#!/usr/bin/env python # -*- coding: utf-8 -*- """ VisualizationPanel - 可视化分析面板 左侧目录树 + 右侧图像查看器，支持多种图表生成。 """ import os import traceback from pathlib import Path from typing import Optional, List, Union import numpy as np import pandas as pd from src.gui.panels._step_path_resolver import get_step_output_path, resolve_step_widget, resolve_subdir from PyQt5.QtCore import Qt, QTimer, QThread, pyqtSignal, QAbstractTableModel from PyQt5.QtGui import QPixmap from PyQt5.QtWidgets import ( QWidget, QVBoxLayout, QHBoxLayout, QGroupBox, QFormLayout, QLabel, QCheckBox, QPushButton, QLineEdit, QMessageBox, QFileDialog, QFrame, QSizePolicy, QDialog, QTreeWidget, QListWidget, QAbstractItemView, QHeaderView,QTreeWidgetItem,QScrollArea ) from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas from matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolbar from matplotlib.figure import Figure PIPELINE_AVAILABLE = True def _viz_training_spectra_csv_path(work_path: Path) -> Path: """可视化光谱/统计及模型散点图使用的训练光谱表路径（与步骤6输出一致）。 注意：步骤5.5（水质指数计算）执行后会覆盖此文件为94维增强版本， 因此下游步骤无需任何修改，直接读取此路径即可。 """ return work_path / "6_Spectral_Feature_Extraction" / "training_spectra.csv" def _viz_infer_wavelength_start_column(df: pd.DataFrame) -> Union[str, int]: """推断光谱起始列（training_spectra 通常以波长数值为列名，未必含 UTM_Y）。""" import re # 兼容前缀：nm / nm_ / wavelength / wl / λ / wave_len（大小写不敏感） _PREFIX_RE = re.compile(r"^(?:nm|wavelength|wl|\u03bb|wave_len)[_\s\-]*", re.IGNORECASE) for i, col in enumerate(df.columns): name = str(col).strip().lstrip("\ufeff") # 1) 直接纯数字（如 "700.0"） try: v = float(name) except ValueError: v = None if v is not None and 200.0 <= v <= 3000.0: return i # 2) 剥前缀后再试（如 "nm_700"、"wavelength_800.0"、"WL 900"） stripped = _PREFIX_RE.sub("", name) if stripped != name: try: v = float(stripped) except ValueError: continue if 200.0 <= v <= 3000.0: return i if "UTM_Y" in df.columns: return "UTM_Y" # 兜底分支触发时打印所有列名，便于下次失败时一眼看到根因 print(f"DEBUG: 尝试解析的列名: {df.columns.tolist()}") return 0 class VisualizationWorkerThread(QThread): """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。""" finished_ok = pyqtSignal(object) failed = pyqtSignal(str) def __init__(self, task: str, work_dir: str, extra: Optional[dict] = None): super().__init__() self.task = task self.work_dir = str(work_dir) self.extra = extra or {} def run(self): mpl_prev = None try: import matplotlib mpl_prev = matplotlib.get_backend() except Exception: pass try: import matplotlib.pyplot as plt plt.switch_backend("Agg") except Exception: mpl_prev = None try: wp = Path(self.work_dir) if self.task == "mask_glint": from src.postprocessing.visualization_reports import WaterQualityVisualization viz = WaterQualityVisualization(output_dir=str(resolve_subdir(self.work_dir, 'visualization'))) preview_paths = viz.generate_glint_deglint_previews( work_dir=str(wp), output_subdir="glint_deglint_previews", ) cnt = len(preview_paths) if preview_paths else 0 self.finished_ok.emit({"task": "mask_glint", "count": cnt, "preview_paths": preview_paths}) elif self.task == "sampling_map": hyperspectral_files = [] deglint_dir = Path(resolve_subdir(self.work_dir, 'deglint')) if deglint_dir.exists(): for ext in ("*.dat", "*.bsq", "*.tif", "*.tiff"): hyperspectral_files.extend(list(deglint_dir.glob(ext))) if not hyperspectral_files: for ext in ("*.dat", "*.bsq", "*.tif", "*.tiff"): hyperspectral_files.extend(list(wp.glob(f"**/{ext}"))) if not hyperspectral_files: self.failed.emit("未找到高光谱影像文件（.dat/.bsq/.tif）。") return hyperspectral_path = str(hyperspectral_files[0]) csv_files = [] processed_dir = wp / "4_processed_data" if processed_dir.exists(): csv_files = list(processed_dir.glob("*.csv")) if not csv_files: csv_files = ( list(wp.glob("**/*sampling*.csv")) + list(wp.glob("**/*point*.csv")) + list(wp.glob("**/*.csv")) ) if not csv_files: self.failed.emit("未找到采样点 CSV 文件。") return csv_path = str(csv_files[0]) from src.postprocessing.point_map import SamplingPointMap map_generator = SamplingPointMap( output_dir=str(Path(resolve_subdir(self.work_dir, 'visualization')) / "sampling_maps"), fast_mode=True, ) map_path = map_generator.create_sampling_point_map( hyperspectral_path=hyperspectral_path, csv_path=csv_path, point_color="red", point_size=100, point_alpha=0.9, show_north_arrow=True, show_scale_bar=True, show_legend=True, downsample=True, dpi=180, ) self.finished_ok.emit( { "task": "sampling_map", "map_path": map_path, "hyperspectral_path": hyperspectral_path, "csv_path": csv_path, } ) elif self.task == "spectrum": from src.postprocessing.visualization_reports import WaterQualityVisualization viz = WaterQualityVisualization(output_dir=str(resolve_subdir(self.work_dir, 'visualization'))) csv_file = self.extra.get("csv_path") wl = self.extra.get("wavelength_start_column", "UTM_Y") n_groups = int(self.extra.get("n_groups", 5)) param_cols = self.extra.get("param_cols") or [] if param_cols: output_paths: List[str] = [] err_lines: List[str] = [] for param_col in param_cols: try: out = viz.plot_spectrum_by_parameter( csv_path=str(csv_file), parameter_column=param_col, wavelength_start_column=wl, n_groups=n_groups, ) output_paths.append(out) except Exception as _ex: err_lines.append(f"{param_col}: {_ex}") if not output_paths: self.failed.emit( "所有参数列的光谱图均生成失败:\n" + "\n".join(err_lines[:20]) ) return self.finished_ok.emit( { "task": "spectrum", "output_paths": output_paths, "errors": err_lines, } ) else: param_col = self.extra.get("param_col") out = viz.plot_spectrum_by_parameter( csv_path=str(csv_file), parameter_column=param_col, wavelength_start_column=wl, n_groups=n_groups, ) self.finished_ok.emit( {"task": "spectrum", "output_path": out, "param_col": param_col} ) elif self.task == "statistics": from src.postprocessing.visualization_reports import WaterQualityVisualization viz = WaterQualityVisualization(output_dir=str(resolve_subdir(self.work_dir, 'visualization'))) csv_file = self.extra.get("csv_path") param_cols = self.extra.get("param_cols") or [] output_paths = viz.plot_statistical_charts( csv_path=str(csv_file), parameter_columns=param_cols, ) self.finished_ok.emit( {"task": "statistics", "output_paths": output_paths} ) elif self.task == "scatter": from src.core.visualization.scatter_plot import generate_model_scatter_plots training_csv_path = (self.extra.get("training_csv_path") or "").strip() models_dir = (self.extra.get("models_dir") or "").strip() if not training_csv_path or not Path(training_csv_path).is_file(): 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。") return if not models_dir or not Path(models_dir).is_dir(): 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。") return scatter_paths = generate_model_scatter_plots( models_dir=models_dir, training_csv_path=training_csv_path, ) self.finished_ok.emit({"task": "scatter", "scatter_paths": scatter_paths or {}}) elif self.task == "generate_all_selected": from src.postprocessing.visualization_reports import WaterQualityVisualization viz = WaterQualityVisualization(output_dir=str(resolve_subdir(self.work_dir, 'visualization'))) parts = [] training_csv_path = (self.extra.get("training_csv_path") or "").strip() if training_csv_path: training_csv = Path(training_csv_path) else: