Xiao Furen
0274e954be
Refactor the cylinder parameter selection and optimization process to improve accuracy and robustness. - Implement `snap_to_discrete_values_xfr` using a KDTree for efficient mapping of continuous diameter and length values to a predefined set of discrete points. - Update `objective_function_xfr` to use the new snapping mechanism and introduce a weighted loss component for diameter and length. - Adjust PSO optimization bounds and search ranges in `run_pso_torch_xfr` to better align with image dimensions and anatomical constraints. - Refine scoring logic in `cl_score_torch_xfr` with updated penalty weights for overlaps and out-of-bone voxels. - Update `config/constant.py` with new allowed diameter and length ranges. - Improve `imaging/preprocessing.py` by making `PROGRESS_FILE` a parameter to allow per-output-directory progress tracking. - Update `xfr_debug.py` with improved error handling and directory paths for batch processing.
326 lines
No EOL
9.6 KiB
Python
326 lines
No EOL
9.6 KiB
Python
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from scipy.spatial import KDTree
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import torch
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import numpy as np
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from config.constant import ALLOWED_DIAMETERS, ALLOWED_LENGTHS
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def create_coordinate_grid(
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shape: tuple[int, int, int],
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device: torch.device,
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dtype: torch.dtype = torch.float32
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) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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"""
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建立固定的 3D voxel coordinate grid
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returns:
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z_t, y_t, x_t with shape = (Z, Y, X)
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"""
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z_t = torch.arange(shape[0], device=device, dtype=dtype)
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y_t = torch.arange(shape[1], device=device, dtype=dtype)
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x_t = torch.arange(shape[2], device=device, dtype=dtype)
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z_t, y_t, x_t = torch.meshgrid(z_t, y_t, x_t, indexing='ij')
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return z_t, y_t, x_t
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def snap_to_discrete_values(diameter_raw, length_raw):
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"""
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將連續值映射到最接近的允許離散值
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Parameters:
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diameter_raw: PSO 給的連續直徑值
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length_raw: PSO 給的連續長度值
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Returns:
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diameter_discrete, length_discrete: 離散化後的值
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"""
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# 找最接近的 diameter
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diameter_discrete = min(ALLOWED_DIAMETERS, key=lambda x: abs(x - diameter_raw))
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# 找最接近的 length
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length_discrete = min(ALLOWED_LENGTHS, key=lambda x: abs(x - length_raw))
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return diameter_discrete, length_discrete
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points = (
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(4.5, 25),
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(4.5, 30),
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(4.5, 35),
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(5.0, 30),
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(5.0, 35),
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(5.0, 40),
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(5.5, 30),
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(5.5, 35),
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(5.5, 40),
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(5.5, 45),
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(5.5, 50),
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(6.0, 35),
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(6.0, 40),
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(6.0, 45),
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(6.0, 50),
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(6.0, 55),
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(6.5, 35),
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(6.5, 40),
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(6.5, 45),
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(6.5, 50),
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(6.5, 55),
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(7.0, 35),
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(7.0, 40),
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(7.0, 45),
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(7.0, 50),
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(7.5, 60),
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(7.5, 70),
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(7.5, 80),
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)
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tree = KDTree(points)
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def snap_to_discrete_values_xfr(diameter_raw, length_raw):
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return snap_to_discrete_values(diameter_raw, length_raw)
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distance, index = tree.query((diameter_raw, length_raw))
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return points[index][0], points[index][1]
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def round_down_to_discrete_values_xfr(diameter_raw, length_raw):
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"""
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將連續值映射到小於等於該值的最接近允許離散值 (向下取整)
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Parameters:
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diameter_raw: PSO 給的連續直徑值
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length_raw: PSO 給的連續長度值
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Returns:
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diameter_discrete, length_discrete: 離散化後的值
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"""
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# 找小於等於 raw 的最接近 diameter，如果都大於 raw 則取最小值
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valid_diameters = [d for d in ALLOWED_DIAMETERS if d <= diameter_raw]
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diameter_discrete = max(valid_diameters) if valid_diameters else min(ALLOWED_DIAMETERS)
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# 找小於等於 raw 的最接近 length，如果都大於 raw 則取最小值
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valid_lengths = [l for l in ALLOWED_LENGTHS if l <= length_raw]
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length_discrete = max(valid_lengths) if valid_lengths else min(ALLOWED_LENGTHS)
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return diameter_discrete, length_discrete
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def generate_cylinder_n_torch(
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diameter: float,
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length: float,
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position_z: float,
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position_y: float,
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position_x: float,
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azimuth: float,
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altitude: float,
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shape: tuple[int, int, int],
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spacing: list[float],
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device: torch.device,
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grid: tuple[torch.Tensor, torch.Tensor, torch.Tensor] | None = None
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) -> torch.Tensor:
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"""
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Generate a "forward" (positive z) cylinder mask in 3D space using PyTorch tensors.
