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MVTEC Halcon 25.11.0.0 Progress Linux x64-linux HALCON25.11.0.0改进版 64位linux版
文件名: halcon-25.11.0.0-x64-linux.zip
文件大小: 9523950778 字节 (8.87 GB)
修改日期: 2025-11-07 00:04
MD5: cbb49529521c8bda5becbb01f45bf84b
SHA1: 08aaf60d0ab73fbc1b21a7ee0e1c30ae6728e1fd
SHA256: bf19fe1ad4356250cb7ef561d51c49b1f825cbf4a170495568409c06ed07c4c2
CRC32: dda0fcc7
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Release Notes for HALCON 25.11.0.0 Progress本文档提供MVTec HALCON 25.11.0.0 Progress版本的发行说明，该版本于2025年11月发布。 目录
HALCON 25.11.0.0 主要新功能改进
持续学习——分类
HALCON 25.11 introduces Continual Learning – Classification, a new technology that makes training and maintaining classification models faster and more flexible. Users can create models with only few images per class and adapt them at any time – for example, to refine existing classes or add new ones.
Unlike conventional deep learning, this approach prevents catastrophic forgetting and keeps maintenance effort low. Based on MVTec’s pretrained models optimized for industrial scenarios, applications can be updated quickly without full retraining. Because the method requires minimal computing power, updates can even be performed directly on edge devices, eliminating the need for external training hardware while ensuring efficient, long-term operation.
The result is a flexible solution that evolves with changing production conditions and remains suitable for embedded and edge environments such as smart cameras, sensors, and inspection modules.
形状匹配的评分可视化
With Score Visualization for Shape Matching in HALCON 25.11, users gain increased transparency when setting up shape matching applications. Instead of only returning an overall score, the feature provides a breakdown of how different model parts contribute to the final result. By configuring color-coded bins, users can immediately see which areas match well and which perform poorly, for example due to shadows or unwanted textures. This visual feedback makes it much easier to refine models, remove problematic parts, and optimize applications – a major usability advantage especially for non-expert users.
The feature can also support advanced scenarios in robotics, helping determine which object in a stack is least covered and should be picked first.
优化深度OCR模型，实现更快、更节省资源的OCR处理
With new Deep OCR recognition models in HALCON 25.11, text reading becomes faster and more resource-efficient without compromising accuracy. The models deliver up to 50× faster inference on embedded devices. All models are pretrained by MVTec on industrial image data, and include the proven alignment preprocessing, which improves recognition when text varies in position or orientation. Thanks to their optimized architecture, they enable real-time OCR applications on low-power devices while maintaining high accuracy. This makes the models ideal for demanding inline applications such as serial number inspection, label verification, or lot tracking OCR tasks, across industries from logistics and packaging to pharmaceuticals, consumer goods, and medical technology.
MobileNetV4分类模型
With HALCON 25.11, MVTec adds support for the MobileNetV4 series, an efficient new generation of deep learning models optimized for resource-constrained systems and edge devices. These models support both classification and object detection tasks and deliver high accuracy while maintaining low computational requirements. Users benefit from fast inference times, lower system costs, and straightforward integration into existing HALCON projects. All models are pretrained by MVTec, ensuring strong performance for various downstream tasks such as quality inspection, product classification, presence detection, and surface defect analysis. Typical industries include automation, electronics, packaging, food, and medical technology.
多种条码读取与打印质量检测的改进
With HALCON 25.11, code reading and print quality inspection (PQI) become even more robust and versatile.
QR code detection has been improved for challenging cases such as curved or deformed surfaces. A more powerful candidate search significantly raises the detection rate, while runtime has been reduced for standard scenarios – enabling reliable reading in industries like logistics, packaging, food production, and bottle labeling.
The bar code reader has also been enhanced for Code 128 and GS1-128, making it more tolerant to irregular bar widths caused by printing variations or local distortions. This increases decoding reliability across diverse industrial applications.
In addition, HALCON now supports the latest print quality inspection standards ISO/IEC 15415:2024 and ISO/IEC 29158:2025. This ensures code quality can be verified according to the most up-to-date requirements in sectors such as pharmaceuticals, food, and logistics.
Together, these enhancements provide compliance, long-term process stability, and higher robustness across a wide range of industrial code reading applications.
内置SBOM，轻松实现合规
With HALCON 25.11, MVTec provides Software Bills of Materials (SBOMs), giving users transparent insight into the software components included in the product. SBOMs are becoming a key requirement under new regulations such as the EU Cyber Resilience Act and are increasingly demanded in process- and safety-critical industries.
By providing SBOMs directly with HALCON, MVTec simplifies compliance and reduces workload for customers. Delivered as machine-readable SPDX JSON files, SBOMs make it easier to perform vulnerability and license analyses, fulfill regulatory obligations, and react quickly to newly discovered risks. The result is less integration effort, lower long-term costs, and greater confidence in meeting both regulatory and customer requirements.
HDevelopEVO 中 HALCON 脚本文件的语法高亮功能
HDevelopEVO 25.11 introduces redesigned syntax highlighting for HALCON Script files, making code easier to read, navigate, and maintain. Instead of uniform coloring, operators, variables, and comments are now displayed in distinct colors, giving scripts a clear visual structure.
This improves orientation in the code, reduces errors, and speeds up debugging and refactoring – resulting in a more efficient workflow and a smoother development experience.
HDevelopEVO中的HALCON脚本引擎与C++ API
With HDevelopEVO 25.11, MVTec introduces the first preview of the HALCON Script Engine, the successor to the HDevEngine. It provides a runtime environment for executing HALCON Script files created in HDevelopEVO. The HALCON Script Engine can initially be integrated into applications via a C++ API. Further interfaces such as .NET and Python are planned for future releases. This bridges the gap between prototyping in HDevelopEVO and productive use in custom solutions.
As a preview version, the HALCON Script Engine already enables embedding HALCON Scripts into applications. While not all language features are supported yet, these will follow in future releases. In the meantime, users can try it out and gain early experience with the new workflow.
HDevelopEVO 25.11 的新增功能
Also included in this release are several improvements that make working with HDevelopEVO more efficient. A new script converter simplifies the migration of existing HDevelop procedures and example programs into HDevelopEVO, supporting stepwise conversion and reuse of established code. Usability has been enhanced with interactive tools: a real-time histogram integrated into the threshold operator for intuitive parameter adjustment, and a live display of grayscale values on mouse hover for instant pixel-level analysis. Together, these features simplify migration, speed up troubleshooting, and streamline everyday image processing workflows.
