🦉 Introduction
In this section we discribe possibilities to build your own younique smart vison system by using the provided infastructur of iam camera.
There are two ways for obtaining an application for iam:
compile native on iam camera: small apps or specific Third Party Libraries.
and more powerfull: cross compiling on a host or server system. Including whole system optimization and FPGA synthesis.
In this section we introduce our example repositories for both cases. By checking those out our Video Tutorials can assist you.
Example Repositories
There are different example Git repository for the iam camera system.
Smart app example with third party library
Example Name | Compiling | Discription | Repository |
opencv_dice | nativ | This app demonstrates dice cube detection and dots counting for each cube. Using OpenCV 3.4.13. After processing the image is sent out via gige - server. | |
sw_scaler_synview | cross | This example uses no hw acceleration. After processing the image is sent out via gige - server. |
Smart app with hardware accelerations and third party libraries
ccode_synview | cross | The example code_synview uses HLS hw acceleration written in c-code. Main processing method can be selected among following choices:
After processing the image is sent out via gige - server. | |
remap_synview | cross | The example remap_synview uses the hw acceleration function xf::cv::remap from the Vitis vision library. After processing the image is sent out via gige - server. | |
dice_synview | cross | The example dice_synview demonstrates dice cube detection and dots counting for each cube. It uses multiple xfOpenCV functions. | |
rtl_threshold_synview | cross | This example uses RTL hw acceleration written in verilog. After processing the image is sent out via gige - server. | |
strm_rtl_threshold_synview | cross | This example uses RTL hw acceleration in the streaming path. After processing the image is sent out via gige - server. |
Smart app example with machine learning
dpuClassify |
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