README.md

Securade.ai HUB Plugins

This directory contains plugins for the Securade.ai HUB camera processing system. Plugins provide different ways to process and analyze camera feeds, from basic image processing to complex computer vision tasks.

Plugin System Overview

The plugin system allows you to create custom image processing modules that can be dynamically loaded and selected in the HUB interface. Each plugin receives an input image and can return a processed/annotated version of that image.

Creating a New Plugin

To create a new plugin:

1. Create a new Python file in this directory (e.g., my_plugin.py)
2. Import the base plugin class: from plugins.base_plugin import BasePlugin
3. Create your plugin class that inherits from BasePlugin
4. Implement the required properties and methods

Here's a template for a new plugin:

import numpy as np
from plugins.base_plugin import BasePlugin

class MyPlugin(BasePlugin):
    # Unique identifier for your plugin
    SLUG = "my_plugin"
    
    # Display name that will appear in the UI
    NAME = "My Custom Plugin"
    
    def run(self, image: np.ndarray, **kwargs) -> np.ndarray:
        """Process the input image
        
        Args:
            image: Input image as numpy array (RGB format)
            **kwargs: Additional arguments passed from the UI
            
        Returns:
            Processed image as numpy array (RGB format)
        """
        # Your image processing code here
        processed_image = image  # Replace with actual processing
        return processed_image

Required Properties and Methods

• SLUG: A unique string identifier for your plugin (lowercase, no spaces)
• NAME: A human-readable name that will be displayed in the UI
• run(self, image, **kwargs): The main processing method that receives and returns an image

Accessing Additional Arguments

Your plugin can access additional arguments through the kwargs parameter. The system provides several common arguments:

def run(self, image: np.ndarray, **kwargs) -> np.ndarray:
    # Access YOLO detector if needed
    detector = kwargs.get('yolo_detector')
    
    # Access other optional parameters
    param1 = kwargs.get('param1', default_value)
    param2 = kwargs.get('param2')
    
    # Check if required parameters are provided
    if param2 is None:
        raise ValueError("Plugin requires 'param2' parameter")

Example Plugins

1. YOLO Detector (yolo_detector.py)

Demonstrates how to use the YOLOv7 detector for object detection:

class YOLODetector(BasePlugin):
    SLUG = "yolo_detector"
    NAME = "YOLO Object Detector"
    
    def run(self, image: np.ndarray, **kwargs) -> np.ndarray:
        detector = kwargs.get('yolo_detector')
        detector.load_cv2mat(image)
        detector.inference()
        return detector.image.copy()

2. Edge Detector (edge_detector.py)

Shows how to implement basic image processing using OpenCV:

class EdgeDetector(BasePlugin):
    SLUG = "edge_detector"
    NAME = "Edge Detector"
    
    def run(self, image: np.ndarray, **kwargs) -> np.ndarray:
        threshold1 = kwargs.get('threshold1', 100)
        threshold2 = kwargs.get('threshold2', 200)
        gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
        edges = cv2.Canny(gray, threshold1, threshold2)
        return cv2.cvtColor(edges, cv2.COLOR_GRAY2RGB)

Best Practices

1. Error Handling: Handle missing dependencies and invalid parameters gracefully
2. Documentation: Include docstrings and comments explaining your plugin's functionality
3. Input Validation: Verify input image format and required parameters
4. Performance: Optimize processing for real-time video analysis when possible
5. Memory Management: Clean up resources and avoid memory leaks

Available Dependencies

Your plugins can use these pre-installed libraries:

• OpenCV (cv2)
• NumPy
• PyTorch
• Other common Python libraries

Debugging Tips

1. Print statements will appear in the server console
2. Use try/except blocks to catch and log errors
3. Test your plugin with different image sizes and formats
4. Verify your plugin works with both still images and video streams

Contributing

When contributing new plugins:

1. Follow the naming conventions and code style of existing plugins
2. Include example usage and documentation
3. Test thoroughly with different inputs
4. Update this README if adding new features or dependencies