兼容性
许可
HALCON 25.11.0.0 Progress requires a valid HALCON Progress license and does not run with licenses of HALCON Steady.
HALCON 库
Compared to HALCON 25.05 Progress, many extensions have been introduced. Thus, the HALCON 25.11.0.0 Progress libraries are not binary compatible with HALCON 25.05 Progress or earlier versions. However, HALCON 25.11.0.0 Progress is mostly source-code compatible to HALCON 25.05 Progress except for the changes listed below: Legacy handle mode has been removed. HALCON now implements the new version of the standard ISO/IEC 15415, namely ISO/IEC15415:2024, such that the output of the operator get_data_code_2d_results with the parameters 'quality_isoiec15415' resp. 'quality_isoiec15415_*' is not compatible with previous HALCON versions:
- get_data_code_2d_results with 'quality_isoiec15415_labels' does not return the label 'Reflectance Margin' anymore, as it has been merged with the parameter 'Modulation' to the new quality parameter 'Modulation' in ISO/IEC 15415:2024.
- The parameter 'quality_isoiec15415_reflectance_margin_module_grades' of get_data_code_2d_results has been removed and replaced by the parameter 'quality_isoiec15415_modulation_module_grades'.
- The parameter 'quality_isoiec15415_additional_reflectance_check' of get_data_code_2d_results has been removed, as the additional reflectance check is removed in the new version of ISO/IEC 15415.
- get_data_code_2d_results with 'quality_isoiec15415_labels' now returns the labels 'Print growth x' and 'Print growth y' instead of 'Print Growth', as the previous print growth assessment is split into vertical and horizontal direction in ISO/IEC 15415:2024.
- The spelling of the labels returned by get_data_code_2d_results with 'quality_isoiec15415_labels' have been changed and now completely correspond to ISO/IEC 15415:2024.
- get_data_code_2d_results with 'quality_isoiec15415' now returns continuous grades from 4.0 to 0.0 in steps of 0.1.
- The symbol threshold defined in ISO/IEC 15415:2024 is now part of the output of the operator get_data_code_2d_results with the parameters 'quality_isoiec15415_intermediate', 'quality_isoiec15415_intermediate_values', and 'quality_isoiec15415_intermediate_labels'.
For the PDF417 reader, the parameter 'quality_isoiec15415_float_grades' of the operator get_data_code_2d_results is no longer available, as the grades are always continuous in the new HALCON version. The update of the NVIDIA CUDA libraries significantly reduces the memory footprint in most cases. However, on some cards there may be performance regressions. The new set_system parameter 'cudnn_tuning' can be used to partially mitigate these issues, for example, by setting it to 'fastest'. For detector models retrieved from a Deep 3D Matching model, normalization and augmentation are now carried out within train_dl_model_batch. All training datasets previously preprocessed have to be preprocessed again in [url=]preprocess_dl_dataset[/url] to be compatible.
Note that these changes do not affect standard detector models, which are not used in Deep 3D Matching. The loss values and gradients of the CTC loss layer of the DL Framework are now normalized by the respective target sequence lengths. To approximate the behavior of previous versions, it may be necessary to multiply the learning rate by the mean target length. More information.HALCON now implements the new version of the standard ISO/IEC 29158, namely ISO/IEC 29158:2025. Consequently, the output of get_data_code_2d_results with the parameters 'quality_isoiec29158' resp. 'quality_isoiec29158_*' is no longer compatible with previous HALCON versions:
- get_data_code_2d_results with 'quality_isoiec29158_labels' does not return the labels 'Reflectance Margin' and 'Print Growth' any longer. The former has been merged with the parameter 'Cell Modulation' to the new quality parameter 'Cell modulation', while 'Print Growth' is no longer assessed in ISO/IEC 29158:2025.
- The spelling of the labels returned by get_data_code_2d_results with 'quality_isoiec29158_labels' have been changed and now completely correspond to ISO/IEC 29158:2025.
- get_data_code_2d_results with 'quality_isoiec29158' or 'quality_isoiec29158_intermediate' now returns continuous grades from 4.0 to 0.0 in steps of 0.1.
- The parameters 'quality_isoiec29158_float_grades', 'quality_isoiec29158_intermediate_float_grades', and 'quality_isoiec29158_reflectance_margin_module_float_grades' are no longer available, as the grading is always continuous in the new HALCON version.
- The parameters 'quality_aimdpm_1_2006', 'quality_aimdpm_1_2006_labels', 'quality_aimdpm_1_2006_values', 'quality_aimdpm_1_2006_reflectance_margin_module_grades', 'quality_aimdpm_1_2006_rows', 'quality_aimdpm_1_2006_cols', 'quality_aimdpm_1_2006_intermediate', 'quality_aimdpm_1_2006_intermediate_labels', and 'quality_aimdpm_1_2006_intermediate_values' have been removed.
Instead, their partly new alternatives 'quality_isoiec29158', 'quality_isoiec29158_labels', 'quality_isoiec29158_values', 'quality_isoiec29158_modulation_module_grades', 'quality_isoiec29158_rows', 'quality_isoiec29158_cols', 'quality_isoiec29158_intermediate', 'quality_isoiec29158_intermediate_labels', and 'quality_isoiec29158_intermediate_values' can be used.
More information.
HALCON应用程序
Please re-compile all C, C++, or .NET programs developed with HALCON 25.05 Progress. The incompatibility with HALCON 25.05 Progress 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.
图像采集接口
In general, HALCON 25.11.0.0 Progress and HALCON 25.05 Progress image acquisition interfaces are library compatible.
数字输入/输出接口
In general, HALCON 25.11.0.0 Progress and HALCON 25.05 Progress digital I/O interfaces are library compatible.
扩展包
Please re-generate your own extension packages developed with HALCON 25.05 Progress.
