Performance:OpenCV:BoofCV

From BoofCV

The following is a comparison of similar algorithms in BoofCV and OpenCV for speed. Ten different algorithms were tuned to produce similar results and then run on three different architectures, desktop computer running on a Core i7-6700, Raspberry PI 3B+, and ODROID XU4. Algorithms covered go from low level image processing (Gaussian blur) to mid-level features (SIFT).

Introduction

It’s been a while since the last runtime comparison was done between BoofCV and OpenCV in 2011. Back then I thought that neural networks (NN) were essentially worthless and C/C++ was the dominant language. Now NN dominate the field and almost everyone use Python. The main event which prompted this benchmark to be done again was concurrency (a.k.a. threads) being added to BoofCV.

The goal of this benchmark is to replicate the speed that an “average” user can expect when using either of these libraries for low level to mid-level image processing/computer vision routines. This will cover image convolution up to feature detectors. NN and machine learning are not included. It is assumed that the average user will install the library using the easiest possible method, cut and paste example code, and do some simple optimizations. For OpenCV this means “pip install opencv-python” and for BoofCV using pre-built jars on Maven Central. If memory or data structures can be easily recycled then they are.

While this approach sounds easy enough it proved to be impossible to follow 100% and exceptions were made, discussed below. Another issue is that none of the algorithms were implemented the same. In fact, only three of them have a chance of producing nearly identical results; Gaussian blur, Sobel, and Histogram. The others have known major differences. For example, BoofCV’s Canny implementation forces you to blur the image while OpenCV doesn’t. BoofCV’s SURF implementation produces significantly better features than OpenCV’s (SURF Benchmark). The default settings in each library can produce drastically different results. Thus tuning criteria are clearly stated and followed in an attempt to produce comparable output.

To replicate the results please carefully read the instructions on this page and in the source code. Especially for architectures with ARM processors, it took about 3 attempts (or 8 hrs) to get a good build of OpenCV running on Raspberry PI. Suggestions for improving the fairness of this comparison are welcomed.

Benchmark Source Code:
https://github.com/lessthanoptimal/SpeedTestCV
Library Version
BoofCV 0.33.1
OpenCV 4.0.1
Device CPU Cores RAM OS
Desktop Core i7-6700 4 32 GB Ubuntu 18.04.2 @ 64bit
Raspberry PI 3B+ Cortex-A53 4 1 GB Raspbian 9.4 @ 32bit
ODROID XU4 Cortex-A15 and A7 4+4 2 GB Ubuntu 16.04.4 @ 32bit


To cite this article use the following:

@misc{BoofVsOpenCV,
  title = {Performance of OpenCV vs BoofCV: March 2019},
  howpublished = {\url{https://boofcv.org/index.php?title=Performance:OpenCV:BoofCV}},
  author = {Peter Abeles},
  originalyear = {03.22.2019}
}

Algorithms, Tuning, and Exceptions

Operation Tuning Target
Gaussian Blur Radius = 5
Sobel Gradient 3x3 Kernel
Local Mean Thresholding Radius = 5
Image Histogram
Canny Edge Output edge pixel chains. ~550,000 unique pixels in chains
Binary Contour External Only. 4-Connect Rule. Find around 1,100,000 points
Good Features Shi-Tomasi corners. Unweighted. Radius = 10 pixels. 3,000 Features
Hough Line Polar Variant. Resolutions: angle = 1 degree, range = 5 pixels. Detect 500 lines.
SIFT Detect and Describe. 5 Octaves. 3 Scales. No doubling of first octave. 10,000 Features
SURF Detect and Describe. 4 Octaves. 4 Scales. 10,000 Features

Two images were used in these test. The first image was 3648 x 2736 pixels of a chessboard pattern with a wood background and was processed as an 8-bit gray scale image. The second was a binary version of the just mentioned image for use by binary operators. This ensured that the binary operators had the same initial input. Tuning parameters and tuning goals mentioned above were selected based on common use cases and to remove potential biases. As an example, one factor that determines how fast a feature detector + descriptor run are the number of features detected since each detected feature must be described.

