
||||
bone = np.zeros_like(b)
|
||||
mmax = float(m.max())
|
||||
metal_img = (m / (mmax + 1e-9)) ** 0.5 if mmax > 0 else m * 0
|
||||
img = np.clip(bone + metal_img, 0, 1)
|
||||
ll, rl = markers[view]
|
||||
h, w = img.shape
|
||||
fig, ax = plt.subplots(figsize=(8.0 * w / h, 8.0), dpi=110)
|
||||
ax.imshow(img, cmap='gray', interpolation='nearest')
|
||||
ax.set_title(f'{view.upper()} projection (spine + screws) - {vid}',
|
||||
color='white', fontsize=13)
|
||||
ax.text(0.01, 0.98, ll, transform=ax.transAxes, color='cyan', fontsize=13,
|
||||
va='top', ha='left', fontweight='bold')
|
||||
ax.text(0.99, 0.98, rl, transform=ax.transAxes, color='cyan', fontsize=13,
|
||||
va='top', ha='right', fontweight='bold')
|
||||
ax.axis('off')
|
||||
fig.tight_layout(pad=0.5)
|
||||
p = os.path.join(out_dir, PROJ_FILENAME[view])
|
||||
fig.savefig(p, format='jpg', facecolor='black')
|
||||
plt.close(fig)
|
||||
paths[view] = p
|
||||
logger.info(f'{vid}: {view} projection -> {p}')
|
||||
return paths
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(
|
||||
description='Write <Output_dir>/<date>/<volume_id>/cbt.nii.gz (screws mapped to native space) '
|
||||
|
|
|
|||
117
xfr_debug.py
117
xfr_debug.py
|
|
@ -8,6 +8,7 @@ import time
|
|||
import queue as queue_module
|
||||
import subprocess
|
||||
import multiprocessing as mp
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
|
||||
import SimpleITK as sitk
|
||||
import torch
|
||||
|
|
@ -27,7 +28,24 @@ azimuth_rotation_dir = '/mnt/1248/open2/cyrou/azimuth_rotation'
|
|||
tilt_contour_dir = '/mnt/1248/open2/cyrou/tilt_contour'
|
||||
Output_dir = '/mnt/1248/open/cyrou/Output'
|
||||
|
||||
LEVELS = ('L1', 'L2', 'L3', 'L4', 'L5')
|
||||
def available_levels(volume_id):
|
||||
"""該 volume 可跑 debug_pso 的全部 lumbar level：rotated/ 中輸入三件
|
||||
（_cortical / _binary_sdf / _roi）齊全的 L\\d。各 volume 層數不同
|
||||
（例：有的只有 L1~L3、有的含 L6），故不用固定 LEVELS 清單。"""
|
||||
rotated_dir = os.path.join(standardized_dir, volume_id, 'rotated')
|
||||
if not os.path.isdir(rotated_dir):
|
||||
return ()
|
||||
levels = []
|
||||
for fn in os.listdir(rotated_dir):
|
||||
m = re.fullmatch(r'(L[1-9]\d*)_cortical\.nii\.gz', fn)
|
||||
if not m:
|
||||
continue
|
||||
level = m.group(1)
|
||||
if (os.path.exists(os.path.join(rotated_dir, f'{level}_binary_sdf.nii.gz'))
|
||||
and os.path.exists(os.path.join(rotated_dir, f'{level}_roi.nii.gz'))):
|
||||
levels.append(level)
|
||||
return tuple(sorted(levels, key=lambda lv: int(lv[1:])))
|
||||
|
||||
|
||||
LOG_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'logs')
|
||||
|
||||
|
|
@ -303,29 +321,49 @@ def gpu_worker(gpu_id, log_path, run_id, task_queue, result_queue):
|
|||
logger.info(f'=== [GPU {gpu_id}] worker finished ===')
|
||||
|
||||
|
||||
def _write_cbt_safe(volume_id, run_id):
|
||||
"""per-volume 收尾：cbt.nii.gz + x-ap/x-lat 投影（錯誤已 log，回傳 ok）"""
|
||||
try:
|
||||
xfr_cbt_native.write_volume_cbt(volume_id, run_id)
