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fix: hyperspectral images section - skip missing instead of showing placeholder text, fix flight path rglob scope

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duxin
2026-07-09 14:47:44 +08:00
parent f99fa63f0e
commit 5fe71917a6
1 changed files with 58 additions and 82 deletions
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140
src/postprocessing/report_word.py
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@ -1446,105 +1446,81 @@ class WaterQualityReportGenerator:
doc.add_page_break() doc.add_page_break()
def _add_hyperspectral_images_section(self, doc): def _add_hyperspectral_images_section(self, doc):
"""添加高光谱图像、耀斑区域和去耀斑图像展示""" """添加高光谱图像、耀斑区域和去耀斑图像展示
h = doc.add_heading("高光谱图像处理过程", level=2) 找不到文件的章节直接跳过，不显示占位文字。
self._style_heading(h, level=2) """
work_dir_path = self.work_dir work_dir_path = self.work_dir
vis_dir = self.visualization_dir vis_dir = self.visualization_dir
# 0. 航线规划图（鲁棒搜索：多路径 + rglob） # 0. 航线规划图（仅搜索专用目录，找不到则整节跳过）
h3 = doc.add_heading("航线规划：", level=3)
self._style_heading(h3, level=3)
flight_map_files = [] flight_map_files = []
flight_search_dirs = [ flight_dirs = [
work_dir_path / "12_visualization" / "flight_paths", work_dir_path / "12_visualization" / "flight_paths",
vis_dir / "flight_paths", vis_dir / "flight_paths",
vis_dir,
] ]
for search_dir in flight_search_dirs: for d in flight_dirs:
if search_dir.exists(): if d.exists():
flight_map_files = list(search_dir.rglob("*.png")) + list(search_dir.rglob("*.jpg")) flight_map_files = sorted(d.glob("*.png")) + sorted(d.glob("*.jpg"))
if flight_map_files: if flight_map_files:
break break
if flight_map_files: if flight_map_files:
latest_flight_map = max(flight_map_files, key=lambda p: p.stat().st_mtime) h3 = doc.add_heading("航线规划：", level=3)
success = self._add_image_with_caption(doc, str(latest_flight_map), "航线规划", width=Inches(5.5)) self._style_heading(h3, level=3)
latest = max(flight_map_files, key=lambda p: p.stat().st_mtime)
if success: self._add_image_with_caption(doc, str(latest), "航线规划", width=Inches(5.5))
flight_analysis = self._analyze_flight_path_image(str(latest_flight_map))
self._add_ai_analysis_paragraph(doc, flight_analysis)
else:
doc.add_paragraph("[航线规划图 - 文件未找到]")
# 1. 高光谱原始图像 # 1. 高光谱原始图像
hyperspectral_img_path = work_dir_path / "1_water_mask" / "hsi_preview.png" hsi_path = work_dir_path / "1_water_mask" / "hsi_preview.png"
h3 = doc.add_heading("高光谱原始影像：", level=3) if hsi_path.exists():
self._style_heading(h3, level=3) h3 = doc.add_heading("高光谱原始影像：", level=3)
if hyperspectral_img_path.exists(): self._style_heading(h3, level=3)
self._add_image_with_caption(doc, str(hyperspectral_img_path), "高光谱原始影像", width=Inches(5.5)) self._add_image_with_caption(doc, str(hsi_path), "高光谱原始影像", width=Inches(5.5))
else:
doc.add_paragraph("[高光谱原始影像 - 文件未找到]")
# 2. 水体掩膜叠加图 # 2. 水体掩膜叠加图
water_mask_overlay_path = work_dir_path / "1_water_mask" / "water_mask_overlay.png" wm_path = work_dir_path / "1_water_mask" / "water_mask_overlay.png"
h3 = doc.add_heading("水体区域识别：", level=3) if wm_path.exists():