遗留功能或不再支持的功能
The following functionality may be discontinued in a future major release: See the reference manual entries of legacy operators for details on how to replace them. The legacy operators and procedures listed below, which used the legacy dl_classifier handle, are not supported any longer. Operators:
- apply_dl_classifier
- clear_dl_classifier
- clear_dl_classifier_result
- clear_dl_classifier_train_result
- create_dl_classifier
- deserialize_dl_classifier
- get_dl_classifier_param
- get_dl_classifier_result
- get_dl_classifier_train_result
- read_dl_classifier
- serialize_dl_classifier
- set_dl_classifier_param
- train_dl_classifier_batch
- write_dl_classifier
Procedures: - apply_dl_classifier_batchwise
- dev_display_dl_classifier_heatmap
- evaluate_dl_classifier
- gen_dl_classifier_heatmap
- gen_interactive_confusion_matrix
- get_dl_classifier_image_results
- plot_dl_classifier_training_progress
- preprocess_dl_classifier_images
- read_dl_classifier_data_set
- select_percentage_dl_classifier_data
- split_dl_classifier_data_set
The legacy handle mode has been announced legacy since HALCON 24.11. The mode shifts the ownership of a handle to the user via the parameter 'legacy_handle_mode' of the set_system operator, and has been removed from HALCON. The following deprecated operators have been removed from HALCON: clear_all_bar_code_models, clear_all_barriers, clear_all_calib_data, clear_all_camera_setup_models, clear_all_class_gmm, clear_all_class_knn, clear_all_class_lut, clear_all_class_mlp, clear_all_class_svm, clear_all_class_train_data, clear_all_color_trans_luts, clear_all_component_models, clear_all_conditions, clear_all_data_code_2d_models, clear_all_deformable_models, clear_all_descriptor_models, clear_all_events, clear_all_lexica, clear_all_matrices, clear_all_metrology_models, clear_all_mutexes, clear_all_ncc_models, clear_all_object_model_3d, clear_all_ocr_class_knn, clear_all_ocr_class_mlp, clear_all_ocr_class_svm, clear_all_sample_identifiers, clear_all_scattered_data_interpolators, clear_all_serialized_items, clear_all_shape_model_3d, clear_all_shape_models, clear_all_sheet_of_light_models, clear_all_stereo_models, clear_all_surface_matching_results, clear_all_surface_models, clear_all_templates, clear_all_text_models, clear_all_text_results, clear_all_training_components, clear_all_variation_models,
close_all_bg_esti, close_all_class_box, close_all_files, close_all_framegrabbers, close_all_measures, close_all_ocrs, close_all_ocvs, close_all_serials, close_all_sockets The following legacy operators have been removed from HALCON: Legacy/Classification
- deserialize_class_box
- serialize_class_box
- write_class_box
- set_class_box_param
- read_sampset
- read_class_box
- learn_sampset_box
- learn_class_box
- get_class_box_param
- clear_sampset
- close_class_box
- create_class_box
- descript_class_box
- test_sampset_box
- enquire_reject_class_box
- enquire_class_box
Legacy/Segmentation - learn_ndim_box
- class_ndim_box
Legacy/Tools The following classes have been removed from HALCON: The following legacy operators have been removed from HALCON:
- create_template_rot
- create_template
- serialize_template
- deserialize_template
- write_template
- read_template
- clear_all_templates
- clear_template
- set_offset_template
- set_reference_template
- adapt_template
- fast_match_mg
- best_match_pre_mg
- best_match_mg
- fast_match
- best_match_rot_mg
- best_match_rot
- best_match
The following classes have been removed from HALCON: The legacy operators set_insert, get_insert, and query_insert have been removed. As announced in previous releases, MVTec has discontinued the support of ARM 32-bit systems with the release of HALCON 25.11 Progress. We recommend switching to ARM 64-bit platforms for future applications.
支持的操作系统
Windows
HALCON 25.11.0.0 Progress has been compiled for the x64-win64 platform version for Windows 10 (x64 editions), 11, Windows Server 2016, 2019, 2022, 2025 on Intel 64 or AMD 64 with SSE2 (AVX2 dispatch) processors.
LinuxHALCON 25.11.0.0 Progress has been compiled for the following Linux platform versions: - x64-linux platform version for Linux x86_64xspace, GLIBC_2.27, GLIBCXX_3.4.24 on Intel 64 or AMD 64 with SSE2 (AVX2 dispatch) processors
- aarch64-linux platform version for Linux aarch64xspace, Kernel with hidraw support, GLIBC_2.27, GLIBCXX_3.4.24 on Armv8-A with AArch64 support
Please refer to the Installation Guide for detailed system requirements corresponding to the different Application Binary Interfaces.
HALCON 25.11.0.0 版本变更详情说明
The changes in HALCON 25.11.0.0 Progress are described with respect to HALCON 25.05. 安全相关主题
The following issues are relevant in the context of cybersecurity: A Software Bill of Materials (SBOM) in the System Package Data Exchange (SPDX) format has been added next to many binary files with the same name, appended with “.spdx.json”. This includes the MVTec HALCON Image Processing Library and all language interfaces (except HALCON/.NET Core). The Installation Guide chapter about cybersecurity has been extended with information regarding the secure handling of read operators and system_call. The release notes now contain an additional section for all issues that are relevant for cybersecurity. Internal security checks identified the security vulnerability CVE-2013-6648 (thirdparty Skia). The corresponding function is not used in the production code path. Exploitation in the context of HALCON is therefore not possible and the CVE is not applicable to our product. Internal security checks identified the security vulnerability CVE-2025-23247 (thirdparty CUDA SDK, cublas). The vulnerability concerns the binary tool cuobjdump, which is shipped alongside the CUDA SDK. HALCON does neither ship nor use this binary. Exploitation in the context of HALCON is therefore not possible and the CVE is for our product not applicable.
HDevelop 错误修复代码导出
GUI
HDevelop示例程序
新的HDevelop示例程序
- hdevelop/Filters/Color/analyze_pills_hyperspectral_canonical_variates.hdev
- hdevelop/Deep-Learning/Classification/continual_learning_for_classification.hdev
- hdevelop/Deep-Learning/Classification/continual_learning_for_classification_on_edge_device.hdev
- hdevelop/Transformations/2D-Transformations/gen_image_warp_map.hdev
- hdevelop/Matching/Shape-Based/get_generic_shape_model_result_score_visualization_contours.hdev
- hdevelop/ImageSource/image_source_chunks.hdev
- hdevelop/Filters/Color/smooth_hyperspectral_images.hdev
新功能
错误修复
HALCON 库
加速
HALCON 25.11 for x86 on Linux and Windows has been compiled with the new Intel clang-based compiler. As a result, many operators are slightly faster than in 25.05, while some are slightly slower. For Linux aarch64, HALCON has been compiled with clang instead of gcc, which also improves overall performance. guided_filter is now up to 115% faster for images of type byte and up to 30% faster for images of type uint2. Due to a different implementation, the results may slightly differ when compared to previous versions. To receive identical results, convert the input images to real. zoom_image_size and zoom_image_factor are now faster for images of type byte by up to 180% for x86/x64-capable CPUs if the input image is zoomed up to twice its dimensions and Interpolation is set to 'bilinear', 'constant', or 'weighted'.