|
||||
return True
|
||||
except Exception as e:
|
||||
logger.error(f'[CBT-NATIVE] {volume_id}: {e}')
|
||||
return False
|
||||
|
||||
|
||||
def _run_sequential(tasks, run_id):
|
||||
"""沒有（或只有一張）GPU 時的回退：單流程串行"""
|
||||
"""沒有（或只有一張）GPU 時的回退：單流程串行。
|
||||
tasks 依 (volume, level, side) 分組排列：一個 volume 的 (level, side)
|
||||
全部跑完後，立刻寫它的 cbt.nii.gz + 投影（不等其他 volume）。"""
|
||||
device = get_device()
|
||||
current_vid = None
|
||||
for volume_id, level, side in tasks:
|
||||
if volume_id != current_vid:
|
||||
if current_vid is not None:
|
||||
_write_cbt_safe(current_vid, run_id)
|
||||
current_vid = volume_id
|
||||
set_task_tag(volume_id, level, 'LEFT' if side == 'L' else 'RIGHT')
|
||||
try:
|
||||
debug_pso(volume_id, level, device, side=side, run_id=run_id)
|
||||
except Exception as e:
|
||||
logger.error(f'Error in {volume_id} {level} {side}: {e}')
|
||||
if current_vid is not None:
|
||||
_write_cbt_safe(current_vid, run_id)
|
||||
|
||||
|
||||
USAGE = 'Usage: python xfr_debug.py [volume_id] [level]'
|
||||
|
||||
|
||||
def parse_args(argv):
|
||||
"""volume_id 可用完整 ID 或末段（如 0005）；level 為 LEVELS 之一（L1~L5）。
|
||||
"""volume_id 可用完整 ID 或末段（如 0005）；level 為 L\\d 形式（L1、L2、…，
|
||||
不限定 L1~L5；實際執行以各 volume 資料中有的 level 為準，見 available_levels）。
|
||||
兩者可省略（＝全部）；level 必須搭配 volume_id 使用。"""
|
||||
vid_arg = argv[0] if len(argv) >= 1 else None
|
||||
level_arg = argv[1] if len(argv) >= 2 else None
|
||||
if len(argv) > 2:
|
||||
sys.exit(f'{USAGE}\nToo many arguments')
|
||||
if level_arg and level_arg.upper() not in LEVELS:
|
||||
sys.exit(f'{USAGE}\nInvalid level: {level_arg} (choose from {"/".join(LEVELS)})')
|
||||
if level_arg and not re.fullmatch(r'L[1-9]\d*', level_arg.upper()):
|
||||
sys.exit(f'{USAGE}\nInvalid level: {level_arg} (expected L1, L2, ...)')
|
||||
if level_arg and not vid_arg:
|
||||
sys.exit(f'{USAGE}\nlevel requires volume_id')
|
||||
return vid_arg, (level_arg.upper() if level_arg else None)
|
||||
|
|
@ -342,7 +380,7 @@ def main():
|
|||
|
||||
# 要處理的 volume 數（並行模式下是「最多嘗試的 volume 數」）
|
||||
MAX_SUCCESSFUL_VOLUMES = 100
|
||||
MAX_SUCCESSFUL_VOLUMES = 10
|
||||
# MAX_SUCCESSFUL_VOLUMES = 10
|
||||
|
||||
# log 檔（console 與檔案同時輸出；各 GPU worker 也會 append 進同一個檔）
|
||||
os.makedirs(LOG_DIR, exist_ok=True)
|
||||
|
|
@ -367,12 +405,23 @@ def main():
|
|||
volumes = vols
|
||||
volumes = volumes[:MAX_SUCCESSFUL_VOLUMES]
|
||||
|
||||
levels = (level_arg,) if level_arg else LEVELS
|
||||
# 各 volume 的 lumbar level：未指定 level 時，取該 volume 實際有的全部 lumbar level
|
||||
# （見 available_levels）；指定 level 時只跑該 level（該 volume 缺檔會直接報錯）
|
||||
vol_levels = {}
|
||||
for vid in volumes:
|
||||
vol_levels[vid] = (level_arg,) if level_arg else available_levels(vid)
|
||||
no_levels = [vid for vid in volumes if not level_arg and not vol_levels[vid]]
|
||||
if no_levels:
|
||||
logger.warning(f'{len(no_levels)} volume(s) have no available lumbar level, '
|
||||
f'skipped: {", ".join(no_levels)}')
