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[MVTEC] MVTEC Halcon 20.11.1.0 Steady Full Version x64-linux/aarch64-linux/armv7a-linux(

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    MVTEC Halcon 20.11.1.0 Steady Full Version x64-linux/aarch64-linux/armv7a-linux(32bit) HALCON20.11.1.0稳定完整版 x64-linux/aarch64-linux/armv7a-linux32位系统

    HALCON 20.11.1.0 Full Version (x64-linux / aarch64-linux / armv7a-linux (32 bit)):

    文件名: halcon-20.11.1.0-linux.tar.gz
    文件大小: 2325443450 字节 (2.17 GB)
    修改日期: 2020-11-23 11:23
    MD5: 8405cc11bb1612a29e7695160dc5e007
    SHA1: da039cac13b93e74ca1857edde73ef6106253554
    SHA256: f96ad75a5fd0c597ca41bde63795bae337f79d1269201735646a062e396716c7
    CRC32: b57a8a3a


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    Release Notes for HALCON 20.11 Steady

    This document provides the release notes for MVTec HALCON 20.11.1.0 Steady, as released in November 2020.

    Contents
    • Major New Features of HALCON 20.11.1.0 Steady
      • Improvements for Shape-Based Matching
      • DotCode Reader
      • Deep OCR
      • Improved Egde-Supported Surface-Based 3D-Matching
      • HALCON/Python
      • HDevelop Facelift
      • Deep Learning Edge Extraction
      • Pruning for Deep Learning
      • Anomaly Detection
      • More Flexibility With Deep Learning
      • More Transparency With the Grad-Cam-Based Heatmap
      • Generic Box Finder for Pick-and-Place Applications
      • More Accurate and Robust Matching Results With Surface-Based 3D Matching
      • Reading Very Small Codes With the Subpixel Bar Code Reader
      • Speedup of the ECC 200 Code Reader
    • Compatibility
      • Licenses
      • HALCON Library
      • HALCON Applications
      • Image Acquisition Interfaces
      • Digital I/O Interfaces
      • Extension Packages
      • Further Compatibility Information
      • Discontinuation of the x86-win32 Platform Version for Windows
    • Supported Operating Systems
    • DetaiLED Description of Changes in HALCON 20.11.1.0 Steady
      • HDevelop
        • New Functionality
        • Bug Fixes
        • HDevelop Example Programs
      • HDevEngine
        • Functionality
        • Bug Fixes
      • HALCON Library
        • Speedup
        • New Functionality
        • Bug Fixes
      • Procedures
      • HALCON/C++
      • HALCON/.NET
      • HALCON/PYTHON
      • Language Interface Example Programs
      • HALCON Variable Inspect
      • Extension Packages
      • HBench
      • Image Acquisition Interfaces
      • Digital I/O Interfaces
      • HALCON for Embedded Vision
      • Documentation
      • Installation
      • Licensing
    • Release Notes of Previous HALCON Versions

    Major New Features of HALCON 20.11.1.0 SteadyImprovements for Shape-Based Matching

    In HALCON 20.11, the core technology of shape-based matching has been improved especially for scenarios with low contrast and high noise. More parameters are now estimated automatically. This increases usability as well as the matching rate and robustness in low contrast and high noise situations.

    Additionally, users can now, for example, define so-called “clutter” regions. These are areas within a search model that should not contain any contours. Adding such clutter information to the search model leads to more robust matching results, for example repetitive structures.

    DotCode Reader

    In HALCON 20.11, the data code reader has been extended by the new code type DotCode. This type of 2D code is based on a matrix of points. Therefore, it can be printed very fast and is applicable especially for high speed applications, like in the pharma or tobacco industry.

    Deep OCR

    With Deep OCR in HALCON 20.11, MVTec introduces a holistic deep-learning-based approach for OCR. This new technology brings machine vision one step closer to human reading. Compared to existing algorithms, Deep OCR can localize characters much more robustly, even regardless of their orientation and font type. The ability to automatically group characters allows the identification of whole words. This strongly increases the recognition performance as, e.g., misinterpretation of characters with similar appearances can be avoided.

    Improved Egde-Supported Surface-Based 3D-Matching

    In HALCON 20.11, the core technology edge-supported surface-based 3D-matching is significantly faster for 3D scenes with many objects and edges. In addition to this speedup, the usability has been improved by removing the need of setting a viewpoint.

    HALCON/Python

    HALCON 20.11 introduces a new HALCON/Python interface. This enables developers who work with Python to easily access HALCON's powerful operator set.

