AddNoise

AddNoise generates film like noise or other effects (like rain) by adding random noise to a video clip. This noise may optionally be horizontally or vertically correlated to cause streaking.

Original https://github.com/HomeOfVapourSynthEvolution/VapourSynth-AddGrain ported from AviSynth plugin http://forum.doom9.org/showthread.php?t=111849
ref type=0 type=1 type=2 type=3 type=4

Installation

pip install vsnoise

Or Releases for Windows builds

Then it can be used normally

import vapoursynth as vs
vs.core.noise.Add(...)

Usage

noise.Add(vnode clip[, int type=0, float var=1.0, float uvar=0.0, float hcorr=0.0, float vcorr=0.0, float xsize = 2.0, float ysize = 2.0, int seed=-1, int constant=False, int every=1, int opt=0])

  • clip: Clip to process. Any format with either integer sample type of 8-16 bit depth or float sample type of 32 bit depth is supported.

  • type: Type of noise.

    • 0 = Gaussian
    • 1 = Perlin
    • 2 = Simplex
    • 3 = Fractional Brownian Motion over simplex
    • 4 = Poisson
      • Use var to control "sensitivity"
      • Should be used for intensity (Y for YUV) or RGB

  • var, uvar: The variance (strength) of the luma and chroma noise, 0 is disabled. uvar does nothing for GRAY and RGB formats.

  • hcorr, vcorr: Horizontal and vertical correlation, which causes a nifty streaking effect. Only for Gaussian noise. Range 0.0-1.0.

  • xsize, ysize: size of Perlin and Simplex noise. Less than 2 for Perlin may generate visible boundaries.

  • seed: Specifies a repeatable noise sequence. Set to at least 0 to use.

  • constant: Specifies a constant noise pattern on every frame.

  • every: Specify the period of frames for each noise pattern.

  • opt: Sets which cpu optimizations to use.

    • 0 = auto detect
    • 1 = use c
    • 2 = use sse2
    • 3 = use avx2
    • 4 = use avx512

The correlation factors are actually just implemented as exponential smoothing which give a weird side affect that I did not attempt to adjust. But this means that as you increase either corr factor you will have to also increase the stddev (noise amount) in order to get the same visible amount of noise, since it is being smooth out a bit.

Increase both corr factors can somewhat give clumps, or larger noise size.

And there is an interesting effect with, say, noise.Add(var=800, vcorr=0.9) or any huge amount of strongly vertical noise. It can make a scene look like it is raining.

Compilation

meson build
ninja -C build
ninja -C build install

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for wwwwwwww from gravatar.com wwwwwwww

Requires

Python >=3.12

Report project as malware

Download files

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

Source Distribution

vsnoise-1.3.tar.gz (275.8 kB view details)

Uploaded Source

Built Distributions

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

vsnoise-1.3-py3-none-win_amd64.whl (31.9 kB view details)

Uploaded Python 3Windows x86-64

vsnoise-1.3-py3-none-musllinux_1_2_x86_64.whl (1.1 MB view details)

Uploaded Python 3musllinux: musl 1.2+ x86-64

vsnoise-1.3-py3-none-musllinux_1_2_aarch64.whl (1.0 MB view details)

Uploaded Python 3musllinux: musl 1.2+ ARM64

vsnoise-1.3-py3-none-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (198.6 kB view details)

Uploaded Python 3manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

vsnoise-1.3-py3-none-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl (183.4 kB view details)

Uploaded Python 3manylinux: glibc 2.27+ ARM64manylinux: glibc 2.28+ ARM64

vsnoise-1.3-py3-none-macosx_11_0_arm64.whl (32.2 kB view details)

Uploaded Python 3macOS 11.0+ ARM64

vsnoise-1.3-py3-none-macosx_10_13_x86_64.whl (36.5 kB view details)

Uploaded Python 3macOS 10.13+ x86-64

File details

Details for the file vsnoise-1.3.tar.gz.

File metadata

  • Download URL: vsnoise-1.3.tar.gz
  • Upload date:
  • Size: 275.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for vsnoise-1.3.tar.gz
Algorithm Hash digest
SHA256 de422d79a13b06708b1664135abf1a004d8a8462c40c7495e28a667c950ad678
MD5 8673f28e00627ebd1c7302f3423cf1c5
BLAKE2b-256 b438ba27b8d6bef87f7999a56a21e74a6781f3f0ebd556a72fdffd87e43d0605

See more details on using hashes here.

Provenance

The following attestation bundles were made for vsnoise-1.3.tar.gz:

Publisher: pypipublish.yml on wwww-wwww/vs-noise

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file vsnoise-1.3-py3-none-win_amd64.whl.

