opencv_dice

• opencv_dice

https://bitbucket.org/net-gmbh/opencv_dice/

• smart app with hardware accelerations and third party libarys

• ccode_synview

• dice_synview

• remap_synview

https://bitbucket.org/net-gmbh/iam_apps/

ccode_synview

The example code_synview uses HLS hw acceleration written in c-code.

Main processing method can be selected among following choices:

• no processing

• simple c-code sw processing

• a optimized c-code version with ARM NEON instructions

• processing with opencv functions

• hw accelerated processing based on a c-code source

After processing the image is sent out via gige - server.

https://bitbucket.org/net-gmbh/iam_apps/

remap_synview

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.

https://bitbucket.org/net-gmbh/iam_apps/

dice_synview

The example dice_synview demonstrates dice cube detection and dots counting for each cube. It uses multiple xfOpenCV functions.

https://bitbucket.org/net-gmbh/iam_apps/

rtl_threshold_synview

This example uses RTL hw acceleration written in verilog.

After processing the image is sent out via gige - server.

https://bitbucket.org/net-gmbh/iam_apps/

strm_rtl_threshold_synview

This example uses RTL hw acceleration in the streaming path.
The module is located between the sensor interface and the dma.
It consists of a 32-Bit master and 32-Bit slave axi-stream interface for sensor pixel data and a 32-Bit axi-lite control interface for register access.

After processing the image is sent out via gige - server.

https://bitbucket.org/net-gmbh/iam_apps/

sw_scaler_synview

This example uses no hw acceleration.
But it uses the same infrastructure like other hw accelerated examples.
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.

https://bitbucket.org/net-gmbh/iam_apps/

dpuClassify

• Reference application for image classifier training and execution.

• Different convolutional models are available (VGG, DenseNet, ResNet, Inception)

• Training code for transfere learning with Keras and Tensorflow

• Vitis Ai model conversion code

• iam application for image classification

https://bitbucket.org/net-gmbh/iam_ml/