The industrial camera manufacturer IDS Imaging Development Systems is currently developing a “Smart GenICam” vision app.
Customers can also integrate and use camera features they have created themselves in a vision-compliant manner.
The function will be available from the second quarter of 2019.
The company told MVPro Magazine that “…when users want to execute self-created functions on intelligent cameras, they face two challenges: on the one hand, they often only have access via proprietary interfaces, which makes it more difficult to process data in third-party applications; on the other hand, only few models offer a high-speed data interface such as GigE Vision.
“However, industrial applications often require both. IDS Imaging Development Systems GmbH therefore combines standard vision industrial cameras with the vision app-based IDS NXT concept. Thanks to a “Smart GenICam App”, data generated by customer-specific programming can be used in exactly the same way as if they were part of the manufacturer-independent standard functional scope of the industrial camera.”
GenICam, as a camera interface, provides standardised camera functions as well as the so called Feature Node Map (camera API) which allows camera manufacturers to integrate special functions.
Users simply upload the new camera function to their IDS NXT camera as a vision app. The camera firmware then integrates the application code into the range of functions. Configuration, control and results of the vision app can easily be processed via the camera’s XML description file in any GenICam-compliant third-party application, such as HALCON, using the full GigE bandwidth at the same time.
The use of vision-compliant applications on the IDS NXT platform also means that application processes can be scaled between camera and host PC. As powerful embedded systems, models such as IDS NXT rio and rome offer the option of partially or completely shifting decision processes from the host PC to the camera (“on the edge”). Advantages include, said IDS, lower network load, hardware-accelerated processing, lower energy consumption and lower system costs.