Object Detection Application

Table of Contents

• Introduction
• Supported plaforms
• Data flow
• Steps to run the application on J721e EVM
• Steps to run the application on PC Linux x86_64
• Sample Output

Introduction

This example shows how to write an OpenVx application which runs pre-processing, post-processing nodes on C66x DSP and runs TIDL node on C7x DSP for Object Detection. The demo is validated on Pelee network available at, https://github.com/Robert-JunWang/Pelee. The trained network is imported using TIDL import tool and raw NV12 input files of resolution 1024x512 is read from SD card or USB 3.0 typec-pen-drive or PCIe memory. The input files are scaled using VPAC multi-scaler MSC to produce a DL resolution of 1024x512 as required by the Pelee network. As a pre-processing step, the scaled NV12 resolution is converted to RGB format and padded as required on C66x_1 DSP. The pre-processed input is given to TIDL running on C7x DSP for inference. As a post-processing step, the TIDL output is read and top detections are rendered as 2D boxes on the provided image on C66x_2 DSP. The final ouput is blended with system performance statistics and displayed on a 1920x1080 screen.

Supported plaforms

Platform	Linux x86_64	Linux+RTOS mode	QNX+RTOS mode	SoC
Support	YES	YES	YES	J721e

Data flow

Steps to run the application on J721e EVM

1. Build the application and related libraries as mentioned in Build Instructions for Linux+TI-RTOS mode
2. A sample "app_od.cfg" for Object detection is provided under "/opt/vision_apps/" on the rootfs partition.
3. Create a folder and keep all the .yuv files used for detection, there are a few images under "/opt/vision_apps/test_data/psdkra/tidl_demo_images".
4. In case a different network is choosen, update the dl_size accordingly for network input resolution.
5. Run the app as shown below

   ./run_app_tidl_od.sh

Steps to run the application on PC Linux x86_64

1. Build the application and related libraries as mentioned in Build Instructions for PC emulation mode
2. A sample "app_od.cfg" for Object detection is provided under "${PSDKR_PATH}/vision_apps/apps/dl_demos/app_tidl_od/config" folder.
3. Create a folder and keep all the .yuv files used for detection, there are a few images under "/opt/vision_apps/test_data/psdkra/tidl_demo_images".
4. In case a different network is chosen, update the dl_size accordingly for network input resolution.
5. Create an output directory

   mkdir app_tidl_od_out

6. Run the app as shown below

   ./vx_app_tidl_od --cfg ${PSDKR_PATH}/vision_apps/apps/dl_demos/app_tidl_od/config/app_od.cfg

7. The output will be written in "app_tidl_od_out" folder in .yuv (NV12) format.

Sample Output

Shown below is a example input and its corresponding output