Skip to main content

fast morphology using kernel subdivision

Project description

[license GPL] [versions] [downloads] [documentation]

Useful links: Binary Installers | Source Repository | Online Documentation |

Description

This module enables efficient morphological erosion and dilatation. It uses the kernel subdivision algorithm implemented in C, with multithreading.

Example of kernel decomposition

Features

  1. Works for any tensor dimension, 2d for images, 3d for videos…

  2. The morphological structuring element decomposition logarithmically reduces temporal complexity.

  3. Functions can be parallelized to take advantage of all the CPU threads, in exchange of higher edge effects.

  4. Functions can be compiled dynamically in C to reduce side-effects and overhead, in exchange for a longer loading time.

Examples

import morphomath
import numpy as np

kernel = morphomath.Kernel([[1, 1], [0, 1]])

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

morphomath-0.0.dev4.tar.gz (58.3 kB view details)

Uploaded Source

File details

Details for the file morphomath-0.0.dev4.tar.gz.

File metadata

  • Download URL: morphomath-0.0.dev4.tar.gz
  • Upload date:
  • Size: 58.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.2

File hashes

Hashes for morphomath-0.0.dev4.tar.gz
Algorithm Hash digest
SHA256 d20778670a982040a624d6c544e8e0d12ff8752cfa0635e7dafb476ca6dbf0a9
MD5 d9d4e2b0d708b7e133f4c8d7a61e9f49
BLAKE2b-256 1a4f9ec83f5ebd10bc52269f1621044ac694b3db92be9129d8e986e82dbbf23b

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