|
||||
# 任務粒度 = (volume, level, side)：同一 level 的 L/R 是兩個獨立任務，
|
||||
# 可被不同 GPU 的 worker 領走並行執行；順序 L1 L -> L1 R -> L2 L -> L2 R -> ...
|
||||
tasks = [(vid, level, s) for vid in volumes for level in levels for s in ('L', 'R')]
|
||||
tasks = [(vid, level, s) for vid in volumes for level in vol_levels[vid] for s in ('L', 'R')]
|
||||
expected = {vid: len(vol_levels[vid]) * 2 for vid in volumes}
|
||||
all_levels = sorted({level for lv in vol_levels.values() for level in lv},
|
||||
key=lambda lv: int(lv[1:]))
|
||||
logger.info(f'Total {len(volumes)} volumes / {len(tasks)} (volume, level, side) tasks '
|
||||
f'(levels: {", ".join(levels)})')
|
||||
f'(levels: {", ".join(all_levels) if all_levels else "(none)"})')
|
||||
if vid_arg or level_arg:
|
||||
logger.info(f'Filter: volume_id={vid_arg!r} level={level_arg!r}')
|
||||
|
||||
|
|
@ -393,6 +442,26 @@ def main():
|
|||
for _ in gpu_ids:
|
||||
task_queue.put(None) # 每個 worker 一個結束哨兵
|
||||
|
||||
# per-volume 收尾：一個 volume 排入的 (level, side) 任務全部回報後
|
||||
# （成功或失敗），立刻在背景執行緒寫它的 cbt.nii.gz + 投影，不等全部 case
|
||||
write_pool = ThreadPoolExecutor(max_workers=4, thread_name_prefix='cbt-write')
|
||||
write_futures = set()
|
||||
pending_writes = set(volumes)
|
||||
remaining = {vid: expected[vid] for vid in volumes}
|
||||
|
||||
def _fire_write(vid):
|
||||
pending_writes.discard(vid)
|
||||
write_futures.add(write_pool.submit(_write_cbt_safe, vid, run_id))
|
||||
|
||||
def _on_task_result(msg):
|
||||
results.append(msg)
|
||||
vid = msg[2]
|
||||
if vid in remaining:
|
||||
remaining[vid] -= 1
|
||||
if remaining[vid] <= 0 and vid in pending_writes:
|
||||
logger.info(f'{vid}: screw tasks complete, writing cbt + projections now')
|
||||
_fire_write(vid)
|
||||
|
||||
procs = [ctx.Process(target=gpu_worker, args=(g, log_path, run_id, task_queue, result_queue), name=f'cbt-gpu-{g}')
|
||||
for g in gpu_ids]
|
||||
for p in procs:
|
||||
|
|
@ -412,7 +481,7 @@ def main():
|
|||
if msg[0] == 'done':
|
||||
finished += 1
|
||||
else:
|
||||
results.append(msg)
|
||||
_on_task_result(msg)
|
||||
|
||||
# 抽乾剩下排進來的結果
|
||||
while True:
|
||||
|
|
@ -421,11 +490,22 @@ def main():
|
|||
except queue_module.Empty:
|
||||
break
|
||||
if msg[0] == 'task':
|
||||
results.append(msg)
|
||||
_on_task_result(msg)
|
||||
|
||||
for p in procs:
|
||||
p.join(timeout=60)
|
||||
|
||||
# 補漏：worker 提早退出、有任務未回報的 volume 仍照舊嘗試寫
|
||||
# （無 side 結果時 write_volume_cbt 會自行 skip）
|
||||
for vid in volumes:
|
||||
if vid in pending_writes:
|
||||
_fire_write(vid)
|
||||
|
||||
# 等待所有 per-volume cbt.nii.gz / 投影寫出完成
|
||||
write_ok = sum(1 for f in write_futures if f.result())
|
||||
write_fail = len(write_futures) - write_ok
