lipan
29f1d45d66
算法更新: delivery v1.2.1、平整度v1.5、测距v2.2、整车配准v1.3 适配器: vehicle-registration 新增、各算法 manifest 和 adapter 更新 后端: taskCadFiles/source 字段迁移、derivedPointClouds 注册、取消任务联动 前端: ElapsedTime 时长显示、两步算法流程、canRun 逻辑修复 文档: 新增5份算法性能分析报告
1270 lines
52 KiB
Python
1270 lines
52 KiB
Python
from __future__ import annotations
|
|
|
|
import importlib.util
|
|
import posixpath
|
|
import time
|
|
from pathlib import Path
|
|
from typing import Any
|
|
from zipfile import ZIP_DEFLATED, ZipFile
|
|
from xml.sax.saxutils import escape
|
|
from xml.etree import ElementTree as ET
|
|
|
|
import numpy as np
|
|
|
|
from cad_converter import CADConverter
|
|
from config_loader import ConfigLoader
|
|
from evaluation import Evaluator
|
|
from io_handler import IOHandler
|
|
from preprocessing.curvature import CurvatureEstimator
|
|
from preprocessing.filters import PreprocessingRunner
|
|
from preprocessing.noise import GaussianNoiseAdder
|
|
from preprocessing.plane import PlaneEstimator
|
|
from registration import PointCloudRegistration
|
|
from visualization import Visualizer
|
|
|
|
|
|
DEFAULT_RESULT_TEMPLATE = Path("excel/测量结果.xlsx")
|
|
DEFAULT_COMBINED_CONTEXT_POINTCLOUD = Path("datasets/train/train_2_10M.ply")
|
|
|
|
|
|
class MeasurementPipeline:
|
|
"""Main workflow entry class for geometry measurement tasks."""
|
|
|
|
def __init__(self, config_path: str | None = None) -> None:
|
|
self.config = ConfigLoader().load(config_path)
|
|
self.io = IOHandler()
|
|
self.cad_converter = CADConverter()
|
|
self.noise_adder = GaussianNoiseAdder()
|
|
self.filters = PreprocessingRunner()
|
|
self.registration = PointCloudRegistration()
|
|
self.evaluator = Evaluator()
|
|
self.curvature = CurvatureEstimator()
|
|
self.plane = PlaneEstimator()
|
|
self.visualizer = Visualizer()
|
|
|
|
def run_cad2mesh(self, cad_path: str | None = None, mesh_path: str | None = None, precision: float | None = None) -> Path:
|
|
paths = self.config["paths"]
|
|
cad_config = self.config["cad"]
|
|
input_step = self.io.require_cad(cad_path or paths["cad"])
|
|
output_stl = self.io.ensure_parent_dir(mesh_path or paths["output_mesh"] or paths["mesh"])
|
|
return self.cad_converter.convert_step_to_stl(
|
|
input_step=input_step,
|
|
output_stl=output_stl,
|
|
precision=float(precision if precision is not None else cad_config["precision"]),
|
|
)
|
|
|
|
def run_denoise(
|
|
self,
|
|
pointcloud_path: str | None = None,
|
|
noise_std: float | None = None,
|
|
no_visualize: bool = False,
|
|
) -> dict[str, Any]:
|
|
path = pointcloud_path or self.config["paths"]["pointcloud"]
|
|
clean = self.io.load_point_cloud(path)
|
|
noise_config = self.config["noise"]
|
|
adder = GaussianNoiseAdder(
|
|
noise_std=float(noise_std if noise_std is not None else noise_config["std"]),
|
|
seed=noise_config.get("seed"),
|
|
)
|
|
noisy = adder.apply(clean)
|
|
noise_level = adder.noise_level(clean, noisy)
|
|
|
|
filter_results = {}
|
|
if self.config["filters"].get("enabled", True):
|
|
filter_results = self.filters.run(noisy, self.config["filters"])
|
|
|
|
print(f"Clean points: {len(clean.points)}")
|
|
print(f"Noisy points: {len(noisy.points)}")
|
|
print(f"Mean noise offset: {noise_level:.6f}")
|
|
for name, result in filter_results.items():
|
|
cd, _, _ = self.evaluator.chamfer_distance(clean, result.pointcloud)
|
|
result.metrics["chamfer_distance"] = cd
|
|
print(f"{name}: keep={result.metrics['keep_ratio']:.2f}% CD={cd:.6f}")
|
|
|
|
cd_noisy, _, _ = self.evaluator.chamfer_distance(clean, noisy)
|
|
print(f"Noisy without filtering: CD={cd_noisy:.6f}")
|
|
|
|
if self._should_visualize(no_visualize):
|
|
first_result = next(iter(filter_results.values()), None)
|
|
self.visualizer.show_denoise(
|
|
clean,
|
|
noisy,
|
|
first_result.pointcloud if first_result else None,
|
|
first_result.name if first_result else "Filtered",
|
|
)
|
|
|
|
return {
|
|
"clean": clean,
|
|
"noisy": noisy,
|
|
"noise_level": noise_level,
|
|
"filter_results": filter_results,
|
|
"noisy_chamfer_distance": cd_noisy,
|
|
}
|
|
|
|
def run_registration(
|
|
self,
|
|
pointcloud_path: str | None = None,
|
|
mesh_path: str | None = None,
|
|
voxel_size: float | None = None,
|
|
roll: float | None = None,
|
|
pitch: float | None = None,
|
|
yaw: float | None = None,
|
|
trans_range: float | None = None,
|
|
no_visualize: bool = False,
|
|
) -> dict[str, Any]:
|
|
paths = self.config["paths"]
|
|
reg_config = self.config["registration"]
|
|
source = self.io.load_point_cloud(pointcloud_path or paths["noisy_pointcloud"] or paths["pointcloud"])
|
|
mesh_file = str(self.io.require_file(mesh_path or paths["mesh"], "Mesh"))
|
|
return self._run_registration_with_pointcloud(
|
|
source=source,
|
|
mesh_file=mesh_file,
|
|
voxel_size=voxel_size,
|
|
roll=roll,
|
|
pitch=pitch,
|
|
yaw=yaw,
|
|
trans_range=trans_range,
|
|
no_visualize=no_visualize,
|
|
reg_config=reg_config,
|
|
)
|
|
|
|
def _run_registration_with_pointcloud(
|
|
self,
|
|
source: Any,
|
|
mesh_file: str,
|
|
voxel_size: float | None,
|
|
roll: float | None,
|
|
pitch: float | None,
|
|
yaw: float | None,
|
|
trans_range: float | None,
|
|
no_visualize: bool,
|
|
reg_config: dict[str, Any],
|
|
) -> dict[str, Any]:
|
|
|
|
result = self.registration.run_simulated_registration(
|
|
source=source,
|
|
voxel_size=float(voxel_size if voxel_size is not None else reg_config["voxel_size"]),
|
|