    HDevelop Facelift

    For enhanced usability, HALCON’s integrated development environment HDevelop has been given a facelift. In HALCON 20.11, more options for individual configuration have been implemented, e.g., a new modern window docking concept. Moreover, themes are now available to improve visual ergonomics and to suit individual preferences.

    Deep Learning Edge Extraction

    Deep learning edge extraction is a new and unique method to robustly extract edges (e.g., object boundaries) that comes with two major use cases. Especially for scenarios where a variety of edges is visible in an image, MVTec's deep learning edge extraction can be trained with only few images to reliably extract the desired edges. Hence, the programming effort to extract specific kinds of edges is highly reduced with MVTec HALCON.
    Besides, the pretrained network is innately able to robustly detect edges in low contrast and high noise situations. This makes it possible to extract edges that usual edge detection filters cannot detect.

    Pruning for Deep Learning

    With network pruning, users have the option to subsequently optimize a fully trained deep learning network in terms of storage requirements and speed. With this feature, it is possible to control the priority of the parameters speed, storage, and accuracy and thus modify the network according to application-specific requirements.

    Anomaly Detection

    This feature significantly facilitates the automated surface inspection for, e.g., detection and segmentation of defects. Here, users face two challenges: getting enough training images of the respective defect and having to label all of these images. However, with HALCON's anomaly detection, you only need a low number of high quality “good” images for training. The technology is able to unerringly and independently localize deviations, i.e., defects of any type, on subsequent images. This means, defects of varying appearance can be detected without any previous knowledge or any preceding labeling efforts.

    More Flexibility With Deep Learning

    With HALCON 20.11, training for all deep learning technologies can be performed on the CPU. By removing the need for a dedicated GPU, standard industrial PCs (that could not house powerful GPUs) can now be used for training as well. This greatly increases customers' flexibility regarding the implementation of deep learning, because training can now be performed directly on the production line allowing for “on the fly” adjustments of the application. Additionally, the inference for all deep learning technologies runs out-of-the-box on Arm® processors.

    More Transparency With the Grad-Cam-Based Heatmap

    Deep learning networks are often considered a black box because users do not know what happens with the data during the inspection process. Therefore, it is very difficult to debug in case of misclassifications. HALCON's newly implemented Grad-CAM-based (Gradient-weighted Class Activation Mapping) heatmap supports you in analyzing which parts of an image influence the classification decision. Since this new heatmap can be calculated on the CPU without significant speed drops, users can analyze their network's class prediction “on the fly”.

    Generic Box Finder for Pick-and-Place Applications

    The generic box finder allows users to locate boxes of different sizes within a predefined range of height, width, and depth, removing the need to train a model. This makes many applications much more efficient – especially within the logistics and pharmaceutical industries, where usually boxes in a large variety of different sizes are used.

    More Accurate and Robust Matching Results With Surface-Based 3D Matching

    Especially in the assembly industry, workpieces must be located robustly and accurately to allow for further processing. Often, properties like small holes are the only unique feature to find the correct orientation of the object. HALCON's surface-based 3D matching can now make use of these features to increase accuracy and robustness of the matching result. Furthermore, edge-supported surface-based matching is now more robust against noisy point clouds: Users can control the impact of surface and edge information via multiple min-scores. If necessary, 3D edge alignment can also be switched off entirely to eliminate the influence of insufficient 3D data on the matching result.

    Reading Very Small Codes With the Subpixel Bar Code Reader

    The bar code reader in HALCON 20.11 features an advanced decoding algorithm, which increases the decoding rate when reading codes with very thin bars. Thanks to this, it is now possible to even read codes with bars smaller than one pixel.

    Speedup of the ECC 200 Code Reader

    In HALCON 20.11, the code reader for ECC 200 codes is significantly faster on multi-core systems. This is especially true for codes that are difficult to detect and read. For such codes, a speedup of up to factor 3 can be reached. This speedup also increases the viability of embedded-based code readers by taking full advantage of existing hardware capacities.

    CompatibilityLicensesAll HALCON 18.11.3 Steady licenses or licenses of earlier versions must be replaced or upgraded. Please contact your local distributor. HALCON 20.11.1.0 Steady licenses will be downwards compatible to HALCON 18.11.3 Steady.

