--- name: nobim-image-generator-f2f8ba162768 slug: "nobim-image-generator" display_name: "Nobim Image Generator" description: "Generate images and visualizations from Revit/IFC files without BIM software. Python-based noBIM tool for batch processing." --- # 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 ```bash pip install pandas matplotlib seaborn plotly ifcopenshell ``` ### Core Functionality ```python import pandas as pd import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D import numpy as np from pathlib import Path from typing import List, Optional, Tuple class NoBIMVisualizer: def __init__(self): self.elements = None self.project_name = "" def load_from_excel(self, xlsx_path: str) -> int: """Load BIM data from converted Excel file.""" self.elements = pd.read_excel(xlsx_path, sheet_name="Elements") self.project_name = Path(xlsx_path).stem return len(self.elements) def generate_3d_scatter(self, output_path: str, color_by: str = "Category", size: Tuple[int, int] = (12, 10)) -> str: """Generate 3D scatter plot of elements.""" if not all(col in self.elements.columns for col in ['BBox_CenterX', 'BBox_CenterY', 'BBox_CenterZ']): raise ValueError("Bounding box data required. Export with 'bbox' option.") fig = plt.figure(figsize=size) ax = fig.add_subplot(111, projection='3d') # Get unique categories for coloring categories = self.elements[color_by].unique() colors = plt.cm.tab20(np.linspace(0, 1, len(categories))) color_map = dict(zip(categories, colors)) for cat in categories: subset = self.elements[self.elements[color_by] == cat] ax.scatter( subset['BBox_CenterX'], subset['BBox_CenterY'], subset['BBox_CenterZ'], c=[color_map[cat]], label=cat[:20], alpha=0.6, s=10 ) ax.set_xlabel('X') ax.set_ylabel('Y') ax.set_zlabel('Z') ax.set_title(f'{self.project_name} - 3D Element Distribution') ax.legend(loc='upper left', fontsize=8, ncol=2) plt.savefig(output_path, dpi=150, bbox_inches='tight') plt.close() return output_path def generate_floor_plan(self, output_path: str, level: str, size: Tuple[int, int] = (14, 10)) -> str: """Generate floor plan visualization for specific level.""" level_elements = self.elements[self.elements['Level'] == level] if level_elements.empty: raise ValueError(f"No elements found for level: {level}") fig, ax = plt.subplots(figsize=size) # Draw walls walls = level_elements[level_elements['Category'] == 'Walls'] for _, wall in walls.iterrows(): rect = plt.Rectangle( (wall['BBox_MinX'], wall['BBox_MinY']), wall['BBox_MaxX'] - wall['BBox_MinX'], wall['BBox_MaxY'] - wall['BBox_MinY'], fill=True, facecolor='gray', edgecolor='black', alpha=0.7 ) ax.add_patch(rect) # Draw rooms rooms = level_elements[level_elements['Category'] == 'Rooms'] for _, room in rooms.iterrows(): center_x = (room['BBox_MinX'] + room['BBox_MaxX']) / 2 center_y = (room['BBox_MinY'] + room['BBox_MaxY']) / 2 ax.annotate(room.get('RoomName', 'Room'), (center_x, center_y), ha='center', fontsize=8) ax.set_aspect('equal') ax.set_title(f'{self.project_name} - {level}') ax.set_xlabel('X (m)') ax.set_ylabel('Y (m)') plt.savefig(output_path, dpi=150, bbox_inches='tight') plt.close() return output_path def generate_category_chart(self, output_path: str, size: Tuple[int, int] = (12, 8)) -> str: """Generate bar chart of element categories.""" cat_counts = self.elements['Category'].value_counts().head(20) fig, ax = plt.subplots(figsize=size) bars = ax.barh(cat_counts.index, cat_counts.values, color=plt.cm.viridis(np.linspace(0, 1, len(cat_counts)))) ax.set_xlabel('Element Count') ax.set_title(f'{self.project_name} - Element Categories') # Add count labels for bar, count in zip(bars, cat_counts.values): ax.text(bar.get_width() + 1, bar.get_y() + bar.get_height()/2, f'{count}', va='center', fontsize=9) plt.tight_layout() plt.savefig(output_path, dpi=150, bbox_inches='tight') plt.close() return output_path def generate_volume_treemap(self, output_path: str) -> str: """Generate treemap of volumes by category.""" import