roll=float(roll if roll is not None else reg_config["roll"]),
|
|
pitch=float(pitch if pitch is not None else reg_config["pitch"]),
|
|
yaw=float(yaw if yaw is not None else reg_config["yaw"]),
|
|
trans_range=float(trans_range if trans_range is not None else reg_config["trans_range"]),
|
|
random_seed=reg_config.get("random_seed"),
|
|
)
|
|
|
|
points = self.io.point_cloud_to_numpy(result.recovered)
|
|
mesh = self.io.load_mesh(mesh_file)
|
|
_, distance_stats = self.evaluator.point_to_mesh_distance_torch(
|
|
points,
|
|
mesh,
|
|
chunk_size=int(self.config["evaluation"]["chunk_size"]),
|
|
triangle_chunk_size=int(self.config["evaluation"]["triangle_chunk_size"]),
|
|
)
|
|
|
|
print("Registration result")
|
|
print(f"Fitness: {result.fitness:.4f}")
|
|
print(f"RMSE: {result.rmse:.6f}")
|
|
print(f"Transformation:\n{result.transformation}")
|
|
self._print_distance_stats(distance_stats)
|
|
|
|
if self._should_visualize(no_visualize):
|
|
self.visualizer.show_registration(source, result.target, result.recovered)
|
|
|
|
return {
|
|
"source": source,
|
|
"registration": result,
|
|
"distance_stats": distance_stats,
|
|
}
|
|
|
|
def run_contour(
|
|
self,
|
|
pointcloud_path: str | None = None,
|
|
cad_path: str | None = None,
|
|
mesh_path: str | None = None,
|
|
mesh_deflection: float | None = None,
|
|
point_chunk_size: int | None = None,
|
|
triangle_chunk_size: int | None = None,
|
|
save_path: str | None = None,
|
|
excel_output_path: str | None = None,
|
|
result_template_path: str | Path | None = DEFAULT_RESULT_TEMPLATE,
|
|
combined_context_pointcloud_path: str | Path | None = DEFAULT_COMBINED_CONTEXT_POINTCLOUD,
|
|
no_visualize: bool = False,
|
|
) -> dict[str, Any]:
|
|
paths = self.config["paths"]
|
|
eval_config = self.config["evaluation"]
|
|
|
|
if mesh_path:
|
|
pointcloud = self.io.load_point_cloud(pointcloud_path or paths["pointcloud"])
|
|
mesh = self.io.load_mesh(mesh_path)
|
|
points = self.io.point_cloud_to_numpy(pointcloud)
|
|
distances, stats = self.evaluator.point_to_mesh_distance_torch(
|
|
points,
|
|
mesh,
|
|
chunk_size=int(point_chunk_size if point_chunk_size is not None else eval_config["chunk_size"]),
|
|
triangle_chunk_size=int(
|
|
triangle_chunk_size if triangle_chunk_size is not None else eval_config["triangle_chunk_size"]
|
|
),
|
|
)
|
|
self._print_distance_stats(stats)
|
|
heatmap = self.evaluator.colorize_distances(points, distances)
|
|
if save_path:
|
|
self.io.save_point_cloud(heatmap, save_path)
|
|
print(f"Saved contour heatmap: {save_path}")
|
|
excel_output = None
|
|
if excel_output_path:
|
|
excel_output = self._save_contour_excel(
|
|
excel_output_path,
|
|
rows=[{
|
|
"task_id": self._contour_task_id_from_sample(Path(mesh_path).stem),
|
|
"mean": stats.mean,
|
|
"status": "成功",
|
|
}],
|
|
result_template_path=result_template_path,
|
|
)
|
|
print(f"Saved contour Excel: {excel_output}")
|
|
if self._should_visualize(no_visualize):
|
|
self.visualizer.show_curvature(heatmap, window_name="Point to Mesh Distance Heatmap")
|
|
return {
|
|
"pointcloud": heatmap,
|
|
"mesh": mesh,
|
|
"distances": distances,
|
|
"distance_stats": stats,
|
|
"excel_output": excel_output,
|
|
}
|
|
|
|
return self.run_contour_distance(
|
|
cad_path=cad_path or paths["cad"],
|
|
pointcloud_path=pointcloud_path or paths["pointcloud"],
|
|
mesh_deflection=mesh_deflection,
|
|
point_chunk_size=point_chunk_size,
|
|
triangle_chunk_size=triangle_chunk_size,
|
|
save_path=save_path,
|
|
excel_output_path=excel_output_path,
|
|
result_template_path=result_template_path,
|
|
no_visualize=no_visualize,
|
|
)
|
|
|
|
def run_contour_distance(
|
|
self,
|
|
cad_path: str | None = None,
|
|
pointcloud_path: str | None = None,
|
|
mesh_deflection: float | None = None,
|
|
point_chunk_size: int | None = None,
|
|
triangle_chunk_size: int | None = None,
|
|
save_path: str | None = None,
|
|
excel_output_path: str | None = None,
|
|
result_template_path: str | Path | None = DEFAULT_RESULT_TEMPLATE,
|
|
combined_context_pointcloud_path: str | Path | None = DEFAULT_COMBINED_CONTEXT_POINTCLOUD,
|
|
no_visualize: bool = False,
|
|
) -> dict[str, Any]:
|
|
paths = self.config["paths"]
|
|
eval_config = self.config["evaluation"]
|
|
input_step = self.io.require_cad(cad_path or paths["cad"])
|
|
pointcloud = self.io.load_point_cloud(pointcloud_path or paths["noisy_pointcloud"] or paths["pointcloud"])
|
|
points = self.io.point_cloud_to_numpy(pointcloud)
|
|
|
|
vertices, faces = self.cad_converter.step_to_numpy_mesh(
|
|
input_step,
|
|
mesh_deflection=float(
|
|
mesh_deflection if mesh_deflection is not None else eval_config["cad_mesh_deflection"]
|
|
),
|
|
)
|
|
print(f"CAD mesh vertices: {vertices.shape[0]}")
|
|
print(f"CAD mesh triangles: {faces.shape[0]}")
|
|
distances, stats = self.evaluator.point_to_triangles_distance_torch(
|
|
points,
|
|
vertices,
|
|
faces,
|
|
point_chunk_size=int(point_chunk_size if point_chunk_size is not None else eval_config["chunk_size"]),
|
|
triangle_chunk_size=int(
|
|
triangle_chunk_size if triangle_chunk_size is not None else eval_config["triangle_chunk_size"]
|
|
),
|
|
)
|
|
self._print_distance_stats(stats)
|
|
|
|
heatmap = self.evaluator.colorize_distances(points, distances)
|
|
output = save_path
|
|
if output:
|
|
self.io.save_point_cloud(heatmap, output)
|
|
print(f"Saved contour heatmap: {output}")
|
|
|
|