    Furthermore, please note the following compatibility issues related to licenses:

    • The following licensing issues are known:
      • After starting applications based on HALCON 20.05 Progress (or later)/HALCON 20.11 Steady (or later), the dongle must be removed and inserted again before starting applications based on HALCON 19.11 (or earlier).
        This problem can be avoided by installing the CodeMeter Runtime from WIBU SYSTEMS AG. The CodeMeter Runtime is not shipped with HALCON and must be downloaded from https://www.wibu.com in the download area.
      • On Arm platforms (aarch64 architecture) with GLIBC 2.21, it is no longer possible to run both 64-bit and 32-bit HALCON processes in parallel because they might block each other.
      More information.

    HALCON LibraryCompared to HALCON 18.11.3, many extensions have been introduced. Thus, the HALCON 20.11.1.0 Steady libraries are not binary compatible with HALCON 18.11.3 or earlier versions. However, HALCON 20.11.1.0 Steady is mostly source-code compatible to HALCON 18.11.3 except for the changes listed below:
    • The signature in the following member functions of the classes HImage and HShapeModel have changed in the language  interfaces HALCON/CPP and HALCON/.NET: FindAnisoShapeModel,  FindScaledShapeModel, and FindShapeModel. The parameter MinScore has changed the type from double to HTuple, so that under certain circumstances ambiguities may occur during a type conversion in the compilation process. In these cases, an explicit cast of your passed variable to double resolves the ambiguity. More information.
    • As the surface-based matching has been extended to be able to calculate a view-based score during the matching and pose refinement step, the interface of the procedure dev_display_surface_matching_results has been extended by the new parameter VisibilityTrained to support the view-based score. More information.
    • The parameter 'batch_size_device' of the deep learning classifier has been replaced by the parameter 'batch_size_multiplier', which has a different semantics. Hence, programs using 'batch_size_device' need to be adapted to use 'batch_size_multiplier' instead. More information.
    • Support for deep learning inference on 32-bit Windows has been removed. Further, the extra library hcpudnn.so/.dylib/.dll has been removed and is not needed anymore. More information.
    • Dev operator implementation classes for HDevEngine have to be extended with an implementation for the new dev_set_contour_style operator. More information.
    • Previously, write_object_model_3d wrote all user added attributes as floats when choosing FileType 'ply'. This behavior has been changed such that all user added attributes are now written as doubles. More information.
    • Extending the functionality of the deep learning model led to changes in the signatures of the following private procedures:
      • dev_display_update_train_dl_model
      • prepare_image_lists
      During this process, parameters have been renamed, which can lead to incompatibilities when running the procedures by the HDevEngine. The following procedures are affected:
      • create_dl_preprocess_param,
      • create_dl_preprocess_param_from_model,
      as well as the following private procedures:
      • get_next_window, and
      • open_next_window.
      More information.
    • triangulate_object_model_3d with Method set to 'xyz_mapping' or 'greedy' now only returns points that are actually used in the output triangulation. Attributes attached to points get thinned out accordingly. The attributes 'lines' and 'polygons' are not copied to the output. To obtain the old behavior, e.g., to return all input points, set the GenParamName 'greedy_output_all_points' or 'xyz_mapping_output_all_points' to 'true'. Alternatively, you can use the old point indices, which are stored in the triangulated 3D object model as an extended attribute named 'original_point_indices' and which can be queried with get_object_model_3d_params. More information.
    • For deep-learning-based object detection models, get_dl_model_param now returns a tuple of values that is as long as the number of classes for the parameter "class_weights" in any case. More information.
    • The interfaces of the procedures disp_menu_ext and inspect_normal_direction are extended by the parameter 'WindowScaling' to scale the text according to the user's setting. More information.
    • As the pretrained deep learning models used in semantic segmentation have been changed, other training results than before must be expected. Although the new models perform better in all our tested application cases, if you have chosen very specific training parameters for the old models, you might need to optimize those parameters again for the new models. More information.
    • The define for the error code 7824 ("DL: Invalid instance type") has been renamed from H_ERR_DL_DETECTOR_INVALID_TYPE to H_ERR_DL_DETECTOR_INVALID_INSTANCE_TYPE. Applications using this define must be adapted. More information.
    • Only NVIDIA cards with compute capability 3.5 are supported. More information.
    • Since find_box_3d is now independent of a viewpoint and the position of the input point cloud in space, the procedure debug_find_box_3d has been adapted accordingly. Furthermore, an error is raised in case that the input GenParam contains a key 'viewpoint'. The returned GrippingPose is set according to the XYZ-mapping.
      If the input point cloud of find_box_3d contains normals, they are not used anymore. More information.
