346 lines
17 KiB
Python
346 lines
17 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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可视化后台线程模块
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包含 VisualizationWorkerThread 后台线程类和辅助函数。
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"""
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from pathlib import Path
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from typing import Optional, List, Union
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from PyQt5.QtCore import QThread, pyqtSignal
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import numpy as np
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def _viz_infer_wavelength_start_column(df) -> Union[str, int]:
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"""推断光谱起始列（training_spectra 通常以波长数值为列名，未必含 UTM_Y）。"""
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import pandas as pd
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for i, col in enumerate(df.columns):
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name = str(col).strip().lstrip("\ufeff")
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try:
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v = float(name)
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except ValueError:
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continue
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if 200.0 <= v <= 3000.0:
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return i
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if "UTM_Y" in df.columns:
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return "UTM_Y"
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return 0
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class VisualizationWorkerThread(QThread):
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"""可视化耗时计算放入后台线程，并临时使用 Agg 后端，避免主界面未响应。"""
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finished_ok = pyqtSignal(object)
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failed = pyqtSignal(str)
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def __init__(self, task: str, work_dir: str, extra: Optional[dict] = None):
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super().__init__()
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self.task = task
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self.work_dir = str(work_dir)
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self.extra = extra or {}
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def run(self):
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mpl_prev = None
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try:
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import matplotlib
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mpl_prev = matplotlib.get_backend()
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except Exception:
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pass
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try:
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import matplotlib.pyplot as plt
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plt.switch_backend("Agg")
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except Exception:
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mpl_prev = None
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try:
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wp = Path(self.work_dir)
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if self.task == "mask_glint":
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from src.postprocessing.visualization_reports import WaterQualityVisualization
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viz = WaterQualityVisualization(output_dir=str(wp / "14_visualization"))
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preview_paths = viz.generate_glint_deglint_previews(
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work_dir=str(wp),
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output_subdir="glint_deglint_previews",
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)
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cnt = len(preview_paths) if preview_paths else 0
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self.finished_ok.emit({"task": "mask_glint", "count": cnt, "preview_paths": preview_paths})
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elif self.task == "sampling_map":
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hyperspectral_files = []
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deglint_dir = wp / "3_deglint"
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if deglint_dir.exists():
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for ext in ("*.dat", "*.bsq", "*.tif", "*.tiff"):
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hyperspectral_files.extend(list(deglint_dir.glob(ext)))
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if not hyperspectral_files:
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for ext in ("*.dat", "*.bsq", "*.tif", "*.tiff"):
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hyperspectral_files.extend(list(wp.glob(f"**/{ext}")))
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if not hyperspectral_files:
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self.failed.emit("未找到高光谱影像文件（.dat/.bsq/.tif）。")
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return
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hyperspectral_path = str(hyperspectral_files[0])
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csv_files = []
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processed_dir = wp / "4_processed_data"
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if processed_dir.exists():
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csv_files = list(processed_dir.glob("*.csv"))
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if not csv_files:
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csv_files = (
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list(wp.glob("**/*sampling*.csv"))
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+ list(wp.glob("**/*point*.csv"))
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+ list(wp.glob("**/*.csv"))
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)
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if not csv_files:
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self.failed.emit("未找到采样点 CSV 文件。")
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return
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csv_path = str(csv_files[0])
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from src.postprocessing.point_map import SamplingPointMap
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map_generator = SamplingPointMap(
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output_dir=str(wp / "14_visualization" / "sampling_maps"),
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fast_mode=True,
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)
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map_path = map_generator.create_sampling_point_map(
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hyperspectral_path=hyperspectral_path,
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csv_path=csv_path,
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point_color="red",
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point_size=100,
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point_alpha=0.9,
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show_north_arrow=True,
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show_scale_bar=True,
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show_legend=True,
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downsample=True,
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dpi=180,
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)
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self.finished_ok.emit(
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{
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"task": "sampling_map",
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"map_path": map_path,
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"hyperspectral_path": hyperspectral_path,
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"csv_path": csv_path,
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}
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)
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elif self.task == "spectrum":
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from src.postprocessing.visualization_reports import WaterQualityVisualization
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viz = WaterQualityVisualization(output_dir=str(wp / "14_visualization"))
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csv_file = self.extra.get("csv_path")
