OpenCV在TI达芬奇以及OMAP平台下的移植与性能评估

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Abstract

In today’s advancing market, the growing

performance and decreasing price of em-

bedded processors are opening many doors

for developers to design highly sophisticat-

ed solutions for different end applications.

The complexities of these systems can cre-

ate bottlenecks for developers in the form of

longer development times, more complicated

development environments and issues with

application stability and quality. Developers

can address these problems using sophisti-

cated software packages such as OpenCV,

but migrating this software to embedded

platforms poses its own set of challenges.

This paper will review how to mitigate some

of these issues, including C++ implemen-

tation, memory constraints, floating-point

support and opportunities to maximize per-

formance using vendor-optimized libraries

and integrated accelerators or co-processors.

Finally, we will introduce a new effort by

Texas Instruments (TI) to optimize vision sys-

tems by running OpenCV on the C6000™

digital signal processor (DSP) architecture.

Benchmarks will show the advantage of us-

ing the DSP by comparing the performance

of a DSP+ARM

system-on-chip (SoC)

processor against an ARM-only device.

OpenCV on TI’s DSP+ARM

platforms: Mitigating the

challenges of porting OpenCV

to embedded platforms

Introduction

OpenCV is a free and open-source computer vision library that offers a broad range of func-

tionality under the permissive Berkeley Software Distribution (BSD) license. The library itself

is written in C++ and is also usable through C or Python language applications. Thousands

of developers use OpenCV to power their own specialized applications, making it the most

widely-used library of its kind. The OpenCV project is under active development, with regular

updates to eliminate bugs and add new functionality. The mainline development effort targets

the x86 architecture and supports acceleration via Intel’s proprietary Integrated Performance

Primitives (IPP) library. A recent release also added support for graphics processing unit (GPU)

acceleration using NVIDIA’s Compute Uniﬁ ed Device Architecture (CUDA) standard.

OpenCV’s greatest asset is the sheer breadth of algorithms included in its standard distri-

bution. Figure 1 on page 2 shows an incomplete list of some of the key function categories in-

cluded in OpenCV. These range from low-level image ﬁ ltering and transformation to sophisti-

cated feature analysis and machine learning functionality. A complete listing of every function

and use case is beyond the scope of this article, but we will consider the unique requirements

of developers in the embedded vision space. For these developers, OpenCV represents an

attractively comprehensive toolbox of useful, well-tested algorithms that can serve as building

blocks for their own specialized applications. The question then becomes whether or not

OpenCV can be used directly in their embedded systems.

Despite its original development focus for use with PC workstations, OpenCV can also

be a useful tool for embedded development. There are vendor-speciﬁ c libraries that offer

OpenCV-like capabilities on various embedded systems, but few can match OpenCV’s ubiquity

in the computer vision ﬁ eld or the sheer breadth of its included algorithms. It should come as

no surprise that OpenCV has already been ported to the ARM

architecture, a popular CPU

choice for embedded processors. It’s certainly possible to cross-compile the OpenCV source

code as-is and use the result with embedded devices, but memory constraints and other

architectural considerations may pose a problem. This white paper will examine some of the

speciﬁ c obstacles that must be overcome for OpenCV to achieve acceptable performance on

Joseph Coombs,

Applications engineering,

Texas Instruments

Rahul Prabhu,

Applications engineering,

Texas Instruments

WHITE PAPER

OpenCV on TI’s DSP+ARM platforms: Mitigating the challenges of porting OpenCV to embedded platforms July 2011

Texas Instruments

an embedded platform. Finally, the paper will describe a new effort by Texas Instruments (TI) to bring OpenCV

to its C6000™ digital signal processor (DSP) architecture. Performance benchmarks will compare TI’s

DSP+ARM

system-on-chip (SoC) processor against the standard ARM-only approach.

The continued growth of embedded vision applications places contradictory demands on embedded develop-

ers. Increasingly sophisticated vision algorithms require more memory and processing power, but price and

deployment constraints require embedded devices that cost less money and consume less power. Embedded

hardware and software expand in complexity while development cycles accelerate and contract. The follow-

ing applications are representative of the current state and future direction of the overall embedded vision

space.

