This project calculates foot sizes using a reference coin and generates a PDF report with measurements of detected feet from an image. The report includes marked images and calculated dimensions, providing a detailed analysis of the detected objects.

• Features
• Requirements
• Installation
• Usage
• Example Images
• File Structure
• Acknowledgments
• License

• Background Removal: Removes the background from the input image.
• Object Detection: Detects feet and coins using YOLO models.
• Feet Measurements: Calculates the width and height of the detected feet in centimeters.
• PDF Report Generation: Creates a PDF report with marked images and measurements.

• Python 3.7+
• OpenCV
• NumPy
• fpdf
• matplotlib
• ultralytics
• flask

1. Clone the repository:

   git clone https://github.com/Xer0bit/BRP-SizeMeasure cd BRP-SizeMeasure

2. Create a virtual environment (optional but recommended):

   python -m venv venv source venv/bin/activate # On Windows, use `venv\Scripts\activate`

3. Install the required packages:

   pip install -r requirements.txt

1. Prepare your YOLO models: Ensure you have the YOLO models for detecting feet and coins in the models/ directory:

   • models/feet.pt
   • models/coin.pt

2. Run the script:

   from image_processing import remove_background, detect_objects from pdf_report import generate_pdf_report # Example usage original_image_path = 'path/to/original/image.jpg' detection_results = detect_objects(remove_background(original_image_path)) output_pdf_path = 'path/to/output/report.pdf' generate_pdf_report(detection_results, output_pdf_path, original_image_path)

3. View the generated PDF: The PDF report will be saved to the path specified in output_pdf_path.

• models/: Directory containing the YOLO model files.
• image_processing.py: Script for image loading, background removal, and object detection.
• pdf_report.py: Script for generating the PDF report.
• requirements.txt: List of required Python packages.
• README.md: Project documentation.
• example_usage.py: Example script demonstrating how to use the project.

• YOLO for the object detection models.
• OpenCV and NumPy for image processing.

For using the YOLO models, ensure that you have the appropriate .pt files for both feet and coins. You can download the pre-trained models from the MODELS.

This project is licensed under the MIT License. See the LICENSE file for details.