diff --git a/IMAGE_CONFIG.md b/IMAGE_CONFIG.md deleted file mode 100644 index 70eb347..0000000 --- a/IMAGE_CONFIG.md +++ /dev/null @@ -1,186 +0,0 @@ -# Logo和横幅图像配置说明 - -## 功能概述 - -本更新实现了以下需求： -1. **Logo在菜单栏左侧** - 和菜单栏在**同一行** -2. **菜单栏在最右侧** - 使用弹性空间布局 -3. **横幅图片撑满整个区域** - 自适应窗口宽度 - -## 布局说明 - -``` -┌─────────────────────────────────────┐ -│ [Logo] 文件 工具 帮助 │ ← 同一行：Logo在左，菜单在右 -├─────────────────────────────────────┤ -│ [软件名称横幅图 - 撑满宽度] │ ← 横幅撑满整个区域 -├─────────────────────────────────────┤ -│ │ -│ 主要内容区 │ -│ │ -``` - -## 配置步骤 - -### 1. 准备图像文件 - -将你的图像文件放在项目目录中。建议的目录结构： - -``` -fengzhuang-ui2/ -├── assets/ # 推荐：创建资源目录 -│ ├── logo.png # 公司logo (建议高度30像素) -│ └── banner.png # 软件名称横幅 (建议高度70像素) -├── src/ -│ └── gui/ -│ └── water_quality_gui.py -└── ... -``` - -### 2. 修改图像路径 - -打开 `src/gui/water_quality_gui.py` 文件，找到以下两处代码： - -#### Logo路径修改（create_logo_bar 方法中，第3928行左右） - -```python -# 修改前： -logo_path = "path/to/your/logo.png" - -# 修改后（使用相对路径）： -logo_path = "assets/logo.png" - -# 或使用绝对路径： -logo_path = r"E:\your\full\path\to\logo.png" -``` - -#### 横幅路径修改（create_banner_widget 方法中，第3978行左右） - -```python -# 修改前： -banner_path = "path/to/your/banner.png" - -# 修改后（使用相对路径）： -banner_path = "assets/banner.png" - -# 或使用绝对路径： -banner_path = r"E:\your\full\path\to\banner.png" -``` - -### 3. 图像规格建议 - -| 名称 | 建议尺寸 | 格式 | 说明 | -|-----|--------|------|------| -| Logo | 高度30像素 | PNG/JPG | 放在顶部Logo栏中，自动按高度缩放保持宽高比 | -| 横幅 | 高度70像素 | PNG/JPG | 占据横幅区域，自动按高度缩放保持宽高比 | - -### 4. 图像加载失败处理 - -如果图像文件未找到或加载失败，系统会自动显示占位符： - -- **Logo占位符**: 显示"Logo"文本，背景为浅灰色 -- **横幅占位符**: 显示"软件名称横幅"文本，背景为蓝色，字体为24号加粗 - -### 5. 自适应缩放说明 - -为了避免图像拉伸，代码使用了 `scaledToHeight()` 方法： -- Logo按高度30像素缩放，自动计算宽度，保持原始宽高比 -- 横幅按高度70像素缩放，自动计算宽度，保持原始宽高比 - -这样可以确保无论原始图像大小如何，都能自然地显示而不会出现拉伸变形。 - -## 常见问题 - -### Q: 如何使用项目内的图像资源？ - -**A**: 在项目中创建 `assets` 或 `resources` 文件夹，并使用相对路径： - -```python -# 假设项目结构： -# fengzhuang-ui2/ -# ├── assets/ -# │ ├── logo.png -# │ └── banner.png -# └── src/gui/water_quality_gui.py - -# 在 water_quality_gui.py 中（第3928行）： -logo_path = "assets/logo.png" - -# 在 water_quality_gui.py 中（第3978行）： -banner_path = "assets/banner.png" -``` - -### Q: Logo或横幅大小不合适？ - -**A**: 修改以下代码调整显示大小： - -```python -# 在 create_logo_bar() 方法中调整Logo大小 -logo_label.setFixedSize(60, 40) # 改为 60×40 - -# 在 create_banner_widget() 方法中调整横幅大小 -banner_label.setMaximumHeight(100) # 改为100像素高 -banner_label.setMinimumHeight(80) # 改为最小80像素高 - -# 调整缩放高度 -scaled_pixmap = logo_pixmap.scaledToHeight(35, Qt.SmoothTransformation) # 改为35 -scaled_pixmap = banner_pixmap.scaledToHeight(85, Qt.SmoothTransformation) # 改为85 -``` - -### Q: 如何隐藏Logo或横幅？ - -**A**: 在 `init_ui()` 方法中注释掉相应的创建方法： - -```python -# 在 init_ui() 中 -# self.create_logo_bar() # 注释此行隐藏Logo -# self.create_banner_widget() # 注释此行隐藏横幅 -``` - -### Q: Logo显示位置不对？ - -**A**: Logo栏是作为独立的工具栏添加在菜单栏下方，不是在菜单栏内。当前的布局顺序是： -1. 菜单栏 (最上方) -2. Logo栏 (菜单栏下方) -3. 横幅区域 (Logo栏下方) -4. 主内容区域 (最下方) - -### Q: 图像在高分辨率屏幕上看起来模糊？ - -**A**: 使用 `Qt.SmoothTransformation` 可以改善图像质量。如果仍然不够清晰，可以准备高分辨率的原始图像。 - -## 代码位置 - -- **Logo栏创建**: `create_logo_bar()` 方法 (第3902行) -- **横幅区域创建**: `create_banner_widget()` 方法 (第3950行) -- **主UI初始化**: `init_ui()` 方法 (第3821行) - -## 支持的图像格式 - -- PNG (推荐，支持透明背景) -- JPG/JPEG -- BMP -- GIF -- TIFF - -## 样式调整 - -如需修改样式（背景色、边框等），编辑以下位置的 `setStyleSheet()` 调用： - -```python -# Logo样式（第3907-3916行） -logo_toolbar.setStyleSheet("""...""") - -# 占位符样式（第3931行） -logo_label.setStyleSheet("...") - -# 横幅占位符样式（第3959-3965行） -banner_label.setStyleSheet("""...""") -``` - -## 更新日期 -2026-03-27 - -## 备注 - -所有的图像路径都可以根据你的实际项目结构灵活调整。建议将图像文件与代码一起版本控制，以确保项目的可维护性。 diff --git a/data/sub/waterindex.csv b/data/sub/waterindex.csv index 0dfee5f..4a04128 100644 --- a/data/sub/waterindex.csv +++ b/data/sub/waterindex.csv @@ -1,4 +1,4 @@ -Formula_Name,Category,Formula,Reference +Formula_Name,Category,Formula,Reference BGA_Am09KBBI,Phycocyanin (BGA_PC),(w686 - w658) / (w686 + w658),"Amin, R.; Zhou, J.; Gilerson, A.; Gross, B.; Moshary, F.; Ahmed, S.; Novel optical techniques for detecting and classifying toxic dinoflagellate Karenia brevis blooms using satellite imagery, Optics Express, 2009, 17, 11, 1-13." BGA_Be162B643sub629,Phycocyanin (BGA_PC),w644 - w629,"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 538." BGA_Be162B700sub601,Phycocyanin (BGA_PC),w700 - w601,"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 539." @@ -12,35 +12,35 @@ BGA_Be16NDPhyI644over615,Phycocyanin (BGA_PC),(w644 - w615) / (w644 + w615),"Bec BGA_Be16NDPhyI644over629,Phycocyanin (BGA_PC),(w644 - w629) / (w644 + w629),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 542." BGA_Be16Phy2BDA644over629,Phycocyanin (BGA_PC),w644 / w629,"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 545." BGA_Da052BDA,Phycocyanin (BGA_PC),w714 / w672,"Wynne, T. T., Stumpf, R. P., Tomlinson, M. C., Warner, R. A., Tester, P. A., Dyble, J.; Relating spectral shape to cyanobacterial blooms in the Laurentian Great Lakes. Int. J. Remote Sens., 2008, 29, 3665-3672." -BGA_Go04MCI,Phycocyanin (BGA_PC),w709 - w681 - (w753 - w681),"Gower, J.F.R.; Brown,L.; Borstad, G.A.; Observation of chlorophyll fluorescence in west coast waters of Canada using the MODIS satellite sensor. Can. J. Remote Sens., 2004, 30 (1), 17闁?5." +BGA_Go04MCI,Phycocyanin (BGA_PC),w709 - w681 - (w753 - w681),"Gower, J.F.R.; Brown,L.; Borstad, G.A.; Observation of chlorophyll fluorescence in west coast waters of Canada using the MODIS satellite sensor. Can. J. Remote Sens., 2004, 30 (1), 17?5." BGA_HU103BDA,Phycocyanin (BGA_PC),(((1 / w615) - (1 / w600)) - w725),"Hunter, P.D.; Tyler, A.N.; Willby, N.J.; Gilvear, D.J.; The spatial dynamics of vertical migration by Microcystis aeruginosa in a eutrophic shallow lake: A case study using high spatial resolution time-series airborne remote sensing. Limn. Oceanogr. 2008, 53, 2391-2406" BGA_Ku15PhyCI,Phycocyanin (BGA_PC),(-1 * (W681 - W665 - (W709 - W665))),"Kudela, R.M., Palacios, S.L., Austerberry, D.C., Accorsi, E.K., Guild, L.S.; Application of hyperspectral remote sensing to cyanobacterial blooms in inland waters, Torres-Perez, J., 2015, Remote Sens. Environ., 2015, 167, 1-10." BGA_Ku15SLH,Phycocyanin (BGA_PC),(w715 - w658) + (w715 - w658),"Kudela, R.M., Palacios, S.L., Austerberry, D.C., Accorsi, E.K., Guild, L.S.; Application of hyperspectral remote sensing to cyanobacterial blooms in inland waters, Torres-Perez, J., 2015, Remote Sens. Environ., 2015, 167, 1-11." -BGA_MI092BDA,Phycocyanin (BGA_PC),w700 / w600,"Mishra, S.; Mishra, D.R.; Schluchter, W. M., A novel algorithm for predicting PC concentrations in cyanobacteria: A proximal hyperspectral remote sensing approach. Remote Sens., 2009, 1, 758闁?75." -BGA_MM092BDA,Phycocyanin (BGA_PC),w724 / w600,"Mishra, S.; Mishra, D.R.; Schluchter, W. M., A novel algorithm for predicting PC concentrations in cyanobacteria: A proximal hyperspectral remote sensing approach. Remote Sens., 2009, 1, 758闁?76." +BGA_MI092BDA,Phycocyanin (BGA_PC),w700 / w600,"Mishra, S.; Mishra, D.R.; Schluchter, W. M., A novel algorithm for predicting PC concentrations in cyanobacteria: A proximal hyperspectral remote sensing approach. Remote Sens., 2009, 1, 758?75." +BGA_MM092BDA,Phycocyanin (BGA_PC),w724 / w600,"Mishra, S.; Mishra, D.R.; Schluchter, W. M., A novel algorithm for predicting PC concentrations in cyanobacteria: A proximal hyperspectral remote sensing approach. Remote Sens., 2009, 1, 758?76." BGA_MM12NDCIalt,Phycocyanin (BGA_PC),(w700 - w658) / (w700 + w658),"Mishra, S.; Mishra, D.R.; A novel remote sensing algorithm to quantify phycocyanin in cyanobacterial algal blooms, Env. Res. Lett., 2014, 9 (11), DOI:10.1088/1748-9326/9/11/114003" BGA_MM143BDAopt,Phycocyanin (BGA_PC),((1 / w629) - (1 / w659)) * w724,"Mishra, S.; Mishra, D.R.; A novel remote sensing algorithm to quantify phycocyanin in cyanobacterial algal blooms, Env. Res. Lett., 2014, 9 (11), DOI:10.1088/1748-9326/9/11/114004" -BGA_SI052BDA,Phycocyanin (BGA_PC),w709 / w620,"Simis, S. G. H.; Peters, S.W. M.; Gons, H. J.; Remote sensing of the cyanobacteria pigment phycocyanin in turbid inland water. Limn. Oceanogr., 2005, 50, 237闁?45" +BGA_SI052BDA,Phycocyanin (BGA_PC),w709 / w620,"Simis, S. G. H.; Peters, S.W. M.; Gons, H. J.; Remote sensing of the cyanobacteria pigment phycocyanin in turbid inland water. Limn. Oceanogr., 2005, 50, 237?45" BGA_SM122BDA,Phycocyanin (BGA_PC),w709 / w600,"Mishra, S. Remote sensing of cyanobacteria in turbid productive waters, PhD Dissertation. Mississippi State University, USA. 2012." -BGA_SY002BDA,Phycocyanin (BGA_PC),w650 / w625,"Schalles, J.; Yacobi, Y. Remote detection and seasonal patterns of phycocyanin, carotenoid and chlorophyll-a pigments in eutrophic waters. Archiv fur Hydrobiologie, Special Issues Advances in Limnology, 2000, 55,153闁?68" +BGA_SY002BDA,Phycocyanin (BGA_PC),w650 / w625,"Schalles, J.; Yacobi, Y. Remote detection and seasonal patterns of phycocyanin, carotenoid and chlorophyll-a pigments in eutrophic waters. Archiv fur Hydrobiologie, Special Issues Advances in Limnology, 2000, 55,153?68" BGA_Wy08CI,Phycocyanin (BGA_PC),(-1 * (W686 - W672 - (W715 - W672))),"Wynne, T. T., Stumpf, R. P., Tomlinson, M. C., Warner, R. A., Tester, P. A., Dyble, J.; Relating spectral shape to cyanobacterial blooms in the Laurentian Great Lakes. Int. J. Remote Sens., 2008, 29, 3665-3672." Chl_Al10SABI,chlorophyll_a,(w857 - w644) / (w458 + w529),"Alawadi, F. Detection of surface algal blooms using the newly developed algorithm surface algal bloom index (SABI). Proc. SPIE 2010, 7825." -Chl_Am092Bsub,chlorophyll_a,w681 - w665,"Amin, R.; Zhou, J.; Gilerson, A.; Gross, B.; Moshary, F.; Ahmed, S. Novel optical techniques for detecting and classifying toxic dinoflagellate Karenia brevis blooms using satellite imagery. Opt. Express 2009, 17, 9126闁?144." +Chl_Am092Bsub,chlorophyll_a,w681 - w665,"Amin, R.; Zhou, J.; Gilerson, A.; Gross, B.; Moshary, F.; Ahmed, S. Novel optical techniques for detecting and classifying toxic dinoflagellate Karenia brevis blooms using satellite imagery. Opt. Express 2009, 17, 9126?144." Chl_Be16FLHblue,chlorophyll_a,w529 - (w644 + (w458 - w644)),"Beck, R.A. and 22 others; Comparison of satellite reflectance algorithms for estimating chlorophyll-a in a temperate reservoir using coincident hyperspectral aircraft imagery and dense coincident surface observations, Remote Sens. Environ., 2016, 178, 15-30." Chl_Be16FLHviolet,chlorophyll_a,w529 - (w644 + (w429 - w644)),"Beck, R.A. and 22 others; Comparison of satellite reflectance algorithms for estimating chlorophyll-a in a temperate reservoir using coincident hyperspectral aircraft imagery and dense coincident surface observations, Remote Sens. Environ., 2016, 178, 15-30." Chl_Be16NDTIblue,chlorophyll_a,(w658 - w458) / (w658 + w458),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 543." Chl_Be16NDTIviolet,chlorophyll_a,(w658 - w444) / (w658 + w444),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 544." Chl_De933BDA,chlorophyll_a,w600 - w648 - w625,"Dekker, A.; Detection of the optical water quality parameters for eutrophic waters by high resolution remote sensing, Ph.D. thesis, 1993, Free University, Amsterdam." Chl_Gi033BDA,chlorophyll_a,((1 / w672) - (1 / w715)) * w757,"Gitelson, A.A.; U. Gritz, and M. N. Merzlyak.; Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J. Plant Phys. 2003, 160, 271-282." -Chl_Kn07KIVU,chlorophyll_a,(w458 - w644) / w529,"Kneubuhler, M.; Frank T.; Kellenberger, T.W; Pasche N.; Schmid M.; Mapping chlorophyll-a in Lake Kivu with remote sensing methods. 2007, Proceedings of the Envisat Symposium 2007, Montreux, Switzerland 23闁?7 April 2007 (ESA SP-636, July 2007)." +Chl_Kn07KIVU,chlorophyll_a,(w458 - w644) / w529,"Kneubuhler, M.; Frank T.; Kellenberger, T.W; Pasche N.; Schmid M.; Mapping chlorophyll-a in Lake Kivu with remote sensing methods. 2007, Proceedings of the Envisat Symposium 2007, Montreux, Switzerland 23?7 April 2007 (ESA SP-636, July 2007)." Chl_MM12NDCI,chlorophyll_a,(w715 - w686) / (w715 + w686),"Mishra, S.; and Mishra, D.R. Normalized difference chlorophyll index: A novel model for remote estimation of chlorophyll-a concentration in turbid productive waters, Remote Sens. Environ., 2012, 117, 394-406" Chl_Zh10FLH,chlorophyll_a,w686 - (w715 + (w672 - w751)),"Zhao, D.Z.; Xing, X.G.; Liu, Y.G.; Yang, J.H.; Wang, L. The relation of chlorophyll-a concentration with the reflectance peak near 700 nm in algae-dominated waters and sensitivity of fluorescence algorithms for detecting algal bloom. Int. J. Remote Sens. 2010, 31, 39-48" Turb_Be16GreenPlusRedBothOverViolet,Turbidity,(w558 + w658) / w444,"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 538" Turb_Be16RedOverViolet,Turbidity,w658 / w444,"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 539" -Turb_Bow06RedOverGreen,Turbidity,w658 / w558,"Bowers, D. G., and C. E. Binding. 2006. 闁炽儲缈籬e Optical Properties of Mineral Suspended Particles: A Review and Synthesis.闁?Estuarine Coastal and Shelf Science 67 (1闁?): 219闁?30. doi:10.1016/j.ecss.2005.11.010" +Turb_Bow06RedOverGreen,Turbidity,w658 / w558,"Bowers, D. G., and C. E. Binding. 2006. 闁炽儲缈籬e Optical Properties of Mineral Suspended Particles: A Review and Synthesis.?Estuarine Coastal and Shelf Science 67 (1?): 219?30. doi:10.1016/j.ecss.2005.11.010" Turb_Chip09NIROverGreen,Turbidity,w857 / w558,"Chipman, J. W.; Olmanson, L.G.; Gitelson, A.A.; Remote sensing methods for lake management: A guide for resource managers and decision-makers. 2009." Turb_Dox02NIRoverRed,Turbidity,w857 / w658,"Doxaran, D., Froidefond, J.-M.; Castaing, P. ; A reflectance band ratio used to estimate suspended matter concentrations in sediment-dominated coastal waters, Remote Sens., 2002, 23, 5079-5085" Turb_Frohn09GreenPlusRedBothOverBlue,Turbidity,(w558 + w658) / w458,"Frohn, R. C., & Autrey, B. C. (2009). Water quality assessment in the Ohio River using new indices for turbidity and chlorophyll-a with Landsat-7 Imagery. Draft Internal Report, US Environmental Protection Agency." -Turb_Harr92NIR,Turbidity,w857,"Schiebe F.R., Harrington J.A., Ritchie J.C. Remote-Sensing of Suspended Sediments闁炽儲鏁刪e Lake Chicot, Arkansas Project. Int. J. Remote Sens. 1992;13:1487闁?509" +Turb_Harr92NIR,Turbidity,w857,"Schiebe F.R., Harrington J.A., Ritchie J.C. Remote-Sensing of Suspended Sediments闁炽儲鏁刪e Lake Chicot, Arkansas Project. Int. J. Remote Sens. 1992;13:1487?509" Turb_Lath91RedOverBlue,Turbidity,w658 / w458,"Lathrop, R. G., Jr., T. M. Lillesand, and B. S. Yandell, 1991. Testing the utility of simple multi-date Thematic Mapper calibration algorithms for monitoring turbid inland waters. International Journal of Remote Sensing" Turb_Moore80Red,Turbidity,w658,"Moore, G.K., Satellite remote sensing of water turbidity, Hydrological Sciences, 1980, 25, 4, 407-422" diff --git a/docs/项目交接文档.md b/docs/项目交接文档.md new file mode 100644 index 0000000..e1cfcc6 --- /dev/null +++ b/docs/项目交接文档.md @@ -0,0 +1,175 @@ +# 水质参数反演分析系统 — 项目交接文档 + +> 文档用途：帮助新负责人快速理解仓库结构、运行方式、核心逻辑与维护要点。 +> 适用仓库：本地工程 **fengzhuang-ui2V3**（水质 GUI + 反演流水线）。 + +--- + +## 1. 项目概述 + +本系统为 **基于遥感影像与机器学习的水质反演桌面应用**：水域掩膜、耀斑检测与去除、CSV/光谱处理、监督与非经验建模、采样与预测、空间制图与报告生成。 + +- **图形界面**：PyQt5，主入口 `src/gui/water_quality_gui.py`。 +- **业务编排**：GUI 使用 `src/core/water_quality_inversion_pipeline_GUI.py` 中的 `WaterQualityInversionPipeline`；命令行/无 GUI 场景可参考 `src/core/water_quality_inversion_pipeline.py`（子目录命名已与 GUI 管线对齐，但 **不含** 步骤 5.5 / 6.5 / 6.75 等扩展目录的完整定义，以 GUI 管线为准）。 +- **打包**：PyInstaller，`scripts/water_quality_gui_file.spec`，单文件 exe，`console=False`。 + +--- + +## 2. 目录结构（精简） + +``` +项目根/ +├── src/ +│ ├── gui/ # PyQt5 主界面、步骤面板、工作线程 +│ ├── core/ # 反演流水线、去耀斑、建模、预测、非经验反演 +│ ├── preprocessing/ # 光谱预处理、水质数据加工 +│ ├── postprocessing/ # 制图、可视化、Word 报告、航迹等 +│ ├── visualization/ # 可视化包初始化（具体实现多在 postprocessing） +│ └── utils/ # 水域提取、指数、采样、工具函数 +├── data/ # 图标、公式子数据等静态资源 +├── scripts/ # PyInstaller spec、构建产物常出现在 scripts/build|dist +├── docs/ # 说明文档（含本文） +├── requirements*.txt # pip/conda 依赖清单 +├── README.md / README-conda.md # 安装与 conda 说明 +└── README_SAMPLING_MAP.md # 采样地图相关说明（若仍写旧目录名，需与下文「工作目录」对照更新） +``` + +--- + +## 3. 运行方式 + +### 3.1 开发运行 GUI + +在项目根目录（或已将 `src` 加入 `PYTHONPATH`）下： + +```bash +python src/gui/water_quality_gui.py +``` + +依赖见 `requirements.txt`；地理栈（GDAL/rasterio 等）建议在 **Conda** 环境按 `README-conda.md` / `requirements-conda.txt` 安装。 + +### 3.2 打包 Windows exe + +在已激活的构建环境中执行（路径按本机调整）： + +```bash +pyinstaller scripts/water_quality_gui_file.spec +``` + +产物一般为 `scripts/dist/water_quality_gui.exe`（以 spec 中 `name` 为准）。spec 中已收集 rasterio 动态库、conda `Library/bin` 下 DLL、`data/icons` 等。 + +### 3.3 多进程说明（Windows / 打包） + +- 若使用 **sklearn `GridSearchCV(n_jobs=-1)`** 等并行，joblib 会起子进程；**冻结 exe 时可能出现控制台窗口闪烁**。缓解思路：线程后端 `parallel_backend("threading")`、或 `n_jobs=1`、或模型内部 `n_jobs` 并行（需改 `modeling_batch.py`，交接时按需评估）。 +- 入口脚本建议在 `if __name__ == "__main__":` 内、启动 GUI 前调用 `multiprocessing.freeze_support()`，以符合 PyInstaller + multiprocessing 规范（是否已加请以当前 `water_quality_gui.py` 为准）。 + +--- + +## 4. GUI 与流水线步骤对应关系 + +界面上的「步骤」通过 `WorkerThread.run_single_step` 映射到管线方法（配置键名与面板一致，如 `step6`、`step5_5`）： + +| 配置键 / 逻辑步骤 | 管线方法名 | +|-------------------|------------| +| step1 | `step1_generate_water_mask` | +| step2 | `step2_find_glint_area` | +| step3 | `step3_remove_glint` | +| step4 | `step4_process_csv` | +| step5 | `step5_extract_training_spectra` | +| step5_5 | `step5_5_calculate_water_quality_indices` | +| step6 | `step6_train_models` | +| step6_5 | `step6_5_non_empirical_modeling` | +| step6_75 | `step6_75_custom_regression` | +| step7 | `step7_generate_sampling_points` | +| step8 | `step8_predict_water_quality` | +| step8_5 | `step8_5_predict_with_non_empirical_models` | +| step8_75 | `step8_75_predict_with_custom_regression` | +| step9 | `step9_generate_distribution_map` | + +单步运行时会注入 `skip_dependency_check=True`，便于独立调试；全流程运行仍应保证输入/前置步骤产物存在。 + +--- + +## 5. 工作目录（work_dir）子文件夹命名 + +所有中间结果默认写在用户选择的 **工作目录** 下。当前代码中约定如下（**若磁盘上仍为旧文件夹名，需手动改名或重新跑流程**）： + +| 子目录名 | 含义 | +|----------|------| +| `1_water_mask` | 水域掩膜 | +| `2_glint` | 耀斑相关 | +| `3_deglint` | 去耀斑影像 | +| `4_processed_data` | 处理后数据 | +| `5_training_spectra` | 训练光谱等 | +| `6_water_quality_indices` | 水质指数（原 `5_5_water_quality_indices`） | +| `7_models` | 监督学习模型（原 `6_models`） | +| `8_non_empirical_models` | 非经验模型（原 `6_5_non_empirical_models`） | +| `9_custom_regression` | 自定义回归结果（原 `6_75_custom_regression`） | +| `10_sampling` | 采样点/光谱（原 `7_sampling`） | +| `11_12_13_predictions` | 预测 CSV 等（原 `8_predictions`） | +| `14_visualization` | 图表、分布图、预览等（原 `9_visualization`） | +| `reports` | 报告输出（原 `10_reports`） | + +**定义位置**：`water_quality_inversion_pipeline_GUI.py` 构造函数中对 `self.*_dir` 的赋值；GUI 中大量路径拼接需与此一致（如 `14_visualization`、`7_models`）。 + +--- + +## 6. 核心模块说明 + +| 模块路径 | 职责 | +|----------|------| +| `src/gui/water_quality_gui.py` | 主窗口、各步骤面板、线程 Worker、图像查看、配置收集与单步/全流程触发 | +| `src/core/water_quality_inversion_pipeline_GUI.py` | GUI 用流水线：步骤实现、目录管理、与可视化/报告类协作 | +| `src/core/water_quality_inversion_pipeline.py` | 无扩展步骤目录时的管线实现，供脚本或非 GUI 参考 | +| `src/core/modeling/modeling_batch.py` | 批量训练、`GridSearchCV`、模型落盘（joblib） | +| `src/core/prediction/inference_batch.py` | 批量预测与模型加载 | +| `src/core/glint_removal/*.py` | Goodman、Hedley、Kutser、SUGAR 等去耀斑算法 | +| `src/postprocessing/visualization_reports.py` | 水质可视化封装（输出目录常指向 `14_visualization`） | +| `src/postprocessing/report_word.py` | Word 报告；默认从 `work_dir/14_visualization` 等读图 | +| `src/postprocessing/map.py` | 分布图、插值相关（内含开发者本地示例路径，勿当生产配置） | + +--- + +## 7. 