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wl = self.extra.get("wavelength_start_column", "UTM_Y")
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n_groups = int(self.extra.get("n_groups", 5))
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param_cols = self.extra.get("param_cols") or []
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if param_cols:
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output_paths: List[str] = []
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err_lines: List[str] = []
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for param_col in param_cols:
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try:
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out = viz.plot_spectrum_by_parameter(
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csv_path=str(csv_file),
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parameter_column=param_col,
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wavelength_start_column=wl,
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n_groups=n_groups,
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)
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output_paths.append(out)
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except Exception as _ex:
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err_lines.append(f"{param_col}: {_ex}")
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if not output_paths:
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self.failed.emit(
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"所有参数列的光谱图均生成失败:\n" + "\n".join(err_lines[:20])
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)
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return
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self.finished_ok.emit(
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{
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"task": "spectrum",
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"output_paths": output_paths,
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"errors": err_lines,
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}
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)
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else:
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param_col = self.extra.get("param_col")
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out = viz.plot_spectrum_by_parameter(
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csv_path=str(csv_file),
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parameter_column=param_col,
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wavelength_start_column=wl,
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n_groups=n_groups,
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)
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self.finished_ok.emit(
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{"task": "spectrum", "output_path": out, "param_col": param_col}
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)
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elif self.task == "statistics":
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from src.postprocessing.visualization_reports import WaterQualityVisualization
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viz = WaterQualityVisualization(output_dir=str(wp / "14_visualization"))
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csv_file = self.extra.get("csv_path")
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param_cols = self.extra.get("param_cols") or []
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output_paths = viz.plot_statistical_charts(
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csv_path=str(csv_file),
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parameter_columns=param_cols,
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)
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self.finished_ok.emit(
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{"task": "statistics", "output_paths": output_paths}
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)
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elif self.task == "scatter":
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from src.core.water_quality_inversion_pipeline_GUI import WaterQualityInversionPipeline
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training_csv_path = (self.extra.get("training_csv_path") or "").strip()
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models_dir = (self.extra.get("models_dir") or "").strip()
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if not training_csv_path or not Path(training_csv_path).is_file():
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self.failed.emit("训练光谱 CSV 无效或不存在，请确认已选择步骤5输出的文件。")
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return
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if not models_dir or not Path(models_dir).is_dir():
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self.failed.emit("模型目录无效或不存在，请确认步骤6已生成 7_Supervised_Model_Training 下的参数子文件夹。")
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return
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pipeline = WaterQualityInversionPipeline(work_dir=str(wp))
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scatter_paths = pipeline.generate_model_scatter_plots(
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training_csv_path=training_csv_path,
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models_dir=models_dir,
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)
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self.finished_ok.emit({"task": "scatter", "scatter_paths": scatter_paths or {}})
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elif self.task == "generate_all_selected":
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from src.postprocessing.visualization_reports import WaterQualityVisualization
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viz = WaterQualityVisualization(output_dir=str(wp / "14_visualization"))
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parts = []
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training_csv = wp / "5_training_spectra" / "training_spectra.csv"
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if self.extra.get("gen_scatter"):
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if training_csv.is_file():
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models_dir = wp / "7_Supervised_Model_Training"
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if models_dir.is_dir() and any(d.is_dir() for d in models_dir.iterdir()):
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from src.core.water_quality_inversion_pipeline_GUI import WaterQualityInversionPipeline
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pipeline = WaterQualityInversionPipeline(work_dir=str(wp))
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scatter_paths = pipeline.generate_model_scatter_plots(
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training_csv_path=str(training_csv),
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models_dir=str(models_dir),
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)
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count = len(scatter_paths) if scatter_paths else 0
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parts.append(f"散点图: {count} 个")
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else:
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parts.append("散点图: 跳过(无模型目录)")
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else:
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parts.append("散点图: 跳过(无训练数据)")
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if self.extra.get("gen_spectrum"):
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if training_csv.is_file():
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import pandas as pd
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df = pd.read_csv(training_csv)
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wl_col = _viz_infer_wavelength_start_column(df)
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if isinstance(wl_col, str):
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idx = int(df.columns.get_loc(wl_col)) + 1
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else:
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idx = int(wl_col)
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param_cols = []
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if idx > 0 and idx < len(df.columns):
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param_cols = [
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c for c in df.columns[:idx]
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if df[c].dtype.kind in 'iuf' and df[c].notna().sum() > 0
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]
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if param_cols:
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spectrum_paths = []
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for param_col in param_cols:
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try:
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path = viz.plot_spectrum_by_parameter(
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csv_path=str(training_csv),
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parameter_column=param_col,
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wavelength_start_column=wl_col,
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n_groups=5,
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)
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if path:
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spectrum_paths.append(path)
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except Exception as e:
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print(f"生成光谱图失败 ({param_col}): {e}")
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count = len(spectrum_paths)
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parts.append(f"光谱图: {count} 个")
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else:
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parts.append("光谱图: 跳过(无可用参数列)")
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else:
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parts.append("光谱图: 跳过(无训练数据)")
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if self.extra.get("gen_boxplots"):
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if training_csv.is_file():
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import pandas as pd
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df = pd.read_csv(training_csv)
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exclude_cols = ['longitude', 'latitude', 'lon', 'lat', 'x', 'y', 'coord', 'coordinate']
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param_cols = [
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c for c in df.select_dtypes(include=[np.number]).columns
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if not any(exc in c.lower() for exc in exclude_cols)
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]
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wl = _viz_infer_wavelength_start_column(df)
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if isinstance(wl, str):
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idx = int(df.columns.get_loc(wl)) + 1
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else:
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idx = int(wl)
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if 0 < idx < len(df.columns):
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meta_set = set(df.columns[:idx])
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param_cols = [c for c in param_cols if c in meta_set]
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if param_cols:
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output_dict = viz.plot_statistical_charts(
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csv_path=str(training_csv),
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parameter_columns=param_cols,
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)
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count = len([v for v in output_dict.values() if v]) if output_dict else 0
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parts.append(f"统计图: {count} 个")
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else:
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parts.append("统计图: 跳过(无可用水质参数列)")
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else:
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parts.append("统计图: 跳过(无训练数据)")
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if self.extra.get("gen_mask_glint"):
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preview_paths = viz.generate_glint_deglint_previews(
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work_dir=str(wp),
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output_subdir="glint_deglint_previews",
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)
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parts.append(f"掩膜/耀斑预览: {len(preview_paths) if preview_paths else 0} 个")
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if self.extra.get("gen_sampling_map"):
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hyperspectral_files = []
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deglint_dir = wp / "3_deglint"
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if deglint_dir.exists():
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for ext in ("*.dat", "*.bsq", "*.tif", "*.tiff"):
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hyperspectral_files.extend(list(deglint_dir.glob(ext)))
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if not hyperspectral_files:
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for ext in ("*.dat", "*.bsq", "*.tif", "*.tiff"):
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hyperspectral_files.extend(list(wp.glob(f"**/{ext}")))
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if hyperspectral_files:
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hyperspectral_path = str(hyperspectral_files[0])
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csv_files = []
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processed_dir = wp / "4_processed_data"
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if processed_dir.exists():
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csv_files = list(processed_dir.glob("*.csv"))
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if not csv_files:
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csv_files = (
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list(wp.glob("**/*sampling*.csv"))
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+ list(wp.glob("**/*point*.csv"))
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+ list(wp.glob("**/*.csv"))
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)
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if csv_files:
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csv_path = str(csv_files[0])
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from src.postprocessing.point_map import SamplingPointMap
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map_generator = SamplingPointMap(
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output_dir=str(wp / "14_visualization" / "sampling_maps"),
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fast_mode=True,
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)
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map_path = map_generator.create_sampling_point_map(
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hyperspectral_path=hyperspectral_path,
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csv_path=csv_path,
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point_color="red",
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point_size=100,
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point_alpha=0.9,
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show_north_arrow=True,
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show_scale_bar=True,
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show_legend=True,
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downsample=True,
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dpi=180,
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)
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parts.append(f"采样点图: {Path(map_path).name}")
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else:
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parts.append("采样点图: 跳过(无CSV)")
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else:
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parts.append("采样点图: 跳过(无影像)")
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self.finished_ok.emit({"task": "generate_all_selected", "parts": parts})
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else:
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self.failed.emit(f"未知可视化任务: {self.task}")
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except Exception as e:
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import traceback
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self.failed.emit(f"{e}\n{traceback.format_exc()}")
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finally:
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if mpl_prev:
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try:
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import matplotlib.pyplot as plt
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plt.switch_backend(mpl_prev)
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except Exception:
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pass |