|
||||
write_pool.shutdown(wait=True)
|
||||
|
||||
total_time = time.time() - start_time
|
||||
ok = [r for r in results if r[5]]
|
||||
fail = [r for r in results if not r[5]]
|
||||
|
|
@ -437,10 +517,11 @@ def main():
|
|||
missing = len(tasks) - len(results)
|
||||
|
||||
# 一個 volume 算「成功」必須它的所有 (level, side) 任務都執行過且全部成功
|
||||
# （expected 為該 volume 實際排入的任務數，各 volume 的 level 數可不同）
|
||||
n_success_volumes = sum(
|
||||
1 for vid in volumes
|
||||
if per_volume.get(vid, set()) == {True}
|
||||
and per_volume_n.get(vid, 0) == len(levels) * 2)
|
||||
and per_volume_n.get(vid, 0) == expected.get(vid, 0))
|
||||
|
||||
print('=' * 60)
|
||||
logger.info(f'Finished in {total_time / 60:.1f} min | '
|
||||
|
|
@ -453,13 +534,11 @@ def main():
|
|||
for _, g, vid, level, side, _, err in fail:
|
||||
logger.error(f'[GPU {g}] {vid} {level} {side}: {err}')
|
||||
|
||||
# 收尾：螺絲位置映回原 CT 空間 -> Output_dir/<run_date>/<volume_id>/cbt.nii.gz
|
||||
# （label 1-10 = L1L L1R L2L L2R ... L5L L5R；無 side 結果的 volume 跳過）
|
||||
for vid in volumes:
|
||||
try:
|
||||
xfr_cbt_native.write_volume_cbt(vid, run_id)
|
||||
except Exception as e:
|
||||
logger.error(f'[CBT-NATIVE] {vid}: {e}')
|
||||
# cbt.nii.gz + x-ap.jpg / x-lat.jpg 已於各 volume 的螺絲任務完成後立刻
|
||||
# 寫出（_fire_write， Output_dir/<run_date>/<volume_id>/；label 1-10 =
|
||||
# L1L L1R L2L L2R ... L5L L5R），不再等全部 case 跑完才統一收尾
|
||||
logger.info(f'CBT writes: {write_ok}/{len(volumes)} volume(s) ok'
|
||||
+ (f', {write_fail} failed (見 [CBT-NATIVE] log)' if write_fail else ''))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
|
|
|||
242
xfr_rerender_spinous.py
Normal file
242
xfr_rerender_spinous.py
Normal file
|
|
@ -0,0 +1,242 @@
|
|||
#!/home/xfr/.conda/envs/cbt/bin/python
|
||||
"""
|
||||
從 optimizer 存的 output.csv 重新渲染既有螺絲模式四視角圖（X-ray 圖）。
|
||||
|
||||
用途：render_bone_figure 改了純顯示層（例如棘突改半透明 + 依深度正確遮擋
|
||||
螺絲路徑）之後，不需要重跑優化，直接從 CSV 的 best_position 重新出圖。
|
||||
原圖先備份到 Output/{date}/backup_opaque_spinous/{vol}/，再重渲染同檔名覆蓋。
|
||||
|
||||
mask 取法與 xfr_debug / xfr_plot_level 相同（level_file_path：
|
||||
binary_sdf 優先、缺則 binary；cortical 同），spacing 讀自 mask 檔。
|
||||
|
||||
Usage:
|
||||
python xfr_rerender_spinous.py # 預設 20260912，多 GPU 並行
|
||||
python xfr_rerender_spinous.py 20260911 # 指定日期
|
||||
python xfr_rerender_spinous.py 20260912 0001 # 只該 volume（全名或末段）
|
||||
python xfr_rerender_spinous.py --dry-run 20260912 # 只列出會重新渲染的圖
|
||||
python xfr_rerender_spinous.py --cpus 20260912 # 純 CPU 循序
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import csv
|
||||
import multiprocessing
|
||||
import os
|
||||
import re
|
||||
import shutil
|
||||
import sys
|
||||
|
||||
import numpy as np
|
||||