training_csv = wp / "6_Spectral_Feature_Extraction" / "training_spectra.csv" if self.extra.get("gen_scatter"): if training_csv.is_file(): models_dir_str = (self.extra.get("models_dir") or "").strip() if models_dir_str: models_dir = Path(models_dir_str) else: models_dir = wp / "8_Supervised_Model_Training" if models_dir.is_dir() and any(d.is_dir() for d in models_dir.iterdir()): from src.core.visualization.scatter_plot import generate_model_scatter_plots scatter_paths = generate_model_scatter_plots( models_dir=str(models_dir), training_csv_path=str(training_csv), ) count = len(scatter_paths) if scatter_paths else 0 parts.append(f"散点图: {count} 个") else: parts.append("散点图: 跳过(无模型目录)") else: parts.append("散点图: 跳过(无训练数据)") if self.extra.get("gen_spectrum"): if training_csv.is_file(): import pandas as pd df = pd.read_csv(training_csv) wl_col = _viz_infer_wavelength_start_column(df) if isinstance(wl_col, str): idx = int(df.columns.get_loc(wl_col)) + 1 else: idx = int(wl_col) param_cols = [] if idx > 0 and idx < len(df.columns): param_cols = [ c for c in df.columns[:idx] if df[c].dtype.kind in 'iuf' and df[c].notna().sum() > 0 ] if param_cols: spectrum_paths = [] for param_col in param_cols: try: path = viz.plot_spectrum_by_parameter( csv_path=str(training_csv), parameter_column=param_col, wavelength_start_column=wl_col, n_groups=5, ) if path: spectrum_paths.append(path) except Exception as e: print(f"生成光谱图失败 ({param_col}): {e}") count = len(spectrum_paths) parts.append(f"光谱图: {count} 个") else: parts.append("光谱图: 跳过(无可用参数列)") else: parts.append("光谱图: 跳过(无训练数据)") if self.extra.get("gen_boxplots"): if training_csv.is_file(): import pandas as pd df = pd.read_csv(training_csv) exclude_cols = ['longitude', 'latitude', 'lon', 'lat', 'x', 'y', 'coord', 'coordinate'] param_cols = [ c for c in df.select_dtypes(include=[np.number]).columns if not any(exc in c.lower() for exc in exclude_cols) ] wl = _viz_infer_wavelength_start_column(df) if isinstance(wl, str): idx = int(df.columns.get_loc(wl)) + 1 else: idx = int(wl) if 0 < idx < len(df.columns): meta_set = set(df.columns[:idx]) param_cols = [c for c in param_cols if c in meta_set] if param_cols: output_dict = viz.plot_statistical_charts( csv_path=str(training_csv), parameter_columns=param_cols, ) count = len([v for v in output_dict.values() if v]) if output_dict else 0 parts.append(f"统计图: {count} 个") else: parts.append("统计图: 跳过(无可用水质参数列)") else: parts.append("统计图: 跳过(无训练数据)") if self.extra.get("gen_mask_glint"): preview_paths = viz.generate_glint_deglint_previews( work_dir=str(wp), output_subdir="glint_deglint_previews", ) parts.append(f"掩膜/耀斑预览: {len(preview_paths) if preview_paths else 0} 个") if self.extra.get("gen_sampling_map"): hyperspectral_files = [] deglint_dir = Path(resolve_subdir(self.work_dir, 'deglint')) if deglint_dir.exists(): for ext in ("*.dat", "*.bsq", "*.tif", "*.tiff"): hyperspectral_files.extend(list(deglint_dir.glob(ext))) if not hyperspectral_files: for ext in ("*.dat", "*.bsq", "*.tif", "*.tiff"): hyperspectral_files.extend(list(wp.glob(f"**/{ext}"))) if hyperspectral_files: hyperspectral_path = str(hyperspectral_files[0]) csv_files = [] processed_dir = wp / "4_processed_data" if processed_dir.exists(): csv_files = list(processed_dir.glob("*.csv")) if not csv_files: csv_files = ( list(wp.glob("**/*sampling*.csv")) + list(wp.glob("**/*point*.csv")) + list(wp.glob("**/*.csv")) ) if csv_files: csv_path = str(csv_files[0]) from src.postprocessing.point_map import SamplingPointMap map_generator = SamplingPointMap( output_dir=str(Path(resolve_subdir(self.work_dir, 'visualization')) / "sampling_maps"), fast_mode=True, ) map_path = map_generator.create_sampling_point_map( hyperspectral_path=hyperspectral_path, csv_path=csv_path, point_color="red", point_size=100, point_alpha=0.9, show_north_arrow=True, show_scale_bar=True, show_legend=True, downsample=True, dpi=180, ) parts.append(f"采样点图: {Path(map_path).name}") else: parts.append("采样点图: 跳过(无CSV)") else: parts.append("采样点图: 跳过(无影像)") if self.extra.get("gen_concentration"): conc_dir = wp / "9_Concentration" conc_csv = conc_dir / "final_concentrations.csv" if conc_csv.is_file(): charts_dir = conc_dir / "charts" charts_dir.mkdir(parents=True, exist_ok=True) try: import pandas as pd df = pd.read_csv(conc_csv) exclude_kw = ( "wavelength", "lon", "lat", "utm_x", "utm_y", "x", "y", "coord", "longitude", "latitude", "sample_id", "id", "index", "name", "pixel", ) conc_cols = [ c for c in df.select_dtypes(include=[np.number]).columns if not any(k in str(c).lower() for k in exclude_kw) ] if conc_cols: orig_out = viz.output_dir viz.output_dir = str(charts_dir) output_dict = viz.plot_statistical_charts( csv_path=str(conc_csv), parameter_columns=conc_cols, ) viz.output_dir = orig_out count = len([v for v in output_dict.values() if v]) parts.append(f"浓度统计图: {count} 个") stats_rows = [] for col in conc_cols: s = df[col].dropna() if len(s) == 0: continue stats_rows.append({ "参数": col, "点位数": len(s), "最小值": round(float(s.min()), 4), "最大值": round(float(s.max()), 4), "均值": round(float(s.mean()), 4), "中位数": round(float(s.median()), 4), "标准差": round(float(s.std()), 4) if len(s) > 1 else 0.0, }) if stats_rows: pd.DataFrame(stats_rows).to_csv( conc_dir / "statistics_summary.csv", index=False, encoding="utf-8-sig", ) parts.append("浓度统计表: 已生成") else: parts.append("浓度统计表: 跳过(无可用列)") else: parts.append("浓度统计图: 跳过(无可用浓度列)") except Exception as e: parts.append(f"浓度统计图: 失败({e})") else: parts.append("浓度统计图: 跳过(无浓度CSV)") if self.extra.get("gen_distribution_map"): dist_dir = wp / "11_Thematic_Map" out_sub = Path(viz.output_dir) / "distribution_maps" out_sub.mkdir(parents=True, exist_ok=True) n_rendered = 0 if dist_dir.exists(): import shutil # 1. 拷贝现成的 PNG for png in list(dist_dir.glob("*_distribution.png")) + list(dist_dir.glob("*_专题图.png")): try: shutil.copy2(png, out_sub / png.name) n_rendered += 1 except Exception: pass # 2. 渲染生成的 TIF tif_files = list(dist_dir.glob("*_distribution.tif")) + list(dist_dir.glob("*_kriging.tif")) if tif_files: from src.postprocessing.map import ContentMapper mapper = ContentMapper() # 优先级1：直接使用 Step 1 面板中缓存的外部原始 .shp 绝对路径！ boundary_path = self.extra.get("boundary_shp_path") # 优先级2：如果没拿到，全局搜索整个工作目录下的 .shp 文件（放宽限制） if not boundary_path: shp_candidates = list(wp.rglob("**/*.shp")) if shp_candidates: boundary_path = str(shp_candidates[0]) # 优先级3：兜底使用 1_water_mask 下的栅格掩膜 if not boundary_path: mask_files = list(wp.rglob("1_water_mask/*")) other_candidates = [f for f in mask_files if f.suffix.lower() in ('.dat', '.bsq', '.tif', '.tiff')] if other_candidates: boundary_path = str(other_candidates[0]) if not boundary_path: print(f"[distribution_maps] 未找到水域边界文件，跳过裁剪") for tif in tif_files: dst_png = out_sub / f"{tif.stem}_rendered.png" try: mapper.visualize_raster( raster_tif_path=str(tif), output_file=str(dst_png), boundary_shp_path=boundary_path, nodata_value=-9999.0, figsize=(14, 10), alpha=0.9 ) n_rendered += 1 except Exception as e: print(f"渲染 TIF 失败 {tif.name}: {e}") parts.append(f"空间分布图: {n_rendered} 个") else: parts.append("空间分布图: 跳过(无 11_Thematic_Map 目录)") self.finished_ok.emit({"task": "generate_all_selected", "parts": parts}) else: self.failed.emit(f"未知可视化任务: {self.task}") except Exception as e: self.failed.emit(f"{e}\n{traceback.format_exc()}") finally: if mpl_prev: try: import matplotlib.pyplot as plt plt.switch_backend(mpl_prev) except Exception: pass class PandasTableModel(QAbstractTableModel): """支持DataFrame的表格模型""" def __init__(self, data_frame: pd.DataFrame): super().