Skip to main content

pixtreme-core: High-Performance GPU Image Processing Core Library

Project description

pixtreme-core

High-Performance GPU Image Processing Core Library

Overview

pixtreme-core provides the fundamental building blocks for GPU-accelerated image processing:

  • I/O: Hardware-accelerated image reading/writing via NVIDIA nvimgcodec
  • Color: Color space conversions (BGR, RGB, HSV, YCbCr, LUT operations)
  • Transform: Geometric transformations (resize, affine, tiling) with 11 interpolation methods
  • Utils: Framework interoperability (NumPy, CuPy, PyTorch) via DLPack

All operations work directly on GPU memory using CuPy arrays for maximum performance.

Installation

Requirements:

  • Python >= 3.12
  • CUDA Toolkit 12.x
  • NVIDIA GPU with compute capability >= 6.0
pip install pixtreme-core

OpenCV Variants

pixtreme-core uses opencv-python by default. For different environments:

  • Headless environments (no GUI): Replace with opencv-python-headless

    pip uninstall opencv-python
    pip install opencv-python-headless
    
  • Contrib modules needed: Replace with opencv-contrib-python

    pip uninstall opencv-python
    pip install opencv-contrib-python
    

All variants provide the same cv2 module and are compatible with pixtreme.

Quick Start

import pixtreme_core as px

# Read image (returns CuPy array on GPU)
img = px.imread("input.jpg")

# Resize with auto-selected interpolation
img = px.resize(img, (512, 512))

# Convert color space
img = px.bgr_to_rgb(img)

# Write image
px.imwrite("output.jpg", img)

Features

Image I/O

  • imread(): Hardware-accelerated JPEG/PNG decoding
  • imwrite(): Efficient image encoding
  • imshow(): Display with matplotlib

Color Conversions

  • BGR ↔ RGB, HSV, YCbCr, Grayscale
  • 3D LUT operations with trilinear/tetrahedral interpolation
  • Video format support (UYVY422, YUV420p, YUV422p10le)
  • Legal/full range YCbCr conversion

Geometric Transforms

  • resize(): 11 interpolation methods including Lanczos, Mitchell, Catmull-Rom
  • affine(): Affine transformations
  • tile_image(), merge_tiles(): Tiling workflow for large images
  • erode(): Morphological erosion

Framework Interoperability

  • to_cupy(), to_numpy(), to_tensor(): Zero-copy conversions via DLPack
  • to_uint8(), to_uint16(), to_float32(): Type conversions with range scaling

License

MIT License - see LICENSE file for details.

Links

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pixtreme_core-0.8.3.tar.gz (43.9 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

pixtreme_core-0.8.3-py3-none-any.whl (49.9 kB view details)

Uploaded Python 3

File details

Details for the file pixtreme_core-0.8.3.tar.gz.

File metadata

  • Download URL: pixtreme_core-0.8.3.tar.gz
  • Upload date:
  • Size: 43.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.8.18

File hashes

Hashes for pixtreme_core-0.8.3.tar.gz
Algorithm Hash digest
SHA256 a5c7483d7312c0d9b533777bad53c603ad4102531aa8c5cfd9a94e7da44e2b38
MD5 1011317732da91b6c998f0cf47ab5b38
BLAKE2b-256 974994143383afeef3263c6d6e48e8d26250456c50512712cfade7d8f84658a0

See more details on using hashes here.

File details

Details for the file pixtreme_core-0.8.3-py3-none-any.whl.

File metadata

File hashes

Hashes for pixtreme_core-0.8.3-py3-none-any.whl
Algorithm Hash digest
SHA256 edae945c1063c39c7da096f89b9bcbdd344d3f78331ca34e23b8a55da2983efe
MD5 ae299f8655bee0428e63d42ba6d6e9e5
BLAKE2b-256 d6945c9e521aa96e12d5d7f6f1dc449a7c72564448ed4b821b9db6dd753f0568

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page