excel_output = None
|
|
if excel_output_path:
|
|
excel_output = self._save_contour_excel(
|
|
excel_output_path,
|
|
cad_path=input_step,
|
|
stats=stats,
|
|
result_template_path=result_template_path,
|
|
)
|
|
print(f"Saved contour Excel: {excel_output}")
|
|
|
|
if self._should_visualize(no_visualize):
|
|
self.visualizer.show_curvature(heatmap, window_name="Point to CAD Distance Heatmap")
|
|
|
|
return {
|
|
"pointcloud": heatmap,
|
|
"distances": distances,
|
|
"distance_stats": stats,
|
|
"cad_vertices": vertices,
|
|
"cad_faces": faces,
|
|
"excel_output": excel_output,
|
|
}
|
|
|
|
def run_contour_batch(
|
|
self,
|
|
task_ids: str | list[int | str] | None = None,
|
|
all_tasks: bool = False,
|
|
data_root: str | Path = "datasets",
|
|
pointcloud_kind: str = "noise",
|
|
mesh_deflection: float | None = None,
|
|
point_chunk_size: int | None = None,
|
|
triangle_chunk_size: int | None = None,
|
|
save_path: str | None = None,
|
|
excel_output_path: str | None = None,
|
|
result_template_path: str | Path | None = DEFAULT_RESULT_TEMPLATE,
|
|
combined_context_pointcloud_path: str | Path | None = DEFAULT_COMBINED_CONTEXT_POINTCLOUD,
|
|
no_visualize: bool = False,
|
|
) -> dict[str, Any]:
|
|
batch_start_time = time.perf_counter()
|
|
data_root = Path(data_root)
|
|
task_inputs = self._resolve_contour_task_inputs(task_ids, all_tasks, data_root, pointcloud_kind)
|
|
task_summaries: dict[str, dict[str, Any]] = {}
|
|
results: dict[str, Any] = {}
|
|
combined_visualizations: list[tuple[str, Any]] = []
|
|
|
|
for task_input in task_inputs:
|
|
task_start_time = time.perf_counter()
|
|
task_id = task_input["task_id"]
|
|
sample_id = task_input["sample_id"]
|
|
cad_path = task_input.get("cad_path")
|
|
mesh_path = task_input.get("mesh_path")
|
|
pointcloud_path = task_input["pointcloud_path"]
|
|
task_save_path = self._contour_task_save_path(save_path, sample_id)
|
|
|
|
summary = task_summaries.setdefault(
|
|
task_id,
|
|
{
|
|
"task_id": task_id,
|
|
"sample_ids": [],
|
|
"point_count": 0,
|
|
"distance_sum": 0.0,
|
|
"max": None,
|
|
"elapsed_sec": 0.0,
|
|
"failures": [],
|
|
"sample_results": {},
|
|
},
|
|
)
|
|
summary["sample_ids"].append(sample_id)
|
|
|
|
print(f"===== contour task {task_id} / sample {sample_id} =====")
|
|
model_path = mesh_path or cad_path
|
|
missing = [str(path) for path in (model_path, pointcloud_path) if path and not path.exists()]
|
|
if missing:
|
|
print("Missing input: " + "; ".join(missing))
|
|
elapsed_sec = time.perf_counter() - task_start_time
|
|
print(f"任务 {task_id} / 样本 {sample_id} 运行时间: {elapsed_sec:.2f} 秒")
|
|
summary["elapsed_sec"] += elapsed_sec
|
|
summary["failures"].append(f"{sample_id}: Missing input: " + "; ".join(missing))
|
|
continue
|
|
|
|
try:
|
|
if mesh_path:
|
|
result = self.run_contour(
|
|
mesh_path=str(mesh_path),
|
|
pointcloud_path=str(pointcloud_path),
|
|
point_chunk_size=point_chunk_size,
|
|
triangle_chunk_size=triangle_chunk_size,
|
|
save_path=str(task_save_path) if task_save_path else None,
|
|
excel_output_path=None,
|
|
no_visualize=True,
|
|
)
|
|
else:
|
|
result = self.run_contour_distance(
|
|
cad_path=cad_path,
|
|
pointcloud_path=pointcloud_path,
|
|
mesh_deflection=mesh_deflection,
|
|
point_chunk_size=point_chunk_size,
|
|
triangle_chunk_size=triangle_chunk_size,
|
|
save_path=str(task_save_path) if task_save_path else None,
|
|
excel_output_path=None,
|
|
no_visualize=True,
|
|
)
|
|
except Exception as exc:
|
|
print(f"Task {task_id} sample {sample_id} failed: {exc}")
|
|
elapsed_sec = time.perf_counter() - task_start_time
|
|
print(f"任务 {task_id} / 样本 {sample_id} 运行时间: {elapsed_sec:.2f} 秒")
|
|
summary["elapsed_sec"] += elapsed_sec
|
|
summary["failures"].append(f"{sample_id}: {exc}")
|
|
continue
|
|
|
|
stats = result["distance_stats"]
|
|
distances = result["distances"]
|
|
elapsed_sec = time.perf_counter() - task_start_time
|
|
print(f"任务 {task_id} / 样本 {sample_id} 运行时间: {elapsed_sec:.2f} 秒")
|
|
summary["point_count"] += len(distances)
|
|
summary["distance_sum"] += float(np.sum(distances))
|
|
summary["max"] = stats.max if summary["max"] is None else max(summary["max"], stats.max)
|
|
summary["elapsed_sec"] += elapsed_sec
|
|
summary["sample_results"][sample_id] = result
|
|
combined_visualizations.append((sample_id, result["pointcloud"]))
|
|
|
|
rows: list[dict[str, Any]] = []
|
|
for task_id in sorted(task_summaries, key=self._contour_sort_key):
|
|
summary = task_summaries[task_id]
|
|
sample_count = len(summary["sample_results"])
|
|
failure_count = len(summary["failures"])
|
|
if sample_count:
|
|
status = "成功" if failure_count == 0 else "部分成功"
|
|
mean = summary["distance_sum"] / summary["point_count"]
|
|
max_value = summary["max"]
|
|
else:
|
|
status = "失败"
|
|
mean = None
|
|
max_value = None
|
|
|
|
rows.append({
|
|
"task_id": task_id,
|
|
"sample_count": sample_count,
|
|
"samples": ",".join(summary["sample_ids"]),
|
|
"max": max_value,
|
|
"mean": mean,
|
|
"status": status,
|
|
"elapsed_sec": summary["elapsed_sec"],
|
|
})
|
|
results[task_id] = summary["sample_results"]
|
|
|
|
excel_output = None
|
|
if excel_output_path:
|
|
excel_output = self._save_contour_excel(
|
|
excel_output_path,
|
|
rows=rows,
|
|
result_template_path=result_template_path,
|
|
)
|
|
print(f"Saved contour Excel: {excel_output}")
|
|
|
|
combined_visualization_output = None
|
|
