Graphical user interfce of Harvester
Even though we just wanted to research image processing algorithms, why did we have to change our image acquisition library every time we change the camera that we use for the research? - Anonymous
Table of Contents
- About Harvester
- How does Harvester GUI us?
- Using Harvester GUI
- Version 1.0.1: Resolves issue #17.
- Version 1.0.0: Resolves issue #16.
- Version 0.4.0: Resolves issue #15.
- Version 0.3.0: Resolves issues #11 and #14.
- Version 0.2.6: Resolves issue #13.
- Version 0.2.5: Use Harvester Core version 0.2.4.
- Version 0.2.4: Use Harvester Core version 0.2.3.
- Version 0.2.3: Use Harvester Core version 0.2.2.
- Version 0.2.2: Resolves issue #5.
- Version 0.2.1: Works with Harvester Core versions >= 0.2.1.
- Version 0.2.0: Works with Harvester Core versions >= 0.2.0.
- Version 0.1.0: Note that this version will be deprecated and the following versions will be incompatible with any of 0.1.n versions.
Harvester was created to be a friendly image acquisition library for all people who those want to learn computer/machine vision. Harvester consists of two Python libraries, Harvester Core and Harvester GUI, and technically speaking, each library is responsible for the following tasks:
- Image acquisition
- Remote device manipulation
- Image data visualization
Harvester consumes image acquisition libraries, so-called GenTL Producers. Just grabbing a GenTL Producer and GenICam compliant machine vision cameras, then Harvester will supply you the acquired image data as numpy array to make your image processing task productive.
You can freely use, modify, distribute Harvester under Apache License-2.0 without worrying about the use of your software: personal, internal or commercial.
Currently, Harvester is being developed by the motivated contributors from all over the world.
Harvester’s name was coming from the great Flemish painter, Pieter Bruegel the Elder’s painting so-called “The Harvesters”. Harvesters harvest a crop every season that has been fully grown and the harvested crop is passed to the consumers. On the other hand, image acquisition libraries acquire images as their crop and the images are passed to the following processes. We found the similarity between them and decided to name our library Harvester.
Apart from anything else, we love its peaceful and friendly name. We hope you also like it ;-)
Pieter Bruegel the Elder, The Harvesters, 1565, (c) 2000–2018 The Metropolitan Museum of Art
Before start talking about the detail, let’s take a look at some important terminologies that frequently appear in this document. These terminologies are listed as follows:
- The GenApi-Python Binding: A Python module that communicates with the GenICam reference implementation.
- A GenTL Producer: A library that has C interface and offers consumers a way to communicate with cameras over physical transport layer dependent technology hiding the detail from the consumer.
- The GenTL-Python Binding: A Python module that communicates with GenTL Producers.
- Harvester: A Python module that consists of Harvester Core and Harvester GUI.
- Harvester Core: A part of Harvester that works as an image acquisition engine.
- Harvester GUI: A part of Harvester that works as a graphical user interface of Harvester Core.
- A GenICam compliant remote device: It’s typically a camera. Just involving the GenICam reference implementation, it offers consumers a way to dynamically configure/control the target remote devices.
The following diagram shows the hierarchy and relationship of the relevant modules:
In this section, we will learn how to instruct procedures to get Harvester work.
The following software modules are required to get Harvester working:
- Either of Python 3.4, 3.5, 3.6, or 3.7
In addition, you will need the following items to let Harvester make something meaningful:
- GenTL Producers
- GenICam compliant machine vision cameras
Harvester has been tested with the following operating systems:
- macOS 10.13
- Ubuntu 14.04
- Red Hat Enterprise Linux Workstation 7.4
- Windows 7
If you want to use Harvester GUI, then please invoke the following command:
$ pip install harvesters_gui
Note that PyQt5 is distributed under LGPL and it may not be ideal for your purpose. In the future, we might support other GUI frameworks which are more or less open and free.
To launch Harvester GUI, let’s create a Python script file, naming harvester.py, that contains the following code:
import sys from PyQt5.QtWidgets import QApplication from harvesters_gui.frontend.pyqt5 import Harvester if __name__ == '__main__': app = QApplication(sys.argv) h = Harvester() h.show() sys.exit(app.exec_())
Then launch harvester.py:
$ python path/to/harvester.py
You will see Harvester GUI pops up.
Harvester GUI works on the top of Harvester Core and offers you high-performance image data visualization on the fly. It involves VisPy for controlling OpenGL functionality and PyQt for providing GUI.
The main features of Harvester GUI are listed as follows:
- Image data visualization of the acquired images
- Image magnification using a mouse wheel or a trackpad
- Image dragging using a mouse or a trackpad
- An arbitrary selection of image displaying point in the data path (Not implemented yet)
Unlike Harvester Core, Harvester GUI limits the number of GenTL Producers to load just one. This is just a limitation to not make the GUI complicated. In general, the user should know which GenTL Producer should be loaded to control his target remote device. It’s not necessary to load multiple GenTL Producers for this use case. However, this is just an idea in an early stage. We might support multiple loading on even Harvester GUI in the future.