File metadata

  • Download URL: vsnoise-1.3-py3-none-win_amd64.whl
  • Upload date:
  • Size: 31.9 kB
  • Tags: Python 3, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for vsnoise-1.3-py3-none-win_amd64.whl
Algorithm Hash digest
SHA256 39201f4c0ed11d4d3f44e4106a039831ae1e72b57a1fa45a313a13369267d779
MD5 8dc618046c65b745ec106db277d5ba7a
BLAKE2b-256 e9b274caf326050f8ae03d8a6e24f810d5cca36f31f18dba5a77eb0c26a8eb02

See more details on using hashes here.

Provenance

The following attestation bundles were made for vsnoise-1.3-py3-none-win_amd64.whl:

Publisher: pypipublish.yml on wwww-wwww/vs-noise

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file vsnoise-1.3-py3-none-musllinux_1_2_x86_64.whl.

File metadata

File hashes

Hashes for vsnoise-1.3-py3-none-musllinux_1_2_x86_64.whl
Algorithm Hash digest
SHA256 4b8da76ecc987e7435b90c3fbc9db02562de7323566fef74008ee1dd84eaeaa3
MD5 adb5c43dbd97b22eab08a789d38f49f3
BLAKE2b-256 6fab62b2135c40231b8f71c48c402c28e0569f2d11c62ef8ec366d4146819110

See more details on using hashes here.

Provenance

The following attestation bundles were made for vsnoise-1.3-py3-none-musllinux_1_2_x86_64.whl:

Publisher: pypipublish.yml on wwww-wwww/vs-noise

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file vsnoise-1.3-py3-none-musllinux_1_2_aarch64.whl.

File metadata

File hashes

Hashes for vsnoise-1.3-py3-none-musllinux_1_2_aarch64.whl
Algorithm Hash digest
SHA256 0365225e4f6a866ec51934c4c54be93e490f9d5d2de47720af6f51ac5d85c480
MD5 9f9ce91cdae3755d125b04c4c49d1ad3
BLAKE2b-256 0e311cc901d16e72c69f010b7fcb2ecb2dac26e3f5c3c497d2f93df85be1647d

See more details on using hashes here.

Provenance

The following attestation bundles were made for vsnoise-1.3-py3-none-musllinux_1_2_aarch64.whl:

Publisher: pypipublish.yml on wwww-wwww/vs-noise

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file vsnoise-1.3-py3-none-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for vsnoise-1.3-py3-none-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 35cc606ef085cd140d62108023f30c16e9dddcea91f23c074cf798db520d66db
MD5 1afba676bd3d3054dfeb92dd0ac66a03
BLAKE2b-256 e386497a531cc75061b609790542de3816cf61e8a902201aa0df909b75f583f7

See more details on using hashes here.

Provenance

The following attestation bundles were made for vsnoise-1.3-py3-none-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl:

Publisher: pypipublish.yml on wwww-wwww/vs-noise

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file vsnoise-1.3-py3-none-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for vsnoise-1.3-py3-none-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 0a9c7b416ac7969937a68acdb9e1c3d02f02e55ec187117db51f727127533ac5
MD5 5d4978b96cf9b8106db28d1b6b2ae79b
BLAKE2b-256 5e5ed1a6016638bd4ca999b91f820a03c0f62ae04b2b57172bb038075470415d

See more details on using hashes here.

Provenance

The following attestation bundles were made for vsnoise-1.3-py3-none-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl:

Publisher: pypipublish.yml on wwww-wwww/vs-noise

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file vsnoise-1.3-py3-none-macosx_11_0_arm64.whl.

File metadata

  • Download URL: vsnoise-1.3-py3-none-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 32.2 kB
  • Tags: Python 3, macOS 11.0+ ARM64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for vsnoise-1.3-py3-none-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 a5c817376a4200428ad896e503cb3a502c59692281f7e7982eb5bd1144eda971
MD5 633e8e594b6b7c49f204293818fb0d2a
BLAKE2b-256 2be493d3829982394444f5031c3af370eaafae10d64474b1f8ac06b2ff809e69

See more details on using hashes here.

Provenance

The following attestation bundles were made for vsnoise-1.3-py3-none-macosx_11_0_arm64.whl:

Publisher: pypipublish.yml on wwww-wwww/vs-noise

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file vsnoise-1.3-py3-none-macosx_10_13_x86_64.whl.

File metadata

File hashes

Hashes for vsnoise-1.3-py3-none-macosx_10_13_x86_64.whl
Algorithm Hash digest
SHA256 b511e13ffd61ea9a894a62ce0b1ae25b8b6999c82f4d831434d9b9309c2c49eb
MD5 32bfdcede7f2be74e96a8ec23e2fac12
BLAKE2b-256 a3d0b9b095d7fa8650aafce540810e8de1ea86f6d2b19986993aa9d26fdc42e1

See more details on using hashes here.

Provenance

The following attestation bundles were made for vsnoise-1.3-py3-none-macosx_10_13_x86_64.whl:

Publisher: pypipublish.yml on wwww-wwww/vs-noise

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Release history Release notifications | RSS feed

This release

1.3 This release

8 files

1.2.1

8 files

1.2

3 files

1.1

3 files

1

1 file

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page