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- Further information on the installation procedure can be obtained from the manual Getting Started with HALCON. Installing HALCON 6.0 on UNIX systems. Insert the CD in your CD-ROM drive and mount the corresponding device. You probably need root privileges to mount the device correctly.
- Navigate the list of programs until you find MVTec HALCON 13.0 or simply click the Search field and type in 'MVTec HALCON 13.0'. If it is installed on your PC the MVTec HALCON.
In order to efficiently access the performance features of the NVIDIA and Raspberry Pi 4 platforms for embedded vision applications, The Imaging Source offers a portfolio of MIPI CSI-2 board cameras. Many embedded machine vision applications (especially multi-camera applications) require cable lengths longer than the maximum 20 cm reached with MIPI CSI-2 cameras. Using the FPD-Link III bridge, cable lengths of up to 15m can be achieved. Image data is transmitted via a thin (Ø 2.8 mm) coaxial cable at up to 4.16 Gbps, with image data, control commands and power supply being transmitted simultaneously. The bandwidth is sufficient, for example, for the transfer of image data from a 5-MP camera at 30 frames/s. Developers can choose between MIPI-CSI-2 and FPD-Link-III board cameras as well as FPD-Link-III cameras with IP67-rated housing. The compact cameras are available as monochrome and color versions with the latest CMOS image sensors from Sony and On Semiconductor (global shutter and rolling shutter with resolutions from 0.3 MP [VGA] - 8.3 MP).
MVTec's software runs smoothly on embedded devices and thus enables high-performing embedded vision products, available on the market as bundles or standard portings. Here, HALCON and MERLIC allow for fast implementation and maximum compatibility.
Why Choose MIPI CSI-2 or FPD-Link-III Camera Interfaces?
The Imaging Source provides MIPI CSI-2 and FPD-Link-III camera carrier boards with up to 6 camera inputs (platform dependent) for connecting the board cameras to the NVIDIA™ Jetson Nano®, Xavier NX, AGX Xavier and Raspberry Pi 4. Some users might be left wondering what the reasons are for using MIPI CSI-2 or FPD-Link-III cameras instead of USB-3 or GigE cameras. The answer lies in the hardware features of the embedded platforms themselves, namely the hardware-accelerated image signal processor (ISP). The embedded platforms' MIPI-CSI-2 interfaces are directly connected to the ISP to avoid latencies and data conversions. The Imaging Source's MIPI-CSI-2 cameras have been specifically designed to pass the raw image data directly to the interface. Since these embedded board cameras include only essential functionality, they are particularly cost effective. The ISP handles hardware-accelerated operations such as de-bayering, color correction, color space conversion, white balance, lens correction and image data compression (e.g. H.264/H.256).
Platforms Bring the Power of AI to Embedded Vision
In addition to their ISPs, the aforementioned embedded computers from NVIDIA (e.g. NVIDIA Jetson Nano, Xavier NX* and AGX Xavier*) offer a GPU with CUDA cores and several MIPI-CSI-2 camera interfaces which make them predestined for AI and demanding machine vision applications. The NVIDIA platforms are capable of running several neural networks in parallel to implement image segmentation, image classification and object recognition. The NVIDIA SDK 'JetPack' supports all NVIDIA Jetson-based embedded platforms.
NVIDIA offers extensive software libraries for deep learning and for image and video processing. The Imaging Source provides the corresponding camera drivers, which are seamlessly integrated into the NVIDIA software framework. This allows image data to be transferred directly to a pre-trained deep learning module.
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For less demanding image processing tasks, the Raspberry Pi 4 with a MIPI-CSI-2 camera interface and an ISP is also an excellent choice. Here too, the MIPI-CSI-2 interface is directly connected to the ISP. The Raspberry Pi 4, for example, is capable of compressing high-resolution H.264 images and sending them via Ethernet or WLAN.
The NVIDIA and Raspberry Pi embedded platforms are supported by a large open source and maker communities. Support questions can be posted in appropriate forums, where developers can exchange information with each other. NVIDIA also offers free online training courses on the subject of deep learning.
*The Imaging Source's latest embedded products for NVIDIA's Xavier NX and AGX Xavier platforms will be available in Q4 2020. Please contact us if you would like to be informed as the products become available.
The above article was published in the September 2020 edition of the German-language industry journal Markt&Technik under the title, FPD-Link III ergänzt MIPI CSI-2.
