MVTec Halcon 9编程指南：商业视觉开发权威手册

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MVTec Halcon 9 Handbook 是由 MVTec Software GmbH 出版的一本详细指南，专注于介绍 MVTec HALCON 软件编程语言接口的使用方法。这是一本针对机器视觉领域的专业文档，适用于商业环境中的视觉应用开发。自 2003 年第一版发布以来，该手册经历了多次更新，反映了软件的不断演进，从最初的 HALCON 7.0 版本到后来的 9.0 版本，直至 2010 年的 9.0.2 版本。在 Halcon 9 的版本中，读者可以期待丰富的功能和优化，包括对 Microsoft Windows 系列操作系统的兼容性，如 Windows NT、Windows 2000、Windows XP、Windows Server 2003、Windows Vista 和 Windows Server。该手册强调了版权保护，提到了多项已授权和待授权的专利，体现了 MVTec 在机器视觉技术领域的创新和法律保护。 Halcon 是一款广泛应用于工业自动化和机器视觉的应用程序，它提供了一系列强大的工具和算法，帮助工程师和开发者设计和实现高效的图像处理和分析系统。通过编程语言接口，用户能够创建自定义的机器视觉解决方案，包括物体检测、识别、测量、定位以及质量控制等方面。手册详细讲解了如何使用这些接口进行任务编程，涉及的知识点包括但不限于： 1. **基础概念**：介绍了Halcon的基本架构，包括核心模块（如图像采集、预处理、特征提取、匹配等）和高级功能（如模板匹配、形状分析、光学字符识别等）。 2. **API使用**：涵盖了HALCON的各种编程接口，如C++、Python、Visual Basic等，以及它们之间的异同和适用场景。 3. **示例代码**：手册提供了大量的实用代码片段和案例，帮助读者快速上手并理解不同功能的实现方式。 4. **调试与优化**：讨论了错误处理、性能调优以及如何解决常见问题的方法，确保用户的项目顺利运行。 5. **最新特性**：每一版更新都会包含新的功能和技术，例如9.0版本可能引入了更先进的机器学习算法或改进的图像处理技术。 6. **版本变迁**：历史版本的比较可以帮助读者了解新版本的进步，以及如何迁移旧代码以适应新的软件环境。 7. **安全性与兼容性**：手册还关注了软件的安全性和与其他硬件和软件平台的兼容性，确保用户能够在各种环境中稳定部署。 MVTec Halcon 9 Handbook 是一个宝贵的资源库，不仅适合经验丰富的开发者深入学习和探索，也是新手入门机器视觉编程的权威指南。通过掌握这本书的内容，读者可以有效地利用 HALCON 进行高效且精确的视觉应用开发，推动企业的自动化和智能制造进程。

