🦉 Introduction
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In this section we discribe possibilities to build your own younique smart vision system by using the provided infrastructur of iam camera. |
There are two ways for obtaining an application for iam:
Compile native on iam: small apps or specific Third Party Libraries .
More powerfull: cross compile on a host or server system, including the entire 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
See section SynView Smart App Example using iAMGigEServer for a step-by-step guide.
Example Name | Compiling | Discription | Repository |
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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_remap_synview | nativ/cross | The example remap_synview uses the function cv::remap from OpenCV. After processing the image is sent out via GigE Server. | |
sw_remap_boxfilter_synview | nativ/cross | The example sw_remap_boxfilter_synview uses the cv::remap and cv::boxfilter function from OpenCV. It demonstrates the execution of multiple sw- and hw-functions in different processing threads. The connection between the threads is established with two image-buffers. One is written, the other is read and vice versa. After processing the image is sent out via GigE Server. | |
sw_scaler_synview | nativ/cross | The main processing consists of a simple c-code scaler for horizontal and vertical rescaling. After processing the image is sent out via GigE Server. |
Smart app with hardware accelerations and third party libraries
See section NET Open Camera Concept (OCC) for iam for a step-by-step guide.
ccode_synview | cross | The example code_synview uses HLS hw acceleration written in C-code. The main processing method can be selected among following choices:
After processing the image is sent out via GigE Server. | |
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remap_synview | cross | The example remap_synview uses the hardware acceleration function xf::cv::remap from the Vitis vision library. After processing, the image is sent out via GigE Server. | |
remap_boxfilter_synview | cross | The example remap_boxfilter_synview uses the hw acceleration function xf::cv::remap from the Vitis vision library and the cv::remap and cv::boxfilter function from OpenCV. After processing, the image is sent out via GigE Server. | |
rtl_threshold_synview | cross | This example uses RTL hardware acceleration written in Verilog. After processing, the image is sent out via GigE Server. | |
strm_boxfilter_synview | cross | This example uses HLS hw acceleration with the xfOpenCV function xf::cv::boxfilter in the streaming path. After processing, the image is sent out via GigE Server. | |
strm_rtl_threshold_synview | cross | This example uses RTL hardware acceleration in the streaming path. After processing, the image is sent out via GigE Server. | |
sw_scaler_synview | cross | This example uses no hardware acceleration. After processing the image is sent out via GigE Server. |
Smart app example with machine learning
See section iam ML - Machine Learning ready for a step-by-step guide.
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