依赖与环境 + +- **Python**：3.8+（README 声明；实际环境以 `requirements-py310.txt` 等为准）。 +- **关键库**：PyQt5、numpy、pandas、scipy、scikit-learn、joblib、matplotlib、rasterio/geopandas（视功能启用）、opencv-python、Pillow 等。 +- **Conda**： GDAL 等二进制依赖推荐用 conda-forge，见 `README-conda.md`。 + +--- + +## 8. 维护与排错提示 + +1. **资源路径（打包后图标不显示）**：冻结运行时勿依赖 `Path(__file__)` 指向源码树；应使用 `sys._MEIPASS` 或项目内统一的 `get_icon_path()` 类逻辑。 +2. **tqdm / 无控制台**：打包 GUI 时若 `sys.stdout` 为 `None`，tqdm 可能报错；`Goodman.py` 等处已对 frozen GUI 做了 `disable` 处理，新增进度条时注意同样处理。 +3. **配置与 YAML**：全流程配置由 GUI 各面板 `get_config()` 汇总；改字段名需同步管线 `**config['stepX']` 形参。 +4. **文档滞后**：`README_SAMPLING_MAP.md` 等若仍出现 `9_visualization` 等旧目录名，应以本文第 5 节为准或更新该文档。 + +--- + +## 9. 交接检查清单 + +- [ ] 已能在本机用 `python src/gui/water_quality_gui.py` 启动 GUI 并完成一次最小流程（或关键单步）。 +- [ ] 已确认 Conda/pip 环境与 `README-conda.md`、`requirements.txt` 一致。 +- [ ] 已阅读 `scripts/water_quality_gui_file.spec`，知悉打包入口与 `datas` 列表。 +- [ ] 已知悉 `work_dir` 子目录当前命名，并能区分「配置键 step6」与「文件夹 `7_models`」。 +- [ ] 已记录客户/内部使用的 **工作目录样例** 与 **典型输入数据格式**（CSV 列、影像格式）。 +- [ ] Git 远程、发布分支、Issue/需求跟踪方式已交接。 +- [ ] 许可证与第三方库合规（MIT 与 README 声明等）已确认。 + +--- + +## 10. 文档修订记录 + +| 日期 | 说明 | +|------|------| +| 2026-04-09 | 初版：目录结构、步骤映射、工作目录新命名、打包与多进程注意事项 | + +--- + +**联系人（请交接时填写）** + +| 角色 | 姓名 | 联系方式 | +|------|------|----------| +| 移交人 | | | +| 接收人 | | | diff --git a/src/core/non_empirical_retrieval.py b/src/core/non_empirical_retrieval.py index 1b37420..ab9b675 100644 --- a/src/core/non_empirical_retrieval.py +++ b/src/core/non_empirical_retrieval.py @@ -246,8 +246,8 @@ def non_empirical_retrieval(algorithm, model_info_path, coor_spectral_path, outp if __name__ == "__main__": algorithm= "chl_a" - model_info_path= r"E:\code\WQ\pipeline_result\work_dir\5_training_spectra\6_5_non_empirical_models\SS\SS_chl_a.json" - coor_spectral_path= r"E:\code\WQ\pipeline_result\work_dir\7_sampling\sampling_spectra.csv" - output_path= r"E:\code\WQ\pipeline_result\work_dir\8_predictions\SS_chl_a.csv" + model_info_path= r"E:\code\WQ\pipeline_result\work_dir\5_training_spectra\8_non_empirical_models\SS\SS_chl_a.json" + coor_spectral_path= r"E:\code\WQ\pipeline_result\work_dir\10_sampling\sampling_spectra.csv" + output_path= r"E:\code\WQ\pipeline_result\work_dir\11_12_13_predictions\SS_chl_a.csv" wave_radius=5.0 non_empirical_retrieval(algorithm, model_info_path, coor_spectral_path, output_path, wave_radius) \ No newline at end of file diff --git a/src/core/water_quality_inversion_pipeline.py b/src/core/water_quality_inversion_pipeline.py index 817c7b7..26d332e 100644 --- a/src/core/water_quality_inversion_pipeline.py +++ b/src/core/water_quality_inversion_pipeline.py @@ -95,11 +95,11 @@ class WaterQualityInversionPipeline: self.deglint_dir = self.work_dir / "3_deglint" self.processed_data_dir = self.work_dir / "4_processed_data" self.training_spectra_dir = self.work_dir / "5_training_spectra" - self.models_dir = self.work_dir / "6_models" - self.sampling_dir = self.work_dir / "7_sampling" - self.prediction_dir = self.work_dir / "8_predictions" - self.visualization_dir = self.work_dir / "9_visualization" - self.reports_dir = self.work_dir / "10_reports" + self.models_dir = self.work_dir / "7_models" + self.sampling_dir = self.work_dir / "10_sampling" + self.prediction_dir = self.work_dir / "11_12_13_predictions" + self.visualization_dir = self.work_dir / "14_visualization" + self.reports_dir = self.work_dir / "reports" # 创建所有子目录 for dir_path in [self.water_mask_dir, self.glint_dir, self.deglint_dir, diff --git a/src/core/water_quality_inversion_pipeline_GUI.py b/src/core/water_quality_inversion_pipeline_GUI.py index 1a5f2db..5677384 100644 --- a/src/core/water_quality_inversion_pipeline_GUI.py +++ b/src/core/water_quality_inversion_pipeline_GUI.py @@ -105,14 +105,14 @@ class WaterQualityInversionPipeline: self.deglint_dir = self.work_dir / "3_deglint" self.processed_data_dir = self.work_dir / "4_processed_data" self.training_spectra_dir = self.work_dir / "5_training_spectra" - self.indices_dir = self.work_dir / "5_5_water_quality_indices" - self.models_dir = self.work_dir / "6_models" - self.non_empirical_models_dir = self.work_dir / "6_5_non_empirical_models" - self.custom_regression_dir = self.work_dir / "6_75_custom_regression" - self.sampling_dir = self.work_dir / "7_sampling" - self.prediction_dir = self.work_dir / "8_predictions" - self.visualization_dir = self.work_dir / "9_visualization" - self.reports_dir = self.work_dir / "10_reports" + self.indices_dir = self.work_dir / "6_water_quality_indices" + self.models_dir = self.work_dir / "7_models" + self.non_empirical_models_dir = self.work_dir / "8_non_empirical_models" + self.custom_regression_dir = self.work_dir / "9_custom_regression" + self.sampling_dir = self.work_dir / "10_sampling" + self.prediction_dir = self.work_dir / "11_12_13_predictions" + self.visualization_dir = self.work_dir / "14_visualization" + self.reports_dir = self.work_dir / "reports" # 创建所有子目录 for dir_path in [self.water_mask_dir, self.glint_dir, self.deglint_dir, @@ -3379,10 +3379,10 @@ class WaterQualityInversionPipeline: else: # 如果output_dir为空，使用工作目录 if hasattr(self, 'work_dir') and self.work_dir is not None: - non_empirical_dir = Path(self.work_dir) / "6_5_non_empirical_models" + non_empirical_dir = Path(self.work_dir) / "8_non_empirical_models" else: # 如果没有工作目录，使用当前目录 - non_empirical_dir = Path.cwd() / "6_5_non_empirical_models" + non_empirical_dir = Path.cwd() / "8_non_empirical_models" non_empirical_dir.mkdir(parents=True, exist_ok=True) # 设置默认参数 @@ -3712,7 +3712,7 @@ class WaterQualityInversionPipeline: if non_empirical_models_dir is not None: final_models_dir = non_empirical_models_dir else: - default_models_dir = str(self.work_dir / "6_5_non_empirical_models") + default_models_dir = str(self.work_dir / "8_non_empirical_models") if Path(default_models_dir).exists(): final_models_dir = default_models_dir else: diff --git a/src/gui/water_quality_gui.py b/src/gui/water_quality_gui.py index 0890e03..58e3372 100644 --- a/src/gui/water_quality_gui.py +++ b/src/gui/water_quality_gui.py @@ -536,7 +536,7 @@ class VisualizationWorkerThread(QThread): wp = Path(self.work_dir) if self.task == "mask_glint": from src.postprocessing.visualization_reports import WaterQualityVisualization - viz = WaterQualityVisualization(output_dir=str(wp / "9_visualization")) + viz = WaterQualityVisualization(output_dir=str(wp / "14_visualization")) preview_paths = viz.generate_glint_deglint_previews( work_dir=str(wp), output_subdir="glint_deglint_previews", @@ -572,7 +572,7 @@ class VisualizationWorkerThread(QThread): csv_path = str(csv_files[0]) from src.postprocessing.point_map import SamplingPointMap map_generator = SamplingPointMap( - output_dir=str(wp / "9_visualization" / "sampling_maps"), + output_dir=str(wp / "14_visualization" / "sampling_maps"), fast_mode=True, ) map_path = map_generator.create_sampling_point_map( @@ -597,7 +597,7 @@ class VisualizationWorkerThread(QThread): ) elif self.task == "spectrum": from src.postprocessing.visualization_reports import WaterQualityVisualization - viz = WaterQualityVisualization(output_dir=str(wp / "9_visualization")) + viz = WaterQualityVisualization(output_dir=str(wp / "14_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)) @@ -641,7 +641,7 @@ class VisualizationWorkerThread(QThread): ) elif self.task == "statistics": from src.postprocessing.visualization_reports import WaterQualityVisualization - viz = WaterQualityVisualization(output_dir=str(wp / "9_visualization")) + viz = WaterQualityVisualization(output_dir=str(wp / "14_visualization")) csv_file = self.extra.get("csv_path") param_cols = self.extra.get("param_cols") or [] output_paths = viz.plot_statistical_charts( @@ -660,7 +660,7 @@ class VisualizationWorkerThread(QThread): 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 6_models 下的参数子文件夹。") + self.failed.emit("模型目录无效或不存在，请确认步骤6已生成 7_models 下的参数子文件夹。") return pipeline = WaterQualityInversionPipeline(work_dir=str(wp)) scatter_paths = pipeline.generate_model_scatter_plots( @@ -670,7 +670,7 @@ class VisualizationWorkerThread(QThread): 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(wp / "9_visualization")) + viz = WaterQualityVisualization(output_dir=str(wp / "14_visualization")) parts = [] if self.extra.get("gen_mask_glint"): preview_paths = viz.generate_glint_deglint_previews( @@ -703,7 +703,7 @@ class VisualizationWorkerThread(QThread): csv_path = str(csv_files[0]) from src.postprocessing.point_map import SamplingPointMap map_generator = SamplingPointMap( - output_dir=str(wp / "9_visualization" / "sampling_maps"), + output_dir=str(wp / "14_visualization" / "sampling_maps"), fast_mode=True, ) map_path = map_generator.create_sampling_point_map( @@ -2699,12 +2699,12 @@ class Step9Panel(QWidget): params_group.setLayout(params_layout) layout.addWidget(params_group) - # 输出目录（可选）：在此目录下生成「CSV文件名_distribution.png」；留空则用工作目录/9_visualization + # 输出目录（可选）：在此目录下生成「CSV文件名_distribution.png」；留空则用工作目录/14_visualization self.output_dir = FileSelectWidget( "输出分布图目录:", "Directories;;All Files (*.