MIVisionX samples using OpenVX and OpenVX extensions. In the samples below we will learn how to run computer vision, inference, and a combination of computer vision & inference efficiently on target hardware.
- C/C++ Samples for OpenVX and OpenVX Extensions
- GDF - Graph Description Format Samples
- Loom 360 Stitch - Radeon Loom 360 Stitch Samples
- Model Compiler Samples - Run Efficient Inference
MIVisionX samples using RunVX
Note:
- To run the samples we need to put MIVisionX executables and libraries into the system path
export PATH=$PATH:/opt/rocm/mivisionx/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/rocm/mivisionx/lib
- To get help on RunVX, use
-hoption
runvx -h
usage:
runvx gdf/skintonedetect.gdf
usage:
runvx gdf/canny.gdf
Using a live camera
usage:
runvx -frames:live gdf/skintonedetect-LIVE.gdf
Using a live camera
usage:
runvx -frames:live gdf/canny-LIVE.gdf
Using live camera
usage:
runvx -frames:live gdf/OpenCV_orb-LIVE.gdf
MIVisionX samples in C/C++
usage:
cd c_samples/canny/
cmake .
make
./cannyDetect --image <imageName>
./cannyDetect --live
usage:
cd c_samples/opencv_orb/
cmake .
make
./orbDetect
MIVisionX samples using LoomShell
Note:
- To run the samples we need to put MIVisionX executables and libraries into the system path
export PATH=$PATH:/opt/rocm/mivisionx/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/rocm/mivisionx/lib
- To get help on loom_shell, use
-helpoption
loom_shell -help
usage:
- Get Data for the stitch
cd loom_360_stitch/sample-1/
python loomStitch-sample1-get-data.py
- Run Loom Shell Script to generate the 360 Image
loom_shell loomStitch-sample1.txt
- Expected Output
loom_shell loomStitch-sample1.txt
loom_shell 0.9.8 [loomsl 0.9.8]
... processing commands from loomStitch-sample1.txt
..ls_context context[1] created
..lsCreateContext: created context context[0]
..lsSetOutputConfig: successful for context[0]
..lsSetCameraConfig: successful for context[0]
OK: OpenVX using GPU device#0 (gfx906+sram-ecc) [OpenCL 2.0 ] [SvmCaps 0 0]
..lsInitialize: successful for context[0] (1380.383 ms)
..cl_mem mem[2] created
..cl_context opencl_context[1] created
..lsGetOpenCLContext: get OpenCL context opencl_context[0] from context[0]
OK: loaded cam00.bmp
OK: loaded cam01.bmp
OK: loaded cam02.bmp
OK: loaded cam03.bmp
..lsSetCameraBuffer: set OpenCL buffer mem[0] for context[0]
..lsSetOutputBuffer: set OpenCL buffer mem[1] for context[0]
OK: run: executed for 100 frames
OK: run: Time: 0.919 ms (min); 1.004 ms (avg); 1.238 ms (max); 1.212 ms (1st-frame) of 100 frames
OK: created LoomOutputStitch.bmp
> stitch graph profile
COUNT,tmp(ms),avg(ms),min(ms),max(ms),DEV,KERNEL
100, 0.965, 1.005, 0.918, 1.237,CPU,GRAPH
100, 0.959, 0.999, 0.915, 1.234,GPU,com.amd.loomsl.warp
100, 0.955, 0.994, 0.908, 1.232,GPU,com.amd.loomsl.merge
OK: OpenCL buffer usage: 324221600, 9/9
..lsReleaseContext: released context context[0]
... exit from loomStitch-sample1.txt
Note: The stitched output image is saved as LoomOutputStitch.bmp
usage:
- Get Data for the stitch
cd loom_360_stitch/sample-2/
python loomStitch-sample2-get-data.py
- Run Loom Shell Script to generate the 360 Image
loom_shell loomStitch-sample2.txt
usage:
- Get Data for the stitch
cd loom_360_stitch/sample-3/
python loomStitch-sample3-get-data.py
- Run Loom Shell Script to generate the 360 Image
loom_shell loomStitch-sample3.txt
In this sample, we will learn how to run inference efficiently using OpenVX and OpenVX Extensions. The sample will go over each step required to convert a pre-trained neural net model into an OpenVX Graph and run this graph efficiently on any target hardware. In this sample, we will also learn about AMD MIVisionX which delivers open source implementation of OpenVX and OpenVX Extensions along with MIVisionX Neural Net Model Compiler & Optimizer.