Skip to main content

Python library for controlling WEOM IR cameras

Project description

WEOM Python interface

version: 1.9.1

WEOMPy is a comprehensive Python library designed to control WEOM cameras using the Python language.

The library can access the following key WEOM thermal core features

  • read device information such as serial number, firmware version, etc...
  • change display parameters (display palette, contrast, brightness, frame rate, gain, etc..)
  • view and capture images
  • update thermal core firmware
  • manage sensor dead pixels

We provide documentation, howtos and tutorials in a form Jupyter notebooks. The notebooks and example can be found in directory where the library is installed using pip. We strongly recommend the use of Python virtual environments to avoid any possible conflicts. To create and activate a clean WEOMPy Python environment run

⚠️ Important

On some systems there might be dependency issues using Python installations from Microsoft Store. We strongly discourage from using those.

python -m venv <venv_directories>/weompy

. <venv_directories>/weompy/bin/activate

now we have a clean Python environment and we can proceed by installing the library

pip install weompy

After these steps you can find the documentation and example in

<venv_directories>/weompy/lib/python<version>/site-packages/weompy/example.py

<venv_directories>/weompy/lib/python<version>/site-packages/weompy/user_documentation.ipynb

Also we provide stubs for IDE code completion in

<venv_directories>/weompy/lib/python<version>/site-packages/weompy/weompy.pyi

Prerequisites:

  • Python 3.8 - 3.12
  • Jupyter extension by Microsoft installed on VSCode, if you want to open user_documentation.ipynb in VSCode

⚠️ Important

WEOMpy communicates with the camera via a serial interface and as such user must be a member of the dialout group.

To use this library with the GigE plugin, Pleora eBUS SDK must be installed.

Project details


Download files

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

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

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

weompy-1.9.1-cp312-cp312-win_amd64.whl (13.7 MB view details)

Uploaded CPython 3.12Windows x86-64

weompy-1.9.1-cp312-cp312-manylinux_2_35_aarch64.whl (32.9 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.35+ ARM64

weompy-1.9.1-cp312-cp312-manylinux_2_28_x86_64.whl (33.0 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ x86-64

weompy-1.9.1-cp312-cp312-macosx_15_0_arm64.whl (30.9 MB view details)

Uploaded CPython 3.12macOS 15.0+ ARM64

weompy-1.9.1-cp311-cp311-win_amd64.whl (13.7 MB view details)

Uploaded CPython 3.11Windows x86-64

weompy-1.9.1-cp311-cp311-manylinux_2_35_aarch64.whl (32.9 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.35+ ARM64

weompy-1.9.1-cp311-cp311-manylinux_2_28_x86_64.whl (33.0 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ x86-64

weompy-1.9.1-cp311-cp311-macosx_15_0_arm64.whl (30.9 MB view details)

Uploaded CPython 3.11macOS 15.0+ ARM64

weompy-1.9.1-cp310-cp310-win_amd64.whl (13.7 MB view details)

Uploaded CPython 3.10Windows x86-64

weompy-1.9.1-cp310-cp310-manylinux_2_35_aarch64.whl (32.9 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.35+ ARM64

weompy-1.9.1-cp310-cp310-manylinux_2_28_x86_64.whl (33.0 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.28+ x86-64

weompy-1.9.1-cp310-cp310-macosx_15_0_arm64.whl (30.9 MB view details)

Uploaded CPython 3.10macOS 15.0+ ARM64

weompy-1.9.1-cp39-cp39-win_amd64.whl (13.7 MB view details)

Uploaded CPython 3.9Windows x86-64

weompy-1.9.1-cp39-cp39-manylinux_2_28_x86_64.whl (33.0 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.28+ x86-64

weompy-1.9.1-cp39-cp39-macosx_15_0_arm64.whl (30.9 MB view details)

Uploaded CPython 3.9macOS 15.0+ ARM64

weompy-1.9.1-cp38-cp38-win_amd64.whl (13.7 MB view details)

Uploaded CPython 3.8Windows x86-64

weompy-1.9.1-cp38-cp38-manylinux_2_28_x86_64.whl (33.0 MB view details)

Uploaded CPython 3.8manylinux: glibc 2.28+ x86-64

weompy-1.9.1-cp38-cp38-macosx_15_0_arm64.whl (30.9 MB view details)

Uploaded CPython 3.8macOS 15.0+ ARM64

File details

Details for the file weompy-1.9.1-cp312-cp312-win_amd64.whl.