Currently, Harvester GUI supports the following pixel formats that are defined by the Pixel Format Naming Convention:
Mono8, Mono10, Mono12, Mono16, RGB8, RGBa8, BayerRG8, BayerGR8, BayerBG8, BayerGB8, BayerRG16, BayerGR16, BayerBG16, BayerGB16
Note that Harvester GUI has not yet supported demosaicing.
In this section, we see some useful windows which Harvester GUI offers you.
The image data visualizer window (below) offers you a visualization of the acquired images. In this screenshot, Harvester is acquiring a 4000 x 3000 pixel of RGB8 image at 30 fps; it means it’s acquiring images at 8.6 Gbps. It’s quite fast, isn’t it?
The attribute controller window (below) offers you to manipulate GenICam feature nodes of the target remote device. Changing exposure time, triggering the target remote device for image acquisition, storing a set of camera configuration so-called User Set, etc, you can manually control the target remote device anytime when you want to. It supports the visibility filter feature and regular expression feature. These features are useful in a case where you need to display only the features you are interested in.
Image data visualizer window :: Toolbar
Most of Harvester GUI’s features can be used through its toolbox. In this section, we describe each button’s functionality and how to use it. Regarding shortcut keys, replace Ctrl with Command on macOS.
Selecting a CTI file
This button is used to select a GenTL Producer file to load. The shortcut key is Ctrl+o.
Updating the remote device information list
This button is used to update the remote device information list; the list will be filled up with the remote devices that are handled by the GenTL Producer that you have loaded on Harvester GUI; sometime it might be empy if there’s no remote device is available. The shortcut key is Ctrl+u. It might be useful when you newly connect a remote device to your system.
Selecting a GenICam compliant remote device
This combo box shows a list of available GenICam compliant remote devices. You can select a remote device that you want to control. The shortcut key is Ctrl+D, i.e., Ctrl+Shift+d.
Connecting a selected remote device to Harvester
This button is used to connect a remote device which is being selected by the former combo box. The shortcut key is Ctrl+c. Once you connect the remote device, the remote device is exclusively controlled.
Disconnecting the connecting remote device from Harvester
This button is used to disconnect the connecting remote device from Harvester. The shortcut key is Ctrl+d.
Starting image acquisition
This button is used to start image acquisition. The shortcut key is Ctrl+j. The acquired images will be drawing in the following canvas pane.
Pausing/Resuming image drawing
This button is used to pausing/resuming drawing images on the canvas pane while it’s keep acquiring images in the background. The shortcut key is Ctrl+k. If you want to resume drawing images, just click the button again. You can do the same thing with the start image acquisition button (Ctrl+j).
Stopping image acquisition
This button is used to stop image acquisition. The shortcut key is Ctrl+l.
Showing the remote device attribute dialog
This button is used to show the remote device attribute dialog. The shortcut key is Ctrl+a. The remote device attribute dialog offers you to a way to intuitively control remote device attribute over a GUI.
Showing the about dialog
This button is used to show the about dialog.
Image data visualizer window :: Canvas
The canvas of Harvester GUI offers you not only image data visualization but also some intuitive object manipulations.
Zooming into the displayed image
If you’re using a mouse, spin the wheel to your pointing finger points at. If you are using a trackpad on a macOS, slide two fingers to the display side.
Zooming out from the displayed image
If you’re using a mouse, spin the wheel to your side. If you are using a trackpad on a macOS, slide two fingers to your side.
Changing the part being displayed
If you’re using a mouse, grab any point in the canvas and drag the pointer as if you’re physically grabbing the image. The image will follow the pointer. If you are using a trackpad on a macOS, it might be useful if you assign the three finger slide for dragging.
The attribute controller offers you an interface to each GenICam feature node that the the target remote device provides.
Attribute controller window :: Toolbar
Filtering GenICam feature nodes by visibility
This combo box offers you to apply visibility filter to the GenICam feature node tree. The shortcut key is Ctrl+v
GenICam defines the following visibility levels:
- Beginner: Features that should be visible for all users via the GUI and API.
- Expert: Features that require a more in-depth knowledge of the camera functionality.
- Guru: Advanced features that might bring the cameras into a state where it will not work properly anymore if it is set incorrectly for the cameras current mode of operation.
- Invisible: Features that should be kept hidden for the GUI users but still be available via the API.
The following table shows each item in the combo box and the visibility status of each visibility level:
|Combo box item||Beginner||Expert||Guru||Invisible|
Filtering GenICam feature nodes by regular expression
This text edit box offers you to filter GenICam feature nodes by regular expression.
Expanding the feature node tree
This button is used to expand the feature node tree. The shortcut key is Ctrl+e.
Collapsing the feature node tree
This button is used to collapse the feature node tree. The shortcut key is Ctrl+c.
Open source resources
Harvester GUI uses the following open source libraries/resources:
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