combined_pointclouds = []
|
|
if combined_visualizations:
|
|
combined_visualization_output = self._contour_combined_save_path(save_path)
|
|
self._save_contour_combined_pointcloud_stream(
|
|
combined_visualizations,
|
|
combined_context_pointcloud_path,
|
|
combined_visualization_output,
|
|
)
|
|
print(f"Saved combined contour heatmap: {combined_visualization_output}")
|
|
|
|
if self._should_visualize(no_visualize) and combined_visualizations:
|
|
combined_pointclouds = self._contour_combined_pointclouds(
|
|
combined_visualizations,
|
|
combined_context_pointcloud_path,
|
|
)
|
|
self.visualizer.show_combined_pointclouds(
|
|
combined_pointclouds,
|
|
labels=[str(index) for index, _ in enumerate(combined_pointclouds, start=1)],
|
|
window_name="Contour Batch Distance Heatmaps",
|
|
)
|
|
|
|
total_elapsed_sec = time.perf_counter() - batch_start_time
|
|
print(f"Contour batch total execution time: {total_elapsed_sec:.2f} seconds")
|
|
|
|
return {
|
|
"rows": rows,
|
|
"results": results,
|
|
"excel_output": excel_output,
|
|
"combined_visualization_output": combined_visualization_output,
|
|
"total_elapsed_sec": total_elapsed_sec,
|
|
}
|
|
|
|
def run_curvature(
|
|
self,
|
|
pointcloud_path: str | None = None,
|
|
radius: float | None = None,
|
|
max_nn: int | None = None,
|
|
color_scale: float | None = None,
|
|
save_path: str | None = None,
|
|
no_visualize: bool = False,
|
|
) -> dict[str, Any]:
|
|
config = self.config["curvature"]
|
|
pointcloud = self.io.load_point_cloud(pointcloud_path or self.config["paths"]["pointcloud"])
|
|
curvatures = self.curvature.compute(
|
|
pointcloud,
|
|
radius=float(radius if radius is not None else config["radius"]),
|
|
max_nn=int(max_nn if max_nn is not None else config["max_nn"]),
|
|
)
|
|
colored = self.curvature.colorize(
|
|
pointcloud,
|
|
curvatures,
|
|
color_scale=float(color_scale if color_scale is not None else config["color_scale"]),
|
|
)
|
|
output = save_path or self.config["paths"].get("curvature_output")
|
|
if output:
|
|
self.io.save_point_cloud(colored, output)
|
|
print(f"Saved curvature point cloud: {output}")
|
|
print(f"Curvature mean={np.mean(curvatures):.8f}, max={np.max(curvatures):.8f}")
|
|
if self._should_visualize(no_visualize):
|
|
self.visualizer.show_curvature(colored)
|
|
return {"pointcloud": colored, "curvatures": curvatures}
|
|
|
|
def run_plane(
|
|
self,
|
|
pointcloud_path: str | None = None,
|
|
threshold_mm: float | None = None,
|
|
max_iterations: int | None = None,
|
|
no_visualize: bool = False,
|
|
) -> dict[str, Any]:
|
|
config = self.config["plane"]
|
|
if pointcloud_path:
|
|
pointcloud = self.io.load_point_cloud(pointcloud_path)
|
|
else:
|
|
pointcloud = self.plane.create_square_plane_with_noise(
|
|
size_mm=float(config["size_mm"]),
|
|
noise_std_mm=float(config["noise_std_mm"]),
|
|
num_points=int(config["num_points"]),
|
|
)
|
|
|
|
result = self.plane.estimate(
|
|
pointcloud,
|
|
threshold_mm=float(threshold_mm if threshold_mm is not None else config["threshold_mm"]),
|
|
max_iterations=int(max_iterations if max_iterations is not None else config["max_iterations"]),
|
|
)
|
|
|
|
print(f"Plane model: {result.model}")
|
|
print(f"Inlier ratio: {result.inlier_ratio:.2f}%")
|
|
print(f"Normal angle error: {result.normal_angle_deg:.6f} deg")
|
|
print(f"Plane RMSE: {result.rmse:.8f} m")
|
|
if self._should_visualize(no_visualize):
|
|
self.visualizer.show_plane(result)
|
|
return {"plane_result": result}
|
|
|
|
def run_full(self, no_visualize: bool = False) -> dict[str, Any]:
|
|
outputs: dict[str, Any] = {}
|
|
if self.config["cad"].get("enabled", False):
|
|
outputs["cad2mesh"] = self.run_cad2mesh()
|
|
if self.config["noise"].get("enabled", True):
|
|
outputs["denoise"] = self.run_denoise(no_visualize=True)
|
|
mesh_file = str(self.io.require_file(self.config["paths"]["mesh"], "Mesh"))
|
|
outputs["registration"] = self._run_registration_with_pointcloud(
|
|
source=outputs["denoise"]["noisy"],
|
|
mesh_file=mesh_file,
|
|
voxel_size=None,
|
|
roll=None,
|
|
pitch=None,
|
|
yaw=None,
|
|
trans_range=None,
|
|
no_visualize=no_visualize,
|
|
reg_config=self.config["registration"],
|
|
)
|
|
else:
|
|
registration_input = self.config["paths"].get("noisy_pointcloud") or self.config["paths"]["pointcloud"]
|
|
outputs["registration"] = self.run_registration(pointcloud_path=registration_input, no_visualize=no_visualize)
|
|
outputs["contour"] = self.run_contour()
|
|
return outputs
|
|
|
|
def _should_visualize(self, no_visualize: bool) -> bool:
|
|
return bool(self.config["visualization"].get("enabled", True)) and not no_visualize
|
|
|
|
def _print_distance_stats(self, stats: Any) -> None:
|
|
print("Distance stats")
|
|
print(f"Mean: {stats.mean:.8f}")
|
|
print(f"Max: {stats.max:.8f}")
|
|
print(f"RMSE: {stats.rmse:.8f}")
|
|
print(f"Min: {stats.min:.8f}")
|
|
print(f"Std: {stats.std:.8f}")
|
|
|
|
def _save_contour_excel(
|
|
self,
|
|
output_path: str | Path,
|
|
*,
|
|
rows: list[dict[str, Any]] | None = None,
|
|
cad_path: str | Path | None = None,
|
|
stats: Any | None = None,
|
|
result_template_path: str | Path | None = DEFAULT_RESULT_TEMPLATE,
|
|
) -> Path:
|
|
output = self._normalize_excel_output_path(output_path)
|
|
if rows is None:
|
|
if cad_path is None or stats is None:
|
|
raise ValueError("cad_path and stats are required when rows is not provided.")