File metadata

  • Download URL: weompy-1.9.1-cp312-cp312-win_amd64.whl
  • Upload date:
  • Size: 13.7 MB
  • Tags: CPython 3.12, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.10.11

File hashes

Hashes for weompy-1.9.1-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 50c8cae533cb31bdcb63ffca408bc0d7b4510a778ec72ec447f36be5f05a20ba
MD5 d726740226feaf4e9bd2787ba99ef281
BLAKE2b-256 d4fc599a9603a794c396ebfb2c86a2c0b83d198769f07a6bfddf54e2e4207964

See more details on using hashes here.

File details

Details for the file weompy-1.9.1-cp312-cp312-manylinux_2_35_aarch64.whl.

File metadata

File hashes

Hashes for weompy-1.9.1-cp312-cp312-manylinux_2_35_aarch64.whl
Algorithm Hash digest
SHA256 95624a7c954042b04d2d510627d15b48a66b6d21d9cbb886a5e7bfa10a62ff5b
MD5 c50a65a95dfc9b2517b23b2289c14ac9
BLAKE2b-256 208f030b09a172e758dd1aad1a0208610d0e376c43288bc1fc4114b409b20ce1

See more details on using hashes here.

File details

Details for the file weompy-1.9.1-cp312-cp312-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for weompy-1.9.1-cp312-cp312-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 9c8c30f5089bb16d43e60d55d5ce806e3f6c1c445bc79010ae0108ac8dd81c3b
MD5 a764c99f64dd7a63443c8ddce7356c02
BLAKE2b-256 60b5fbe95efd8c06299d0f46559aff5e78436149f6cb5b0439d02df8bb351e6c

See more details on using hashes here.

File details

Details for the file weompy-1.9.1-cp312-cp312-macosx_15_0_arm64.whl.

File metadata

File hashes

Hashes for weompy-1.9.1-cp312-cp312-macosx_15_0_arm64.whl
Algorithm Hash digest
SHA256 84d04b5fe171e0cf935c51132c6303abfc5a40f2f5d8faef4742da22969b8fbd
MD5 3c2666021be0a568da92640bbb66ac69
BLAKE2b-256 3344365b7fe5aae05da91b2a06695ff85742b0aabbc2c051edd4ba8907f4b1d5

See more details on using hashes here.

File details

Details for the file weompy-1.9.1-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: weompy-1.9.1-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 13.7 MB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.10.11

File hashes

Hashes for weompy-1.9.1-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 49592f36f4e750237fd7d0a838674f632c3669c1f7e936d7df90c6d8f33326f3
MD5 9fc88df923d97e02cd996bfabf39998d
BLAKE2b-256 85c6abfa71acc593453b9dabf3ed6d54ab175a980c65d0b2e9568aa33d1fcce1

See more details on using hashes here.

File details

Details for the file weompy-1.9.1-cp311-cp311-manylinux_2_35_aarch64.whl.

File metadata

File hashes

Hashes for weompy-1.9.1-cp311-cp311-manylinux_2_35_aarch64.whl
Algorithm Hash digest
SHA256 11962c47c4d534b4312c7219611a585ee51360cb37282a8846399a1b797b5f62
MD5 e790a7ccf79b230235a976230099c869
BLAKE2b-256 6e06dcc34be473be7ba6fa622a9a2a7c4fa40b0bd7a76f8730ee297417dd46bf

See more details on using hashes here.

File details

Details for the file weompy-1.9.1-cp311-cp311-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for weompy-1.9.1-cp311-cp311-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 beee907c5a30c1730703456270e5c734f7359434760858c6ee369164cd7cf0eb
MD5 832f19de70e48ce61e7972ef810af478
BLAKE2b-256 02557dcfde8e16a9e8dfd73232f78286e048e9e02c1f8f3f5795bce5a5d1c647

See more details on using hashes here.

File details

Details for the file weompy-1.9.1-cp311-cp311-macosx_15_0_arm64.whl.

File metadata

File hashes

Hashes for weompy-1.9.1-cp311-cp311-macosx_15_0_arm64.whl
Algorithm Hash digest
SHA256 83100f399620feab3612ee63d8518ba08790c34423281e05906f5dfe3633abf0
MD5 22516de3322089f7c0be2abff95be6e7
BLAKE2b-256 8d1b6a4a6aa1c7c6cf125f85785981a21967a05552bb3fc2622b9a5644b075c8

See more details on using hashes here.

File details

Details for the file weompy-1.9.1-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: weompy-1.9.1-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 13.7 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.10.11

File hashes

Hashes for weompy-1.9.1-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 6d719605786d146d894ed04fcbcb0599e90f7594c345f4b6b21ad74164ae7d65
MD5 b17b81e959e6f4f88c1f6efe752a454d
BLAKE2b-256 ee9069024825520cf30413f0d8a281202cbd10b9e7306ef21790420ef3dbfcd8

See more details on using hashes here.