新功能
3DThe deep learning procedures [url=]dev_display_dl_data[/url] and [url=]dev_display_dl_data_tiled[/url] have been extended for training samples of a 3D pose estimation model retrieved from a Deep 3D Matching model. Now, during the training, result poses on some random samples are displayed in addition to the training curves. The procedures [url=]create_scene_engine_run_params[/url], [url=]set_scene_engine_run_param[/url], and [url=]check_scene_engine_run_param[/url] have been improved regarding parameter checks and error messages. train_dl_model_batch has been extended with an automatic augmentation of (typically synthetically generated) training samples for detector models retrieved from a Deep 3D Matching model.
The deep learning procedures [url=]create_dl_train_param[/url] and [url=]preprocess_dl_model_images[/url] have been adapted so that augmentation in [url=]augment_dl_samples[/url] is disabled and no normalization is performed.
Note that these changes do not affect standard detector models, which are not used in Deep 3D Matching. Note that this change affects the compatibility. Read more.open_scene_engine has been extended with the generic parameter 'debug_log_file'. When setting a string specifying filename and location of a debug file, the opened Scene Engine instance creates the corresponding file and writes debug information to it.
分类
In the Handle Inspect window, now some information about handles of type class_lut is visible. This information can be queried for debugging purposes using the operators get_handle_param and get_handle_tuple.
深度学习
set_dl_model_param now supports a new parameter 'fuse_conv_bn' to fuse pairs of convolution and batch normalization layers. This fusion can improve the runtime performance and memory consumption of the model during inference. read_dl_model has been extended such that the ONNX MatMul operation can now be read as a MatMul layer in HALCON. For some deep learning functionality, now the “cuDNN FrontEnd” (FE) API by NVIDIA is used. This implies that a computational graph is compiled at runtime (JIT) for specific operations and given GPU hardware. Due to this online optimization process, the device initialization of a given model using such a layer or functionality takes much longer than before. create_dl_layer_elementwise now supports unidirectional broadcasting for the elementwise operation 'sum'. Further, for all commutative elementwise operations, unidirectional broadcasting is supported for any order of input layers. HALCON has been expanded to include Continual Learning for all deep learning classification models. This function allows models to be incrementally updated with new data while retaining previously learned knowledge. The HDevelop examples hdevelop/Deep-Learning/Classification/continual_learning_for_classification.hdev and hdevelop/Deep-Learning/Classification/continual_learning_for_classification_on_edge_device.hdev have been added to demonstrate how to configure and utilize a classification deep learning model with this feature. The reference manual entry "Deep Learning" > "Continual Learning" has been added, providing instructions on how to configure and use a classification model with Continual Learning functionality. set_dl_model_param now supports a new parameter 'max_gradient_norm' to set a limit for the gradient norm of the weights and biases of the model during training. This is also known as gradient clipping. create_dl_layer_permutation and create_dl_layer_slice have been extended to support a new behavior, which allows specifying a permutation or axes in 'nchw' format. This behavior can be enabled by setting the GenParam 'axes_format' to 'nchw'. The NVIDIA CUDA libraries used by HALCONs Deep Learning Framework have been updated to CUDA 12.8 and cuDNN 9.10 on x64-linux and x64-win64 platforms. With this release, the NVIDIA Blackwell architecture is supported. On aarch64 systems, Jetpack 6.2 is required. The TensorRT AI² interface has been updated to use TensorRT 10.12.0 to support the Blackwell architecture as well. Note that this change affects the compatibility. Read more.set_dl_model_param now supports a new parameter 'weight_decay' to enable decoupled weight decay, a popular regularization method for model training. create_dl_layer_loss_ctc now supports a new parameter 'zero_infinity' to suppress batch items with infinite loss, which can stabilize the training. Furthermore, the CTC loss layer now provides better determinism and better balancing of samples with different sequence lengths. This improves the training of Deep OCR recognition models. Note that this change affects the compatibility. Read more.HALCON has been extended with pretrained deep learning classification models from the MobileNetV4 family. Depending on runtime and accuracy requirements, the user can choose between pretrained_dl_model_mobilenet_v4_small.hdl, pretrained_dl_model_mobilenet_v4_medium.hdl, and pretrained_dl_model_mobilenet_v4_large.hdl. All models are also available as backbones for object detection. The DL framework now contains the new operator create_dl_layer_gather, which creates a Gather layer within a custom DL model. The layer gathers values from the input data along a specified axis according to the given indices.
read_dl_model has been extended such that the ONNX Gather operation can now be read as a Gather layer in HALCON as long as the indices have a rank smaller than 2. create_dl_layer_input and set_dl_model_layer_param now support a new generic parameter 'keep_batch_size_const', which can be set to 'true' for input layers of type 'constant'. In this case, the batch size of this layer is not affected by changes of the model batch size. HALCON now supports reading and running inference of YOLO v8/v9/v11 Deep Learning object detection models (axis-aligned or oriented bounding boxes). YOLO models are known for their real-time speed and high accuracy, making them ideal for applications requiring quick and reliable object detections. To use this feature, a trained YOLO ONNX model is required.
The existing example hdevelop/Deep-Learing/Detection/dl_detection_inference_yolo.hdev has been extended, which guides you through the workflow. The GenParam scaling parameter of gen_dl_model_heatmap has been extended to include the option 'scale_without_relu'. In addition, the default value for this parameter is now set to 'scale_without_relu'. The scaling parameter is now also applicable when HeatmapMethod is set to 'guided_grad_cam'. create_dl_layer_activation has been extended with the activation modes 'sin' and 'cos'.
read_dl_model has been extended such that the ONNX Sin and Cos operations can now be read as activation layer in HALCON. create_dl_layer_activation now supports 31 more activation modes:
'asin', 'acos', 'atan', 'abs', 'ceil', 'celu', 'clip', 'cos', 'cosh', 'elu', 'erf', 'exp', 'floor', 'gelu', 'hard_sigmoid', 'hard_swish', 'log', 'mish', 'neg', 'pow', 'reciprocal', 'round', 'sin', 'sinh', 'softplus', 'softsign', 'sqrt', 'swish', 'tan', 'tanh', 'thresholded_relu'.