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Returns a binary mask (torch.uint8) on the specified device.
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"""
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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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# Shift
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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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# Apply rotation (same formula as your NumPy version, but in torch)
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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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# Handle spacing
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# You can expand or generalize for more spacing options
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if spacing == [1, 1, 1]:
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radius = diameter / 2.0
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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 <= length)
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)
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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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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 <= length * 2)
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)
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else:
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raise ValueError(f"Unsupported spacing: {spacing}")
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# Convert boolean mask to uint8
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cylinder_mask = mask.to(torch.uint8)
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return cylinder_mask
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def generate_cylinder_o_torch(
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diameter: float,
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length: float,
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position_z: float,
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position_y: float,
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position_x: float,
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azimuth: float,
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altitude: float,
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shape: tuple[int, int, int],
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spacing: list[float],
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device: torch.device,
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grid: tuple[torch.Tensor, torch.Tensor, torch.Tensor] | None = None
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) -> torch.Tensor:
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"""
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Generate an "opposite" (negative z) cylinder mask in 3D space using PyTorch tensors.
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Returns a binary mask (torch.uint8) on the specified device.
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"""
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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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# Convert angles to torch
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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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# Shift
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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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# Apply rotation
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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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# Handle spacing
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if spacing == [1, 1, 1]:
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radius = diameter / 2.0
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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 <= length)
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)
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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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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 >= -length * 2)
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)
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else:
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raise ValueError(f"Unsupported spacing: {spacing}")
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cylinder_mask_o = mask.to(torch.uint8)
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return cylinder_mask_o
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def generate_cylinder_numpy(diameter, length, position_z, position_y, position_x, azimuth, altitude, shape, spacing):
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azimuth = np.radians(azimuth)
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altitude = np.radians(altitude)
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cylinder_mask = np.zeros(shape, dtype=np.uint8)
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z, y, x = np.mgrid[0:shape[0], 0:shape[1], 0:shape[2]].astype(np.float64)
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z -= float(position_z)
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y -= float(position_y)
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x -= float(position_x)
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x_rot = x * np.cos(azimuth) * np.cos(altitude) + y * np.sin(azimuth) * np.cos(altitude) - z * np.sin(altitude)
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y_rot = -x * np.sin(azimuth) + y * np.cos(azimuth)
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z_rot = x * np.cos(azimuth) * np.sin(altitude) + y * np.sin(azimuth) * np.sin(altitude) + z * np.cos(altitude)
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if spacing == [1, 1, 1]:
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radius = diameter / 2.0
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cylinder = (x_rot**2 + y_rot**2 <= radius**2) & (z_rot >= 0) & (z_rot <= length)
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elif spacing == [0.5, 0.5, 0.5]:
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radius = diameter / 2.0 * 2 # *2 is for resampling
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cylinder = (x_rot**2 + y_rot**2 <= radius**2) & (z_rot >= 0) & (z_rot <= length * 2)
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cylinder_mask[cylinder] = 1
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return cylinder_mask
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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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azimuth, altitude,
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shape, spacing, device, grid=None,
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tip_ratio=0.2 # 取末端 20% 當尖端
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) -> torch.Tensor:
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"""只生成圓柱末端的 mask"""
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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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if spacing == [0.5, 0.5, 0.5]:
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radius = (diameter / 2.0) * 2
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total_length = length * 2
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else:
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radius = diameter / 2.0
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total_length = length
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tip_start = total_length * (1 - tip_ratio) # 末端 20% 開始的位置
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mask = (
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(x_rot**2 + y_rot**2 <= radius**2)
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& (z_rot >= tip_start) # 只取末端
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& (z_rot <= total_length)
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)
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return mask.to(torch.uint8) |