|
|
rows = [{
|
|
"task_id": self._contour_task_id_from_sample(Path(cad_path).stem),
|
|
"max": stats.max,
|
|
"mean": stats.mean,
|
|
"status": "成功",
|
|
}]
|
|
|
|
if result_template_path and Path(result_template_path).exists():
|
|
sheet_name, table = self._build_measurement_table_from_template(rows, Path(result_template_path))
|
|
else:
|
|
sheet_name = "contour"
|
|
table = [
|
|
[
|
|
"任务号",
|
|
"样本数",
|
|
"样本",
|
|
"最大值",
|
|
"平均值",
|
|
"状态",
|
|
"elapsed_sec",
|
|
]
|
|
]
|
|
for row in rows:
|
|
table.append([
|
|
row.get("task_id"),
|
|
row.get("sample_count"),
|
|
row.get("samples"),
|
|
row.get("max"),
|
|
row.get("mean"),
|
|
row.get("status"),
|
|
row.get("elapsed_sec"),
|
|
])
|
|
|
|
self._write_xlsx(table, output, sheet_name=sheet_name)
|
|
return output
|
|
|
|
def _build_measurement_table_from_template(
|
|
self,
|
|
rows: list[dict[str, Any]],
|
|
template_path: Path,
|
|
) -> tuple[str, list[list[Any]]]:
|
|
template_sheet_name, headers, template_rows = self._read_measurement_template(template_path)
|
|
task_col = self._find_header_index(headers, ["任务号", "任务编号", "task_id"])
|
|
result_col = self._find_header_index(headers, ["测量结果"])
|
|
conclusion_col = self._find_header_index(headers, ["测量结论", "结论", "状态"])
|
|
|
|
result_by_task = {
|
|
self._normalize_template_task_id(row.get("task_id")): row
|
|
for row in rows
|
|
if row.get("task_id") is not None
|
|
}
|
|
used_task_ids = set()
|
|
table = [headers]
|
|
|
|
for template_row in template_rows:
|
|
output_row = list(template_row)
|
|
if len(output_row) < len(headers):
|
|
output_row.extend([""] * (len(headers) - len(output_row)))
|
|
|
|
task_id = self._normalize_template_task_id(output_row[task_col])
|
|
result_row = result_by_task.get(task_id)
|
|
if result_row is None:
|
|
continue
|
|
|
|
output_row[result_col] = self._row_measurement_mean(result_row)
|
|
output_row[conclusion_col] = self._row_measurement_conclusion(result_row)
|
|
table.append(output_row)
|
|
used_task_ids.add(task_id)
|
|
|
|
for task_id, result_row in result_by_task.items():
|
|
if task_id in used_task_ids:
|
|
continue
|
|
|
|
output_row = [""] * len(headers)
|
|
output_row[task_col] = task_id
|
|
output_row[result_col] = self._row_measurement_mean(result_row)
|
|
output_row[conclusion_col] = self._row_measurement_conclusion(result_row)
|
|
table.append(output_row)
|
|
|
|
return template_sheet_name, table
|
|
|
|
def _read_measurement_template(self, template_path: Path) -> tuple[str, list[Any], list[list[Any]]]:
|
|
ns = {
|
|
"a": "http://schemas.openxmlformats.org/spreadsheetml/2006/main",
|
|
"r": "http://schemas.openxmlformats.org/officeDocument/2006/relationships",
|
|
}
|
|
|
|
with ZipFile(template_path) as xlsx:
|
|
names = xlsx.namelist()
|
|
shared_strings = []
|
|
if "xl/sharedStrings.xml" in names:
|
|
shared_root = ET.fromstring(xlsx.read("xl/sharedStrings.xml"))
|
|
for item in shared_root.findall("a:si", ns):
|
|
shared_strings.append(
|
|
"".join((text.text or "") for text in item.findall(".//a:t", ns))
|
|
)
|
|
|
|
workbook_root = ET.fromstring(xlsx.read("xl/workbook.xml"))
|
|
first_sheet = workbook_root.find("a:sheets/a:sheet", ns)
|
|
if first_sheet is None:
|
|
raise RuntimeError(f"模板中没有工作表: {template_path}")
|
|
|
|
sheet_name = first_sheet.attrib.get("name", "Sheet1")
|
|
relation_id = first_sheet.attrib[
|
|
"{http://schemas.openxmlformats.org/officeDocument/2006/relationships}id"
|
|
]
|
|
|
|
rels_root = ET.fromstring(xlsx.read("xl/_rels/workbook.xml.rels"))
|
|
relation_targets = {
|
|
rel.attrib["Id"]: rel.attrib["Target"]
|
|
for rel in rels_root
|
|
}
|
|
sheet_path = self._xlsx_rel_target_to_path(relation_targets[relation_id])
|
|
|
|
sheet_root = ET.fromstring(xlsx.read(sheet_path))
|
|
parsed_rows = []
|
|
max_col_count = 0
|
|
for row in sheet_root.findall("a:sheetData/a:row", ns):
|
|
values_by_col = {}
|
|
for cell in row.findall("a:c", ns):
|
|
col_index = self._xlsx_col_to_index(cell.attrib["r"])
|
|
values_by_col[col_index] = self._xlsx_cell_value(cell, shared_strings, ns)
|
|
max_col_count = max(max_col_count, col_index)
|
|
if values_by_col:
|
|
parsed_rows.append(values_by_col)
|
|
|
|
table = []
|
|
for values_by_col in parsed_rows:
|
|
table.append([
|
|
values_by_col.get(col_index, "")
|
|
for col_index in range(1, max_col_count + 1)
|
|
])
|
|
|
|
if not table:
|
|
raise RuntimeError(f"模板为空: {template_path}")
|
|
|
|
return sheet_name, table[0], table[1:]
|
|
|
|
def _row_measurement_mean(self, row: dict[str, Any]) -> Any:
|
|
for key in ("mean", "mean_dist", "average_distance", "测量结果"):
|
|
if key in row:
|
|
return row.get(key)
|
|
return None
|
|
|
|
def _row_measurement_conclusion(self, row: dict[str, Any]) -> str:
|
|
status = str(row.get("status", "")).strip()
|
|
if status and status != "成功":
|
|
return "失败"
|
|
|
|
value = self._row_measurement_mean(row)
|
|
if value is None:
|
|
return "失败"
|
|
try:
|
|
if not np.isfinite(float(value)):
|
|
return "失败"
|
|
except (TypeError, ValueError):
|
|
return "失败"
|
|
return "成功"
|
|
|
|
def _normalize_template_task_id(self, value: Any) -> str:
|
|
text = str(value).strip()
|
|
if text.endswith(".0") and text[:-2].isdigit():
|
|
return text[:-2]
|
|
return text
|
|
|
|
def _find_header_index(self, headers: list[Any], candidates: list[str]) -> int:
|
|
normalized = [str(header).strip() for header in headers]
|
|
for candidate in candidates:
|
|
if candidate in normalized:
|
|
return normalized.index(candidate)
|
|
raise ValueError(f"模板缺少列: {'/'.join(candidates)}")
|
|
|
|
def _resolve_contour_task_inputs(
|
|
self,
|
|
task_ids: str | list[int | str] | None,
|
|
all_tasks: bool,
|
|
data_root: Path,
|
|
pointcloud_kind: str,
|
|
) -> list[dict[str, Any]]:
|
|
if all_tasks:
|
|
task_values = self._discover_contour_task_values(data_root)
|
|
elif task_ids is None:
|
|
raise ValueError("Batch contour requires --tasks or --all.")