The corresponding ONNX operations are supported in the operator read_dl_model. fit_dl_out_of_distribution has been updated to support datasets containing fewer than 15 training samples per class, as long as validation samples for that class are available. The forward pass on CPUs of the elementwise layer has been accelerated by parallelizing the elementwise operations 'sum' with broadcasting, 'minimum', 'maximum', and 'division'. The pretrained Deep OCR recognition model has been improved and extended. The runtime and memory consumption have been significantly reduced, while the accuracy has been slightly improved.
Additionally, a new compact recognition model has been added. This compact model is optimized for inference on low-power devices, prioritizing computational efficiency over accuracy. Both models can be retrained using Deep OCR recognition training. set_deep_ocr_param has been extended with the option to specify ‘compact’ or ‘default’ for the parameter ‘recognition_model’. The following pretrained model has been updated: dl/pretrained_deep_ocr_recognition.hdl The following pretrained model has been added: dl/pretrained_deep_ocr_recognition_compact.hdl HALCON has been enhanced with Continual Learning for all deep learning classification models. This function allows models to be incrementally updated with new data while retaining previously learned knowledge.
In addition to the extension of the deep learning operators and procedures with the new Continual Learning functionality, the operators init_dl_continual_learning and extend_dl_continual_learning have been introduced. The HDevelop examples hdevelop/Deep-Learning/Classification/continual_learning_for_classification.hdev and hdevelop/Deep-Learning/Classification/continual_learning_for_classification_on_edge_device.hdev have been added to demonstrate how to configure and utilize a classification deep learning model with this feature. The reference manual entry “Deep Learning” > “Continual Learning” has been added, providing instructions on how to configure and use a classification model with Continual Learning functionality.
滤波器
HALCON now provides ways to smooth images in channel direction as well as to compute derivatives in channel direction. This is especially helpful for multispectral and hyperspectral images. The new operator convol_channels can be used to apply a linear convolution filter in channel direction. The new operator gen_savitzky_golay_filter can be used to create filter parameters for such a convolution.
The new HDevelop example hdevelop/Filters/Color/smooth_hyperspectral_images.hdev shows how to use the new operators to smooth the data and to compute derivatives in channel direction. HALCON now provides a way to use canonical variates analysis on the channels of an image to reduce the dimensionality while keeping target classes separable. This is especially helpful in the analysis of multi- or hyperspectral images. The new operator gen_canonical_variates_trans uses the canonical variates analysis to compute a linear transformation, which can be applied using linear_trans_color.
The new HDevelop example hdevelop/Filters/Color/analyze_pills_hyperspectral_canonical_variates.hdev shows how to use this functionality to reduce hyperspectral images to RGB images.
识别 > 条形码
The bar code reader has been improved for Code 128 and GS1-128 codes with irregular element widths. Now, the bar code model parameter 'element_size_variable' influences the decoding of these code types. Setting 'element_size_variable' to 'true' enables find_bar_code to compensate small variations in the element size, which can help to increase the decoding rate. Further, the parameter 'element_size_variable' can now be specifically set and queried for different bar code types by using the operators set_bar_code_param_specific and get_bar_code_param_specific.
识别 > 数据码
HALCON now implements the newest standard ISO/IEC 15415:2024 for print quality inspection of two-dimensional barcodes by calling the operator get_data_code_2d_results with the parameter 'ResultNames' set to 'quality_isoiec15415'. The new version of the print quality inspection standard has an impact on the Data Matrix ECC 200, QR Code, Micro QR Code, Aztec Code, and PDF417 code readers and the related operators, parameters, and results:
- The previous version of the aforementioned standard, namely ISO/IEC 15415:2011, is no longer supported.
- The grades returned by get_data_code_2d_results with parameter 'ResultNames' set to 'quality_isoiec15415' are continuous from 0.0 to 4.0 in steps of 0.1.
- The quality parameters 'Reflectance Margin' and 'Modulation' are now merged to the new quality parameter 'Modulation'.
- The quality parameter 'Print Growth' is split into the new quality parameters 'Print growth x' and 'Print growth y', which now contribute to the overall symbol grade.
- The labels returned by get_data_code_2d_results with the parameters 'quality_isoiec15415_labels' and 'quality_isoiec15415_intermediate_labels' have been changed such that they now completely correspond to the spelling in ISO/IEC 15415:2024.
- The additional reflectance check has been removed in the new version of ISO/IEC 15415 and, consequently, the parameter 'quality_isoiec15415_additional_reflectance_check' of get_data_code_2d_results has been removed from HALCON.
- The computation of the symbol threshold and the two reflectance values Rmin and Rmax has been changed.
- The symbol threshold is now part of the intermediate grades, values, and labels of the print quality inspection, which can be queried by get_data_code_2d_results by using the parameters 'quality_isoiec15415_intermediate', 'quality_isoiec15415_intermediate_values', and 'quality_isoiec15415_intermediate_labels'. Note that the symbol threshold is no longer the mean of Rmin and Rmax, but represents the Otsu threshold as described in the new standard version.
- The parameters 'quality_isoiec15415_intermediate', 'quality_isoiec15415_intermediate_values', and 'quality_isoiec15415_intermediate_labels' are now also available for the QR Code, Micro QR Code, and the Aztec Code reader.
- The parameter 'quality_isoiec15415_reflectance_margin_module_grades' of get_data_code_2d_results has been removed and replaced by the new parameter 'quality_isoiec15415_modulation_module_grades', as the quality parameters 'Reflectance Margin' and 'Modulation' have been merged to the new quality parameter 'Modulation' in the new version of ISO/IEC 15415.
- The procedures [url=]grade_data_code_2d[/url] and [url=]dev_display_data_code_2d_print_quality_results[/url] have been adapted according to the changes mentioned above.
- For the PDF417 reader, the parameter 'quality_isoiec15415_float_grades' of the operator get_data_code_2d_results has been removed, as the grades are always continuous in HALCON 25.11 for two-dimensional barcodes.