__init__() self._data = data_frame.copy() if self._data.empty: self._data = pd.DataFrame() self._data.fillna("", inplace=True) self._columns = [str(col) for col in self._data.columns] def rowCount(self, parent=None): return len(self._data) def columnCount(self, parent=None): return len(self._columns) def data(self, index, role=Qt.DisplayRole): if not index.isValid() or role != Qt.DisplayRole: return None value = self._data.iat[index.row(), index.column()] if pd.isna(value): return "" return str(value) def headerData(self, section, orientation, role=Qt.DisplayRole): if role != Qt.DisplayRole: return None if orientation == Qt.Horizontal: if section < len(self._columns): return self._columns[section] return str(section) return str(section + 1) def flags(self, index): if not index.isValid(): return Qt.NoItemFlags return Qt.ItemIsEnabled | Qt.ItemIsSelectable class ChartViewerDialog(QDialog): """图表查看器对话框""" def __init__(self, title="图表查看器", parent=None): super().__init__(parent) self.setWindowTitle(title) self.resize(1000, 700) self.init_ui() def init_ui(self): layout = QVBoxLayout() self.figure = Figure(figsize=(10, 7)) self.canvas = FigureCanvas(self.figure) self.canvas.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Expanding) self.toolbar = NavigationToolbar(self.canvas, self) layout.addWidget(self.toolbar) layout.addWidget(self.canvas) btn_layout = QHBoxLayout() self.save_btn = QPushButton("保存图表") self.save_btn.clicked.connect(self.save_chart) btn_layout.addWidget(self.save_btn) btn_layout.addStretch() self.close_btn = QPushButton("关闭") self.close_btn.clicked.connect(self.close) btn_layout.addWidget(self.close_btn) layout.addLayout(btn_layout) self.setLayout(layout) def display_image(self, image_path): """显示图片""" self.figure.clear() ax = self.figure.add_subplot(111) try: import matplotlib.image as mpimg img = mpimg.imread(image_path) ax.imshow(img) ax.axis('off') self.figure.tight_layout() self.canvas.draw() self.current_image_path = image_path except Exception as e: ax.text(0.5, 0.5, f'加载图片失败:\n{str(e)}', ha='center', va='center', transform=ax.transAxes) self.canvas.draw() def display_custom_plot(self, plot_func): """显示自定义绘图函数""" self.figure.clear() try: plot_func(self.figure) self.canvas.draw() except Exception as e: ax = self.figure.add_subplot(111) ax.text(0.5, 0.5, f'绘图失败:\n{str(e)}', ha='center', va='center', transform=ax.transAxes) self.canvas.draw() def save_chart(self): """保存图表""" file_path, _ = QFileDialog.getSaveFileName( self, "保存图表", "", "PNG图片 (*.png);;JPG图片 (*.jpg);;PDF文件 (*.pdf);;所有文件 (*.*)" ) if file_path: try: self.figure.savefig(file_path, dpi=300, bbox_inches='tight') QMessageBox.information(self, "成功", f"图表已保存到:\n{file_path}") except Exception as e: QMessageBox.critical(self, "错误", f"保存失败:\n{str(e)}") class ImageCategoryTree(QTreeWidget): """现代化的图像分类目录树 - 支持智能归类、筛选和高颜值样式""" def __init__(self, parent=None): super().__init__(parent) self._work_path = None self._all_image_files = [] # 缓存所有扫描到的图片路径 self.setHeaderHidden(True) # 隐藏表头，显得更清爽 self.setAlternatingRowColors(True) # 斑马纹交替背景 self.setMaximumWidth(340) self.setMinimumWidth(280) # 核心：高颜值现代化 CSS 样式 self.setStyleSheet(""" QTreeWidget { border: 1px solid #E2E8F0; border-radius: 8px; background-color: #FFFFFF; alternate-background-color: #F8FAFC; font-family: "Microsoft YaHei", "Segoe UI"; font-size: 13px; padding: 4px; } QTreeWidget::item { height: 32px; border-radius: 6px; margin: 2px 4px; } QTreeWidget::item:hover { background-color: #F1F5F9; } QTreeWidget::item:selected { background-color: #E0F2FE; color: #0369A1; font-weight: bold; } QTreeWidget::branch:has-children:!has-siblings:closed, QTreeWidget::branch:closed:has-children:has-siblings { border-image: none; image: none; } QTreeWidget::branch:open:has-children:!has-siblings, QTreeWidget::branch:open:has-children:has-siblings { border-image: none; image: none; } """) def _parse_file_info(self, file_path: Path): """智能解析文件名，提取【水质参数】和【图表类型】""" name_upper = file_path.name.upper() # 1. 提取图表类型 chart_type = "其他图表" if "DISTRIBUTION" in name_upper or "专题图" in name_upper or "RENDERED" in name_upper: chart_type = "空间分布图" elif "SCATTER" in name_upper or "散点" in name_upper: chart_type = "模型散点图" elif "SPECTRUM" in name_upper or "光谱" in name_upper: chart_type = "光谱曲线图" elif "HEATMAP" in name_upper or "热力图" in name_upper: chart_type = "相关性热力图" elif "BOXPLOT" in name_upper or "箱线" in name_upper: chart_type = "统计箱线图" elif "HISTOGRAM" in name_upper or "直方" in name_upper: chart_type = "分布直方图" elif "SAMPLING" in name_upper or "采样" in name_upper: chart_type = "采样点地图" elif "GLINT" in name_upper or "MASK" in name_upper or "PREVIEW" in name_upper: chart_type = "掩膜与预览" # 2. 提取参数名（扩展版：覆盖 12 项常见水质参数前缀） param_name = "综合/未分类" # ★ 匹配规则：短关键字（≤3 字符）必须在词边界（开头或 _ - . 之后）出现， # 杜绝 "TT" 命中 "scaTTer"、"CL" 命中 "ChL" 等误匹配。 # 长关键字（≥4 字符）保持简单子串匹配，兼容历史行为。 import re as _re def _match_key(name: str, key: str) -> bool: clean = key.replace('-', '') # 长关键字（≥4）：简单子串匹配，历史行为一致 if len(key) >= 4 or len(clean) >= 4: if key in name: return True if clean != key and clean in name: return True return False # 短关键字（≤3）：必须出现在词边界（行首或 _ - . 之后）。 # 若 key 本身以 _ - . 结尾，则不要求后缀边界（_ 已是分隔符）。 suffix = r'' if key[-1] in '_.-' else r'(?![a-zA-Z0-9])' pattern = _re.compile( r'(?:^|[_.\-])' + _re.escape(key) + suffix ) if pattern.search(name): return True if clean != key: clean_suffix = r'' if clean[-1] in '_.-' else r'(?![a-zA-Z0-9])' pattern2 = _re.compile( r'(?:^|[_.\-])' + _re.escape(clean) + clean_suffix ) if pattern2.search(name): return True return False params_map = { # ── 叶绿素 a ── 'CHLOROPHYLL': '叶绿素a (Chl-a)', 'CHL-A': '叶绿素a (Chl-a)', 'CHL_A': '叶绿素a (Chl-a)', 'CHLA': '叶绿素a (Chl-a)', 'CHL_CONC': '叶绿素a (Chl-a)', 'CHL_': '叶绿素a (Chl-a)', # ── 总悬浮物 ── 'SUSPENDED': '总悬浮物 (TSM)', 'TSM_CONC': '总悬浮物 (TSM)', 'TSM_': '总悬浮物 (TSM)', 'TSM': '总悬浮物 (TSM)', # ── 藻蓝蛋白 ── 'PHYCOCYANIN': '藻蓝蛋白 (PC)', 'PHYCO': '藻蓝蛋白 (PC)', 'BGA': '藻蓝蛋白 (PC)', 'PC_CONC': '藻蓝蛋白 (PC)', 'PC_': '藻蓝蛋白 (PC)', # ── 浊度 ── 'TURBIDITY': '浊度 (Turbidity)', 'TURB_CONC': '浊度 (Turbidity)', 'TURB_': '浊度 (Turbidity)', # ── 有色可溶性有机物 ── 'CDOM': '有色可溶性有机物 (CDOM)', # ── 透明度 ── 'SECCHI': '透明度 (SDD)', 'SDD_CONC': '透明度 (SDD)', 'SDD_': '透明度 (SDD)', 'SDD': '透明度 (SDD)', # ── 氮磷类 ── 'NITROGEN': '总氮 (TN)', 'TN_CONC': '总氮 (TN)', 'TN_': '总氮 (TN)', 'PHOSPHORUS': '总磷 (TP)', 'TP_CONC': '总磷 (TP)', 'TP_': '总磷 (TP)', 'NH3-N': '氨氮 (NH3-N)', 'NH3_CONC': '氨氮 (NH3-N)', 'NH3_': '氨氮 (NH3-N)', 'NH3N': '氨氮 (NH3-N)', 'NO3-N': '硝态氮 (NO3-N)', 'NO3N': '硝态氮 (NO3-N)', # ── 其他需氧量与离子 ── 'COD': '化学需氧量 (COD)', 'DISSOLVED_OXYGEN': '溶解氧 (DO)', 'DO_CONC': '溶解氧 (DO)', 'DO_': '溶解氧 (DO)', 'CL-': '氯离子 (Cl-)', 'CL_': '氯离子 (Cl-)', # ── 基础物理与特征指标 ── 'PH': '酸碱度 (pH)', 'TEMPERATURE': '温度 (Temperature)', 'SPCOND': '电导率 (spCond)', 'TDS': '总溶解固体 (TDS)', 'TT': '特征指标 (TT)', } for key, display_name in params_map.items(): if _match_key(name_upper, key): param_name = display_name break return param_name, chart_type def scan_directory(self, work_dir: str): """全量扫描文件并缓存（不直接构建树，而是交给 rebuild_tree 渲染）""" try: if not work_dir: return self._work_path = Path(work_dir) if not self._work_path.exists(): return # 仅扫描用于视觉展示的常规图片格式，屏蔽科学栅格 TIF 以免无法渲染报错 image_extensions = ['*.png', '*.jpg', '*.jpeg', '*.bmp'] # 扩展扫描路径 scan_roots = [ Path(resolve_subdir(str(self._work_path), 'visualization')), Path(resolve_subdir(str(self._work_path), 'prediction_dir')), Path(resolve_subdir(str(self._work_path), 'regression_modeling')), self._work_path / "10_feature_construction", self._work_path / "5_training_spectra", Path(resolve_subdir(str(self._work_path), 'glint_detection')), Path(resolve_subdir(str(self._work_path), 'deglint')), Path(resolve_subdir(str(self._work_path), 'water_mask')), self._work_path / "9_water_quality_prediction", self._work_path / "9_Concentration", self._work_path / "11_Thematic_Map" ] scan_roots = [p for p in scan_roots if p.is_dir()] if not scan_roots: scan_roots.append(self._work_path) seen_norm = set() self._all_image_files = [] for root in scan_roots: for ext in image_extensions: for p in root.rglob(ext): key = os.path.normcase(os.path.normpath(str(p.resolve()))) if key in seen_norm: continue seen_norm.add(key) if p.name.startswith('.') or 'thumb' in p.name.lower(): continue self._all_image_files.append(p) self._all_image_files.sort(key=lambda x: x.name) # 默认构建模式 self.rebuild_tree(group_mode='parameter', filter_type='all') except Exception as e: print(f"目录扫描出错: {e}") def rebuild_tree(self, group_mode='parameter', filter_type='all'): """根据下拉框的【模式】和【筛选条件】实时重新构建 UI 树""" self.blockSignals(True) self.clear() root_nodes = {} for img_file in self._all_image_files: param, chart_type = self._parse_file_info(img_file) # 1. 应用筛选器逻辑 if filter_type != 'all' and filter_type != chart_type: continue # 2. 决定分组基准 if group_mode == 'parameter': group_key = param group_icon = "💧" if param != "综合/未分类" else "📁" display_name = f"[{chart_type}] {img_file.name}" elif group_mode == 'type': group_key = chart_type group_icon = "📊" display_name = f"[{param.split(' ')[0]}] {img_file.name}" else: # 物理文件夹原样模式 try: rel_path = img_file.relative_to(self._work_path) group_key = str(rel_path.parent) if len(rel_path.parts) > 1 else "根目录" except: group_key = "其他" group_icon = "📂" display_name = img_file.name # 3. 创建父节点 if group_key not in root_nodes: root_item = QTreeWidgetItem(self) root_item.setText(0, f"{group_icon} {group_key}") root_item.setExpanded(True) font = root_item.font(0) font.setBold(True) root_item.setFont(0, font) root_item.setData(0, Qt.UserRole, {"type": "root"}) root_nodes[group_key] = root_item parent_item = root_nodes[group_key] # 4. 挂载子节点及专属图标 icon = "🖼️" if "散点" in chart_type: icon = "📌" elif "光谱" in chart_type: icon = "📈" elif "分布" in chart_type: icon = "🗺️" elif "箱线" in chart_type: icon = "📉" image_item = QTreeWidgetItem(parent_item) image_item.setText(0, f" {icon} {display_name}") image_item.setData(0, Qt.UserRole, {"type": "image", "path": str(img_file)}) image_item.setToolTip(0, str(img_file)) # 统计数量 for i in range(self.topLevelItemCount()): root_item = self.topLevelItem(i) count = root_item.childCount() old_text = root_item.text(0) root_item.setText(0, f"{old_text} ({count})") self.blockSignals(False) def get_selected_image_path(self) -> Optional[str]: selected_item = self.currentItem() if not selected_item: return None data = selected_item.data(0, Qt.UserRole) if data and data.get("type") == "image": return data.get("path") return None class ImageViewerWidget(QWidget): """图像查看器组件 - 支持缩放、平移""" def __init__(self, parent=None): super().__init__(parent) self.current_image_path = None self.scale_factor = 1.0 self._update_timer = QTimer() self._update_timer.setSingleShot(True) self._update_timer.timeout.connect(self._do_update_display) self._pending_scale = None self.setup_ui() def setup_ui(self): layout = QVBoxLayout() layout.setContentsMargins(0, 0, 0, 0) toolbar = QHBoxLayout() self.refresh_btn = QPushButton("🔄 刷新目录") self.refresh_btn.setToolTip("重新扫描工作目录中的图像文件") toolbar.addWidget(self.refresh_btn) separator = QFrame() separator.setFrameShape(QFrame.VLine) separator.setFrameShadow(QFrame.Sunken) toolbar.addWidget(separator) self.zoom_in_btn = QPushButton("🔍+") self.zoom_in_btn.setToolTip("放大") self.zoom_in_btn.setMaximumWidth(50) toolbar.addWidget(self.zoom_in_btn) self.zoom_out_btn = QPushButton("🔍-") self.zoom_out_btn.setToolTip("缩小") self.zoom_out_btn.setMaximumWidth(50) toolbar.addWidget(self.zoom_out_btn) self.fit_btn = QPushButton("⬜ 适应窗口") self.fit_btn.setToolTip("适应窗口大小") toolbar.addWidget(self.fit_btn) self.original_btn = QPushButton("1:1 原始大小") self.original_btn.setToolTip("原始大小") toolbar.addWidget(self.original_btn) self.hint_label = QLabel("💡 提示: 按住 Ctrl+滚轮 可以实现放大缩小") self.hint_label.setStyleSheet(""" QLabel { color: #444444; font-size: 14px; font-weight: bold; padding-left: 15px; } """) toolbar.addWidget(self.hint_label) toolbar.addStretch() self.save_btn = QPushButton("💾 保存") self.save_btn.setToolTip("保存当前图像") toolbar.addWidget(self.save_btn) layout.addLayout(toolbar) self.scroll_area = QScrollArea() self.scroll_area.setWidgetResizable(True) self.scroll_area.setHorizontalScrollBarPolicy(Qt.ScrollBarAsNeeded) self.scroll_area.setVerticalScrollBarPolicy(Qt.ScrollBarAsNeeded) self.scroll_area.setStyleSheet("background-color: white;") self.image_label = QLabel() self.image_label.setAlignment(Qt.AlignCenter) self.image_label.setStyleSheet("background-color: white;") self.scroll_area.setWidget(self.image_label) # 全方位事件拦截：给所有可能触发滚轮的子组件全部挂载过滤器 self.image_label.installEventFilter(self) self.scroll_area.viewport().installEventFilter(self) self.scroll_area.installEventFilter(self) self.scroll_area.verticalScrollBar().installEventFilter(self) self.scroll_area.horizontalScrollBar().installEventFilter(self) layout.addWidget(self.scroll_area, 1) self.setLayout(layout) self.zoom_in_btn.clicked.connect(self.zoom_in) self.zoom_out_btn.clicked.connect(self.zoom_out) self.fit_btn.clicked.connect(self.fit_to_window) self.original_btn.clicked.connect(self.original_size) self.save_btn.clicked.connect(self.save_image) def load_image(self, image_path: str): """加载并显示图像""" if not image_path or not Path(image_path).exists(): self.image_label.setText("图像不存在") return self.current_image_path = image_path self.scale_factor = 1.0 pixmap = QPixmap(image_path) if pixmap.isNull(): self.image_label.setText("无法加载图像") return self.original_pixmap = pixmap self.fit_to_window() file_info = Path(image_path).stat() size_mb = file_info.st_size / (1024 * 1024) def update_image_display(self): """更新图像显示 - 使用防抖避免频繁重绘卡顿""" self._update_timer.stop() self._pending_scale = self.scale_factor self._update_timer.start(50) def _do_update_display(self): """实际执行图像更新""" if not hasattr(self, 'original_pixmap') or self.original_pixmap.isNull(): return if self._pending_scale is None: return if self._pending_scale > 2.0 or self._pending_scale < 0.5: transform = Qt.FastTransformation else: transform = Qt.SmoothTransformation scaled_pixmap = self.original_pixmap.scaled( int(self.original_pixmap.width() * self._pending_scale), int(self.original_pixmap.height() * self._pending_scale), Qt.KeepAspectRatio, transform ) self.image_label.setPixmap(scaled_pixmap) self._pending_scale = None def eventFilter(self, obj, event): from PyQt5.QtCore import QEvent, Qt if event.type() == QEvent.Wheel: if event.modifiers() == Qt.ControlModifier: if obj is self.scroll_area.viewport() or obj is self.image_label: delta = event.angleDelta().y() if delta > 0: if getattr(self, 'scale_factor', 1.0) < 5.0: self.scale_factor = min(self.scale_factor * 1.1, 5.0) self.update_image_display() else: if getattr(self, 'scale_factor', 1.0) > 0.1: self.scale_factor = max(self.scale_factor / 1.1, 0.1) self.update_image_display() return True return super().eventFilter(obj, event) def zoom_in(self): """放大""" if self.scale_factor < 5.0: self.scale_factor = min(self.scale_factor * 1.25, 5.0) self.update_image_display() def zoom_out(self): """缩小""" if self.scale_factor > 0.1: self.scale_factor = max(self.scale_factor / 1.25, 0.1) self.update_image_display() def fit_to_window(self): """适应窗口""" if not hasattr(self, 'original_pixmap') or self.original_pixmap.isNull(): return view_size = self.scroll_area.viewport().size() img_size = self.original_pixmap.size() scale_w = view_size.width() / img_size.width() scale_h = view_size.height() / img_size.height() self._fit_scale = min(scale_w, scale_h) self.scale_factor = self._fit_scale self.update_image_display() def original_size(self): """原始大小""" self.scale_factor = 1.0 self._fit_scale = None self.update_image_display() def save_image(self): """保存图像""" if not self.current_image_path: return file_path, _ = QFileDialog.getSaveFileName( self, "保存图像", Path(self.current_image_path).name, "PNG图片 (*.png);;JPG图片 (*.jpg);;所有文件 (*.*)" ) if file_path: try: import shutil shutil.copy(self.current_image_path, file_path) except Exception as e: QMessageBox.critical(self, "错误", f"保存失败: {e}") class ChartBrowserDialog(QDialog): """图表浏览器对话框""" def __init__(self, chart_files, parent=None): super().__init__(parent) self.chart_files = sorted(chart_files, key=lambda x: x.stat().st_mtime, reverse=True) self.current_index = 0 self.setWindowTitle("图表浏览器") self.resize(1200, 800) self.init_ui() self.show_chart(0) def init_ui(self): layout = QVBoxLayout() list_group = QGroupBox(f"图表列表 (共 {len(self.chart_files)} 个)") list_layout = QHBoxLayout() self.chart_list = QListWidget() self.chart_list.setMaximumHeight(150) for chart_file in self.chart_files: self.chart_list.addItem(chart_file.name) self.chart_list.currentRowChanged.connect(self.show_chart) list_layout.addWidget(self.chart_list) list_group.setLayout(list_layout) layout.addWidget(list_group) self.figure = Figure(figsize=(12, 8)) self.canvas = FigureCanvas(self.figure) self.canvas.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Expanding) self.toolbar = NavigationToolbar(self.canvas, self) layout.addWidget(self.toolbar) layout.addWidget(self.canvas, 1) btn_layout = QHBoxLayout() self.prev_btn = QPushButton("◀ 上一个") self.prev_btn.clicked.connect(self.prev_chart) btn_layout.addWidget(self.prev_btn) self.next_btn = QPushButton("下一个 >") self.next_btn.clicked.connect(self.next_chart) btn_layout.addWidget(self.next_btn) btn_layout.addStretch() self.save_btn = QPushButton("💾 保存当前图表") self.save_btn.clicked.connect(self.save_current_chart) btn_layout.addWidget(self.save_btn) self.close_btn = QPushButton("关闭") self.close_btn.clicked.connect(self.close) btn_layout.addWidget(self.close_btn) layout.addLayout(btn_layout) self.setLayout(layout) def show_chart(self, index): """显示指定索引的图表""" if 0 <= index < len(self.chart_files): self.current_index = index self.chart_list.setCurrentRow(index) chart_file = self.chart_files[index] self.figure.clear() ax = self.figure.add_subplot(111) try: import matplotlib.image as mpimg img = mpimg.imread(str(chart_file)) ax.imshow(img) ax.axis('off') ax.set_title(chart_file.name, fontsize=12, pad=10) self.figure.tight_layout() self.canvas.draw() except Exception as e: ax.text(0.5, 0.5, f'加载图片失败:\n{str(e)}', ha='center', va='center', transform=ax.transAxes) self.canvas.draw() self.prev_btn.setEnabled(index > 0) self.next_btn.setEnabled(index < len(self.chart_files) - 1) def prev_chart(self): """上一个图表""" if self.current_index > 0: self.show_chart(self.current_index - 1) def next_chart(self): """下一个图表""" if self.current_index < len(self.chart_files) - 1: self.show_chart(self.current_index + 1) def save_current_chart(self): """保存当前图表""" if 0 <= self.current_index < len(self.chart_files): current_file = self.chart_files[self.current_index] file_path, _ = QFileDialog.getSaveFileName( self, "保存图表", current_file.name, "PNG图片 (*.png);;JPG图片 (*.jpg);;所有文件 (*.*)" ) if file_path: try: import shutil shutil.copy(str(current_file), file_path) QMessageBox.information(self, "成功", f"图表已保存到:\n{file_path}") except Exception as e: QMessageBox.critical(self, "错误", f"保存失败:\n{str(e)}") class Step12VizPanel(QWidget): """步骤12：可视化展示""" def __init__(self, parent=None): super().__init__(parent) self.work_dir = None self.chart_viewer = None self._viz_thread = None self.init_ui() def _viz_set_busy(self, busy: bool): for w in ( getattr(self, "gen_all_btn", None), getattr(self, "scan_btn", None), ): if w is not None: w.setEnabled(not busy) def _start_visualization_thread(self, task: str, extra: Optional[dict] = None) -> bool: if not self.work_dir: QMessageBox.warning(self, "警告", "请先选择工作目录！") return False work_path = Path(self.work_dir) if not work_path.exists(): QMessageBox.warning(self, "警告", "工作目录不存在！") return False if self._viz_thread and self._viz_thread.isRunning(): QMessageBox.information(self, "提示", "可视化任务正在运行，请稍候。") return False self._viz_thread = VisualizationWorkerThread(task, str(work_path), extra or {}) self._viz_thread.finished_ok.connect(self._on_visualization_worker_ok, Qt.QueuedConnection) self._viz_thread.failed.connect(self._on_visualization_worker_fail, Qt.QueuedConnection) self._viz_thread.finished.connect(lambda: self._viz_set_busy(False), Qt.QueuedConnection) self._viz_set_busy(True) self._viz_thread.start() return True def _spectrum_meta_param_columns(self, df: pd.DataFrame) -> List[str]: """光谱图可选的水质参数列（光谱波段列之前、且为数值型）。""" wl = _viz_infer_wavelength_start_column(df) if isinstance(wl, str): idx = int(df.columns.get_loc(wl)) + 1 else: idx = int(wl) if idx <= 0 or idx >= len(df.columns): numeric = df.select_dtypes(include=[np.number]).columns.tolist() return [ c for c in numeric if not any(x in str(c).lower() for x in ("utm", "lat", "lon", "x", "y")) ] meta = list(df.columns[:idx]) return [c for c in meta if pd.api.types.is_numeric_dtype(df[c])] def _statistics_param_columns(self, df: pd.DataFrame) -> List[str]: """统计图用的参数列：只统计水质参数列（数值型），排除波长列。""" numeric_cols = df.select_dtypes(include=[np.number]).columns.tolist() wl = _viz_infer_wavelength_start_column(df) if isinstance(wl, str): idx = int(df.columns.get_loc(wl)) + 1 else: idx = int(wl) coord_kw = ("utm", "lat", "lon") if 0 < idx < len(df.columns): meta_set = set(df.columns[:idx]) return [ col for col in numeric_cols if col in meta_set and not any(x in str(col).lower() for x in coord_kw) ] return [ col for col in numeric_cols if not any(x in str(col).lower() for x in coord_kw + ("x", "y")) ] def _on_visualization_worker_ok(self, payload): if not isinstance(payload, dict): self.scan_work_directory() return t = payload.get("task") if t == "mask_glint": cnt = int(payload.get("count") or 0) if cnt > 0: QMessageBox.information( self, "成功", f"掩膜和耀斑缩略图生成完成，共 {cnt} 个预览图。