    • Due to changes to the calculation of the anomaly score histogram in evaluate_dl_model (in order to solve a display issue), the following parameter keys have become obsolete and hence unavailable for the procedure's generic parameter dictionary GenParam:
      • 'anomaly_num_bins'
      • 'anomaly_min_value'
      • 'anomaly_max_value'
      More information.
    • The default value for the texture inspection model parameter 'gmm_preprocessing' has been changed from 'none' to 'normalization'. When using gray scale images, identical results compared to previous HALCON versions can be obtained by calling set_texture_inspection_model_param(TextureInspectionModel, 'gmm_preprocessing', 'none') before executing train_texture_inspection_model(). More information.
    • sort_region with SortMode 'character' previously took the larger one of two overlapping regions for the calculation of the overlap. This can cause an issue in case the two regions greatly differ in their sizes. Now, the smaller region is used to compute the overlap. Hence, in cases with overlapping regions that are very different in their sizes, this change can cause a different behavior of the operator.
      Also note that the extension of the parameter SortMode with a second optional value affects the compatibility as the operator signature changed for language interfaces. Affected interfaces are C, C++ and .NET. More information.
    • Because of a bug fix regarding the calculation of the mean average precision (mean_ap) measure during the evaluation of deep-learning-based object detection, the deep-learning-based object detection evaluation results might be different. To obtain the same results for 'mean_ap' as before, the evaluation parameter 'interpolate_pr_curves' can be set to false (default: true). More information.
    • As edges_object_model_3d is now by default independent of a viewpoint, a 'viewpoint' contained in GenParamName is silently ignored. To obtain the old behavior, add 'estimate_viewpose' and 'false' to GenParamName and GenParamValue, respectively.
      To obtain the old behavior in case no manual viewpoint has been set, add ['estimate_viewpose', 'viewpoint'] and ['false', '0 0 0'] to GenParamName and GenParamValue, respectively. More information.
    • In previous versions, train_dl_model_batch expected the parameter 'DLSampleBatch' as single dictionary in the C++ and .NET interface. While the operator now expects a tuple of dictionaries, with the new signature it is still possible to use a single dictionary for 'DLSampleBatch'. More information.
    • The default value for the new parameter 'max_num_samples' in the deep learning procedure determine_dl_model_detection_param has been set to 1500. For datasets with more than 1500 samples, this can lead to different detection parameters determined by the procedure. To reproduce the previous behavior, set 'max_num_samples' to '-1'. More information.
    • A set 'viewpoint' in GenParamName of find_surface_model, find_surface_model_image, refine_surface_model_pose, and refine_surface_model_pose_image has no effect anymore, except for the calculation of the view-based score if specified by 'use_view_based' set to 'true'. The procedure debug_find_surface_model has been adapted accordingly.
      If a surface-based model has been trained for use of edges, and the input point cloud of find_surface_model and find_surface_model_image contains a mapping, scene normals are automatically generated flipped inwards consistently w.r.t. the mapping. Normals contained in the input point cloud are not used anymore. Furthermore, the GenParamName 'scene_invert_normals' should not be needed anymore. Typically, 'scene_invert_normals' should be set to 'false' or be removed from GenParamName.
      In rare cases, edge-supported surface-based matching returns slightly different results.
      More information.
    • Using NVIDIA GPUs under Windows 7 is not supported anymore. More information.
    • Due to the change of the default value for the parameter 'rectif_interpolation' when using stereo models, identical results compared to previous HALCON versions can be obtained by calling set_stereo_model_param(StereoModel, 'rectif_interpolation', 'none') and set_stereo_model_image_pairs before reconstructing the surface using reconstruct_surface_stereo. More information.
    • The default encoding of 2D data codes has been changed from 'utf8' to 'latin1'. Programs that expect UTF-8 encoded messages must be adapted by setting the parameter 'string_encoding' to 'utf8' with set_data_code_2d_param. More information.
    • get_data_code_2d_results now returns the value '-1' for all result-specific parameters if 'CandidateHandle' is 'general'. Applications that expect another value or an empty tuple need to be adapted. More information.
    • In very rare cases, the results of the operators watersheds, watersheds_threshold, and watersheds_marker might have changed for basins at the boundary of the ROI that are one pixel large. More information.
    • HALCON's camera models for perspective line scan cameras now support the polynomial distortion model. As a result of this change, the camera model type string 'line_scan' has been replaced by 'line_scan_division'. The old camera type string 'line_scan' is still supported as input to provide backward compatibility. However, applications should be modified to use 'line_scan_division' instead of 'line_scan'. In particular, programs that test for 'line_scan' in a camera parameter tuple that has been output by HALCON must be rewritten to test for 'line_scan_division'. Furthermore, the procedure gen_cam_par_line_scan has been deprecated. It should be replaced by a call to the new procedure gen_cam_par_line_scan_division. More information.