File details

Details for the file weompy-1.9.1-cp310-cp310-manylinux_2_35_aarch64.whl.

File metadata

File hashes

Hashes for weompy-1.9.1-cp310-cp310-manylinux_2_35_aarch64.whl
Algorithm Hash digest
SHA256 e40a1b2d1a472985115190a5f0c893f830d5415fc93c5c269f365a6b9125eab1
MD5 8625538be054d02d6bbe914a5b31ffe1
BLAKE2b-256 5e9f783dc4801570be450a43c951305e56fa65fbf3f1be1504551909e537d5f7

See more details on using hashes here.

File details

Details for the file weompy-1.9.1-cp310-cp310-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for weompy-1.9.1-cp310-cp310-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 ae3585f858af493710db62db0c70a65f06fffe981d0c01e38391dd72b6e01079
MD5 037e8e2010bfed0d8fa597d34061434c
BLAKE2b-256 07e1e7422cf10486dedd5f2a9258c5454944211ae51fae123fea9b4751246f4f

See more details on using hashes here.

File details

Details for the file weompy-1.9.1-cp310-cp310-macosx_15_0_arm64.whl.

File metadata

File hashes

Hashes for weompy-1.9.1-cp310-cp310-macosx_15_0_arm64.whl
Algorithm Hash digest
SHA256 fa09ea257f3fc71cd8f21cd63e0a82d014adbce79c0a95cbe76120482e8bf950
MD5 d8e317464dd28827d52c7c5e6bc7c250
BLAKE2b-256 e3157ff2317af7e84ebb621e4d97e74d95d5af840da868013ee8be5083f2798f

See more details on using hashes here.

File details

Details for the file weompy-1.9.1-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: weompy-1.9.1-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 13.7 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.10.11

File hashes

Hashes for weompy-1.9.1-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 5cd2a4aa97a05f70ae70e1cc6de2bb5deebbce86285073231328419acbf7ecb8
MD5 731cc9a7433cd6a6a10176b40c7c5647
BLAKE2b-256 6ed02459902798cb294a944f6912fcaabf2553b97dedbe1318e3614a8a91dd92

See more details on using hashes here.

File details

Details for the file weompy-1.9.1-cp39-cp39-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for weompy-1.9.1-cp39-cp39-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 f2f6a7236ff8128ae2df626accfbf705cd2ba88c5fb60443ddd3fe19fb754dba
MD5 b389a34a6b4189861ee08af7bc6016ad
BLAKE2b-256 a68205c124e9e1fe246c7ed61a328b0ba2f0e79a4e9c6985c6d0c8fcb116dce2

See more details on using hashes here.

File details

Details for the file weompy-1.9.1-cp39-cp39-macosx_15_0_arm64.whl.

File metadata

File hashes

Hashes for weompy-1.9.1-cp39-cp39-macosx_15_0_arm64.whl
Algorithm Hash digest
SHA256 1c59b24486b5ad905a9c6b6d8ccf7bfd0e64a6baf203922164c40b6f0c0589d5
MD5 e0483641e1c129db2edde991b2c22e94
BLAKE2b-256 049c8da554b4c969dc9da7e5bf7f5415b1d6008400a87fd4b3bf54c4046aaa67

See more details on using hashes here.

File details

Details for the file weompy-1.9.1-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: weompy-1.9.1-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 13.7 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.10.11

File hashes

Hashes for weompy-1.9.1-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 7c9ce972b1ff4706f97d1784b95e5a0010c011d9171e95ba8d7606112614c0de
MD5 49536e38611252eaa955b2418a47889d
BLAKE2b-256 6950643e4057c5cdf220e80159b3b447c4fda0ad8aa58a0aef792722a55dfd5c

See more details on using hashes here.

File details

Details for the file weompy-1.9.1-cp38-cp38-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for weompy-1.9.1-cp38-cp38-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 a38869f948338cf74eedf4d1401d5e63f9d2dc7627d98eb520fb37af936331fb
MD5 50a728aefde3c859e5e2b49801e31f7b
BLAKE2b-256 3fe06567c57c47f3fcc6ed02680d7efdb55d832b5cb4c9021e94cf11150c9456

See more details on using hashes here.

File details

Details for the file weompy-1.9.1-cp38-cp38-macosx_15_0_arm64.whl.

File metadata

File hashes

Hashes for weompy-1.9.1-cp38-cp38-macosx_15_0_arm64.whl
Algorithm Hash digest
SHA256 5631f9c3188e260783c20d883d7e090e193efd39ff87c95843d36c32ca8a0ec1
MD5 1d626c4fee6b975c632122152745b5f8
BLAKE2b-256 c78f36cb8cc0339344e48338030b3c6064e54409ce3c3d289821664d2640ecd4

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