- The grading of two-dimensional multi-row barcode symbols has only been adapted with respect to changes in ISO/IEC 15415, mainly focusing on continuous grading and adapted tables. Changes introduced by ISO/IEC 15416:2025 “Bar code print quality test specification” standard have not yet been considered and will follow in an upcoming HALCON release.
Furthermore, the following standard examples were adapted with respect to ISO/IEC 15415:2024: - hdevelop/Identification/Data-Code/ecc200_print_quality_intermediate_results.hdev
- hdevelop/Identification/Data-Code/ecc200_print_quality_modules.hdev
- hdevelop/Identification/Data-Code/ecc200_print_quality.hdev
- hdevelop/Identification/Data-Code/print_quality_reflectance_reference.hdev
- hdevelop/Identification/Data-Code/print_quality_smallest_module_size.hdev
Note that this change affects the compatibility. Read more.HALCON's QR Code reader is now even more robust on deformed codes when setting the parameter 'deformation_tolerance' to 'curved'. By using a more powerful method to find code candidates in the image, the detection rate has been increased significantly, especially for challenging scenarios like images with strongly distorted codes.
Further, the detection rate has been significantly increased for standard and enhanced recognition without using the parameter 'deformation_tolerance', while simultaneously reducing the run time. HALCON now implements the newest standard ISO/IEC 29158:2025 for assessing the symbol quality of direct marked parts (DPM). The assessment can be executed by calling the operator get_data_code_2d_results with the parameter 'ResultNames' set to 'quality_isoiec29158'. The new version and the changes made affect the assessment of the Data Matrix ECC 200, QR Code, Micro QR Code, and Aztec Code. The affected operators, procedures, parameters, and results are:
- The previous versions of the ISO/IEC 29158 standard are no longer supported or available. Therefore, the following 'ResultNames' have been removed from get_data_code_2d_results: 'quality_aimdpm_1_2006', 'quality_aimdpm_1_2006_labels', 'quality_aimdpm_1_2006_values', 'quality_aimdpm_1_2006_reflectance_margin_module_grades', 'quality_aimdpm_1_2006_rows', 'quality_aimdpm_1_2006_cols', 'quality_aimdpm_1_2006_intermediate', 'quality_aimdpm_1_2006_intermediate_labels', and 'quality_aimdpm_1_2006_intermediate_values'. This has been done as the major changes in ISO/IEC 29158:2025 no longer justify these deprecated names. However, note that all previous '_aimdpm_1_2006' parameters map onto an '_isoiec29158' counterpart. Therefore, it is possible to simply replace these names to migrate to the new standard.
- The deprecated predecessor to 'quality_isoiec29158', namely 'quality_isoiec_tr_29158', and all the derived parameter names have also been completely removed and should be replaced by their 'quality_isoiec29158' counterparts.
- The new ISO/IEC 29158:2025 standard together with ISO/IEC 15415:2024 make continuous grading mandatory. Consequently, the following 'ResultNames' have been removed: 'quality_isoiec29158_float_grades', 'quality_isoiec29158_intermediate_float_grades', and 'quality_isoiec29158_reflectance_margin_module_float_grades'. In general, the variants without the '_float_grades' suffix account for their purpose, except for 'quality_isoiec29158_reflectance_margin_module_float_grades', which has been replaced by 'quality_isoiec29158_modulation_module_grades'.
- With respect to continuous grading, the rounding has been revised to always round down. This is in accordance to the new version of ISO/IEC 15415:2024.
- The labels returned by get_data_code_2d_results with the parameters 'quality_isoiec29158_labels' and 'quality_isoiec29158_intermediate_labels' have been changed to follow more closely the spelling in ISO/IEC29158:2025.
- The quality grade 'Print Growth' is not graded and not included in the final grade.
- Similar to the ISO/IEC15415:2024, the 'Reflectance Margin' and 'Cell Modulation' grades have been merged to the new 'Cell modulation' grade.
- The parameter 'quality_isoiec29158_reflectance_margin_module_grades' of get_data_code_2d_results has been removed and replaced by the new parameter 'quality_isoiec29158_modulation_module_grades', as the quality parameters 'Reflectance Margin' and 'Cell Modulation' are merged to the new quality parameter 'Cell modulation' in the new version of ISO/IEC 29158.
- The methods to compute the thresholds 'T2' of ISO/IEC29158:2025 and the 'Symbol threshold' of ISO/IEC15415:2024 have been aligned. This can lead to small changes for the ISO/IEC 29158 standard, even though the standardized algorithm did not change.
- The parameters 'quality_isoiec29158_intermediate', 'quality_isoiec29158_intermediate_values', and 'quality_isoiec29158_intermediate_labels' are now also available for the QR Code, Micro QR Code, and the Aztec Code reader.
- The threshold 'T2', the mean dark value 'MD', as well as the mean light value 'MLtarget' are now also available for the QR Code, Micro QR Code, and Aztec Code via get_data_code_2d_results with the parameter 'quality_isoiec29158_intermediate_values'. In addition, 'Rmax' is also returned for all code types supporting the ISO/IEC 29158 standard. This new value represents the maximum reflectance measured over all grid intersection points and is an important ingredient for the calibration process.
- The procedures [url=]grade_data_code_2d[/url] and [url=]dev_display_data_code_2d_print_quality_results[/url] have been adapted according to the changes mentioned above.
Furthermore, the following standard examples have been adapted and renamed with respect to ISO/IEC 29158:2025: - hdevelop/Identification/Data-Code/calibration_aimdpm_1_2006.hdev, which is now named calibration_isoiec29158.hdev
- hdevelop/Identification/Data-Code/print_quality_aimdpm_1_2006.hdev, which is now named print_quality_isoiec29158.hdev
Similarly, the images datacode/ecc200/ecc200_quality_aimdpm_[01-08].png have been renamed to ecc200_quality_isoiec29158_[01-08].png. Note that this change affects the compatibility. Read more.The behavior of the 'strict_quiet_zone' check, which can be enabled via set_data_code_2d_param, changed due to the adaptations made regarding the print quality inspection standards ISO/IEC 15415 and ISO/IEC 29158. This affects all 2D code types supporting this parameter.
图像
paint_xld is now more accurate if called with multiple contours that contain touching edges. The runtime of paint_xld is now independent of the setting of 'init_new_image'. access_channel now allows selecting multiple channels of the input image. Additionally, multiple multi-channel input images can be passed, in which case the same channels are selected from all of them.
