3D-to-FloorPlan-Converter

A Python program to convert 3D images into 2D floor plans using image processing techniques.

Problem Statement

The goal is to develop a Python program that can convert 3D images into floor plans. This involves extracting meaningful information from the 3D images to generate a 2D representation of the floor layout. The challenge includes handling image processing, edge detection, and contour extraction to produce accurate floor plans.

Approach

To address this problem, we will use the following approach:

1. Image Loading: Load the 3D image from the specified path.
2. Grayscale Conversion: Convert the loaded image to grayscale to simplify processing.
3. Gaussian Blur: Apply Gaussian blur to the grayscale image to reduce noise and enhance edge detection.
4. Edge Detection: Use the Canny edge detection algorithm to find edges in the image.
5. Contour Detection: Find contours in the edge-detected image to outline the shapes present.
6. Contour Drawing: Draw the detected contours on the original image to visualize the floor plan.
7. Display Results: Display the original image, edge-detected image, and image with contours side by side for comparison.

Solution

The solution involves implementing the approach using Python and the OpenCV library for image processing. The program includes the following steps:

1. Loading the Image: The program loads an image from a specified path. It includes error handling to ensure the image file is found and loaded correctly.
2. Converting to Grayscale: The loaded image is converted to a grayscale image, which simplifies subsequent processing steps.
3. Applying Gaussian Blur: A Gaussian blur is applied to the grayscale image to reduce noise and improve the accuracy of edge detection.
4. Edge Detection: The Canny edge detection algorithm is used to identify edges in the blurred grayscale image.
5. Finding Contours: Contours are detected in the edge-detected image, outlining the significant shapes.
6. Drawing Contours: The detected contours are drawn on the original image to visualize the detected shapes.
7. Displaying Images: The original image, edge-detected image, and image with contours are displayed side by side for comparison.

Instructions for Running

1. Install Required Libraries: Ensure you have Python installed on your system along with the necessary libraries. You can install the required libraries using pip:

   pip install opencv-python numpy matplotlib

2. Prepare Images: Save the 3D images you want to process in the same directory as the script or provide the correct path to the images.

3. Run the Script: Execute the script using a Python interpreter. For example, run the following command in your command line or terminal:

   python script_name.py

   Make sure to replace script_name.py with the actual name of your Python script file.

4. Modify Image Path: If your images are not named 3d_interior.jpg, update the image_path variable in the process_image function with the correct path to your image file.

Enhancements

To further improve the solution, consider the following enhancements:

• Perspective Correction: Implement techniques to correct perspective distortion in 3D images.
• Feature Detection: Use machine learning models to detect and label specific architectural features (walls, doors, windows) to improve floor plan accuracy.
• Plan Generation: Generate a vector-based floor plan from the detected contours using libraries like svgwrite for scalable vector graphics.

By following these steps, you can create a more robust and complex program for converting 3D images into floor plans.