Generate images and visualizations from Revit/IFC files without BIM software. Python-based noBIM tool for batch processing.
npx skills add https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill nobim-image-generator
Creating visualizations from BIM models typically requires:
noBIM tool extracts data and generates visualizations using Python libraries, processing hundreds of projects without BIM software.
pip install pandas matplotlib seaborn plotly ifcopenshell
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
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")
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")
| 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 |
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
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Take datadrivenconstruction/nobim-image-generator from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference pip.
Without those the skill loads but fails at the first command.