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Image Acquisition Library for GenICam-based Machine Vision System
| Date | Stars |
|---|---|
| 2026-07-24 | 609 |
| 2026-07-25 | 609 |
| 2026-07-28 | 609 |
| 2026-07-30 | 609 |
| 2026-08-06 | 609 |
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.. figure:: https://user-images.githubusercontent.com/8652625/40595190-1e16e90e-626e-11e8-9dc7-207d691c6d6d.jpg
:align: center
:alt: The Harvesters
Pieter Bruegel the Elder, The Harvesters, 1565, (c) The Metropolitan Museum of Art
.. image:: https://readthedocs.org/projects/harvesters/badge/?version=latest
:target: https://harvesters.readthedocs.io/en/latest/?badge=latest
.. image:: https://img.shields.io/pypi/v/harvesters.svg
:target: https://pypi.org/project/harvesters
.. image:: https://zenodo.org/badge/133908095.svg
:target: https://zenodo.org/badge/latestdoi/133908095
----
.. contents:: Table of Contents
:depth: 1
----
About Harvester
===============
Harvester is a Python library that aims to make the image acquisition process in your computer vision application breathtakingly easy. Like the peasants/harvesters in the above drawing, it gathers the image data as its harvest and fills up your bucket/buffer.
You can freely use, modify, distribute Harvester under `Apache License-2.0 <https://www.apache.org/licenses/LICENSE-2.0>`_ without worrying about the use of your software: personal, internal or commercial.
Tasks Harvester Does for You
============================
The main features of Harvester are:
* Image acquisition through GenTL Producers
* Multiple loading of GenTL Producers in a single Python script
* GenICam feature node manipulation
Note that the second item implies you can involve various types of transport layers in your Python script. Each transport layer has own advantages and disadvantages and you should choose appropriate one based on your application's requirement. You just need to acquire images for some purposes and the GenTL Producers deliver the images somehow. It truly is the great benefit of the GenTL Standard! And of course, not only GenTL Producers but Harvester offer you a way to manipulate multiple GenICam compliant entities such as a camera in a single Python script with an intuitive manner.
Need a GUI?
===========
Do you need a GUI? Harvester has a sister project that is called **Harvester GUI**. Please visit there if you are interested in it:
https://github.com/genicam/harvesters_gui
.. image:: https://user-images.githubusercontent.com/8652625/43035346-c84fe404-8d28-11e8-815f-2df66cbbc6d0.png
:align: center
:alt: Image data visualizer
Asking Questions
================
We have prepared an FAQ page. Perhaps your issue could be resolved just reading through it:
https://github.com/genicam/harvesters/wiki/FAQ
If any article was not mentioning about the issue you are facing, please try to visit the following page and check if there's a ticket that is relevant to the issue. If nothing has been mentioned yet, feel free to create an issue ticket so that we can help you:
https://github.com/genicam/harvesters/issues
Links
=====
.. list-table::
- - Documentation
- https://harvesters.readthedocs.io/en/latest/
- - Digital Object Identifier
- https://zenodo.org/record/3554804#.Xd4HSi2B01I
- - EMVA website
- https://www.emva.org/standards-technology/genicam/genicam-downloads/
- - Harvester GUI
- https://github.com/genicam/harvesters_gui
- - Issue tracker
- https://github.com/genicam/harvesters/issues
- - PyPI
- https://pypi.org/project/harvesters/
- - Source repository
- https://github.com/genicam/harvesters
Harvester on IPython
====================
The following code block shows Harvester is running on IPython. An acquired image is delivered as the payload of a buffer and the buffer can be fetched by calling the ``fetch`` method of the ``ImageAcquirer`` class. Once you get an image you should be able to immediately start image processing. If you're running on the Jupyter notebook, you should be able to visualize the image data using Matplotlib. This step should be helpful to check what's going on your trial in the image processing flow.
.. code-block:: python
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:1d89e22cb784568a, topic:computer-vision, readme:computer vision