In this proof of concept, we want to show you how to easily integrate a QR code reader on an embedded device using MVTec HALCON and HDevEngine. In our example, we used a Raspberry Pi 3 Model B with the standard camera module v2.
Prerequisites
- Rasperry Pi 3 running Raspbian
- Raspberry Camera incl. a mounting device to attach it to the RPi case
- MVTec HALCON to create the machine vision application on your desktop machine (You can download a free trial version atwww.halcon.com/now)
- MVTec HALCON for ARM-based Platforms (www.mvtec.com/products/halcon-embedded/arm/)
- Video4Linux2 interface (use the driver that is included in the HALCON for ARM-based Platforms installer)
- Any ftp client or usb thumb drive for transfering the source files onto the RPi
- Makefile to build the application for the desired target platform (ARMv8)
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Creating the machine vision part with HDevelop
In the final application, we want to use two HDevelop procedures: One that initializes the camera and the data code reader, and one that finds the data codes (in our case, QR codes) and returns a result message. Let's have a look how easily these procedures can be created using HDevelop.
First, we create the procedure init_acquisition.hdvp. Here, we open and configure the image acquisition device. The output variable AcqHandle can later be used to grab images of the camera.
open_framegrabber ('Video4Linux2', 4, 4, 0, 0, 0, 0, 'default', -1, 'gray', -1, 'default', 'default', 'default', -1, -1, AcqHandle)
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Secondly, we create the handle for the data code reader. We specify that we want to detect QR codes.
create_data_code_2d_model ('QR Code', [], [], DataCodeHandle)
Thus, the output of this procedure are the handles AcqHandle and DataCodeHandle.
Then, we create the procedure find_qr_code.hdvp. Here, we grab an image using the AcqHandle.
grab_image (Image, AcqHandle)
Then, we find QR codes in this image using the DataCodeHandle.
find_data_code_2d (Image, SymbolXLDs, DataCodeHandle, [], [], ResultHandles, DecodedDataStrings)
The rest of the program simply assembles the output message, that we want to return in a shell.
get_system_time (MSecond, Second, Minute, Hour, Day, YDay, Month, Year) Date := Year + '-' + Month + '-' + Day $ '.2d' + ' ' + Hour $ '.2d' + ':' + Minute $ '.2d' + ':' + Second $ '.2d' if (DecodedDataStrings []) ResultMessage := 'No QR code found.' + ' (' + Date + ')' else ResultMessage := 'Decoded QR code(s): ' + DecodedDataStrings + ' (' + Date + ')' endif
Incorporating these procedures in a C++ project using HDevEngine
In the C++ project, we use these HDevelop procedures directly, with HDevEngine. First, we define the location of the procedures.
std::string ext_proc_path = './hdevelop_procedures'; HDevEngine().SetProcedurePath(ext_proc_path.c_str());
Then, we call the first procedure and get the output variables.
HTuple acq_handle; HTuple data_code_handle; InitializeAcquisition(acq_handle, data_code_handle);
The function InitializeAcquisition looks like this:
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HDevProcedure proc('init_acquisition'); HDevProcedureCall proc_call(proc); proc_call.Execute(); proc_call.GetOutputCtrlParamTuple('AcqHandle', &acq_handle); proc_call.GetOutputCtrlParamTuple('DataCodeHandle', &data_code_handle);
Similarly, we call the second procedure in a loop to find the QR codes and return a result message.
Deck software for mac. while (keepRunning) { HString result_message; FindQRCodes(proc, acq_handle, data_code_handle, result_message); printf(result_message); printf('n'); }
Building the application
When building and running the application, we need to set some parameters. Thus, we created a simple setup.env file.
export HALCONROOT=$(pwd)/halcon export HALCONARCH=armv7ahfneon-linux-gcc48 export PATH=$HALCONROOT/bin/$HALCONARCH:$PATH export LD_LIBRARY_PATH=$HALCONROOT/lib/$HALCONARCH
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export NATIVE=yes export DISPLAY=192.168.0.1:0 export XLOCALEDIR=$(pwd)/arm-xilinx-linux-gnueabi/share/X11/locale export LD_LIBRARY_PATH=$(pwd)/arm-xilinx-linux-gnueabi/lib:$LD_LIBRARY_PATH
# To use the Video4Linux API, load the driver sudo modprobe bcm2835-v4l2
The result
When executing the application, the output looks something like this:
Note: If more QR codes are within the camera's field of view, the QR code reader uses the code with the best quality.
The Raspberry Pi 3 with the camera module