import SimpleITK as sitk
|
||||
|
||||
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
|
||||
|
||||
from imaging.transforms import level_file_path
|
||||
from visualization.res_bone_figure import render_bone_figure
|
||||
|
||||
OUTPUT_BASE = '/mnt/1248/open2/cyrou/Output'
|
||||
MASK_DIR = '/mnt/1248/open2/cyrou/CBT/Seg/Resample/standardized-xfr'
|
||||
BACKUP = 'backup_opaque_spinous'
|
||||
|
||||
# {level}_{way}_L{d}_{l}_R{d}_{l}_{swarm}_{iter}.png（單側跑法該側可為空）
|
||||
PNG_RE = re.compile(
|
||||
r'^(?P<level>[A-Z]\d+?)_(?P<way>CBT|TPS)'
|
||||
r'_L(?P<dl>\d+(?:\.\d+)?|)_(?P<ll>\d+(?:\.\d+)?|)'
|
||||
r'_R(?P<dr>\d+(?:\.\d+)?|)_(?P<lr>\d+(?:\.\d+)?|)'
|
||||
r'_(?P<sw>\d+|)_(?P<it>\d+|)\.png$')
|
||||
|
||||
|
||||
def _f(s):
|
||||
s = (s or '').strip()
|
||||
return float(s) if s else None
|
||||
|
||||
|
||||
def _parse_pos(s):
|
||||
"""CSV '(x, y, z)' -> (z, y, x)（best_position 慣例）。"""
|
||||
v = re.findall(r'[-+]?\d*\.?\d+(?:[eE][-+]?\d+)?', s or '')
|
||||
if len(v) < 3:
|
||||
return None
|
||||
x, y, z = (float(t) for t in v[:3])
|
||||
return z, y, x
|
||||
|
||||
|
||||
def _pos(row):
|
||||
if row is None:
|
||||
return None
|
||||
p = _parse_pos(row.get('Position_XYZ'))
|
||||
if p is None:
|
||||
return None
|
||||
az, alt = _f(row.get('Raw_Azimuth')), _f(row.get('Raw_Altitude'))
|
||||
if az is None or alt is None:
|
||||
return None
|
||||
return (p[0], p[1], p[2], az, alt)
|
||||
|
||||
|
||||
def read_csv_rows(vol_out_dir):
|
||||
path = os.path.join(vol_out_dir, 'output.csv')
|
||||
if not os.path.isfile(path):
|
||||
return None
|
||||
with open(path, newline='') as f:
|
||||
return [r for r in csv.DictReader(f) if any(c.strip() for c in r.values())]
|
||||
|
||||
|
||||
def find_row(rows, side, d, l):
|
||||
if d is None or l is None:
|
||||
return None
|
||||
for r in rows:
|
||||
if r.get('Side') != side:
|
||||
continue
|
||||
rd, rl = _f(r.get('Diameter')), _f(r.get('Length'))
|
||||
if rd is not None and rl is not None \
|
||||
and abs(rd - d) < 1e-6 and abs(rl - l) < 1e-6:
|
||||
return r
|
||||
return None
|
||||
|
||||
|
||||
def collect_figs(date_dir, vol_filter=None):
|
||||
"""回傳 (tasks, skipped)：tasks 為可重新渲染的圖（dict），skipped 為 (原因, 路徑)。"""
|
||||
vols = sorted(d for d in os.listdir(date_dir)
|
||||
if os.path.isdir(os.path.join(date_dir, d)) and d != BACKUP)
|
||||
if vol_filter:
|
||||
vols = [v for v in vols
|
||||
if v == vol_filter or v.rsplit('.', 1)[-1] == vol_filter]
|
||||
if not vols:
|
||||
raise SystemExit(f'volume not found: {vol_filter}')
|
||||
tasks, skipped = [], []
|
||||
for vol in vols:
|
||||
vdir = os.path.join(date_dir, vol)
|
||||
rows = read_csv_rows(vdir) or []
|
||||
