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Introduction

GTC2015_stitching project is a simple Panorama Stitching pipeline that demonstrates various CPU and GPU optimizations possible on the Jetson TK1 platform.

The original implementation is written in C++ for Tegra CPU. We will measure its performance and then try to improve it using the CUDA technology. We will see that even the NEON-optimized code is less efficient than GPU implementation. However, memory transfers are quite expensive, so it is not enough to just replace bottlenecks and better to port the entire pipeline to the GPU.

Prerequisites

For Unix: SSH to the Jetson TK1 board (user: ubuntu, password: ubuntu):

$ ssh ubuntu@<board address>

For Windows:

  1. Go to www.putty.org, click "You can download PuTTY here", download putty.zip (NOT .exe, but .zip)
  2. Extract putty.zip, open the extracted directory and run putty.exe
  3. Enter board address and press "Open"
  4. Enter user name and password (user: ubuntu, password: ubuntu).

For ALL OS: open yet another terminal on desktop for files sharing. Use Putty tools folder on Windows to use pscp tool without environment changes.

Get the source code and input images from GitHub:

$ wget https://github.com/Itseez/gtc-2015-lab/archive/master.zip
$ unzip master.zip

Turn off power management on Tegra in order to get reliable performance measurements:

$ cd ./gtc-2015-lab-master
$ sudo ./freqScale_l4t.sh

Build & Run

Execute the following commands on your Jetson TK1 board:

$ make -j3
$ ./stitching ../images/*.jpg

You will see the performance report in your console. You can also check the final panorama exported to the result.jpg file.

Working on the lab

By default the application works on CPU. And it has three major bottleneck functions:

  • Seam finding cv::detail::VoronoiSeamFinder::find
  • Composition cv::detail::SphericalWarper::warp and detail::MultiBandBlender::blend

Source code contains time measurements that allow to detect bottlenecks and all potential optimization steps. We will try to switch from CPU to GPU version using CUDA and then tune it for the Jetson TK1 platform.

OpenCV provides GPU-based implementations for Warping and Blending steps that give speedup ranging from 1.5x to 2.5x. VoronoiSeamFinder::find method does not have GPU implementation, but switch to the GPU pipeline allows to significantly improve performance of the seam search procedure in general.

Detailed instructions

Step 1. Profile CPU implementation

Run the application and record performance data.

Step 2. Switch to GPU pipeline

In file stitching/stitching.hpp:11 uncomment #define USE_GPU_COMPOSITION line. It switches the pipeline to GPU version.

Major changes:

  • cv::Mat => cv::gpu::GpuMat
  • Blending on GPU
  • Warping on GPU
  • Utility image processing steps on GPU

Conclusion

  • OpenCV for Tegra helps to prototype and optimize computational pipelines
  • GPU greatly helps in real-time Computer Vision apps
  • It helps even better on mobile devices,
  • where we are usually power/performance bound
  • Memory transfers are very expensive, try to avoid it