匹配
HALCON shape-based matching has been extended with Score Visualization for Shape Matching. To query if a shape model supports the feature, the call get_generic_shape_model_param has been extended with the parameter 'score_visualization_enabled'. The new example
hdevelop/Matching/Shape-Based/get_generic_shape_model_result_score_visualization_contours.hdev
has been added to show how to use this feature in HALCON.
其他
错误修复
3D
connection_object_model_3d did not raise an error when more than one Feature was passed or Value was out of bounds. This problem has been fixed. set_deep_matching_3d_param with GenParamName 'camera_pose N' only worked correctly for poses of type 0. Pose types 1 to 13 did not work correctly. This problem has been fixed. remove_object_model_3d_attrib and remove_object_model_3d_attrib_mod with attributes 'triangles', 'lines', or 'polygons' did not remove extended attributes attached to these triangles, lines, or polygons. This inconsistent stage could lead to problems in subsequent operators like select_points_object_model_3d or union_object_model_3d. This problem has been fixed. Now, extended attributes attached to triangles, lines, or polygons are removed if 'triangles', 'lines', or 'polygons' are removed, respectively. create_deep_matching_3d with symmetry poses of non-mixed tuple type or poses of types not equal to 0 did not work correctly. This problem has been fixed. The procedure [url=]create_dl_train_param[/url] raised an error for models of type '3d_pose_estimation' for empty GenParamName and GenParamValue. In particular, it raised an error for missing generic evaluation parameters even if EvaluationIntervalEpochs was set to 0. This problem has been fixed. apply_deep_matching_3d returned the error 8406 ("Point cannot be projected") in rare cases. This problem has been fixed. The procedures check_dl_preprocess_param, [url=]preprocess_dl_dataset[/url], and [url=]gen_dl_samples[/url] did not treat the parameter 'min_visibility' correctly, leading to errors in the detection training in example deep_3d_matching_training_workflow.hdev. This problem has been fixed. For open_scene_engine, the default timeout of 10 seconds was insufficient on some systems. The default timeout has been increased to 60 seconds. The procedure [url=]set_scene_engine_run_param[/url] sometimes threw error 1401 ("Wrong number of values of control parameter 1") in operation 'max'. This problem has been fixed. prepare_object_model_3d with Purpose set to 'shape_based_matching_3d' in rare cases added an empty shape attribute instead of raising an error.
create_shape_model_3d in rare cases added an empty shape attribute when it raised an error. These problems have been fixed. When passing a 3D object model with pre-defined vertex normals to the scene engine, those normals were not always used and copied when the physics engine stage was active. This problem has been fixed. run_scene_engine returned object instance visibilities that contain NaN ("Not a Number") values in some cases. This problem has been fixed. All instance visibilities returned by run_scene_engine are now in the range 0 to 1. When using run_scene_engine on a system without a suitable GPU, the Scene Engine returned black images and printed errors about missing shaders to the console. This problem has been fixed. Now, the required shaders are part of the installation, and images can be rendered on CPU. However, using a GPU is recommended for performance reasons. In some cases, the brightness of images rendered with the scene engine could vary when rendering repeatedly with the same scene engine instance. This problem has been fixed. The image brightness is now consistent, even when rendering multiple images. When running run_scene_engine, in some cases parts of the ground and the distractor objects were rendered too dark. This problem has been fixed.
深度学习
While training neural networks with the procedure [url=]train_dl_model[/url], the evaluation of the model after a specific epoch could happen a single iteration too late. This problem has been fixed. During the training of the neural networks, the learning rate was not displayed correctly when the learning rate changed. This problem has been fixed. fit_dl_out_of_distribution in some cases leaked memory if it failed because the model could not correctly be applied to the samples in the dataset. This problem has been fixed. Reading invalid legacy models with read_dl_model could lead to crashes. This problem has been fixed. Switching the new alignment of the Deep OCR Recognition model across multiple threads on a GPU led to an error. This problem has been fixed. set_text_model_param with 'separate_touching_chars' set to 'enhanced' crashed if the HALCON deep learning library could not be loaded. This problem has been fixed. In the HDevelop example deep_ocr_workflow.hdev, the procedure display_recognition_alphabet did not show special characters if all capital letters were removed from the recognition alphabet. This problem has been fixed. The error handling of set_deep_ocr_param and set_dl_model_param used together with the 'recognition_model' has been aligned. Both operators will throw the same errors if an invalid shape adjustment is recognized. Furthermore, it was possible to change the image height of the recognition model's input layer by bypassing the checks in set_deep_ocr_param via set_dl_model_param together with the parameter 'input_dimensions'. These problems have been fixed.
文件
Corrupted image files in PNM format have not always been detected. This problem has been fixed. Using values exceeding the maximum image size for the read_sequence parameters SourceWidth and SourceHeight caused an error. This problem has been fixed. On Windows, copy_file did not copy access rights and file timestamps of the source file. This problem has been fixed. Under certain conditions, reading corrupted TIFF files caused a crash. This problem has been fixed.
滤波器
convol_image led to an error for very large filter sizes and mirror margin. This problem has been fixed. gauss_filter returned incorrect results for int4 images on 32-bit systems. This problem has been fixed. gauss_filter handled inner and border region of 'real' images inconsistently. This problem has been fixed. mean_image_shape in rare cases returned slightly incorrect results for byte images due to rounding problems on aarch64 with neon enabled. This problem has been fixed. polar_trans_image_ext produced incorrect results for images of type 'real' if int_zooming was set to 'true' and the 'fused multiply-add' (FMA3) and 'Advanced Vector Extension 2' (AVX2) instruction sets were not enabled on x86 processors. This problem has been fixed.
图形
draw_region handled sub-pixel coordinates incorrectly. Whenever the operator was executed on a strongly magnified image, the resulting region depended on which part of the pixel was actually touched: The lower-right quadrant of the image pixel resulted in the expected region pixel, whereas the other parts of the image pixel caused the resulting region to be moved by 1 to the left and/or up. This problem has been fixed.
识别 > 概述
识别 > 条形码
Some labels for print quality inspection according to ISO/IEC 15416 contained the term 'minimal' instead of 'minimum'. This problem has been fixed. In rare cases, a successful decode of a barcode decoding attempt using the small_elements_robustness decode feature together with the quiet_zone check could lead to the unexpected error 3100 ("Wrong segmentation threshold"). This problem has been fixed. find_bar_code may have failed to successfully decode certain readable barcodes in very rare cases. The issue affected only barcodes suffering from print growth or loss. This problem has been fixed. find_bar_code crashed for CodeType 'GS1 DataBar Expanded Stacked' in very rare cases. This problem has been fixed. find_bar_code could crash in very rare case. This problem has been fixed.
