framepump 0.2.0

High-performance video processing built on PyAV

FramePump

A Python library for high-performance video processing, built on PyAV (Python bindings for FFmpeg's libraries). It provides a simple and efficient way to read, write, and manipulate video files.

Installation

pip install framepump

Usage

Reading Video Frames

The main entry point for reading videos is the VideoFrames class. It allows for efficient, slice-based access to video frames.

from framepump import VideoFrames
import numpy as np

frames = VideoFrames('my_video.mp4')  # This is lazy, it only reads some metadata.

# Get basic information
print(f"Shape: {frames.imshape}")
print(f"FPS: {frames.fps}")
print(f"Number of frames: {len(frames)}")

# Iterate over all frames — this is where decoding begins
for frame in frames:
    # frame is a numpy array of shape (height, width, 3) and dtype uint8
    pass

# Slice the video to get every second frame within the first 100 frames
subset_frames = frames[:100:2]
print(f"Number of frames in subset: {len(subset_frames)}")

# Resize the video on the fly (this creates a new VideoFrames instance, no frames are read yet)
resized_frames = frames.resized((128, 128))
print(f"Resized shape: {resized_frames.imshape}")

# Change the data type (e.g., to float32 for neural network processing)
float_frames = VideoFrames('my_video.mp4', dtype=np.float32)

# Use GPU acceleration for decoding (requires a CUDA-enabled ffmpeg build and a GPU).
# Frames are decoded on GPU and returned as numpy arrays (on CPU).
frames = VideoFrames('my_video.mp4', gpu=True)

# For fully GPU-resident frames (no GPU→CPU transfer), use VideoFramesCuda instead.
# This returns DecodedFrame objects that stay in GPU memory — useful when feeding
# directly into GPU pipelines (e.g. NVENC encoding, CUDA processing).
# from framepump import VideoFramesCuda
# cuda_frames = VideoFramesCuda('my_video.mp4')

Writing Videos

You can write a sequence of frames to a video file using the VideoWriter class. It handles the writing process in a separate thread for better performance.

import numpy as np
from framepump import VideoWriter

# Use VideoWriter as a context manager
with VideoWriter('output.mp4', fps=30) as writer:
    for i in range(100):
        # Generate a 100x100 black frame with a moving white square
        frame = np.zeros((100, 100, 3), dtype=np.uint8)
        frame[i:i+10, i:i+10] = 255
        writer.append_data(frame)

Including Audio

You can copy the audio stream from another video file into your output video by providing the audio_source_path argument to the VideoWriter. The audio will be copied without re-encoding.

import numpy as np
from framepump import VideoWriter

# Create a silent video and then mux it with audio from another file
with VideoWriter('output_with_audio.mp4', fps=30, audio_source_path='input_with_audio.mp4') as writer:
    for i in range(100):
        frame = np.zeros((100, 100, 3), dtype=np.uint8)
        writer.append_data(frame)

Getting Video Information

Several utility functions are available to get information about a video file.

from framepump import get_fps, get_duration, num_frames, video_extents

video_path = 'my_video.mp4'

fps = get_fps(video_path)
duration = get_duration(video_path)
n_frames = num_frames(video_path)
width, height = video_extents(video_path)

print(f"FPS: {fps}, Duration: {duration}s, Frames: {n_frames}, Dimensions: {width}x{height}")

Video Manipulation

Trimming

Cut a portion of a video.

from framepump import trim_video

trim_video('input.mp4', 'output_trimmed.mp4', start_time='00:00:10', end_time='00:00:20')

Muxing Audio and Video

Combine the video stream from one file with the audio stream from another.

from framepump import video_audio_mux

video_audio_mux(
    vidpath_audiosource='video_with_audio.mp4',
    vidpath_imagesource='silent_video.mp4',
    out_video_path='output_muxed.mp4'
)

Core Abstractions

VideoFrames

This is the central class for reading video frames. It's a lazy, sliceable, and chainable frame sequence.

• Lazy: Frames are only read from the file when you iterate over them.
• Sliceable: You can use standard Python slicing ([start:stop:step]) to select a range of frames. This is also lazy and does not read the frames into memory. The resulting object is also sliceable, so you can chain slicing operations, for example frames[::4][:10].
• Chainable: Methods like resized(), repeat_each_frame(), and slicing return a new VideoFrames instance, allowing you to chain operations.

VideoWriter

This class handles writing frames to a video file. It uses a separate thread to encode and write the video, which prevents the main thread from blocking on I/O and improves performance. It can be used as a context manager for easy setup and teardown.

Resampling Frame Rate

You can resample a video to a constant frame rate using the constant_framerate parameter. This is useful for ML pipelines that expect a fixed number of frames per second.

from framepump import VideoFrames

# Resample to 10 fps (drops/duplicates frames as needed)
frames = VideoFrames('my_video.mp4', constant_framerate=10.0)
print(f"FPS: {frames.fps}, Frames: {len(frames)}")

# Ensure constant frame rate at the original fps (useful for VFR videos)
frames = VideoFrames('my_vfr_video.mp4', constant_framerate=True)

Zero-Copy GPU Encoding with NVENC

For real-time rendering applications, GLVideoWriter encodes OpenGL textures directly to video using NVIDIA's hardware encoder (NVENC), without any CPU memory transfers.

from framepump import GLVideoWriter

with GLVideoWriter('output.mp4', fps=30) as writer:
    for _ in render_loop:
        render_to_texture(texture)
        ctx.finish()  # Ensure GPU is done rendering
        writer.append_data(texture)  # Encode directly from GPU memory

Key features:

• Zero-copy: Pixel data never leaves the GPU
• Hardware encoding: Uses dedicated NVENC hardware, not CUDA cores
• Headless support: Works with both GLX (X11) and EGL (headless/containerized) contexts

Requirements:

• NVIDIA GPU with NVENC support
• Linux with NVIDIA driver
• For headless: pip install framepump[nvenc-cuda]

See the NVENC documentation for details.

High Bit Depth Support

The library supports high bit depth video (e.g., 10-bit) by using the numpy.uint16 data type for frames.

When reading videos, you can specify dtype=np.uint16 in the VideoFrames constructor. This will decode the video into 16-bit RGB frames.

# Read a video as 16-bit integer frames
uint16_frames = VideoFrames('my_high_bit_depth_video.mp4', dtype=np.uint16)

When writing, if you provide the first frame with a dtype of np.uint16, framepump will automatically encode the video using a 10-bit YUV pixel format (yuv420p10le), which is suitable for high dynamic range (HDR) content.

import numpy as np
from framepump import VideoWriter

# Use VideoWriter as a context manager
with VideoWriter('output_10bit.mp4', fps=30) as writer:
    for i in range(100):
        # Generate a 16-bit frame (e.g., a gradient)
        frame = np.zeros((100, 100, 3), dtype=np.uint16)
        gradient = np.linspace(0, 65535, 100, dtype=np.uint16)
        frame[:, :, 0] = gradient
        frame[:, :, 1] = gradient.T
        writer.append_data(frame)