for name in sorted(os.listdir(vdir)):
|
||||
m = PNG_RE.match(name)
|
||||
if not m:
|
||||
continue
|
||||
png = os.path.join(vdir, name)
|
||||
level, way = m['level'], m['way']
|
||||
d_l, l_l = _f(m['dl']), _f(m['ll'])
|
||||
d_r, l_r = _f(m['dr']), _f(m['lr'])
|
||||
row_l = find_row(rows, 'L', d_l, l_l)
|
||||
row_r = find_row(rows, 'R', d_r, l_r)
|
||||
if (d_l is not None and row_l is None) or (d_r is not None and row_r is None) \
|
||||
or (d_l is None and d_r is None):
|
||||
skipped.append((vol, name, 'CSV 無對應 L/R 行'))
|
||||
continue
|
||||
mask_dir = os.path.join(MASK_DIR, vol)
|
||||
sdf = level_file_path(mask_dir, level, 'binary_sdf')
|
||||
binary_path = sdf if os.path.exists(sdf) \
|
||||
else level_file_path(mask_dir, level, 'binary')
|
||||
if not os.path.exists(binary_path):
|
||||
skipped.append((vol, name, f'無 bone mask: {binary_path}'))
|
||||
continue
|
||||
cortical_path = level_file_path(mask_dir, level, 'cortical')
|
||||
row_any = row_l or row_r
|
||||
tt = _f(row_any.get('Total_Time'))
|
||||
tt = None if (tt is None or not np.isfinite(tt)) else tt
|
||||
tasks.append({
|
||||
'vol': vol, 'name': name, 'png': png, 'level': level, 'way': way,
|
||||
'd_l': d_l, 'l_l': l_l, 'd_r': d_r, 'l_r': l_r,
|
||||
'pos_l': _pos(row_l), 'pos_r': _pos(row_r),
|
||||
'binary': binary_path,
|
||||
'cortical': cortical_path if os.path.exists(cortical_path) else None,
|
||||
'swarm': int(_f(m['sw']) or 0), 'iter': int(_f(m['it']) or 0),
|
||||
'time': tt,
|
||||
})
|
||||
return tasks, skipped
|
||||
|
||||
|
||||
def _run_batch(batch):
|
||||
"""一個 worker 固定綁一個 GPU，依序渲染分到的圖（CUDA_VISIBLE_DEVICES
|
||||
須在 torch 首次 init CUDA 前設好，故綁定後不再變）。"""
|
||||
gpu, g_tasks = batch
|
||||
os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu)
|
||||
os.environ.setdefault('OMP_NUM_THREADS', '4')
|
||||
import matplotlib
|
||||
matplotlib.use('Agg')
|
||||
return [render_one(t) for t in g_tasks]
|
||||
|
||||
|
||||
def render_one(task):
|
||||
import torch
|
||||
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
|
||||
img = sitk.ReadImage(task['binary'], sitk.sitkUInt8)
|
||||
spacing = list(img.GetSpacing())
|
||||
|
||||
# 備份原圖（同目錄同檔名會重複跑時，備份檔保留第一份）
|
||||
backup_dir = os.path.join(os.path.dirname(os.path.dirname(task['png'])), BACKUP, task['vol'])
|
||||
bpath = os.path.join(backup_dir, task['name'])
|
||||
os.makedirs(backup_dir, exist_ok=True)
|
||||
if not os.path.exists(bpath):
|
||||
shutil.move(task['png'], bpath)
|
||||
|
||||
try:
|
||||
out = render_bone_figure(
|
||||
task['vol'], task['level'], task['binary'], task['cortical'],
|
||||
base_folder=OUTPUT_BASE, spacing=spacing, way=task['way'],
|
||||
best_position_l=task['pos_l'], best_position_r=task['pos_r'],
