# Applications

MIVisionX has a number of applications built on top of OpenVX and its modules, it uses AMD optimized libraries to build applications which can be used as prototypes or used as models to develop products.

## Cloud Application
This sample [application](./cloud_inference#cloud-inference-application) does inference using a client-server system.

[![Radeon Inference](../docs/images/inferenceVideo.png)](http://www.youtube.com/watch?v=0GLmnrpMSYs)

## DG Test
This sample [application](./dg_test#amd-dgtest) is used to recognize hand written digits.

<p align="center">
  <img src="../docs/images/digits1.png">
  <img src="../docs/images/digits2.png">
  <img src="../docs/images/DGtest.png">
</p>

## MIVisionX OpenVX Classsification
This sample [application](./mivisionx_openvx_classifier/README.md) shows how to run supported pre-trained caffe models with MIVisionX RunTime.

<p align="center"> <img src="./mivisionx_openvx_classifier/data/MIVisionX-ImageClassification.png"></p>

## MIVisionX WinML Classification
This sample [application](./mivisionx_winml_classifier/README.md) shows how to run supported ONNX models with MIVisionX RunTime on Windows.

<p align="center"> <img src="./mivisionx_winml_classifier/images/MIVisionX-ImageClassification-WinML.png"> </p>

## MIVisionX WinML YoloV2
This sample [application](./mivisionx_winml_yolov2#yolov2-using-amd-winml-extension) shows how to run tiny yolov2(20 classes) with MIVisionX RunTime on Windows.

<p align="center"> <img src="./mivisionx_winml_yolov2/image/cat-yolo.jpg"> </p>

## rali-unittests
Simple application for running unit tests for RALI library.
Usage: rali-unittests input-image-folder output-image-name augmentation-number display-on-off gpu-or-cpu rgb-or-grayscale
Example: rali-unittests input-images/ output-image-test.png 0 1 1 1
The above example will run the first augmentation in the app on the image located in input-images and output the result to
output-image-test.png. It will be shown in the OpenCV display window, it will use the GPU and it will be in color. 
Example 2: rali-unittests input-images/ output-image-test2.png 0 1 0 0
Same as the first example but the processing will be done on the CPU and the image will be in grayscale.

## External Application

* [MIVisionX-Classifier](https://github.com/kiritigowda/MIVisionX-Classifier) - This application runs know CNN image classifiers on live/pre-recorded video stream.
* [YOLOv2](https://github.com/kiritigowda/YoloV2NCS) - Run tiny yolov2 (20 classes) with AMD's MIVisionX
* [Traffic Vision](https://github.com/srohit0/trafficVision#traffic-vision) - This app detects cars/buses in live traffic at a phenomenal 50 frames/sec with HD resolution (1920x1080) using deep learning network Yolo-V2. The model used in the app is optimized for inferencing performance on AMD-GPUs using MIVisionX toolkit.
* [RGBDSLAMv2-MIVisionX](https://github.com/ICURO-AI-LAB/RGBDSLAMv2-MIVisionX) - This is an implementation of RGBDSLAM_V2 that utilizes AMD MIVisionX for feature detection and ROCm OpenCL for offloading computations to Radeon GPUs. This application is used to create 3D maps using RGB-D Cameras.