double-buffer-shared-memory-manager 1.0.0

Package for communication with RabbitMQ

DoubleBufferedSharedMemoryManager is a Python class designed to manage double-buffered shared memory for concurrent read/write operations, particularly suited for handling video frames stored as NumPy ndarrays.

Features

• Double-Buffered Memory: Allows one buffer to be read while the other is being written to, minimizing read-write conflicts.

• Thread-Safe Access: Utilizes reader-writer locks to allow multiple readers and a single writer.

• Dynamic Memory Management: Dynamically creates or connects to shared memory segments based on the provided base name.

• Customizable Data Handling: Supports specifying the shape and data type of the NumPy ndarray.

Requirements

• Python 3.x

• NumPy

• multiprocessing

• readerwriterlock

• colorlog (for enhanced logging with color)

Installation

Ensure you have the required libraries:

pip install numpy readerwriterlock colorlog

Usage

Initialization

Import and initialize the manager:

from double_buffered_shared_memory import DoubleBufferedSharedMemoryManager
import numpy as np

# Initialize with default parameters
manager = DoubleBufferedSharedMemoryManager('image_buffer')

# Initialize with custom shape and dtype
manager = DoubleBufferedSharedMemoryManager('image_buffer', shape=(576, 720, 3), dtype=np.uint16)

Writing to Shared Memory

# Create a random HD image
img = np.random.randint(255, size=(1080, 1920, 3), dtype=np.uint8)

# Write to the active buffer
manager[0] = img

0 is a convention, you can use any number, but for clarity use 0

Reading from Shared Memory

# Switch the active buffer
manager.__switch()

# Read from the non-active buffer
img_copy = manager[0]

0 is a convention, you can use any number, but for clarity use 0

Cleanup

It’s important to clean up shared memory resources when they’re no longer needed:

manager.cleanup()

Using with Context Manager

The class supports context management protocol for automatic resource management:

with DoubleBufferedSharedMemoryManager('image_buffer') as manager:
    # Your code to use the manager
    pass

Logging

The class utilizes colorlog for enhanced logging. Log messages will vary in color based on the log level to provide a clearer and more intuitive understanding of the operations and events.

Contributing

Contributions, issues, and feature requests are welcome. Feel free to check issues page if you want to contribute.

Author

Massimo Ghiani [e-mail]

License

Distributed under the MIT License. See LICENSE for more information.

Acknowledgements

• NumPy

• readerwriterlock

• colorlog