|
||||
diameter_l=task['d_l'], length_l=task['l_l'],
|
||||
diameter_r=task['d_r'], length_r=task['l_r'],
|
||||
image2_path=None, device=device,
|
||||
swarm_size=task['swarm'], max_iter=task['iter'], total_time=task['time'],
|
||||
write_csv=False, output_path=task['png'])
|
||||
except Exception as e:
|
||||
if not os.path.exists(task['png']) and os.path.exists(bpath):
|
||||
shutil.move(bpath, task['png'])
|
||||
return (task['vol'], task['name'], 'fail', f'{type(e).__name__}: {e}')
|
||||
if out is None:
|
||||
if os.path.exists(bpath):
|
||||
shutil.move(bpath, task['png'])
|
||||
return (task['vol'], task['name'], 'fail', 'render 回傳 None（無/空遮罩）')
|
||||
if out != task['png']:
|
||||
os.replace(out, task['png'])
|
||||
return (task['vol'], task['name'], 'ok', f'{out} ({device})')
|
||||
|
||||
|
||||
def main():
|
||||
ap = argparse.ArgumentParser(description='從 output.csv 重新渲染螺絲模式四視角圖')
|
||||
ap.add_argument('date', nargs='?', default='20260912')
|
||||
ap.add_argument('volume', nargs='?', default=None)
|
||||
ap.add_argument('--dry-run', action='store_true')
|
||||
ap.add_argument('--cpus', action='store_true', help='CPU 循序（不佔 GPU）')
|
||||
args = ap.parse_args()
|
||||
|
||||
date_dir = os.path.join(OUTPUT_BASE, args.date)
|
||||
if not os.path.isdir(date_dir):
|
||||
raise SystemExit(f'no such date dir: {date_dir}')
|
||||
tasks, skipped = collect_figs(date_dir, args.volume)
|
||||
print(f'{args.date}: {len(tasks)} figure(s) to re-render, {len(skipped)} skip(s)')
|
||||
for vol, name, why in skipped:
|
||||
print(f' [skip] {vol}/{name}: {why}')
|
||||
if args.dry_run:
|
||||
for t in tasks:
|
||||
print(f" [dry] {t['vol']}/{t['name']} "
|
||||
f"L=({t['d_l']},{t['l_l']}) R=({t['d_r']},{t['l_r']})")
|
||||
return
|
||||
|
||||
if not tasks:
|
||||
return
|
||||
|
||||
if args.cpus or not _cuda_count():
|
||||
os.environ['CUDA_VISIBLE_DEVICES'] = ''
|
||||
for i, t in enumerate(tasks, 1):
|
||||
r = render_one(t)
|
||||
print(f'[{i:3d}/{len(tasks)}] {r[2]:4s} {r[0]}/{r[1]} {r[3]}', flush=True)
|
||||
else:
|
||||
n = min(_cuda_count(), 4)
|
||||
ctx = multiprocessing.get_context('spawn')
|
||||
batches = [(i, tasks[i::n]) for i in range(n)]
|
||||
with ctx.Pool(n) as pool:
|
||||
for results in pool.map(_run_batch, batches):
|
||||
for vol, name, status, detail in results:
|
||||
print(f'[{status:4s}] {vol}/{name} {detail}', flush=True)
|
||||
print('=' * 60)
|
||||
print('Done.')
|
||||
|
||||
|
||||
def _cuda_count():
|
||||
try:
|
||||
import torch
|
||||
return torch.cuda.device_count()
|
||||
except Exception:
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
Loading…
Reference in a new issue