16 Basic Information About Programming with HALCON

HALCONARCH Operating System, Processor Compiler

x86-win32

Windows

NT/2000/XP/2003/Vista/2008/7,

on Intel Pentium or compatible

Visual Studio 6.0 or

higher

x86sse2-win32

Windows

NT/2000/XP/2003/Vista/2008/7,

on x86 processor with SSE2 extension,

e.g., Intel Pentium 4 / AMD Athlon64 or

newer

Visual Studio 6.0 or

higher

x64-win64

Windows XP/2003/Vista/2008/7 x64

Edition,

on Intel EM64T or AMD64

Visual Studio 2005

or higher

x86-linux2.4-gcc40

Linux, Kernel 2.4 or higher,

libc.so.6 (GLIBC_2.3.2 or higher),

libstdc++.so.6 (GLIBCXX_3.4 or higher),

on Intel Pentium or compatible

gcc 3.4/4.x

x86sse2-linux2.4-gcc40

Linux, Kernel 2.4 or higher,

libc.so.6 (GLIBC_2.3.2 or higher),

libstdc++.so.6 (GLIBCXX_3.4 or higher),

on x86 processor with SSE2 extension,

e.g.,

Intel Pentium 4 / AMD Athlon64 or newer

gcc 3.4/4.x

x64-linux2.4-gcc40

Linux x86_64, Kernel 2.4 or higher,

libc.so.6 (GLIBC_2.3.4 or higher),

libstdc++.so.6 (GLIBCXX_3.4 or higher),

on Intel EM64T or AMD64

gcc 3.4/4.x

x86-linux2.4-gcc33

∗

Linux, Kernel 2.4 or higher,

libc.so.6 (GLIBC_2.3.2 or higher),

libstdc++.so.5 (GLIBCPP_3.2 or higher),

on Intel Pentium or compatible

gcc 3.2/3.3

x86sse2-linux2.4-gcc33

∗

Linux, Kernel 2.4 or higher,

libc.so.6 (GLIBC_2.3.2 or higher),

libstdc++.so.5 (GLIBCPP_3.2 or higher),

on x86 processor with SSE2 extension,

e.g.,

Intel Pentium 4 / AMD Athlon64 or newer

gcc 3.2/3.3

x64-linux2.4-gcc33

∗

Linux x86_64, Kernel 2.4 or higher,

libc.so.6 (GLIBC_2.3.4 or higher),

libstdc++.so.5 (GLIBCPP_3.2 or higher),

on Intel EM64T or AMD64

gcc 3.2/3.3

sparc-sun-solaris9

Solaris 9,

on UltraSPARC Workstations

Sun Studio 9 or

higher

(*) corresponding version only available via download

Table 1.3: Values of HALCONARCH and detailed system requirements.

18 Parallel Programming and HALCON

helps to evaluate dependencies between an operator’s input parameter characteristics (e.g. the size of an

input image) and the efﬁciency of its parallel processing. This examination may take up to 10 minutes,

depending on your computer.

The extracted information is stored in the registry (under Windows) or in the ﬁle .halcon_par_info

in the directory $HALCONROOT (under Linux/UNIX). Please note, that on some operating sys-

tems you need special privileges to initialize HALCON successfully, otherwise the operator

check_par_hw_potential is not able to store the extracted information. Under Windows 2000, you

need administrator privileges or the group privileges Power User. Under Linux/UNIX, per default

only the user who installed HALCON can create (or modify) the ﬁle .halcon_par_info. Please note,

that the operator check_par_hw_potential returns without an error if it cannot store the extracted

information. In contrast, the program hcheck_parallel checks privileges and writing permissions be-

forehand and returns with a corresponding error message. Note that in order to execute command line!

tools with administrator privileges under Windows Vista and higher, you will need to select “Run

as Administrator” (even if you are already logged in as administrator).

2.1.2 The Three Methods of Automatic Parallelization

For the automatic parallelization of operators, HALCON exploits data parallelism, i.e., the property that

parts of the input data of an operator can be processed independently of each other. Data parallelism can

be found at three levels:

1. tuple level

If an operator is called with iconic input parameters containing tuples, i.e., arrays of images,

regions, or XLDs, it can be parallelized by distributing the tuple elements, i.e., the individual

images, regions, or XLDs, on parallel threads. This method requires that all input parameters

contain the same number of tuple elements (or contain a single iconic object or value).

2. channel level

If an operator is called with input images containing multiple channels, it can be parallelized by

distributing the channels on parallel threads. This method requires that all input image objects

contain the same number of channels or a single channel image.

3. domain level

Every operator can be parallelized by dividing its domain and distributing its parts on parallel

threads.

The description of a HALCON operator in the Reference Manuals contains an entry called ’Paralleliza-

tion Information’, which speciﬁes its behavior when using HALCON on a multi-core or multi-processor

hardware. This entry indicates whether the operator will be automatically parallelized by HALCON and

by which method (tuple, channel, or domain).

2.2 Parallel Programming Using HALCON

HALCON supports parallel programming by being thread-safe and reentrant, i.e., different threads can

call HALCON operators simultaneously without having to wait. However, not all operators are fully

2.2 Parallel Programming 19

reentrant. This section takes a closer look at the reentrancy of HALCON. Furthermore, it points out

issues that should be kept in mind when writing parallel programs that use HALCON.

The example program example_multithreaded1.c in the directory example\c shows how to use

multithreading to extract different types of components on a board in parallel using HALCON/C.

2.2.1 A Closer Look at Reentrancy

In fact there are different “levels” of reentrancy for HALCON operators:

1. reentrant

An operator is fully reentrant if it can be called by multiple threads simultaneously independent

of the data it is called with.

Please note that you must take special care when multiple threads use the same data objects, e.g.,

the same image variable. In this case, you must synchronize the access to this variable manually

using the corresponding parallel programming mechanisms (mutexes, semaphores). Better still

is to avoid such cases as far as possible, i.e., to use local variables. Note that this is no special

problem of HALCON but of parallel programming in general.