|
|
elif isinstance(task_ids, str):
|
|
task_values = [item.strip() for item in task_ids.split(",") if item.strip()]
|
|
else:
|
|
task_values = [str(item).strip() for item in task_ids if str(item).strip()]
|
|
|
|
task_inputs: list[dict[str, Any]] = []
|
|
for task_value in task_values:
|
|
task_inputs.extend(
|
|
self._contour_inputs_for_task_value(data_root, task_value, pointcloud_kind)
|
|
)
|
|
|
|
return sorted(
|
|
task_inputs,
|
|
key=lambda item: (self._contour_sort_key(item["task_id"]), item["sample_id"]),
|
|
)
|
|
|
|
def _discover_contour_task_values(self, data_root: Path) -> list[str]:
|
|
mesh_dir = data_root / "Mesh"
|
|
if mesh_dir.exists():
|
|
sample_ids = []
|
|
for pattern in ("*.stl", "*.obj", "*.ply"):
|
|
sample_ids.extend(path.stem for path in mesh_dir.glob(pattern))
|
|
if sample_ids:
|
|
return sorted(
|
|
{self._contour_task_id_from_sample(sample_id) for sample_id in sample_ids},
|
|
key=self._contour_sort_key,
|
|
)
|
|
|
|
cad_dir = data_root / "CAD"
|
|
if not cad_dir.exists():
|
|
raise FileNotFoundError(f"CAD or Mesh directory not found under: {data_root}")
|
|
return sorted((path.stem for path in cad_dir.glob("*.step")), key=self._contour_sort_key)
|
|
|
|
def _contour_inputs_for_task_value(
|
|
self,
|
|
data_root: Path,
|
|
task_value: str,
|
|
pointcloud_kind: str,
|
|
) -> list[dict[str, Any]]:
|
|
mesh_paths = self._contour_mesh_paths_for_task_value(data_root, task_value)
|
|
if mesh_paths:
|
|
return [
|
|
{
|
|
"task_id": self._contour_task_id_from_sample(mesh_path.stem),
|
|
"sample_id": mesh_path.stem,
|
|
"mesh_path": mesh_path,
|
|
"pointcloud_path": self._contour_pointcloud_path(
|
|
data_root,
|
|
mesh_path.stem,
|
|
pointcloud_kind,
|
|
),
|
|
}
|
|
for mesh_path in mesh_paths
|
|
]
|
|
|
|
sample_id = task_value
|
|
return [
|
|
{
|
|
"task_id": self._contour_task_id_from_sample(sample_id),
|
|
"sample_id": sample_id,
|
|
"cad_path": data_root / "CAD" / f"{sample_id}.step",
|
|
"pointcloud_path": self._contour_pointcloud_path(
|
|
data_root,
|
|
sample_id,
|
|
pointcloud_kind,
|
|
),
|
|
}
|
|
]
|
|
|
|
def _contour_mesh_paths_for_task_value(self, data_root: Path, task_value: str) -> list[Path]:
|
|
mesh_dir = data_root / "Mesh"
|
|
if not mesh_dir.exists():
|
|
return []
|
|
|
|
mesh_paths = []
|
|
for pattern in ("*.stl", "*.obj", "*.ply"):
|
|
mesh_paths.extend(mesh_dir.glob(pattern))
|
|
|
|
if "_" in task_value:
|
|
matched = [path for path in mesh_paths if path.stem == task_value]
|
|
else:
|
|
matched = [
|
|
path for path in mesh_paths
|
|
if self._contour_task_id_from_sample(path.stem) == task_value
|
|
]
|
|
|
|
return sorted(matched, key=lambda path: (self._contour_sort_key(path.stem), path.stem))
|
|
|
|
def _contour_task_id_from_sample(self, sample_id: str) -> str:
|
|
return str(sample_id).split("_", 1)[0]
|
|
|
|
def _contour_sort_key(self, value: str) -> tuple[int, int | str]:
|
|
text = str(value)
|
|
base = self._contour_task_id_from_sample(text)
|
|
if base.isdigit():
|
|
return (0, int(base))
|
|
return (1, base)
|
|
|
|
def _contour_pointcloud_path(self, data_root: Path, task_id: str, pointcloud_kind: str) -> Path:
|
|
if pointcloud_kind == "clean":
|
|
return data_root / "PointCloud" / f"{task_id}.ply"
|
|
if pointcloud_kind == "noise":
|
|
return data_root / "Noise_PointCloud" / f"{task_id}_noisy.ply"
|
|
raise ValueError("pointcloud_kind must be 'noise' or 'clean'.")
|
|
|
|
def _contour_task_save_path(self, save_path: str | None, task_id: str) -> Path | None:
|
|
if not save_path:
|
|
return None
|
|
|
|
output = Path(save_path)
|
|
if output.suffix.lower() != ".ply":
|
|
output.mkdir(parents=True, exist_ok=True)
|
|
return output / f"{task_id}_contour.ply"
|
|
|
|
output.parent.mkdir(parents=True, exist_ok=True)
|
|
if "{task_id}" in output.name:
|
|
return output.with_name(output.name.replace("{task_id}", task_id))
|
|
return output.with_name(f"{output.stem}_{task_id}{output.suffix}")
|
|
|
|
def _contour_combined_save_path(self, save_path: str | None) -> Path:
|
|
if not save_path:
|
|
return Path("vis") / "combined_contour.ply"
|
|
|
|
output = Path(save_path)
|
|
if output.suffix.lower() != ".ply":
|
|
return output / "combined_contour.ply"
|
|
if "{task_id}" in output.name:
|
|
return output.with_name(output.name.replace("{task_id}", "combined"))
|
|
return output.with_name(f"{output.stem}_combined{output.suffix}")
|
|
|
|
def _save_contour_combined_pointcloud_stream(
|
|
self,
|
|
task_pointclouds: list[tuple[str, Any]],
|
|
context_pointcloud_path: str | Path | None,
|
|
output_path: str | Path,
|
|
) -> Path:
|
|
output = self.io.ensure_parent_dir(output_path)
|
|
context_path = Path(context_pointcloud_path) if context_pointcloud_path else None
|
|
context_point_count = 0
|
|
context_header_size = None
|
|
|
|
if context_path:
|
|
if context_path.exists():
|
|
context_point_count, context_header_size = self._read_binary_xyz_ply_header(context_path)
|
|
print(f"Streaming context point cloud into combined contour: {context_path}")
|
|
else:
|
|
print(f"Context point cloud not found, skipped: {context_path}")
|
|
context_path = None
|
|
|
|
task_point_count = sum(len(pointcloud.points) for _, pointcloud in task_pointclouds)
|
|
total_point_count = context_point_count + task_point_count
|
|
if total_point_count == 0:
|
|
raise ValueError("No point clouds to save in combined contour.")
|
|
|
|
header = (
|
|
"ply\n"
|
|
"format binary_little_endian 1.0\n"
|
|
f"element vertex {total_point_count}\n"
|
|
"property float x\n"
|
|
"property float y\n"
|
|
"property float z\n"
|
|
"property uchar red\n"
|
|
"property uchar green\n"
|
|
"property uchar blue\n"
|
|
"end_header\n"
|
|
)
|
|
|
|
with open(output, "wb") as output_file:
|
|
output_file.write(header.encode("ascii"))
|
|
if context_path:
|
|
self._write_context_xyz_ply_stream(
|
|
context_path,
|
|
output_file,
|
|
context_header_size,
|
|
context_point_count,
|
|
)
|
|
self._write_contour_pointclouds_stream(task_pointclouds, output_file)
|
|
|
|
return output
|
|
|
|
def _read_binary_xyz_ply_header(self, ply_path: Path) -> tuple[int, int]:
|
|
with open(ply_path, "rb") as file:
|
|
header_lines = []
|
|
while True:
|
|
line = file.readline()
|
|
if not line:
|
|
raise ValueError(f"Invalid PLY header: {ply_path}")
|
|
header_lines.append(line)
|
|
if line.strip() == b"end_header":
|
|
break
|
|
header_size = file.tell()
|
|
|
|
text_lines = [
|
|
line.decode("ascii", errors="replace").strip()
|
|
for line in header_lines
|
|
]
|
|
if len(text_lines) < 3 or text_lines[0] != "ply":
|
|
raise ValueError(f"Not a PLY file: {ply_path}")
|
|
if text_lines[1] != "format binary_little_endian 1.0":
|
|
raise ValueError(
|
|
"Only binary_little_endian PLY context point clouds can be streamed."
