How to Identify Entities Inside or Crossing a Given Closed Polyline with Ezdxf in Python

What will you learn?

In this tutorial, you will learn how to efficiently identify entities that are either completely inside or crossing a specified closed polyline using the Ezdxf module in Python. By the end of this guide, you will have a solid understanding of: – Analyzing geometric shapes and relationships in DXF files – Utilizing the Ezdxf library for reading and writing DXF files – Implementing mathematical checks to determine entity containment or intersection within a closed polyline boundary

Introduction to Problem and Solution

Imagine working on a CAD project where you need to identify which elements lie within or intersect a specific area defined by a closed polyline. This task is crucial for tasks like collision detection, spatial analysis, or data filtering. To address this challenge effectively, we turn to the powerful ezdxf library in Python.

By leveraging ezdxf, we can iterate through entities in a DXF file and apply mathematical calculations to ascertain if they fall within or cross our designated closed polyline boundary. This approach enables us to extract relevant entities based on their spatial relationship with the defined boundary.


import ezdxf

def find_entities_within_polyline(dxf_filepath, polyline_handle):
    # Load the DXF document
    doc = ezdxf.readfile(dxf_filepath)
    msp = doc.modelspace()

    # Retrieve the boundary polyline using its handle
    boundary_polyline = msp.entitydb[polyline_handle]

    # List for storing entities within or crossing the polyline
    contained_entities = []

    # Iterate over all entities in model space
    for entity in msp:
        if entity.dxftype() == 'LWPOLYLINE':
            if entity.handle != boundary_polyline.handle:
                points_inside = 0

                # Check each vertex of current entity against boundary polyline
                for point in entity.get_points():
                    if boundary_polyline.point_inside(point):
                        points_inside += 1

                # Add entity if any point is inside (or modify logic as needed)
                if points_inside > 0:

    return contained_entities

# Example usage: Replace 'your_dxf_file_path.dxf' with your actual file path and 'polyline_handle' with actual handle.
entities_within_boundary = find_entities_within_polyline('your_dxf_file_path.dxf', 'polyline_handle')

# Copyright PHD


To tackle the problem of identifying entities inside or crossing a closed polyline, we follow these key steps: 1. Loading Document: Open the specified DXF file using ezdxf. 2. Accessing Model Space: Retrieve all drawable entities from the document. 3. Finding Boundary Polyline: Locate the target closed polyline using its handle ID. 4. Iterating Over Entities: Check each LWPOLYLINE entity against the boundary polyline. 5. Checking Points Inside: Verify if any vertex of an entity lies within or crosses the defined boundary.

Entities meeting these criteria are stored in contained_entities, providing a comprehensive list of relevant items based on spatial relationships.

    1. What modules do I need installed?

      • You’ll need ezdxf installed. Install it using pip: pip install ezdxf.
    2. Can I use this code for heavy polylines?

      • Yes! Customize ‘LWPOLYLINE’ check according to your requirements.
    3. How can I improve performance?

      • Consider utilizing spatial indexing libraries like R-tree via rtree module for large datasets.
    4. Does it work on circles or arcs as well?

      • Additional geometric calculations are needed for non-linear shapes like circles or arcs within polylines.
    5. Is there error handling involved here?

      • While not explicitly shown, implementing error handling is recommended when dealing with file operations and handle access.
    6. Can I filter types of entities returned?

      • Modify conditional checks within loop based on .dxftype() output against desired types such as “LINE” or “CIRCLE”.

Mastering the identification of enclosed or intersecting geo-spatial data is invaluable across various domains like urban planning and gaming physics engines. By combining basic iteration techniques with geometric checks, you gain significant capabilities in programmatically manipulating design data using Python alongside modules like EZDXF.

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