2. local

This level of reentrancy is only relevant under Windows.

Under Windows, operators marked as local should be called only from the thread that instantiates

the corresponding objects. Typical examples are operators that use graphical I/O functions, which

should only be used in the main thread. The reason is that under Windows, there exists a direct

mapping between program threads and graphical elements, such as windows, dialog boxes or

button controls. In short, a graphical element only exists in the context of its associated thread.

This can cause severe problems (for example, hang the application) if a thread tries to perform

user interactions via graphical elements that belong to another thread. For example, you may get

a deadlock if one thread opens a window via open_window and another thread tries to get input

from this window via draw_circle.

3. single write multiple read

A certain group of operators should be called simultaneously only if the different calling threads

work on different data. For example, threads should not try to modify the same template for

pattern matching simultaneously by calling adapt_template with the same handle. Exactly the

same applies to the case that one thread should not modify a data set that is simultaneously read

by another thread. Other groups of operators with this behavior are concerned with ﬁle I/O (e.g.,

write_image – read_image, fwrite_string – fread_string but also non-HALCON ﬁle

commands) or background estimation (e.g., update_bg_esti – give_bg_esti).

As this thread behavior is not recommended quite generally, HALCON does not actively prevent

it and thus saves overhead. This means that if you (accidentally) call such operators simultane-

ously with the same data no thread will block, but you might get unwelcome effects.

4. mutual exclusive

Some operators cannot be called simultaneously by multiple threads but may be executed in

parallel to other HALCON operators. Examples for mutual exclusive operators are com-

bine_roads_xld, pouring, or concat_ocr_trainf.

General Issues

20 Parallel Programming and HALCON

5. completely exclusive

A group of operators is executed exclusively by HALCON, i.e., while such an operator is exe-

cuted, all other threads cannot call another HALCON operator. Examples are all OCR/OCV

operators that modify OCR classiﬁers, all operators that create or delete ﬁles, and the operator

reset_obj_db. The latter is switched off by default, together with the HALCON database of

iconic objects. If, however, you switch the database on via set_system, you must assure that all

operators that are to be executed before reset_obj_db is called have already ﬁnished, because

this operator resets HALCON and therefore strongly inﬂuences its behavior.

As mentioned already, the description of a HALCON operator in the Reference Manuals contains an

entry called ’Parallelization Information’, which speciﬁes its behavior when using HALCON. This entry

speciﬁes the level of reentrancy as described above.

2.2.2 Style Guide

The following tips are useful for multithreaded programming in general:

• Number of threads ≤ number of processors or cores

If you use more threads than there are processors or cores, your application might actually be

slower than before because of the synchronization overhead. Note that when counting threads only

the so-called worker threads are relevant, i.e., threads that are running / working continuously.

• Local variables

If possible, use local variables, i.e., instantiate variables in the thread that uses them. If multiple

threads use the same variable, you must synchronize their access to the variable using the appro-

priate parallel programming constructs (mutexes, semaphores; please refer to the documentation

of your programming language for details).

An exception are COM applications that use the so-called “Single-Threaded Apartment” mode,

because here calls are synchronized automatically. This mode, however, has other disadvantages,

as described in more detail in the tip for HALCON/COM below.

When using HALCON, please keep the following tips in mind:

• Initialization

Before calling HALCON operators in parallel in a multithreaded program, you have to call one

operator exclusively. This is necessary to allow HALCON to initialize its internal data structures.

• I/O and visualization

Under Windows, use I/O operators (including graphics operators like open_window or

disp_image) locally, i.e., in the same thread, otherwise you might get a deadlock. This means

that you should not open a window in one thread and request a user interaction in it from another

thread. In the Reference Manual, these operators are marked as locally reentrant (see section 2.2.1

on page 19).

For HALCON/.NET, this typically means that all visualization must be performed in the main

thread, because HWindowControl (see section 10.3 on page 90) is instantiated in the main

thread. However, other threads can also “delegate” the display to the main thread as shown,

e.g., in the example program examples\c#\MultiThreading or examples\hdevengine\c#\

MultiThreading (the latter is described in detail in section 22.2.5.1 on page 198, the delegation

of display on page 205).

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