    HALCON ApplicationsPlease re-compile all C, C++, or .NET programs developed with HALCON 18.11.3. The incompatibility with HALCON 18.11.3 or earlier versions mainly concerns the binaries, with only few changes in the language interfaces. If you encounter problems during recompiling your programs, please check the detailed description of changes below.
    Image Acquisition Interfaces

    In general, HALCON 20.11.1.0 Steady, HALCON 18.11.3, and HALCON 18.11.x image acquisition interfaces are library compatible.

    HALCON 20.11.1.0 Steady includes only a subset of available manufacturer-independent image acquisition interfaces. Image acquisition interfaces that are included are: DirectFile, DirectShow, File, GenICamTL, GigeVision2, GStreamer, USB3Vision, and Video4Linux2. You can download additional proprietary interfaces from our web server.


    Digital I/O Interfaces

    In general, HALCON 20.11.1.0 Steady, HALCON 18.11.3, and HALCON 18.11.x digital I/O interfaces are library compatible.

    HALCON 20.11.1.0 Steady includes only a subset of available manufacturer-independent digital I/O interfaces. Digital I/O interfaces that are included are: Linux-GPIO, OPC_UA, and Hilscher-cifX. You can download additional proprietary interfaces from our web server.


    Extension Packages
    Please re-generate your own extension packages developed with HALCON 18.11.3.

    Note also the following compatibility issues:

    • Previously, in user-provided .def files of extension packages, parameters could be declared with default_type only, i.e., without declaring sem_type. This has been changed. sem_type is now a required field for all parameters. More information.

    Further Compatibility Information
    • The legacy include file "HSync.h" has been removed from the fileset. Therefore, the legacy macro definitions for CRITICAL_SECTIONS that were not used by HALCON for several versions are not available anymore. More information.
    • String literals in HDevelop scripts must not span multiple lines. Previously, the use of line continuation (backslash + newline) within a string literal was not treated as an error but led to corrupted strings with extra spaces. Now it is reported as an error and leads to invalid lines. Note that you can break long string literals by concatenating multiple string literals. More information.
    • The new revision 19.11.1 of the OPC_UA interface is not backwards compatible to older HALCON versions. The interface heavily uses HALCON Dictionaries, which are only available since HALCON 19.11 Progress/HALCON 20.11 Steady for image acquisition and digital I/O interfaces. Further, the OpenSSL version required by the interface is not compatible with the one included in previous releases of HDevelop. More information.
    • When exporting a program or library as source code for HALCON/.NET, private procedures will now be exported with the "private" access modifier. If your application relies on calling such a procedure you may need to configure it as "Public" explicitly. More information.


    Discontinuation of the x86-win32 Platform Version for Windows

    With HALCON 20.11, the x86-win32 platform version for Windows is discontinued. Switch any existing x86-win32 applications to the x64-win64 platform version for Windows to be able to use HALCON 20.11.


    Supported Operating SystemsWindows

    HALCON 20.11.1.0 Steady has been compiled for the x64-win64 platform version for Windows 7/8.1/10 or Windows Server 2008 R2/2012 R2/2016/2019 x64 Edition on Intel 64 or AMD 64 processors.


    Linux

    HALCON 20.11.1.0 Steady has been compiled for the following Linux platform versions:

    • x64 platform version for Linux x86_64, GLIBC_2.17, GLIBCXX_3.4.21, on Intel 64 or AMD 64 processors
    • armv7a platform version for Linux armv7a, Kernel with hidraw support, hard-float ABI, GLIBC_2.17, GLIBCXX_3.4.21 on Armv7-A processors with NEON support
    • aarch64 platform version for Linux aarch64, Kernel with hidraw support, GLIBC_2.17, GLIBCXX_3.4.21 on AArch64 processors

    Please refer to the Installation Guide for detailed system requirements corresponding to the different Application Binary Interfaces.


    macOS

    HALCON 20.11.1.0 Steady has been compiled for the x64 platform version of macOS 10.15 on Intel 64.


    Detailed Description of Changes in HALCON 20.11.1.0 Steady

    The changes in HALCON 20.11.1.0 Steady are described with respect to HALCON 18.11.3.

