nobim-image-generator

name	nobim-image-generator
description	Generate images and visualizations from Revit/IFC files without BIM software. Python-based noBIM tool for batch processing.
homepage	https://datadrivenconstruction.io
metadata	{"openclaw":{"emoji":"🖼️","os":["win32"],"homepage":"https://datadrivenconstruction.io","requires":{"bins":["python3"],"anyBins":["ifcopenshell"]}}}

noBIM Image Generator

Business Case

Problem Statement

Creating visualizations from BIM models typically requires:

• Expensive BIM software licenses
• Manual screenshot capture
• Time-consuming rendering
• Impossible to batch process

Solution

noBIM tool extracts data and generates visualizations using Python libraries, processing hundreds of projects without BIM software.

Business Value

• No license required - Pure Python solution
• Batch processing - Generate images for 1000s of projects
• Customizable - Create exactly the visualizations you need
• Automatable - Integrate into data pipelines

Technical Implementation

Installation

pip install pandas matplotlib seaborn plotly ifcopenshell

Core Functionality

 pandas  pd
 matplotlib.pyplot  plt
 mpl_toolkits.mplot3d  Axes3D
 numpy  np
 pathlib  Path
 typing  , , 

 :
     ():
        .elements = 
        .project_name = 

     () -> :
        
        .elements = pd.read_excel(xlsx_path, sheet_name=)
        .project_name = Path(xlsx_path).stem
         (.elements)

     () -> :
        
          (col  .elements.columns
                    col  [, , ]):
             ValueError()

        fig = plt.figure(figsize=size)
        ax = fig.add_subplot(, projection=)

        
        categories = .elements[color_by].unique()
        colors = plt.cm.tab20(np.linspace(, , (categories)))
        color_map = ((categories, colors))

         cat  categories:
            subset = .elements[.elements[color_by] == cat]
            ax.scatter(
                subset[],
                subset[],
                subset[],
                c=[color_map[cat]],
                label=cat[:],
                alpha=,
                s=
            )

        ax.set_xlabel()
        ax.set_ylabel()
        ax.set_zlabel()
        ax.set_title()
        ax.legend(loc=, fontsize=, ncol=)

        plt.savefig(output_path, dpi=, bbox_inches=)
        plt.close()
         output_path

     () -> :
        
        level_elements = .elements[.elements[] == level]

         level_elements.empty:
             ValueError()

        fig, ax = plt.subplots(figsize=size)

        
        walls = level_elements[level_elements[] == ]
         _, wall  walls.iterrows():
            rect = plt.Rectangle(
                (wall[], wall[]),
                wall[] - wall[],
                wall[] - wall[],
                fill=, facecolor=, edgecolor=, alpha=
            )
            ax.add_patch(rect)

        
        rooms = level_elements[level_elements[] == ]
         _, room  rooms.iterrows():
            center_x = (room[] + room[]) / 
            center_y = (room[] + room[]) / 
            ax.annotate(room.get(, ),
                       (center_x, center_y), ha=, fontsize=)

        ax.set_aspect()
        ax.set_title()
        ax.set_xlabel()
        ax.set_ylabel()

        plt.savefig(output_path, dpi=, bbox_inches=)
        plt.close()
         output_path

     () -> :
        
        cat_counts = .elements[].value_counts().head()

        fig, ax = plt.subplots(figsize=size)
        bars = ax.barh(cat_counts.index, cat_counts.values,
                       color=plt.cm.viridis(np.linspace(, , (cat_counts))))

        ax.set_xlabel()
        ax.set_title()

        
         bar, count  (bars, cat_counts.values):
            ax.text(bar.get_width() + , bar.get_y() + bar.get_height()/,
                   , va=, fontsize=)

        plt.tight_layout()
        plt.savefig(output_path, dpi=, bbox_inches=)
        plt.close()
         output_path

     () -> :
        
         plotly.express  px

        vol_by_cat = .elements.groupby()[].().reset_index()
        vol_by_cat = vol_by_cat[vol_by_cat[] > ].sort_values(, ascending=)

        fig = px.treemap(
            vol_by_cat.head(),
            path=[],
            values=,
            title=
        )

        fig.write_image(output_path)
         output_path

     () -> []:
        
        output_dir = Path(output_dir)
        output_dir.mkdir(parents=, exist_ok=)

        generated = []
         xlsx  xlsx_files:
            :
                .load_from_excel(xlsx)
                base_name = Path(xlsx).stem

                
                .generate_3d_scatter((output_dir / ))
                .generate_category_chart((output_dir / ))

                generated.append(base_name)
                ()

             Exception  e:
                ()

         generated