*)" ) - self.output_dir.line_edit.setPlaceholderText("留空→工作目录/9_visualization") + self.output_dir.line_edit.setPlaceholderText("留空→工作目录/14_visualization") # 修改浏览按钮为选择目录 self.output_dir.browse_btn.clicked.disconnect() self.output_dir.browse_btn.clicked.connect(self.browse_output_dir) @@ -3404,10 +3404,10 @@ class ImageCategoryTree(QTreeWidget): if not work_path.exists(): return - # 查找所有图像文件：9_visualization 为主，同时扫描步骤产出目录（如 1_water_mask 下的预览/叠置图） + # 查找所有图像文件：14_visualization 为主，同时扫描步骤产出目录（如 1_water_mask 下的预览/叠置图） image_extensions = ['*.png', '*.jpg', '*.jpeg', '*.tif', '*.tiff', '*.bmp'] scan_roots: List[Path] = [] - _viz = work_path / "9_visualization" + _viz = work_path / "14_visualization" if _viz.is_dir(): scan_roots.append(_viz) _wm = work_path / "1_water_mask" @@ -3761,7 +3761,7 @@ class VisualizationPanel(QWidget): 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: 9_visualization/glint_deglint_previews/", + f"保存位置: 14_visualization/glint_deglint_previews/", ) else: QMessageBox.warning( @@ -3776,7 +3776,7 @@ class VisualizationPanel(QWidget): "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: 9_visualization/sampling_maps/", + "路径: 14_visualization/sampling_maps/", ) if map_path: self.show_chart_viewer(map_path, "采样点分布图") @@ -3787,7 +3787,7 @@ class VisualizationPanel(QWidget): 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/9_visualization/" + f"保存目录: 工作目录/14_visualization/" ) if errs: msg += f"\n\n以下列未生成或出错 ({len(errs)} 项，详见日志):\n" @@ -3818,14 +3818,14 @@ class VisualizationPanel(QWidget): self, "成功", f"已生成 {len(ok_paths)} 个模型评估散点图。\n" - f"保存位置: 9_visualization/scatter_plots/", + f"保存位置: 14_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 6_models 下已有各参数子目录及模型文件，" + "未生成任何散点图。请确认 7_models 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": @@ -3935,7 +3935,7 @@ class VisualizationPanel(QWidget): self.gen_scatter_btn = QPushButton("📊 散点图") self.gen_scatter_btn.setToolTip( - "基于工作目录下 5_training_spectra/training_spectra.csv 与 6_models 生成模型评估散点图" + "基于工作目录下 5_training_spectra/training_spectra.csv 与 7_models 生成模型评估散点图" ) self.gen_scatter_btn.clicked.connect(lambda: self.generate_chart('scatter')) specific_layout.addWidget(self.gen_scatter_btn) @@ -4000,7 +4000,7 @@ class VisualizationPanel(QWidget): self.image_tree.scan_directory(str(work_path)) # 如果有图像，自动选择第一个 - viz_dir = work_path / "9_visualization" + viz_dir = work_path / "14_visualization" if viz_dir.exists(): image_files = list(viz_dir.glob("**/*.png")) + list(viz_dir.glob("**/*.jpg")) if image_files: @@ -4082,7 +4082,7 @@ class VisualizationPanel(QWidget): ) return training_csv = training_spectra_csv - models_dir = work_path / "6_models" + models_dir = work_path / "7_models" if not models_dir.is_dir() or not any( d.is_dir() for d in models_dir.iterdir() ): @@ -4188,9 +4188,9 @@ class VisualizationPanel(QWidget): return work_path = Path(self.work_dir) - viz_dir = work_path / "9_visualization" - viz_dir2 = work_path / "9_visualization/boxplots" - viz_dir3 = work_path / "9_visualization/scatter_plots" + viz_dir = work_path / "14_visualization" + viz_dir2 = work_path / "14_visualization/boxplots" + viz_dir3 = work_path / "14_visualization/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。") @@ -4338,7 +4338,7 @@ class ReportGenerationPanel(QWidget): layout.setSpacing(10) intro = QLabel( - "根据工作目录下的可视化结果（9_visualization 等）生成 Word 分析报告。" + "根据工作目录下的可视化结果（14_visualization 等）生成 Word 分析报告。" "需已存在可视化图表；AI 分析通过 Ollama /api/chat 调用本地或远程服务。" ) intro.setWordWrap(True) @@ -4350,7 +4350,7 @@ class ReportGenerationPanel(QWidget): wd_row = QHBoxLayout() self.work_dir_edit = QLineEdit() - self.work_dir_edit.setPlaceholderText("选择流程工作目录（含 9_visualization）…") + self.work_dir_edit.setPlaceholderText("选择流程工作目录（含 14_visualization）…") wd_browse = QPushButton("浏览…") wd_browse.clicked.connect(self.browse_work_dir) sync_btn = QPushButton("同步主窗口工作目录") @@ -4362,7 +4362,7 @@ class ReportGenerationPanel(QWidget): out_row = QHBoxLayout() self.output_dir_edit = QLineEdit() - self.output_dir_edit.setPlaceholderText("留空则保存到 工作目录/9_visualization") + self.output_dir_edit.setPlaceholderText("留空则保存到 工作目录/14_visualization") out_browse = QPushButton("浏览…") out_browse.clicked.connect(self.browse_output_dir) out_row.addWidget(self.output_dir_edit, 1) @@ -4494,7 +4494,7 @@ class ReportGenerationPanel(QWidget): if not wd or not os.path.isdir(wd): QMessageBox.warning(self, "提示", "请选择有效的工作目录。") return - viz = Path(wd) / "9_visualization" + viz = Path(wd) / "14_visualization" if not viz.is_dir(): QMessageBox.warning( self, @@ -4755,7 +4755,7 @@ class Step6_5Panel(QWidget): "输出模型目录:", "Directories;;All Files (*.