DO YOU NEED ASSISTANCE
RECOMMENDED HARDWARE
Runtime
- For typical requirements - fast processing and convenient usage we recommend to use NVIDIA GPU with enough memory. A rough estimation is 3GB per deep-learning module. To be on the safe side we recommend to use NVIDIA GeForce® RTX 2080 Ti.
- For price sensitive use-cases it is possible to use cheaper NVIDIA GPU.
- For embedded use-cases we support embedded hardware too - e.g. ARM based devices like NVIDIA TX2 or Xavier. Contact us for more information.
Development
- For typical requirements - fast processing and convenient usage we recommend to use NVIDIA GPU with enough memory. A rough estimation is 3GB per deep-learning module. To be on the safe side we recommend to use NVIDIA GeForce® RTX 2080 Ti.
HOW TO INTEGRATE PEKAT VISION
Camera is directly connected to PEKAT VISION (GenICam camera)
This option is the easiest. PEKAT VISION processes input from the camera in a loop. You have to enable the camera and set what should happen after evaluation (output).
PEKAT VISION is middleware
You use additional software for capturing images or processing. This method has 3 steps and requires programming knowledge.
- Send images to PEKAT
- You can use SDK from https://github.com/pekat-vision/. We support Python, C# and C++ programming languages.
- You can use LabVIEW plugin http://sine.ni.com/nips/cds/view/p/lang/en/nid/218329
- You can send images to HTTP API. We created some examples.
- Pekat processes the image. It returns data structure (JSON) which describes the result.
- Your script from step 1 processes the response. In the response, there could be information about objects which were found or overall evaluation. You can use the inspection tab for debugging.