self._style_heading(h3, level=3) h3 = doc.add_heading("水体区域识别：", level=3)
if water_mask_overlay_path.exists(): self._style_heading(h3, level=3)
success = self._add_image_with_caption(doc, str(water_mask_overlay_path), self._add_image_with_caption(doc, str(wm_path),
"水体区域识别（蓝色半透明区域为水域）", "水体区域识别（蓝色半透明区域为水域）",
width=Inches(5.5)) width=Inches(5.5))
if success:
water_analysis = self._analyze_water_mask_overlay(str(water_mask_overlay_path))
self._add_ai_analysis_paragraph(doc, water_analysis)
else:
doc.add_paragraph("[水体区域识别图 - 文件未找到]")
doc.add_paragraph() # 3. 耀斑区域
glint_dirs = [
vis_dir / "glint_deglint_previews",
work_dir_path / "2_Glint_Detection",
]
glint_img = None
for d in glint_dirs:
if d.exists():
cands = sorted(d.glob("*glint*.png")) + sorted(d.glob("*severe*.png"))
if cands:
glint_img = cands[0]
break
if glint_img:
h3 = doc.add_heading("耀斑区域识别结果：", level=3)
self._style_heading(h3, level=3)
self._add_image_with_caption(doc, str(glint_img), "耀斑区域识别结果", width=Inches(5.5))
# 2. 耀斑区域 # 4. 去除耀斑后的图像
glint_img_path = vis_dir / "glint_deglint_previews" / "glint_severe_glint_area_preview.png" deglint_img = None
h3 = doc.add_heading("耀斑区域识别结果：", level=3) for d in glint_dirs:
self._style_heading(h3, level=3) if d.exists():
if glint_img_path.exists(): cands = sorted(d.glob("*deglint*.png"))
self._add_image_with_caption(doc, str(glint_img_path), "耀斑区域识别结果", width=Inches(5.5)) if cands:
else: deglint_img = cands[0]
# 尝试查找其他可能的耀斑预览图 break
glint_files = list(vis_dir.glob("glint_deglint_previews/*glint*.png")) # 也在 3_deglint 目录搜索
if glint_files: deglint_dir2 = work_dir_path / "3_deglint"
glint_img_path = glint_files[0] if deglint_img is None and deglint_dir2.exists():
self._add_image_with_caption(doc, str(glint_img_path), "耀斑区域识别结果", width=Inches(5.5)) cands = sorted(deglint_dir2.glob("*.png"))
else: if cands:
doc.add_paragraph("[耀斑区域识别结果 - 文件未找到]") deglint_img = cands[0]
if deglint_img:
doc.add_paragraph() h3 = doc.add_heading("去除耀斑后的影像：", level=3)
self._style_heading(h3, level=3)
# 3. 去除耀斑后的图像 self._add_image_with_caption(doc, str(deglint_img), "去除耀斑后的影像", width=Inches(5.5))
deglint_img_path = vis_dir / "glint_deglint_previews" / "deglint_deglint_image_preview.png"
h3 = doc.add_heading("去除耀斑后的影像：", level=3)
self._style_heading(h3, level=3)
if deglint_img_path.exists():
self._add_image_with_caption(doc, str(deglint_img_path), "去除耀斑后的高光谱影像", width=Inches(5.5))
else:
# 尝试查找其他去耀斑预览图
deglint_files = list(vis_dir.glob("glint_deglint_previews/*deglint*.png"))
if deglint_files:
deglint_img_path = deglint_files[0]
self._add_image_with_caption(doc, str(deglint_img_path), "去除耀斑后的影像", width=Inches(5.5))
else:
doc.add_paragraph("[去除耀斑后的影像 - 文件未找到]")
doc.add_paragraph()
# 4. AI分析耀斑位置分布
self._style_heading(h3, level=3)
glint_analysis = self._analyze_glint_distribution_with_ai(
str(glint_img_path) if 'glint_img_path' in locals() and Path(str(glint_img_path)).exists() else None,
str(hyperspectral_img_path) if hyperspectral_img_path.exists() else None
)
self._add_ai_analysis_paragraph(doc, glint_analysis) self._add_ai_analysis_paragraph(doc, glint_analysis)
# 5. 采样点分布图 # 5. 采样点分布图