pixelflux 1.3.2

A performant web native pixel delivery pipeline for diverse sources, blending VNC-inspired parallel processing of pixel buffers with flexible modern encoding formats.

pixelflux

A performant web native pixel delivery pipeline for diverse sources, blending VNC-inspired parallel processing of pixel buffers with flexible modern encoding formats.

This module provides a Python interface to a high-performance C++ capture library. It captures pixel data from a source (currently X11 screen regions), detects changes, and encodes modified stripes into JPEG or H.264. It supports CPU-based encoding (libx264, libjpeg-turbo) as well as hardware-accelerated H.264 encoding via NVIDIA's NVENC and VA-API for Intel/AMD GPUs. The resulting data is delivered efficiently to your Python application via a callback mechanism.

Installation

This module relies on a native C++ extension that is compiled during installation.

1. Prerequisites (for Debian/Ubuntu): Ensure you have a C++ compiler (g++) and development files for Python, X11, Xfixes, XShm, libjpeg-turbo, and libx264. These are required for the base software encoding functionality.

   sudo apt-get update && \
   sudo apt-get install -y \
     g++ \
     libdrm-dev \
     libjpeg-turbo8-dev \
     libva-dev \
     libx11-dev \
     libx264-dev \
     libxext-dev \
     libxfixes-dev \
     libyuv-dev \
     python3-dev
   

2. Hardware Acceleration (Optional but Recommended):

   • NVIDIA (NVENC): No extra packages are needed at compile time. If you have the NVIDIA driver installed, the library will be detected and used automatically at runtime.
   • Intel/AMD (VA-API): The libva-dev and libdrm-dev packages listed above are sufficient for compilation. Ensure you have the correct VA-API drivers for your hardware installed (e.g., intel-media-va-driver-non-free for Intel, mesa-va-drivers for AMD).

3. Install the Package: You can install directly from PyPI or from a local source clone.

   Option A: Install from PyPI

   pip install pixelflux
   

   Option B: Install from a local source directory

   # From the root of the project repository
   pip install .
   

   Note: The current backend is designed and tested for Linux/X11 environments.

Usage

Capture Settings

The CaptureSettings class allows for detailed configuration of the capture process.

# All attributes of the CaptureSettings object are standard ctypes properties.
settings = CaptureSettings()

# --- Core Capture ---
settings.capture_width = 1920
settings.capture_height = 1080
settings.capture_x = 0
settings.capture_y = 0
settings.target_fps = 60.0
settings.capture_cursor = True

# --- Encoding Mode ---
# 0 for JPEG, 1 for H.264
settings.output_mode = 1
# Force CPU encoding and ignore hardware encoders
capture_settings.use_cpu = False

# --- JPEG Settings ---
settings.jpeg_quality = 75              # Quality for changed stripes (0-100)
settings.paint_over_jpeg_quality = 90   # Quality for static "paint-over" stripes (0-100)

# --- H.264 Settings ---
settings.h264_crf = 25                   # CRF value (0-51, lower is better quality/higher bitrate)
settings.h264_paintover_crf = 18         # CRF for H.264 paintover on static content. Must be lower than h264_crf to activate.
settings.h264_paintover_burst_frames = 5 # Number of high-quality frames to send in a burst when a paintover is triggered.
settings.h264_fullcolor = False          # Use I444 (full color) instead of I420 for software encoding
settings.h264_fullframe = True           # Encode full frames (required for HW accel) instead of just changed stripes
settings.h264_streaming_mode = False     # Bypass all VNC logic and work like a normal video encoder, higher constant CPU usage for fullscreen gaming/videos

# --- Hardware Acceleration ---
# Set to >= 0 to enable VA-API on a specific /dev/dri/renderD* node.
# Set to -1 to disable VA-API and let the system try NVENC if available.
settings.vaapi_render_node_index = -1

# --- Change Detection & Optimization ---
settings.use_paint_over_quality = True  # Enable paint-over/IDR requests for static regions
settings.paint_over_trigger_frames = 15 # Frames of no motion to trigger paint-over
settings.damage_block_threshold = 10    # Consecutive changes to trigger "damaged" state
settings.damage_block_duration = 30     # Frames a stripe stays "damaged"

# --- Watermarking ---
# Must be a bytes object. The path to your PNG image.
settings.watermark_path = b"/path/to/your/watermark.png" 
# 0:None, 1:TopLeft, 2:TopRight, 3:BottomLeft, 4:BottomRight, 5:Middle, 6:Animated
settings.watermark_location_enum = 4 

Stripe Callback and Data Structure

Your callback function receives a ctypes.POINTER(StripeEncodeResult). You must access its fields via the .contents attribute.

The StripeEncodeResult struct has the following fields:

# This is an illustrative Python representation of the C++ struct.
class StripeEncodeResult(ctypes.Structure):
    _fields_ = [
        ("type", ctypes.c_int),             # 1 for JPEG, 2 for H.264
        ("stripe_y_start", ctypes.c_int),
        ("stripe_height", ctypes.c_int),
        ("size", ctypes.c_int),             # The size of the data in bytes
        ("data", ctypes.POINTER(ctypes.c_ubyte)), # Pointer to the encoded data
        ("frame_id", ctypes.c_int),         # Frame counter for this stripe
    ]

Memory Management: The data pointed to by result.contents.data is valid only within the scope of your callback function. The C++ library automatically frees this memory after your callback returns. To use the data, you must copy it, for example by using ctypes.string_at(result.contents.data, result.contents.size).

Features

• Efficient Pixel Capture: Leverages a native C++ module using XShm for optimized X11 screen capture performance.
• Flexible Encoding Backends:
  • Software: libx264 (H.264) and libjpeg-turbo (JPEG).
  • Hardware: NVIDIA NVENC and VA-API (Intel, AMD).
• Stripe-Based Processing: For software encoding, can divide the screen into horizontal stripes and process them in parallel across CPU cores.
• Change Detection: Encodes only stripes that have changed (based on an XXH3 hash comparison) since the last frame, significantly reducing processing load and bandwidth for software encoding modes.
• Dynamic Watermarking: Overlay a PNG image on the captured video. The watermark can be pinned to a corner, centered, or animated to bounce around the screen.
• Dynamic Quality Optimizations:
  • Paint-Over for Static Regions: After a region remains static for paint_over_trigger_frames, it is resent at high quality (JPEG) or as a new IDR frame (H.264) to correct any compression artifacts.
  • Damage Throttling: For highly active regions, the system can temporarily reduce the frequency of change detection to save CPU cycles.
• Direct Callback Mechanism: Provides encoded stripe data directly to your Python code for real-time processing or streaming.

Example: Real-time H.264 Streaming with WebSockets

A comprehensive example, screen_to_browser.py, is located in the example directory of this repository. This script demonstrates robust, real-time screen capture, H.264 encoding, and streaming via WebSockets. It sets up:

• An asyncio-based WebSocket server to stream encoded H.264 frames.
• An HTTP server to serve a client-side HTML page for viewing the stream.
• The pixelflux module to perform the screen capture and encoding.

To run this example:

Note: This example assumes you are on a Linux host with a running X11 session.

1. First, ensure you have the websockets library installed:

   pip install websockets
   

2. Navigate to the example directory within the repository:

   cd example
   

3. Execute the Python script:

   python3 screen_to_browser.py
   

4. Open your web browser and go to the URL indicated by the script's output (usually http://localhost:9001) to view the live stream.

License

This project is licensed under the Mozilla Public License Version 2.0. A copy of the MPL 2.0 can be found at https://mozilla.org/MPL/2.0/.