Samples

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 Inference Deploy Samples

GDF - Graph Description Format

MIVisionX samples using RunVX

Note:

• To run the samples we need to put MIVisionX executables and libraries into the system path

export PATH=$PATH:/opt/rocm/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/rocm/lib

• To get help on RunVX, use -h option

runvx -h

skintonedetect.gdf

usage:

runvx gdf/skintonedetect.gdf

canny.gdf

usage:

runvx gdf/canny.gdf

skintonedetect-LIVE.gdf

Using a live camera

usage:

runvx -frames:live gdf/skintonedetect-LIVE.gdf

canny-LIVE.gdf

Using a live camera

usage:

runvx -frames:live gdf/canny-LIVE.gdf

OpenCV_orb-LIVE.gdf

Using a live camera

usage:

runvx -frames:live gdf/OpenCV_orb-LIVE.gdf

C/C++ Samples for OpenVX and OpenVX Extensions

MIVisionX samples in C/C++

Canny

usage:

cd c_samples/canny/
cmake .
make
./cannyDetect --image <imageName> 
./cannyDetect --live

Orb Detect

usage:

cd c_samples/opencv_orb/
cmake .
make
./orbDetect

Loom 360 Stitch - Radeon Loom 360 Stitch Samples

MIVisionX samples using LoomShell

Note:

• To run the samples we need to put MIVisionX executables and libraries into the system path

export PATH=$PATH:/opt/rocm/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/rocm/lib

• To get help on loom_shell, use -help option

loom_shell -help

Sample - 1

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

Sample - 2

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

Sample - 3

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

Model Compiler Samples - Run Efficient Inference

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.

Sample-1: Classification Using Pre-Trained ONNX Model

Sample-2: Detection Using Pre-Trained Caffe Model

Sample-3: Classification Using Pre-Trained NNEF Model

Sample-4: Classification Using Pre-Trained Caffe Model

MV Object Detect Samples

This sample shows how to run video decoding and object detection using pre-trained YoloV2 Caffe Model

The sample demonstrates the use of mv_compile utility to do video decoding and inference.