# 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](#cc-samples-for-openvx-and-openvx-extensions)
* [GDF - Graph Description Format Samples](#gdf---graph-description-format)
* [Loom 360 Stitch - Radeon Loom 360 Stitch Samples](#loom-360-stitch---radeon-loom-360-stitch-samples)
* [Model Compiler Samples - Run Efficient Inference](#model-compiler-samples---run-efficient-inference)

## GDF - Graph Description Format

MIVisionX samples using [RunVX](../utilities/runvx#amd-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 `-h` option

``` 
runvx -h
```

### skintonedetect.gdf

<p align="center"><img width="90%" src="images/skinToneDetect_image.PNG" /></p>

usage:

``` 
runvx gdf/skintonedetect.gdf
```

### canny.gdf

<p align="center"><img width="90%" src="images/canny_image.PNG" /></p>

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](../utilities/loom_shell#radeon-loomshell)

[![Loom Stitch](../docs/images/loom-4.png)](https://youtu.be/E8pPU04iZjw)

**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 `-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

<p align="center"><img width="80%" src="../docs/images/modelCompilerWorkflow.png" /></p>

In this [sample](model_compiler_samples#mivisionx-model-compiler-samples), 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](model_compiler_samples#mivisionx-model-compiler-samples), 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](model_compiler_samples#sample-1---classification-using-pre-trained-onnx-model)

### [Sample-2: Detection Using Pre-Trained Caffe Model](model_compiler_samples#sample-2---detection-using-pre-trained-caffe-model) 

### [Sample-3: Classification Using Pre-Trained NNEF Model](model_compiler_samples#sample-3---classification-using-pre-trained-nnef-model)

### [Sample-4: Classification Using Pre-Trained Caffe Model](model_compiler_samples#sample-4---classification-using-pre-trained-caffe-model)