VisionGuard

Overview

VisionGuard is a comprehensive Python library for computer vision tasks, specializing in object detection, image classification, and real-time video analysis. It provides a modular framework for building, training, and deploying robust vision models, leveraging popular libraries like OpenCV and PyTorch. The project aims to offer high-performance solutions for security, surveillance, and automated inspection systems.

Features

• Real-time Object Detection: Implementations of popular object detection models (e.g., YOLO, SSD) for live video streams.
• Image Classification: Tools for training and evaluating image classification models on custom datasets.
• Advanced Preprocessing: Utilities for image augmentation, normalization, and resizing.
• Model Training & Evaluation: Customizable training loops with support for various loss functions and metrics.
• Deployment Ready: Optimized for efficient inference on edge devices and cloud platforms.
• Interactive Visualization: Functions for drawing bounding boxes, displaying class labels, and visualizing model predictions.

Installation

To get started with VisionGuard, clone the repository and install the required dependencies:

git clone https://github.com/Eation5/VisionGuard.git
cd VisionGuard
pip install -r requirements.txt

Usage

Here's a quick example of how to use VisionGuard for real-time object detection:

import cv2
import torch
from visionguard.models import YOLOv5
from visionguard.utils import draw_boxes

# 1. Initialize YOLOv5 model (pre-trained)
model = YOLOv5(model_path=\'yolov5s.pt\') # Assuming yolov5s.pt is downloaded

# 2. Open video capture (0 for webcam, or path to video file)
cap = cv2.VideoCapture(0)

if not cap.isOpened():
    print("Error: Could not open video stream.")
    exit()

while True:
    ret, frame = cap.read()
    if not ret:
        break

    # 3. Perform inference
    detections = model.predict(frame)

    # 4. Draw bounding boxes on the frame
    annotated_frame = draw_boxes(frame, detections)

    # 5. Display the result
    cv2.imshow("VisionGuard Object Detection", annotated_frame)

    # Break loop on 'q' key press
    if cv2.waitKey(1) & 0xFF == ord(\'q\'):
        break

# 6. Release resources
cap.release()
cv2.destroyAllWindows()

Project Structure

VisionGuard/
├── README.md
├── requirements.txt
├── setup.py
├── visionguard/
│   ├── __init__.py
│   ├── models.py
│   ├── preprocessing.py
│   ├── utils.py
│   └── datasets.py
└── tests/
    ├── __init__.py
    └── test_models.py

Contributing

We welcome contributions! Please see CONTRIBUTING.md for details on how to get started.

License

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

Contact

For any inquiries, please open an issue on GitHub or contact Matthew Wilson at matthew.wilson.ai@example.com.