*)" ) - self.output_dir.line_edit.setPlaceholderText("6_5_non_empirical_models") + self.output_dir.line_edit.setPlaceholderText("8_non_empirical_models") # 修改浏览按钮为选择目录 self.output_dir.browse_btn.clicked.disconnect() self.output_dir.browse_btn.clicked.connect(self.browse_output_dir) @@ -4825,9 +4825,9 @@ class Step6_5Panel(QWidget): # 如果output_dir为空，使用工作目录或当前目录 main_window = self.parent().window() if hasattr(main_window, 'work_dir') and main_window.work_dir: - output_dir = str(Path(main_window.work_dir) / "6_5_non_empirical_models") + output_dir = str(Path(main_window.work_dir) / "8_non_empirical_models") else: - output_dir = str(Path.cwd() / "6_5_non_empirical_models") + output_dir = str(Path.cwd() / "8_non_empirical_models") config['output_dir'] = output_dir # 添加训练数据路径（用于独立运行） @@ -5044,7 +5044,7 @@ class Step6_75Panel(QWidget): output_layout = QFormLayout() self.output_dir = QLineEdit() - self.output_dir.setText("6_75_custom_regression") + self.output_dir.setText("9_custom_regression") output_layout.addRow("输出目录名:", self.output_dir) output_group.setLayout(output_layout) @@ -5202,7 +5202,7 @@ class Step6_75Panel(QWidget): checkbox.setChecked(method in selected_methods) if 'output_dir' in config: - self.output_dir.setText(config['output_dir'] or "6_75_custom_regression") + self.output_dir.setText(config['output_dir'] or "9_custom_regression") if 'enabled' in config: self.enable_checkbox.setChecked(config['enabled']) diff --git a/src/postprocessing/flight_path.py b/src/postprocessing/flight_path.py index c93e060..9e78932 100644 --- a/src/postprocessing/flight_path.py +++ b/src/postprocessing/flight_path.py @@ -534,7 +534,7 @@ if __name__ == "__main__": print(" )") - visualizer = FlightPathVisualizer(output_dir=r"E:\code\WQ\pipeline_result\work_dir\9_visualization\flight_maps") + visualizer = FlightPathVisualizer(output_dir=r"E:\code\WQ\pipeline_result\work_dir\14_visualization\flight_maps") # 生成飞行轨迹图 map_path = visualizer.create_flight_path_map( gps_folder=r"D:\BaiduNetdiskDownload\20250902\gps", # GPS文件夹路径 diff --git a/src/postprocessing/map.py b/src/postprocessing/map.py index 6d37d37..7862f35 100644 --- a/src/postprocessing/map.py +++ b/src/postprocessing/map.py @@ -2130,9 +2130,9 @@ def main(): mapper = ContentMapper() # 示例1：处理单个文件 - csv_file = r"E:\code\WQ\pipeline_result\tests1\8_predictions\BGA.csv" # 采样点的预测值 + csv_file = r"E:\code\WQ\pipeline_result\tests1\11_12_13_predictions\BGA.csv" # 采样点的预测值 shp_file = r"D:\BaiduNetdiskDownload\yaobao\roi\roi.shp" # 水体边界shapefile路径 - output_file = r"E:\code\WQ\pipeline_result\work_dir\8_predictions\BGA.png" # 输出图片路径 + output_file = r"E:\code\WQ\pipeline_result\work_dir\11_12_13_predictions\BGA.png" # 输出图片路径 # mapper.process_data( csv_file=csv_file, diff --git a/src/postprocessing/report_word.py b/src/postprocessing/report_word.py index f36eb8c..80e1e57 100644 --- a/src/postprocessing/report_word.py +++ b/src/postprocessing/report_word.py @@ -79,7 +79,7 @@ class WaterQualityReportGenerator: self.work_dir = Path(work_dir) # 基于工作目录设置各子目录 - self.visualization_dir = self.work_dir / "9_visualization" + self.visualization_dir = self.work_dir / "14_visualization" # 设置报告保存位置：默认为可视化目录（visualization_dir） self._output_dir_is_default = output_dir is None @@ -585,12 +585,12 @@ class WaterQualityReportGenerator: output_path: Optional[str] = None) -> str: """ 生成 Word 报告 - 所有数据均来自工作目录（work_dir） - 可视化图片、统计数据等均从 work_dir/9_visualization 和 work_dir/4_processed_data 中读取 + 可视化图片、统计数据等均从 work_dir/14_visualization 和 work_dir/4_processed_data 中读取 """ # 设置工作目录（整个流程的核心） if work_dir is not None: self.work_dir = Path(work_dir) - self.visualization_dir = self.work_dir / "9_visualization" + self.visualization_dir = self.work_dir / "14_visualization" if getattr(self, "_output_dir_is_default", False): self.output_dir = self.visualization_dir self.output_dir.mkdir(parents=True, exist_ok=True) @@ -1050,7 +1050,7 @@ class WaterQualityReportGenerator: vis_dir = self.visualization_dir # 0. 航线规划图 - flight_path_img_path = work_dir_path / "9_visualization" / "flight_maps" + flight_path_img_path = work_dir_path / "14_visualization" / "flight_maps" h3 = doc.add_heading("航线规划：", level=3) self._style_heading(h3, level=3) diff --git a/src/postprocessing/visualization_reports.py b/src/postprocessing/visualization_reports.py index 01bcf51..5904b5e 100644 --- a/src/postprocessing/visualization_reports.py +++ b/src/postprocessing/visualization_reports.py @@ -423,7 +423,7 @@ class WaterQualityVisualization: - 2_glint文件夹：单波段二值耀斑掩膜，使用红色高亮显示 - 3_deglint文件夹：多波段去耀斑影像，使用RGB合成显示 - 自动识别文件类型并应用相应的可视化方案 - - 输出保存至9_visualization/glint_deglint_previews/ + - 输出保存至14_visualization/glint_deglint_previews/ Args: work_dir: 工作目录路径 @@ -713,7 +713,7 @@ class WaterQualityVisualization: from src.postprocessing.point_map import SamplingPointMap # 如果没有提供路径，自动查找 - work_dir = self.output_dir.parent # 9_visualization的父目录就是工作目录 + work_dir = self.output_dir.parent # 14_visualization的父目录就是工作目录 if hyperspectral_path is None: # 查找高光谱影像