plotly.express as px vol_by_cat = self.elements.groupby('Category')['Volume'].sum().reset_index() vol_by_cat = vol_by_cat[vol_by_cat['Volume'] > 0].sort_values('Volume', ascending=False) fig = px.treemap( vol_by_cat.head(30), path=['Category'], values='Volume', title=f'{self.project_name} - Volume Distribution' ) fig.write_image(output_path) return output_path def batch_generate(self, xlsx_files: List[str], output_dir: str) -> List[str]: """Generate standard visualizations for multiple projects.""" output_dir = Path(output_dir) output_dir.mkdir(parents=True, exist_ok=True) generated = [] for xlsx in xlsx_files: try: self.load_from_excel(xlsx) base_name = Path(xlsx).stem # Generate all visualizations self.generate_3d_scatter(str(output_dir / f"{base_name}_3d.png")) self.generate_category_chart(str(output_dir / f"{base_name}_categories.png")) generated.append(base_name) print(f"Generated visualizations for: {base_name}") except Exception as e: print(f"Error processing {xlsx}: {e}") return generated ``` ## Usage Examples ### Single Project ```python viz = NoBIMVisualizer() viz.load_from_excel("C:/Projects/Office.xlsx") # Generate 3D view viz.generate_3d_scatter("office_3d.png", color_by="Category") # Generate floor plan viz.generate_floor_plan("office_level1.png", level="Level 1") # Generate category breakdown viz.generate_category_chart("office_categories.png") ``` ### Batch Processing ```python from pathlib import Path viz = NoBIMVisualizer() # Find all converted files xlsx_files = list(Path("C:/ConvertedProjects").glob("*.xlsx")) # Generate visualizations for all generated = viz.batch_generate( [str(f) for f in xlsx_files], output_dir="C:/Visualizations" ) print(f"Generated visualizations for {len(generated)} projects") ``` ## Output Examples | Visualization | Use Case | |---------------|----------| | 3D Scatter | Overall project structure | | Floor Plan | Level-by-level layout | | Category Chart | Element distribution | | Volume Treemap | Material quantities | | Level Comparison | Multi-floor analysis | ## Integration with Reporting ```python from reportlab.pdfgen import canvas from reportlab.lib.pagesizes import A4 def create_project_report(xlsx_path: str, output_pdf: str): """Generate PDF report with all visualizations.""" viz = NoBIMVisualizer() viz.load_from_excel(xlsx_path) # Generate images images = { '3D View': viz.generate_3d_scatter("temp_3d.png"), 'Categories': viz.generate_category_chart("temp_cat.png"), } # Create PDF c = canvas.Canvas(output_pdf, pagesize=A4) c.drawString(100, 800, f"Project Report: {viz.project_name}") y_pos = 700 for title, img_path in images.items(): c.drawString(100, y_pos, title) c.drawImage(img_path, 100, y_pos - 300, width=400, height=280) y_pos -= 350 c.save() return output_pdf ``` ## Resources - **GitHub**: [Revit-IFC-Creating-images](https://github.com/datadrivenconstruction/Revit-IFC-Creating-images) - **Examples**: See repository for Jupyter notebooks --- Source attribution: kbarbel640-del Original project: https://github.com/kbarbel640-del/skills/blob/e7e336ac628c3e3bd771aff1aaa469c93778f94a/skills/datadrivenconstruction/nobim-image-generator/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Dave AI. ## Upstream license notice MIT License Copyright (c) 2026 clawdbot Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Dave AI resource metadata Creator / publisher: kbarbel640-del Original project: https://github.com/kbarbel640-del/skills/blob/e7e336ac628c3e3bd771aff1aaa469c93778f94a/skills/datadrivenconstruction/nobim-image-generator/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Development & Engineering, Product Management, Project & Program Management Roles: Data Analyst, Frontend Developer, ML / AI Engineer, Product Manager, Project Manager Specialties: API Tools: Ifcopenshell, Matplotlib, Pandas, Plotly, Seaborn Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 932e9c7a4de1dc0f5a871556fd9af882273b38256e7952d56b83136db2d67002