|
|
)
|
|
|
|
vertex_count = None
|
|
vertex_properties = []
|
|
in_vertex = False
|
|
for line in text_lines[2:]:
|
|
parts = line.split()
|
|
if not parts:
|
|
continue
|
|
if parts[0] == "element":
|
|
in_vertex = parts[1] == "vertex"
|
|
if in_vertex:
|
|
vertex_count = int(parts[2])
|
|
continue
|
|
if in_vertex and parts[0] == "property":
|
|
if len(parts) >= 2 and parts[1] == "list":
|
|
raise ValueError("List properties are not supported in streamed PLY.")
|
|
if len(parts) >= 3:
|
|
vertex_properties.append((parts[1], parts[2]))
|
|
|
|
expected = [("float", "x"), ("float", "y"), ("float", "z")]
|
|
if vertex_count is None or vertex_properties[:3] != expected:
|
|
raise ValueError(
|
|
"Context PLY must start with vertex properties: float x, float y, float z."
|
|
)
|
|
if len(vertex_properties) != 3:
|
|
raise ValueError(
|
|
"Context PLY must contain only float x/y/z vertices for streamed merge."
|
|
)
|
|
|
|
return vertex_count, header_size
|
|
|
|
def _write_context_xyz_ply_stream(
|
|
self,
|
|
input_path: Path,
|
|
output_file: Any,
|
|
header_size: int,
|
|
point_count: int,
|
|
) -> None:
|
|
record_size = 12
|
|
chunk_points = 1_000_000
|
|
gray = np.array([158, 158, 158], dtype=np.uint8)
|
|
|
|
with open(input_path, "rb") as input_file:
|
|
input_file.seek(header_size)
|
|
remaining = point_count
|
|
while remaining > 0:
|
|
current = min(chunk_points, remaining)
|
|
data = input_file.read(current * record_size)
|
|
if len(data) != current * record_size:
|
|
raise IOError(f"Unexpected end of context PLY: {input_path}")
|
|
points = np.frombuffer(data, dtype="<f4").reshape(-1, 3)
|
|
self._write_xyz_rgb_chunk(output_file, points, gray)
|
|
remaining -= current
|
|
|
|
def _write_contour_pointclouds_stream(
|
|
self,
|
|
task_pointclouds: list[tuple[str, Any]],
|
|
output_file: Any,
|
|
) -> None:
|
|
chunk_points = 1_000_000
|
|
gray = np.array([158, 158, 158], dtype=np.uint8)
|
|
|
|
for _, pointcloud in task_pointclouds:
|
|
points = np.asarray(pointcloud.points)
|
|
colors = np.asarray(pointcloud.colors) if pointcloud.has_colors() else gray
|
|
for start in range(0, len(points), chunk_points):
|
|
end = min(start + chunk_points, len(points))
|
|
self._write_xyz_rgb_chunk(output_file, points[start:end], colors if colors.ndim == 1 else colors[start:end])
|
|
|
|
def _write_xyz_rgb_chunk(self, output_file: Any, points: np.ndarray, colors: np.ndarray) -> None:
|
|
points = np.asarray(points)
|
|
colors = np.asarray(colors)
|
|
count = len(points)
|
|
if count == 0:
|
|
return
|
|
|
|
vertex_dtype = np.dtype([
|
|
("x", "<f4"),
|
|
("y", "<f4"),
|
|
("z", "<f4"),
|
|
("red", "u1"),
|
|
("green", "u1"),
|
|
("blue", "u1"),
|
|
])
|
|
vertices = np.empty(count, dtype=vertex_dtype)
|
|
vertices["x"] = points[:, 0].astype(np.float32, copy=False)
|
|
vertices["y"] = points[:, 1].astype(np.float32, copy=False)
|
|
vertices["z"] = points[:, 2].astype(np.float32, copy=False)
|
|
|
|
if colors.ndim == 1:
|
|
rgb = colors
|
|
if np.issubdtype(rgb.dtype, np.floating):
|
|
rgb = np.clip(rgb * 255.0 + 0.5, 0, 255).astype(np.uint8)
|
|
else:
|
|
rgb = rgb.astype(np.uint8, copy=False)
|
|
vertices["red"] = int(rgb[0])
|
|
vertices["green"] = int(rgb[1])
|
|
vertices["blue"] = int(rgb[2])
|
|
else:
|
|
rgb = colors
|
|
if np.issubdtype(rgb.dtype, np.floating):
|
|
rgb = np.clip(rgb * 255.0 + 0.5, 0, 255).astype(np.uint8)
|
|
else:
|
|
rgb = rgb.astype(np.uint8, copy=False)
|
|
vertices["red"] = rgb[:, 0]
|
|
vertices["green"] = rgb[:, 1]
|
|
vertices["blue"] = rgb[:, 2]
|
|
|
|
output_file.write(vertices.tobytes())
|
|
|
|
def _contour_combined_pointclouds(
|
|
self,
|
|
task_pointclouds: list[tuple[str, Any]],
|
|
context_pointcloud_path: str | Path | None,
|
|
) -> list[Any]:
|
|
pointclouds = []
|
|
if context_pointcloud_path:
|
|
context_path = Path(context_pointcloud_path)
|
|
if context_path.exists():
|
|
context = self.io.load_point_cloud(context_path)
|
|
if not context.has_colors():
|
|
context.paint_uniform_color([0.62, 0.62, 0.62])
|
|
pointclouds.append(context)
|
|
print(f"Added context point cloud to combined contour: {context_path}")
|
|
else:
|
|
print(f"Context point cloud not found, skipped: {context_path}")
|
|
|
|
pointclouds.extend(item[1] for item in task_pointclouds)
|
|
return pointclouds
|
|
|
|
def run_translate_align_pointcloud_to_cad(
|
|
self,
|
|
cad_path: str | None = None,
|
|
pointcloud_path: str | None = None,
|
|
output_dir: str = "results",
|
|
axis: str = "x",
|
|
crop_ratio: float = 0.01,
|
|
mesh_deflection: float = 0.1,
|
|
cad_sample_points: int = 30000,
|
|
local_sample_points: int = 1000,
|
|
scale_pointcloud: float = 1.0,
|
|
no_visualize: bool = False,
|
|
) -> dict[str, Any]:
|
|
"""Run the archived front/tail centroid translation alignment workflow."""