    HDevelopNew FunctionalityAssistants
    • A new reference manual chapter entry 'Calibration' was added. It gathers data applicable to different calibration setups. Among them are calibration recommendations, which are now centralized for improved readability. This allowed to prune other documentation parts and enhance their focus. As part of this work, the recommended values have been reviewed.
      The HDevelop example program 'hdevelop/Calibration/Multi-View/check_calib_image_quality.hdev' as well as the HDevelop Calibration Assistant have been updated and are in line with the current recommendations. As a consequence, the score for the image coverage by a single plate has been removed.
    • HALCON has been extended by a new camera model for line scan cameras with telecentric lenses:
      • 'line_scan_telecentric_division'
      • 'line_scan_telecentric_polynomial'.
      New HDevelop procedures to generate camera parameter tuples for each camera type have been added:
      • gen_cam_par_line_scan_telecentric_division
      • gen_cam_par_line_scan_telecentric_polynomial.
      The new HDevelop example program hdevelop/Calibration/Multi-View/line_scan_telecentric_calibration.hdev shows how to use the new functionality.
      The HDevelop Calibration Assistant has been extended accordingly.

    Code Export
    • Code exported from HDevelop added a call to the Xlib function XInitThreads on Linux systems, which has been unnecessary since HALCON 13.0. This has been adapted accordingly.
    • Within the HDevelop User's Guide, the chapter about Code Export has been improved. Further, the section about the Library Project Export now also provides a link to the tutorial video.
    • When exporting a program or library as source code for HALCON/.NET, private procedures will now be exported with the "private" access modifier. Note that this change affects the compatibility. Read more.

    GUI
    • The Qt version used by HDevelop has been upgraded to Qt 5.12.5. A known problem is that on macOS, HDevelop does not use the macOS style buttons for closing, minimizing, and maximizing in the title bars.
    • HDevelop's Start Dialog has been extended with a direct link to the complete collection of MVTec's video tutorials.
    • The About dialog has been improved. Previously, the license status was not displayed. Now, the used license and, if applicable, the expiration date are displayed.
    • HALCON has been extended with means to configure the fill style for displaying XLD contours. The operators set_contour_style, get_contour_style, and the respective HDevelop operator dev_set_contour_style have been added to enable this.
      The pie charts displayed by the evaluation procedures for deep learning have been adapted to use this new functionality. Note that this change affects the compatibility. Read more.
    • The HDevelop window modes MDI and SDI have been replaced by a new docking framework. This allows users to dock, float, and combine windows. Further, it better supports the usage of a second screen and to attach windows on the left or right of the desktop.
    • The font size in the Program Window of HDevelop can now be adjusted via
      • the mouse wheel + CTRL or via
      • CTRL+Plus to increment the font size and
      • CTRL+Minus to decrement the font size.
    • Procedures that have a non-empty warning section in their general documentation now get displayed in HDevelop in the same manner as operators with a non-empty warning slot already do. In particular:
      • Calls to such a procedure get marked with a warning sign in the program listing.
      • The procedures get marked with a warning sign in the Program Window's procedure selection box.
      • The warning text is prominently displayed in the Operator Window.
    • Since HDevelop has been updated to Qt 5.12.5 and now uses the QtWebEngine, it is required that the linux system provides the correct version of the libdbus-1.so3 library. Otherwise an error like below may be displayed:
      "hdevelop: relocation error: ../libQt5DBusMVTec.so.5: symbol dbus_message_get_allow_interactive_authorization, version LIBDBUS_1_3 not defined in file libdbus-1.so.3 with link time reference"
    • High DPI displays are now much better supported. HDevelop adapts appropriately to the Windows screen settings "Scale and layout".
    • The Handle Inspect window in HDevelop no longer shows internal data containers, which are only used to structure the presented data in a tree, as modified. Instead, only the data contained by them is shown as modified if it was changed in the last step or run operation.
    • The GUI of HDevelop has been modernized in the following way:
      • HDevelop's icons have been updated.
      • HDevelop now supports a dark and a light theme. This can be set via menu > Edit Preferences > User Interface > Themes.
      • HDevelop now shows the MVTec logo in the status bar. Clicking the logo opens the HALCON product page of the MVTec website in a browser.
    • The status bar of HDevelop now contains a new bell icon, which signals whether an update of the currently used version is available.
      The update check is done during the start of HDevelop.
    • HDevelop now logs internal errors, which can be viewed in the output console of HDevelop.
    • 3D object models can now be visualized directly from within handle inspect windows. This allows