Let’s start with industrial vision applications. One common industrial vision task is assembly line inspec-

tion, which detects, classiﬁ es and sorts objects to maximize manufacturing speed and quality. These vision

algorithms are often run on costly computer workstations; migrating to an embedded DSP is one obvious

way to save on price and power consumption. Even applications that are already implemented with embed-

ded systems can be improved by condensing discrete logic into the DSP. For example, many industrial vision

systems share the basic shape illustrated by Figure 2. The image signal processor (ISP) is a ﬁ eld program-

mable gate array (FPGA) that performs time-critical pre-processing on incoming data before it reaches the

DSP. This FPGA becomes more expensive and consumes more power proportional to its workload. One way

to maximize the efﬁ ciency of the overall embedded system is to integrate as much pre-processing as pos-

sible into the DSP. The challenge then becomes keeping up with rapid improvements in the physical system.

Changing requirements

of embedded vision

applications

Figure 1. Partial overview of the OpenCV library.

Figure 2. Typical embedded vision system, including camera, pre-processing FPGA and DSP.

ISP DSP

Texas Instruments

Next-generation systems must process more data in less time to accommodate improved camera resolution

and frame rate as well as faster assembly line speeds.

Video surveillance applications provide another perspective on the evolution of embedded vision. Tra-

ditional surveillance systems are less concerned with vision analytics than they are with simply encoding

and recording video data. However, as vision algorithms improve, video surveillance will incorporate more

automated monitoring and analysis of this recorded data. Examples range from motion and camera tamper

detection to people counting and license plate reading. These algorithms enable so-called metadata stream-

ing, or creating automated logs of detected activity to accompany streamed and recorded video data. As

vision algorithms become more capable and reliable, video surveillance systems will become more auto-

mated and sophisticated. This presents a particular challenge to embedded video surveillance systems, since

cutting-edge algorithms that are developed on PCs may require considerable rework and optimization to run

efﬁ ciently on an embedded device. Consequently, many embedded video surveillance applications are limited

to the simpler encode-and-record paradigm.

One last example application from the broad category of embedded vision is automotive vision. Unlike

the previously discussed application spaces, automotive vision is almost exclusively the domain of embed-

ded processors. Many automotive vision systems can be reduced to a block diagram similar to Figure 2,

essentially consisting of a camera, a pre-processing FPGA and a DSP to apply intensive vision algorithms.

Reliability is the key concern in applications such as lane departure warning, steering assistance and

proximity detection. The vision algorithms used in automotive vision are under constant, active development

using high-level PC software, but running the ﬁ nal application on a PC is simply not an option. The transition

from PC to DSP is a critical step in the development of automotive vision applications. Writing and rewrit-

ing algorithms to achieve acceptable real-time performance is a major development focus. This only gets

more difﬁ cult as embedded systems become more sophisticated, incorporating multiple camera inputs and

multiple processing cores.

Efﬁ cient DSP software plays a critical role in all embedded vision applications. The prospect of using high-

level software like OpenCV to facilitate rapid algorithm development is appealing, but optimizing that software

for a new platform is a critical sticking point. Conversely, achieving acceptable performance with un-optimized

DSP software is simply unrealistic. In the next section of this article, we consider the key challenges associ-

ated with porting and optimizing sophisticated PC software — particularly the OpenCV library — to run on an

embedded device.

Since OpenCV is open source and written entirely in C/C++, the library has been cross compiled and ported

as-is to a variety of platforms. However, simply rebuilding the library for an embedded platform may not yield

the real-time performance demanded in that space. At the same time, rewriting and manually optimizing the

entire OpenCV library for a new architecture represents an enormous amount of work. Device-appropriate

optimizing compilers are critical to navigate between these opposing challenges. The ubiquitous GNU Com-

piler Collection (GCC) has been used to successfully port OpenCV to ARM platforms, but GCC is not available

OpenCV on TI’s DSP+ARM platforms: Mitigating the challenges of porting OpenCV to embedded platforms July 2011

Challenges of

porting OpenCV to

embedded devices

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2013-10-25

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