识别 > 数据码
In rare cases, get_data_code_2d_results returned wrong start/stop pattern grades of the print quality inspection of PDF 417 codes. This problem has been fixed. For Data Matrix ECC 200 symbols, get_data_code_2d_results returned random values for 'structured_append' if queried for unsuccessful candidates. This problem has been fixed. Now, it always returns an empty tuple for 'structured_append' if queried for an unsuccessful candidate. This is true independent of the symbol type. I.e., the behavior is now consistent for Data Matrix ECC 200, QR Code, Micro QR Code, Aztec Code, and DotCode. The Aztec Code reader in rare cases did not decode small symbols close to the image border. This problem has been fixed. In rare cases, the Aztec Code reader returned the error 9205 ('Matrix is singular'). This problem has been fixed. find_data_code_2d could show unexpected behavior if 'strict_quiet_zone' was set to 'yes' via set_data_code_2d_param and the symbol XLD of the candidate touched or crossed the image borders. In rare cases and under the mentioned circumstances, the strict quiet zone check may not have worked correctly. The issue affected all code types supporting 'strict_quiet_zone'. This problem has been fixed. get_data_code_2d_objects returned wrong results for 'module_1_rois' and 'module_0_rois' for some Data Matrix codes with a square shape in rare cases. Additionally, get_data_code_2d_results returned wrong results for 'bin_module_data' for the same cases. These problems have been fixed.
匹配
create_ncc_model added a rotation step more than necessary and hence increased the angle width. This problem has been fixed.
测量
measure_projection could return values differing more than one gray value when compared to its values with another system setting for int_zooming. This problem has been fixed. When performing measurements on int4 images with very large pixel values, overflows could occur. This problem has been fixed. The metrology model parameter 'scale' and the metrology object parameter 'distance_threshold' were previously not always serialized depending on which other model parameters were set. This problem has been fixed.
其他
区域
region_features(Regions : : Features : Value) crashed for multiple features when computed on a region that extends to the limits of the available coordinate range. This problem has been fixed.
变换
For affine_trans_image, the parameter 'AdaptImageSize' wrongly accepted string values other than 'true' and 'false'. In this case, the value 'false' was assumed. This problem has been fixed. Now, the operator returns an error if another value than 'true' or 'false' is used.
XLDWhen using intersection_region_contour_xld in HALCON XL with large coordinates, the returned intersections were not always correct. This problem has been fixed.
语言接口
常规
错误修复
For HALCON/C++ and HALCON/C, the HBase.h header file redefined the TRUE and FALSE macros to true and false, respectively. This could lead to compiler warnings on Windows. Now, HBase.h will no longer redefine TRUE or FALSE if the HC_NO_LEGACY_TYPES macro is defined.
HALCON/C
错误修复
In some cases, a HALCON/C application could crash when destroying an application thread previously used to execute HALCON operators. A necessary condition was that at least one operator, typically the last one executed in the thread, returned an error result. This problem has been fixed.
语言接口示例程序
错误修复
HALCON 变量检查
错误修复
User settings were not stored correctly. This problem has been fixed. The HALCON Variable Inspect application was inconsistent about when it was responsive or not. Now, it is only responsive during debugging and while at a break, as intended. Further, on first open, the variables to inspect were not loaded correctly. These problems have been fixed.
图像采集
图像采集接口
The latest information about new interface revisions and newly supported image acquisition devices can be found on MVTec's web server. Please refer to the release notes within the documentation of the individual image acquisition interfaces for information about improvements, bugfixes, or whether a new revision of the corresponding device driver is required.
图片来源
功能
错误修复
Reading unavailable registers through get_image_source_param has been improved to deal with possible unexpected problems when accessing the register length.
数字输入/输出接口
The latest information about new interface revisions and newly supported digital I/O interfaces can be found on MVTec's web server. Please refer to the release notes within the documentation of the individual digital I/O interfaces for information about improvements, bugfixes, or whether a new revision of the corresponding device driver is required.
文档
程序员手册
用户指南
The Installation Guide chapter about cybersecurity has been extended with information regarding the secure handling of read operators and system_call.
参考手册
The Deep 3D Matching documentation and examples have been extended with some helpful information on the usage of the method. The reference manual entry of create_deep_matching_3d now provides a simple example on how to set symmetries of the 3D object model. The reference manual entry of get_deep_matching_3d_param now mentions the importance of having camera_pose in the same unit as the 3D object model. The reference manual entry of get_dl_model_param did not mention the parameter 'inference_outputs'. This problem has been fixed. The reference manual entry for set_spy mentioned the option “error” although the option was effectless. This problem has been fixed. The outdated entry has been removed. The reference manual entry of the signature for the callback used with the 'do_low_error' set_system parameter was incorrect. This problem has been fixed.
发行文档
其他
安装
The files HalconUninst.dll and hcheck_cpu.exe in misc/x86-win32 were not used anymore in newer versions of HALCON and have been removed from the file set, including the containing folder. The installation of the package "Deep Learning Core" erroneously did not depend on the installation of "Microsoft Visual C++ Redistributable". This problem has been fixed. The Microsoft Visual C++ redistributable installed with HALCON has been updated to version v14.44.35211.0. This version supports all applications created using Visual Studio 2015, 2017, 2019, or 2022.
第三方库
The NVIDIA CUDA libraries used by HALCONs Deep Learning Framework have been updated to CUDA 12.8 and cuDNN 9.10 on x64-linux and x64-win64 platforms. With this release, the NVIDIA Blackwell architecture is supported. On aarch64 systems, Jetpack 6.2 is required. The TensorRT AI² interface has been updated to use TensorRT 10.12.0 to support the Blackwell architecture as well. Note that this change affects the compatibility. Read more.The Cycles library used in the Scene Engine has been updated to version 4.4.0. mimalloc has been updated to version 2.2.4. HALCON now uses version 8.30a of the CodeMeter Runtime. LibTIFF has been updated to version 4.7.1.
勘误说明
HALCON 旧版本发行说明
Follow this link to read about the changes of previous HALCON versions.
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