|
|
paths = self.config["paths"]
|
|
input_step = self.io.require_cad(cad_path or paths["cad"])
|
|
input_pointcloud = self._resolve_translate_align_pointcloud(
|
|
pointcloud_path or paths["noisy_pointcloud"] or paths["pointcloud"]
|
|
)
|
|
|
|
module = self._load_archive_module("translate_align_pointcloud_to_cad.py")
|
|
return module.translate_align_by_front_tail(
|
|
cad_path=str(input_step),
|
|
pointcloud_path=str(input_pointcloud),
|
|
output_dir=output_dir,
|
|
axis=axis,
|
|
crop_ratio=float(crop_ratio),
|
|
mesh_deflection=float(mesh_deflection),
|
|
cad_sample_points=int(cad_sample_points),
|
|
local_sample_points=int(local_sample_points),
|
|
scale_pointcloud=float(scale_pointcloud),
|
|
visualize=not no_visualize,
|
|
)
|
|
|
|
def _resolve_translate_align_pointcloud(self, pointcloud_path: str | Path) -> Path:
|
|
path = Path(pointcloud_path)
|
|
if path.is_dir():
|
|
preferred = path / "shifted_whole_pointcloud.ply"
|
|
if preferred.exists():
|
|
return preferred
|
|
|
|
candidates = sorted(path.glob("*.ply"))
|
|
if len(candidates) == 1:
|
|
return candidates[0]
|
|
if not candidates:
|
|
raise FileNotFoundError(f"No PLY point cloud found in directory: {path}")
|
|
names = ", ".join(candidate.name for candidate in candidates)
|
|
raise ValueError(f"Multiple PLY files found in {path}; specify one file explicitly. Found: {names}")
|
|
|
|
return self.io.require_file(path, "PointCloud")
|
|
|
|
def _load_archive_module(self, filename: str) -> Any:
|
|
script_path = Path(__file__).resolve().parent / "archive" / filename
|
|
if not script_path.exists():
|
|
raise FileNotFoundError(f"Archive script not found: {script_path}")
|
|
|
|
module_name = f"_archive_{script_path.stem}"
|
|
spec = importlib.util.spec_from_file_location(module_name, script_path)
|
|
if spec is None or spec.loader is None:
|
|
raise ImportError(f"Cannot load archive script: {script_path}")
|
|
|
|
module = importlib.util.module_from_spec(spec)
|
|
spec.loader.exec_module(module)
|
|
return module
|
|
|
|
def _normalize_excel_output_path(self, output_path: str | Path) -> Path:
|
|
output = Path(output_path)
|
|
if output.suffix.lower() != ".xlsx":
|
|
output = output / "contour.xlsx"
|
|
elif output.parent == Path("."):
|
|
output = Path("excel") / output
|
|
output.parent.mkdir(parents=True, exist_ok=True)
|
|
return output
|
|
|
|
def _write_xlsx(self, rows: list[list[Any]], output_path: Path, sheet_name: str) -> None:
|
|
sheet_rows = []
|
|
for row_index, row_values in enumerate(rows, start=1):
|
|
cells = []
|
|
for col_index, value in enumerate(row_values, start=1):
|
|
cell_ref = self._xlsx_cell_ref(row_index, col_index)
|
|
if isinstance(value, (int, float, np.integer, np.floating)) and not isinstance(value, bool):
|
|
cells.append(f'<c r="{cell_ref}"><v>{value}</v></c>')
|
|
else:
|
|
text = escape("" if value is None else str(value))
|
|
cells.append(f'<c r="{cell_ref}" t="inlineStr"><is><t>{text}</t></is></c>')
|
|
sheet_rows.append(f'<row r="{row_index}">{"".join(cells)}</row>')
|
|
|
|
workbook_xml = (
|
|
'<?xml version="1.0" encoding="UTF-8" standalone="yes"?>'
|
|
'<workbook xmlns="http://schemas.openxmlformats.org/spreadsheetml/2006/main" '
|
|
'xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships">'
|
|
f'<sheets><sheet name="{escape(sheet_name[:31])}" sheetId="1" r:id="rId1"/></sheets>'
|
|
'</workbook>'
|
|
)
|
|
worksheet_xml = (
|
|
'<?xml version="1.0" encoding="UTF-8" standalone="yes"?>'
|
|
'<worksheet xmlns="http://schemas.openxmlformats.org/spreadsheetml/2006/main">'
|
|
'<sheetData>'
|
|
f'{"".join(sheet_rows)}'
|
|
'</sheetData>'
|
|
'</worksheet>'
|
|
)
|
|
content_types_xml = (
|
|
'<?xml version="1.0" encoding="UTF-8" standalone="yes"?>'
|
|
'<Types xmlns="http://schemas.openxmlformats.org/package/2006/content-types">'
|
|
'<Default Extension="rels" ContentType="application/vnd.openxmlformats-package.relationships+xml"/>'
|
|
'<Default Extension="xml" ContentType="application/xml"/>'
|
|
'<Override PartName="/xl/workbook.xml" '
|
|
'ContentType="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet.main+xml"/>'
|
|
'<Override PartName="/xl/worksheets/sheet1.xml" '
|
|
'ContentType="application/vnd.openxmlformats-officedocument.spreadsheetml.worksheet+xml"/>'
|
|
'</Types>'
|
|
)
|
|
root_rels_xml = (
|
|
'<?xml version="1.0" encoding="UTF-8" standalone="yes"?>'
|
|
'<Relationships xmlns="http://schemas.openxmlformats.org/package/2006/relationships">'
|
|
'<Relationship Id="rId1" '
|
|
'Type="http://schemas.openxmlformats.org/officeDocument/2006/relationships/officeDocument" '
|
|
'Target="xl/workbook.xml"/>'
|
|
'</Relationships>'
|
|
)
|
|
workbook_rels_xml = (
|
|
'<?xml version="1.0" encoding="UTF-8" standalone="yes"?>'
|
|
'<Relationships xmlns="http://schemas.openxmlformats.org/package/2006/relationships">'
|
|
'<Relationship Id="rId1" '
|
|
'Type="http://schemas.openxmlformats.org/officeDocument/2006/relationships/worksheet" '
|
|
'Target="worksheets/sheet1.xml"/>'
|
|
'</Relationships>'
|
|
)
|
|
with ZipFile(output_path, "w", ZIP_DEFLATED) as xlsx:
|
|
xlsx.writestr("[Content_Types].xml", content_types_xml)
|
|
xlsx.writestr("_rels/.rels", root_rels_xml)
|
|
xlsx.writestr("xl/workbook.xml", workbook_xml)
|
|
xlsx.writestr("xl/_rels/workbook.xml.rels", workbook_rels_xml)
|
|
xlsx.writestr("xl/worksheets/sheet1.xml", worksheet_xml)
|
|
|
|
def _xlsx_cell_ref(self, row_index: int, col_index: int) -> str:
|
|
letters = ""
|
|
col = col_index
|
|
while col:
|
|
col, remainder = divmod(col - 1, 26)
|
|
letters = chr(65 + remainder) + letters
|
|
return f"{letters}{row_index}"
|
|
|
|
def _xlsx_col_to_index(self, cell_ref: str) -> int:
|
|
letters = "".join(ch for ch in cell_ref if ch.isalpha())
|
|
index = 0
|
|
for letter in letters:
|
|
index = index * 26 + ord(letter.upper()) - ord("A") + 1
|
|
return index
|
|
|
|
def _xlsx_rel_target_to_path(self, target: str) -> str:
|
|
target = target.replace("\\", "/").lstrip("/")
|
|
if target.startswith("xl/"):
|
|
return target
|
|
return posixpath.normpath(posixpath.join("xl", target))
|
|
|
|
def _xlsx_cell_value(self, cell: ET.Element, shared_strings: list[str], ns: dict[str, str]) -> str:
|
|
value_node = cell.find("a:v", ns)
|
|
inline_node = cell.find("a:is/a:t", ns)
|
|
cell_type = cell.attrib.get("t")
|
|
|
|
if cell_type == "s" and value_node is not None:
|
|
return shared_strings[int(value_node.text)]
|
|
if cell_type == "inlineStr" and inline_node is not None:
|
|
return inline_node.text or ""
|
|
if value_node is not None:
|
|
return value_node.text or ""
|
|
return ""
|