\n" f"保存位置: 12_visualization/glint_deglint_previews/", ) else: QMessageBox.warning( self, "警告", "未找到可处理的影像文件（2_Glint_Detection/3_deglint 等）。", ) elif t == "sampling_map": map_path = payload.get("map_path") QMessageBox.information( self, "成功", "采样点地图生成完成。\n" f"输出: {Path(map_path).name if map_path else ''}\n" "路径: 12_visualization/sampling_maps/", ) if map_path: self.show_chart_viewer(map_path, "采样点分布图") elif t == "spectrum": multi = payload.get("output_paths") if isinstance(multi, list) and multi: ok_paths = [p for p in multi if p and Path(str(p)).is_file()] errs = payload.get("errors") or [] msg = ( f"已为 {len(ok_paths)} 个水质参数生成光谱对比图。\n" f"保存目录: 工作目录/12_visualization/" ) if errs: msg += f"\n\n以下列未生成或出错 ({len(errs)} 项，详见日志):\n" msg += "\n".join(str(e) for e in errs[:8]) if len(errs) > 8: msg += "\n..." QMessageBox.information(self, "成功", msg) if ok_paths: self.show_chart_viewer(ok_paths[0], "光谱曲线对比（首张）") else: outp = payload.get("output_path") param = payload.get("param_col", "") QMessageBox.information(self, "成功", f"光谱图已生成:\n{outp}") if outp: self.show_chart_viewer(outp, f"{param} - 光谱曲线对比") elif t == "statistics": outp = payload.get("output_paths") or {} QMessageBox.information( self, "成功", f"统计图表已生成，共 {len(outp)} 项。" ) if isinstance(outp, dict) and "boxplot" in outp: self.show_chart_viewer(outp["boxplot"], "水质参数箱线图") elif t == "scatter": paths = payload.get("scatter_paths") or {} ok_paths = [p for p in paths.values() if p and Path(str(p)).is_file()] if ok_paths: QMessageBox.information( self, "成功", f"已生成 {len(ok_paths)} 个模型评估散点图。\n" f"保存位置: 12_visualization/scatter_plots/", ) self.show_chart_viewer(ok_paths[0], "模型评估散点图") else: QMessageBox.warning( self, "提示", "未生成任何散点图。请确认 7_Supervised_Model_Training 下已有各参数子目录及模型文件，" "且训练 CSV 与建模时一致。", ) elif t == "generate_all_selected": parts = payload.get("parts") or [] QMessageBox.information( self, "完成", "批量可视化已执行：\n" + "\n".join(parts) if parts else "（无选中项或已跳过）", ) # ★ 延迟 400ms 再扫描目录，确保后台渲染线程已将图像文件 flush 到磁盘 QTimer.singleShot(400, self.scan_work_directory) def _on_visualization_worker_fail(self, err: str): QMessageBox.critical(self, "错误", f"可视化任务失败:\n{err[:1200]}") def init_ui(self): """初始化UI - 使用全新的三列布局（控制参数 | 独立满高目录树 | 图像查看器）""" # 1. 补上缺失的主题样式导入！ from src.gui.styles import ModernStylesheet # 2. 增加一句热重载安全清理逻辑，彻底消除 QLayout 冲突警告 if self.layout() is not None: QWidget().setLayout(self.layout()) main_layout = QHBoxLayout() main_layout.setSpacing(16) main_layout.setContentsMargins(20, 20, 20, 20) # ========================================== # 第一列：控制面板（目录选择 + 生成配置） # ========================================== control_panel = QWidget() control_layout = QVBoxLayout() control_layout.setContentsMargins(0, 0, 0, 0) control_layout.setSpacing(16) # 1. 工作目录选择 dir_group = QGroupBox("工作目录") dir_layout = QHBoxLayout() self.work_dir_edit = QLineEdit() self.work_dir_edit.setPlaceholderText("选择工作目录...") self.work_dir_edit.setReadOnly(True) dir_browse_btn = QPushButton("浏览") dir_browse_btn.setStyleSheet(ModernStylesheet.get_button_stylesheet('normal')) dir_browse_btn.clicked.connect(self.browse_work_dir) dir_layout.addWidget(self.work_dir_edit, 1) dir_layout.addWidget(dir_browse_btn) dir_group.setLayout(dir_layout) control_layout.addWidget(dir_group) # 2. 图像目录选择 img_dir_group = QGroupBox("图像目录") img_dir_layout = QHBoxLayout() self.img_dir_edit = QLineEdit() self.img_dir_edit.setPlaceholderText("预测结果目录（自动填充）…") self.img_dir_edit.setReadOnly(True) img_dir_browse_btn = QPushButton("浏览") img_dir_browse_btn.setStyleSheet(ModernStylesheet.get_button_stylesheet('normal')) img_dir_browse_btn.clicked.connect(self.browse_img_dir) img_dir_layout.addWidget(self.img_dir_edit, 1) img_dir_layout.addWidget(img_dir_browse_btn) img_dir_group.setLayout(img_dir_layout) control_layout.addWidget(img_dir_group) # 3. 可视化配置 config_group = QGroupBox("⚙️ 可视化配置") config_layout = QVBoxLayout() config_layout.setSpacing(12) config_layout.setContentsMargins(16, 20, 16, 16) self.gen_scatter = QCheckBox("模型评估散点图") self.gen_scatter.setChecked(True) config_layout.addWidget(self.gen_scatter) self.gen_spectrum = QCheckBox("光谱曲线图") self.gen_spectrum.setChecked(True) config_layout.addWidget(self.gen_spectrum) self.gen_boxplots = QCheckBox("统计图表") self.gen_boxplots.setChecked(True) config_layout.addWidget(self.gen_boxplots) self.gen_mask_glint = QCheckBox("掩膜和耀斑缩略图") self.gen_mask_glint.setChecked(True) config_layout.addWidget(self.gen_mask_glint) self.gen_sampling_map = QCheckBox("采样点地图") self.gen_sampling_map.setChecked(True) config_layout.addWidget(self.gen_sampling_map) self.gen_distribution_map = QCheckBox("空间分布图 (Step 11 产物)") self.gen_distribution_map.setChecked(True) self.gen_distribution_map.setToolTip("渲染并汇总 Step 11 生成的 TIF 分布图") config_layout.addWidget(self.gen_distribution_map) config_layout.addSpacing(6) line = QFrame() line.setFrameShape(QFrame.HLine) line.setStyleSheet("color: #E2E8F0;") config_layout.addWidget(line) config_layout.addSpacing(6) # 彻底移除硬编码内联样式，全面接入 ModernStylesheet 主题 self.gen_all_btn = QPushButton("🚀 生成全部") self.gen_all_btn.setStyleSheet(ModernStylesheet.get_button_stylesheet('primary')) self.gen_all_btn.setMinimumHeight(36) self.gen_all_btn.clicked.connect(self.generate_all_visualizations) config_layout.addWidget(self.gen_all_btn) self.scan_btn = QPushButton("📁 扫描目录") self.scan_btn.setStyleSheet(ModernStylesheet.get_button_stylesheet('normal')) self.scan_btn.setMinimumHeight(36) self.scan_btn.clicked.connect(self.scan_work_directory) config_layout.addWidget(self.scan_btn) config_group.setLayout(config_layout) control_layout.addWidget(config_group) control_layout.addStretch() control_panel.setLayout(control_layout) control_panel.setMaximumWidth(290) control_panel.setMinimumWidth(250) main_layout.addWidget(control_panel, 0) # ========================================== # 第二列：满高独立的目录树（选图与筛选面板） # ========================================== from PyQt5.QtWidgets import QComboBox tree_panel = QWidget() tree_layout = QVBoxLayout() tree_layout.setContentsMargins(0, 0, 0, 0) tree_group = QGroupBox("🖼️ 图像浏览与筛选") group_layout = QVBoxLayout() group_layout.setSpacing(12) group_layout.setContentsMargins(16, 20, 16, 16) filter_layout = QFormLayout() filter_layout.setContentsMargins(0, 0, 0, 0) filter_layout.setSpacing(10) # 彻底摘除下拉框的硬编码外壳，交由系统的全局 CSS 完美接管 self.view_mode_cb = QComboBox() self.view_mode_cb.addItems(["按水质参数归类", "按图表类型归类", "按物理文件夹"]) self.view_mode_cb.currentIndexChanged.connect(self.update_image_tree_view) self.chart_filter_cb = QComboBox() self.chart_filter_cb.addItems(["全部图表", "空间分布图", "模型散点图", "光谱曲线图", "统计箱线图", "分布直方图", "相关性热力图", "掩膜与预览", "采样点地图"]) self.chart_filter_cb.currentIndexChanged.connect(self.update_image_tree_view) filter_layout.addRow("视图模式:", self.view_mode_cb) filter_layout.addRow("类型筛选:", self.chart_filter_cb) group_layout.addLayout(filter_layout) self.image_tree = ImageCategoryTree() self.image_tree.itemClicked.connect(self.on_tree_item_clicked) group_layout.addWidget(self.image_tree, 1) tree_group.setLayout(group_layout) tree_layout.addWidget(tree_group, 1) tree_panel.setLayout(tree_layout) tree_panel.setMaximumWidth(340) tree_panel.setMinimumWidth(280) main_layout.addWidget(tree_panel, 0) # ========================================== # 第三列：图像查看器 # ========================================== right_panel = QWidget() right_layout = QVBoxLayout() right_layout.setContentsMargins(0, 0, 0, 0) # 给画板穿上卡片外衣，确保三列视觉上的绝对统称和统一 viewer_group = QGroupBox("👁️ 图像预览区") viewer_layout = QVBoxLayout() viewer_layout.setContentsMargins(8, 20, 8, 8) self.image_viewer = ImageViewerWidget() self.image_viewer.refresh_btn.clicked.connect(self.scan_work_directory) viewer_layout.addWidget(self.image_viewer, 1) viewer_group.setLayout(viewer_layout) right_layout.addWidget(viewer_group, 1) right_panel.setLayout(right_layout) main_layout.addWidget(right_panel, 1) self.setLayout(main_layout) def update_image_tree_view(self): """响应下拉框改变，重新渲染树状图""" if not hasattr(self, 'image_tree') or not self.image_tree._all_image_files: return # 1. 提取当前选中的分组模式 mode_idx = self.view_mode_cb.currentIndex() if mode_idx == 0: group_mode = 'parameter' elif mode_idx == 1: group_mode = 'type' else: group_mode = 'folder' # 2. 提取当前的图表筛选条件 filter_type = self.chart_filter_cb.currentText() if filter_type == "全部图表": filter_type = 'all' # 3. 触发重绘 self.image_tree.rebuild_tree(group_mode, filter_type) self._load_first_image_from_tree() def set_work_dir(self, work_dir): """设置工作目录""" self.work_dir = work_dir self.work_dir_edit.setText(str(work_dir)) if work_dir: QTimer.singleShot(100, self.scan_work_directory) def _get_default_work_dir(self): """获取 work_dir，优先用 panel 自身缓存的，否则尝试从主窗口取""" if hasattr(self, 'work_dir') and self.work_dir: return str(self.work_dir) mw = self.window() if mw and hasattr(mw, 'work_dir') and mw.work_dir: return str(mw.work_dir) return "" def browse_work_dir(self): """浏览工作目录""" default = self._get_default_work_dir() dir_path = QFileDialog.getExistingDirectory(self, "选择工作目录", default) if dir_path: self.work_dir = dir_path self.work_dir_edit.setText(dir_path) self.scan_work_directory() def browse_img_dir(self): """手动浏览图像目录""" default = self._get_default_work_dir() dir_path = QFileDialog.getExistingDirectory(self, "选择图像目录", default) if dir_path: self.img_dir_edit.setText(dir_path) self.image_tree.scan_directory(dir_path) self._load_first_image_from_tree() def update_from_config(self, work_dir=None, pipeline=None): """从全局配置自动推断并填入图像目录，然后自动加载目录内容。 推断优先级： 1. {work_dir}/9_ML_Prediction（机器学习预测） 2. {work_dir}/10_WaterIndex_CSV（水色指数反演） 3. {work_dir}/11_Thematic_Map（专题分布图） 4. {work_dir}/12_visualization（可视化目录） 5. {work_dir}（工作目录根） """ try: if work_dir: self.work_dir = work_dir self.work_dir_edit.setText(str(work_dir)) elif not self.work_dir: return work_path = Path(self.work_dir) # 按优先级寻找存在的目录 candidates = [ Path(resolve_subdir(self.work_dir, 'ml_prediction')), Path(resolve_subdir(self.work_dir, 'watercolor')), Path(resolve_subdir(self.work_dir, 'step11_map')), Path(resolve_subdir(self.work_dir, 'visualization')), work_path, ] detected_dir = None for candidate in candidates: if candidate.exists() and candidate.is_dir(): detected_dir = candidate break if detected_dir: detected_str = str(detected_dir) self.img_dir_edit.setText(detected_str) self.image_tree.scan_directory(detected_str) else: # 无预测目录时扫描整个工作目录 self.image_tree.scan_directory(self.work_dir) # 自动触发加载第一张图像 self._load_first_image_from_tree() except Exception as e: import traceback print(f"可视化面板 update_from_config 出错: {e}") traceback.print_exc() def _load_first_image_from_tree(self): """自动加载树状图中的第一张有效图片（兼容物理目录层级结构）""" try: tree = getattr(self, 'image_tree', None) if not tree: return from PyQt5.QtCore import Qt def find_first_image(item): # 检查当前节点是否是图片节点 data = item.data(0, Qt.UserRole) if isinstance(data, dict) and data.get("type") == "image": return item # 如果不是，递归检查所有子节点 for i in range(item.childCount()): found = find_first_image(item.child(i)) if found: return found return None # 遍历所有顶层节点 for i in range(tree.topLevelItemCount()): first_img_item = find_first_image(tree.topLevelItem(i)) if first_img_item: tree.setCurrentItem(first_img_item) # 主动触发一次点击槽函数，以在右侧渲染图片 self.on_tree_item_clicked(first_img_item, 0) return except Exception as e: import traceback print(f"自动加载首张图片失败: {e}") traceback.print_exc() def scan_work_directory(self): """扫描工作目录中的图像文件""" if not self.work_dir: return work_path = Path(self.work_dir) if not work_path.exists(): return print(f"扫描工作目录: {work_path}") self.image_tree.scan_directory(str(work_path)) self._setup_prediction_output_dirs(work_path) viz_dir = Path(resolve_subdir(str(work_path), 'visualization')) if viz_dir.exists(): image_files = list(viz_dir.glob("**/*.png")) + list(viz_dir.glob("**/*.jpg")) if image_files: self.image_viewer.load_image(str(image_files[0])) def _setup_prediction_output_dirs(self, work_path: Path): """收集预测输出目录路径信息（不创建目录，仅用于日志/调试）。 2026-06-30 修复：移除 mkdir 调用，不再在未运行 pipeline 时创建空目录。 目录创建统一留给各 pipeline 步骤在实际执行时处理。 """ try: ml_dir = Path(resolve_subdir(str(work_path), 'ml_prediction')) watercolor_dir = Path(resolve_subdir(str(work_path), 'watercolor')) thematic_dir = Path(resolve_subdir(str(work_path), 'step11_map')) # 仅输出信息，不创建目录 existing = [str(d) for d in (ml_dir, watercolor_dir, thematic_dir) if d.is_dir()] if existing: print(f"预测输出目录已存在: {existing}") except Exception as e: print(f"读取预测输出目录信息失败: {e}") def on_tree_item_clicked(self, item, column): """目录树项点击事件""" data = item.data(0, Qt.UserRole) if not data: return if data.get("type") == "image": image_path = data.get("path") if image_path and Path(image_path).exists(): self.image_viewer.load_image(image_path) def generate_all_visualizations(self): """生成所有可视化图表""" if not self.work_dir: QMessageBox.warning(self, "警告", "请先选择工作目录！") return work_path = Path(self.work_dir) if not work_path.exists(): QMessageBox.warning(self, "警告", "工作目录不存在！") return if not (self.gen_scatter.isChecked() or self.gen_spectrum.isChecked() or self.gen_boxplots.isChecked() or self.gen_mask_glint.isChecked() or self.gen_sampling_map.isChecked() or self.gen_distribution_map.isChecked()): QMessageBox.information(self, "提示", "请至少勾选一项可视化配置选项以生成图表。") return reply = QMessageBox.question( self, "确认生成", "将根据左侧勾选项在后台生成可视化图表，可能需要较长时间。