As previously mentioned, tuning these two libraries to produce similar results is a very difficult if not impossible problem. An attempt was made to be fair. See in code comments for specific details for why values were selected. The best way to ensure that two implementations are "equivalent" is to apply them to the same task and measure their performance. That approach is very labor intensive and often impossible due to difference in quality between two implementations, see the SURF Benchmark as an example, and was not done here.

Exceptions to the Rules

SIFT and SURF are covered by patents (or were, SIFT’s just expired this month) and not included in the pip package. That means you need to build OpenCV from scratch. Thus, on Desktop, those two operations were running code custom built for my architecture breaking the "average user" rule. Major issues were found on ARM architectures where there was no version of OpenCV 4 that could be easily installed and for BoofCV, the default JVM included lack optimizations for ARM making it run very slow!

The build settings for OpenCV on ARM are included below. An attempt was made to find the best settings and different websites had different recommendations. I picked one which explicitly enabled CPU specific optimizations.

cmake -D CMAKE_BUILD_TYPE=RELEASE     -D CMAKE_INSTALL_PREFIX=/usr/local     -D INSTALL_PYTHON_EXAMPLES=ON     -D OPENCV_EXTRA_MODULES_PATH=~/opencv_contrib-4.0.1/modules     -D ENABLE_NEON=ON     -D ENABLE_VFPV3=ON     -D WITH_FFMPEG=ON     -D WITH_GSTREAMER=ON     -D BUILD_EXAMPLES=ON -D OPENCV_ENABLE_NONFREE=ON ..

Instructions for installing the JVM used on ARM architectures:

jdk11.0.2-linux-arm32-vfp-hflt

While outside of the scope of this benchmark, building OpenCV on your specific architecture does provide significant performance boost for some operations. Gaussian blur ran about 2x faster on Desktop when custom built.

Results

Results are shown below for Intel Core i7, Odroid XU4, and Raspberry PI 3B+. Click on the arrow to change which results you are viewing.

OpenCV does very well in the Gaussian Blur test due to its hand crafted SIMD instructions being multi-threaded. For other low level SIMD friendly operations the speed difference isn't as great between Java and the C code (GCC does a better job optimizing for SIMD than JVM), so it tends to come down to threading performance. SURF doesn't lend itself towards SIMD optimization, meaning that the compiler is less important and algorithm more important. The main surprise is SIFT, which should have crushed BoofCV because the most computationally expensive part is applying Gaussian blur many times.

Results between architectures are more consistent than I thought they would be. OpenCV on desktop used the generic version contained in pypy (except for SIFT and SURF) while OpenCV for ARM architectures had been custom built for each architecture. Winners and near ties are effectively the same. OpenCV's SIFT was unable to finish computing on ARM processors, threw out of memory error or just died. OpenCV's SIFT code has not been inspect to root cause this problem, but BoofCV's implementation was designed to recycle images as much as possible.

Conclusions

In this benchmark, BoofCV was the top performer in 6 out of 10, there was a tie in 2 operations, and OpenCV did best in 2 operations. This means on average BoofCV was the top overall performer. OpenCV does well in low level SIMD friendly operation due to superior optimization by GCC and hand written SIMD instructions. However, even with that advantage BoofCV now out performs it in many low level operations due to better coverage of concurrent algorithms. For algorithms which are not dominated by SIMD friendly operations, BoofCV does very well and out performs mostly due to more efficient algorithms.

End Comment

The last time I published this benchmark I was a bit surprised at the lack of reading comprehension exhibited by academic authors. The results were clearly split down the middle, yet most people somehow concluded that OpenCV was the winner! The real answer to which library is faster/better is “it depends”. If you ignore programming language preference then the following would be true. Is your problem heavy in pure image convolution? OpenCV is best for you! Do you want to use a fast and stable QR code detector (see these results) then BoofCV is for you.