floorplan-epc-detector 1.1.2

A package for making predictions using a custom-trained ONNX floorplan/epc/proprty_image model

Smart Floorplan Predictor

A Python package for classifying images as EPCs, floorplans, property photos, or exterior shots using a pre-trained ONNX model.

Prerequisites

• Python 3.7 or higher

Installation

pip install floorplan-epc-detector

Features

• Image Classification into 4 categories:
  • EPC (Energy Performance Certificate)
  • Floorplans
  • Property Interior Images
  • Property Exterior Images
• Confidence Scoring
• Automatic model download from private/public repositories
• Progress bar for model downloads
• Configurable confidence thresholds
• Raw probability access for advanced use cases

Usage

import os
from floorplan_epc_detector import FloorplanPredictor, FloorplanPredictorError

# --- Configuration for Private Repositories --- 
# If the model is hosted in a private GitHub repository, 
# you MUST provide a GitHub Personal Access Token (PAT).
# Set it as an environment variable:
# export GITHUB_TOKEN="your_github_pat_here"
# Ensure the token has the 'repo' scope to access private repository content.

github_token = os.environ.get("GITHUB_TOKEN")

try:
    # Initialize the predictor with options
    predictor = FloorplanPredictor(
        github_token=github_token,  # Optional: For private repos
        model_path="custom/path/model.onnx",  # Optional: Custom model path
        skip_download=False  # Optional: Skip auto-download
    )

    # Basic prediction (returns just the class)
    predicted_class = predictor.predict("path/to/your/image.jpg")
    print(f"Predicted Class: {predicted_class}")

    # Prediction with confidence score
    predicted_class, confidence = predictor.predict_with_confidence(
        "path/to/your/image.jpg",
        confidence_threshold=0.7  # Optional: Default is 0.7
    )
    print(f"Predicted Class: {predicted_class}")
    print(f"Confidence: {confidence:.2%}")

    # Get raw probabilities for all classes
    probabilities = predictor.get_raw_probabilities("path/to/your/image.jpg")
    print("Raw probabilities:", probabilities)

except FloorplanPredictorError as e:
    print(f"An error occurred: {e}")
    # Handle specific errors:
    # - ModelDownloadError
    # - ImageLoadError
    # - InferenceError

Model Downloading

The package supports two ways to use the model:

1. Automatic Download: The model will be automatically downloaded from the configured repository on first use
2. Local Model: Provide a local path to an existing model file

• The package automatically attempts to download the model.onnx file using the GitHub API if it's not found locally
• Progress bar shows download progress for large model files
• Supports Git LFS for efficient model file handling
• Private Repositories: Downloading from private GitHub repositories requires a GITHUB_TOKEN environment variable or direct token provision

Error Handling

The package provides specific error types for better error handling:

• ModelDownloadError: Issues with model downloading
• ImageLoadError: Problems with image loading or preprocessing
• InferenceError: Issues during model inference
• FloorplanPredictorError: Base class for all package errors

Common Errors & Troubleshooting

• ModelDownloadError: ... 404 Not Found ...:

  • Check if the GITHUB_TOKEN environment variable is set correctly (if accessing a private repo)
  • Verify the token is valid, not expired, and has the repo scope enabled
  • Confirm the repository and model path settings are correct

• ModelDownloadError: ... 403 Forbidden ...:

  • Usually indicates the provided GITHUB_TOKEN lacks necessary permissions

• FloorplanPredictorError: GITHUB_TOKEN is required...:

  • Set the GITHUB_TOKEN environment variable for private repository access

• FileNotFoundError or ImageLoadError:

  • Ensure the image path is correct and the file exists
  • Verify the image format is supported (JPEG, PNG, etc.)

• ONNX Runtime Issues:

  • Ensure onnxruntime is installed correctly for your OS
  • Check Python version compatibility

Running Tests (Development)

1. Clone the repository
2. Install development dependencies: pip install -r requirements.txt
3. Set the GITHUB_TOKEN environment variable if testing with private repo
4. Place test images in the root directory
5. Run the test script: python test.py

Requirements

• Python >= 3.7
• onnxruntime >= 1.12.0
• numpy >= 1.19.0
• requests >= 2.25.0
• Pillow >= 10.0.0
• tqdm >= 4.65.0

License

MIT License

Author

Oliver Brown