\n是否继续？", QMessageBox.Yes | QMessageBox.No ) if reply != QMessageBox.Yes: return extra = { "gen_scatter": self.gen_scatter.isChecked(), "gen_spectrum": self.gen_spectrum.isChecked(), "gen_boxplots": self.gen_boxplots.isChecked(), "gen_mask_glint": self.gen_mask_glint.isChecked(), "gen_sampling_map": self.gen_sampling_map.isChecked(), "gen_distribution_map": self.gen_distribution_map.isChecked(), } main_window = self.window() factory = getattr(main_window, '_panel_factory', None) if main_window else None # [新增] 直接从 Step 1 面板读取原始 .shp 的绝对路径，突破工作目录限制 step1_panel = factory.get_panel('step1') if factory else None if step1_panel: s1_conf = step1_panel.get_config() s1_mask = s1_conf.get('mask_path') # 确保文件存在且是shp格式，存入extra透传给后台线程 if s1_mask and Path(s1_mask).is_file() and str(s1_mask).lower().endswith('.shp'): extra["boundary_shp_path"] = str(s1_mask) step6_panel = factory.get_panel('step6_feature') if factory else None if step6_panel and getattr(step6_panel, 'output_file', None): _resolved_csv = step6_panel.output_file.get_path() if _resolved_csv: extra["training_csv_path"] = _resolved_csv step8_panel = factory.get_panel('step8_ml_train') if factory else None if step8_panel and getattr(step8_panel, 'output_path', None): _resolved_models_dir = step8_panel.output_path.get_path() if _resolved_models_dir: extra["models_dir"] = _resolved_models_dir self._start_visualization_thread("generate_all_selected", extra) def generate_chart(self, chart_type): """生成图表""" if not self.work_dir: QMessageBox.warning(self, "警告", "请先选择工作目录！") return work_path = Path(self.work_dir) if not work_path.exists(): QMessageBox.warning(self, "警告", "工作目录不存在！") return try: main_window = self.window() factory = getattr(main_window, '_panel_factory', None) if main_window else None step6_panel = factory.get_panel('step6_feature') if factory else None if step6_panel and getattr(step6_panel, 'output_file', None) and step6_panel.output_file.get_path(): training_spectra_csv = Path(step6_panel.output_file.get_path()) else: training_spectra_csv = _viz_training_spectra_csv_path(work_path) if chart_type == 'scatter': if not training_spectra_csv.is_file(): QMessageBox.warning( self, "警告", "未找到 6_Spectral_Feature_Extraction\\training_spectra.csv。\n" "请先执行步骤6（光谱特征提取）生成该文件。", ) return training_csv = training_spectra_csv models_dir = work_path / "7_Supervised_Model_Training" if not models_dir.is_dir() or not any(d.is_dir() for d in models_dir.iterdir()): mdir = QFileDialog.getExistingDirectory( self, "选择模型根目录（内含各水质参数子文件夹）", str(work_path)) if not mdir: return models_dir = Path(mdir) self._start_visualization_thread( "scatter", {"training_csv_path": str(training_csv), "models_dir": str(models_dir)}, ) return if chart_type == 'spectrum': if not training_spectra_csv.is_file(): QMessageBox.warning( self, "警告", "未找到 6_Spectral_Feature_Extraction\\training_spectra.csv。\n" "光谱分析固定使用该文件，请先执行步骤6（光谱特征提取）。", ) return csv_file = training_spectra_csv df = pd.read_csv(csv_file) columns = self._spectrum_meta_param_columns(df) if not columns: QMessageBox.warning( self, "警告", "当前 CSV 中没有可用的数值型水质参数列，无法按参数分组绘制光谱图。", ) return wl_col = _viz_infer_wavelength_start_column(df) self._start_visualization_thread( "spectrum", {"csv_path": str(csv_file), "param_cols": columns, "wavelength_start_column": wl_col, "n_groups": 5}, ) return if chart_type == 'statistics': if not training_spectra_csv.is_file(): QMessageBox.warning( self, "警告", "未找到 6_Spectral_Feature_Extraction\\training_spectra.csv。\n" "统计分析固定使用该文件，请先执行步骤6（光谱特征提取）。", ) return csv_file = training_spectra_csv df = pd.read_csv(csv_file) param_cols = self._statistics_param_columns(df) if not param_cols: QMessageBox.warning(self, "警告", "未找到可用的水质参数列！") return self._start_visualization_thread( "statistics", {"csv_path": str(csv_file), "param_cols": param_cols}, ) return if chart_type == 'sampling_map': self.generate_sampling_point_map() return except ImportError: QMessageBox.critical( self, "错误", "无法导入可视化模块！\n请确保 visualization_reports.py 文件存在。", ) except Exception as e: QMessageBox.critical( self, "错误", f"生成图表时出错:\n{str(e)}\n\n{traceback.format_exc()}", ) def generate_mask_glint_previews(self): """生成掩膜和耀斑缩略图""" self._start_visualization_thread("mask_glint") def generate_sampling_point_map(self): """生成采样点地图""" self._start_visualization_thread("sampling_map") def view_chart(self, chart_type): """查看图表""" if not self.work_dir: QMessageBox.warning(self, "警告", "请先选择工作目录！") return work_path = Path(self.work_dir) viz_dir = Path(resolve_subdir(self.work_dir, 'visualization')) viz_dir2 = viz_dir / "boxplots" viz_dir3 = viz_dir / "scatter_plots" if not viz_dir.exists(): QMessageBox.warning(self, "警告", f"可视化目录不存在:\n{viz_dir}\n\n请先生成图表。") return chart_files = [] if chart_type == 'scatter': chart_files = list(viz_dir3.glob("*scatter*.png")) elif chart_type == 'spectrum': chart_files = list(viz_dir.glob("*spectrum*.png")) elif chart_type == 'statistics': chart_files = list(viz_dir2.glob("*boxplot.png")) + \ list(viz_dir.glob("*histogram.png")) + \ list(viz_dir.glob("*heatmap.png")) elif chart_type == 'distribution': chart_files = list(viz_dir.glob("**/*distribution.png")) elif chart_type == 'mask_glint': glint_dir = viz_dir / "glint_deglint_previews" chart_files = list(glint_dir.glob("*preview.png")) if glint_dir.exists() else \ list(viz_dir.glob("*preview.png")) + \ list(viz_dir.glob("*glint*.png")) + \ list(viz_dir.glob("*mask*.png")) elif chart_type == 'sampling_map': sampling_dir = viz_dir / "sampling_maps" chart_files = list(sampling_dir.glob("*sampling_map.png")) if sampling_dir.exists() else \ list(viz_dir.glob("*sampling*.png")) if not chart_files: QMessageBox.warning(self, "警告", f"未找到{chart_type}类型的图表文件！\n\n请先生成图表。") return if len(chart_files) > 1: from PyQt5.QtWidgets import QInputDialog file_names = [f.name for f in chart_files] file_name, ok = QInputDialog.getItem( self, "选择图表", "请选择要查看的图表:", file_names, 0, False) if ok: selected_file = next(f for f in chart_files if f.name == file_name) self.show_chart_viewer(str(selected_file), file_name) else: self.show_chart_viewer(str(chart_files[0]), chart_files[0].name) def browse_all_charts(self): """浏览所有图表""" if not self.work_dir: QMessageBox.warning(self, "警告", "请先选择工作目录！") return work_path = Path(self.work_dir) chart_files = list(work_path.glob("**/*.png")) + list(work_path.glob("**/*.jpg")) if not chart_files: QMessageBox.warning(self, "警告", "未找到图表文件！") return dialog = ChartBrowserDialog(chart_files, self) dialog.exec_() def show_chart_viewer(self, image_path, title="图表查看器"): """显示图表查看器""" viewer = ChartViewerDialog(title=title, parent=self) viewer.display_image(image_path) viewer.exec_() def get_config(self): """获取配置""" return { 'generate_scatter': self.gen_scatter.isChecked(), 'generate_boxplots': self.gen_boxplots.isChecked(), 'generate_spectrum': self.gen_spectrum.isChecked(), 'generate_glint_previews': self.gen_mask_glint.isChecked(), 'generate_sampling_maps': self.gen_sampling_map.isChecked(), 'generate_distribution_maps': self.gen_distribution_map.isChecked(), 'scatter_config': { 'metric': 'test_r2', 'feature_start_column': 13, 'test_size': 0.2, 'random_state': 42 }, 'boxplot_config': { 'data_start_column': 4, 'save_individual': True, 'use_seaborn': True }, 'glint_preview_config': { 'work_dir': None, 'output_subdir': 'glint_deglint_previews', 'generate_glint': True, 'generate_deglint': True } } def set_config(self, config): """设置配置""" if not config: return if 'generate_scatter' in config: self.gen_scatter.setChecked(config['generate_scatter']) if 'generate_boxplots' in config: self.gen_boxplots.setChecked(config['generate_boxplots']) if 'generate_spectrum' in config: self.gen_spectrum.setChecked(config['generate_spectrum']) if 'generate_glint_previews' in config: self.gen_mask_glint.setChecked(config['generate_glint_previews']) if 'generate_sampling_maps' in config: self.gen_sampling_map.setChecked(config.get('generate_sampling_maps', True)) if 'generate_distribution_maps' in config: self.gen_distribution_map.setChecked(config.get('generate_distribution_maps', True))