Memory-mapped files using the boost library

9
8 months ago
by Erik Smistad in

The objective of memory mapping files is to increase I/O performance. Memory mapping a file creates a pointer to a segment in virtual memory and the actual loading is performed by the Operating System one page at a time. For large files, this is much faster than using traditional methods in C such as fopen/fread/fwrite.

In this post, I show an example of how to use the boost iostreams library to create a memory mapped file that, unlike mmap, works for both Windows and Linux.

Start with installing the boost iostreams library. On ubuntu this is done by installing the libboost-iostreams-dev package.

sudo apt-get install libboost-iostreams-dev

The example below will create a memory mapping of 1000000 integers for the file filename.raw. The integers will be available from the pointer called data.

#include <boost/iostreams/device/mapped_file.hpp>
#include <iostream>
 
int main() {
 
    boost::iostreams::mapped_file_source file;
    int numberOfElements = 1000000;
    int numberOfBytes = numberOfElements*sizeof(int);
    file.open("filename.raw", numberOfBytes);
 
    // Check if file was successfully opened
    if(file.is_open()) {
        // Get pointer to the data
        int * data = (int *)file.data();
 
        // Do something with the data
        for(int i = 0; i < numberOfElements; i++)
            std::cout << data[i] << " ";
 
        // Remember to unmap the file
        file.close();
    } else {
        std::cout << "could not map the file filename.raw" << std::endl;
    }
}

Here is a minimal CMakeLists.txt file for compiling this example together with the boost iostreams library.

cmake_minimum_required(VERSION 2.8)
find_package(Boost COMPONENTS iostreams REQUIRED)
 
add_executable(memory-map main.cpp)
target_link_libraries(memory-map ${Boost_LIBRARIES})

As usual you can download/clone the code and the sample raw file from my GitHub page

GPU-based Gradient Vector Flow using OpenCL

3
11 months ago
by Erik Smistad in ,

Illustration of Gradient Vector Flow performed on an image. The colors represents the vector direction.

Gradient Vector Flow (GVF) is a feature-preserving diffusion of gradient information. It was originally introduced by Xu and Prince to drive snakes, or active contours, towards edges of interest in image segmentation. However, GVF is also used for detection of tubular structures and skeletonization.

I just recently published an article in the Journal of Real-Time Image Processing entitled “Real-time gradient vector flow on GPUs using OpenCL” describing an optimized OpenCL implementation of Gradient Vector Flow (GVF) that runs on GPUs and CPUs for both 2D and 3D.
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Gaussian Blur using OpenCL and the built-in Images/Textures

16
11 months ago
by Erik Smistad in ,
If used correctly, OpenCL images / textures can give you large speedups on GPUs. In this post, I’ll show you a very short example of how to use OpenCL to blur/smooth an image. The goal is to show how images/textures are used in OpenCL and the benefits of using them.

The source code can be download from by GitHub page.
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Measuring runtime in milliseconds using the C++ 11 chrono library

0
11 months ago
by Erik Smistad in

I have been playing around with the new C++ 11 standard. It includes a nice new library called chrono which includes some useful clocks and timers. Below is an example of some macros you can use to time your applications in milliseconds and print out the result. Timing can be turned off by removing the #define TIMING line. Remember to compile the program with C++11 (or C++0x) enabled. For GCC this should be:

g++ main.cpp -std=c++0x
#include <iostream>
#include <chrono>
 
#define TIMING
 
#ifdef TIMING
#define INIT_TIMER auto start = std::chrono::high_resolution_clock::now();
#define START_TIMER  start = std::chrono::high_resolution_clock::now();
#define STOP_TIMER(name)  std::cout << "RUNTIME of " << name << ": " << \
    std::chrono::duration_cast<std::chrono::milliseconds>( \
            std::chrono::high_resolution_clock::now()-start \
    ).count() << " ms " << std::endl; 
#else
#define INIT_TIMER
#define START_TIMER
#define STOP_TIMER(name)
#endif
 
int main() {
    INIT_TIMER
    START_TIMER
    sleep(2);
    STOP_TIMER("sleeping for 2 seconds")
    START_TIMER
    long unsigned int b = 0;
    for(int i = 0; i < 10000000; i++) {
        b += i;
    }
    STOP_TIMER("some long loop")
}

Example output:

RUNTIME of sleeping for 2 seconds: 2000 ms 
RUNTIME of some long loop: 24 ms

Getting started with Google Test (GTest) on Ubuntu

7
11 months ago
by Erik Smistad in

Google test is a framework for writing C++ unit tests. In this short post, I explain how to set it up in Ubuntu.

Start by installing the gtest development package:

sudo apt-get install libgtest-dev

Note that this package only install source files. You have to compile the code yourself to create the necessary library files. These source files should be located at /usr/src/gtest. Browse to this folder and use cmake to compile the library:

sudo apt-get install cmake # install cmake
cd /usr/src/gtest
sudo cmake CMakeLists.txt
sudo make
 
# copy or symlink libgtest.a and libgtest_main.a to your /usr/lib folder
sudo cp *.a /usr/lib

Lets say we now want to test the following simple squareRoot function:

// whattotest.cpp
#include <math.h>
 
double squareRoot(const double a) {
    double b = sqrt(a);
    if(b != b) { // nan check
        return -1.0;
    }else{
        return sqrt(a);
    }
}

In the following code, we create two tests that test the function using a simple assertion. There exists many other assertion macros in the framework (see http://code.google.com/p/googletest/wiki/Primer#Assertions). The code contains a small main function that will run all of the tests automatically. Nice and simple!

// tests.cpp
#include "whattotest.cpp"
#include <gtest/gtest.h>
 
TEST(SquareRootTest, PositiveNos) { 
    ASSERT_EQ(6, squareRoot(36.0));
    ASSERT_EQ(18.0, squareRoot(324.0));
    ASSERT_EQ(25.4, squareRoot(645.16));
    ASSERT_EQ(0, squareRoot(0.0));
}
 
TEST(SquareRootTest, NegativeNos) {
    ASSERT_EQ(-1.0, squareRoot(-15.0));
    ASSERT_EQ(-1.0, squareRoot(-0.2));
}
 
int main(int argc, char **argv) {
    testing::InitGoogleTest(&argc, argv);
    return RUN_ALL_TESTS();
}

The next step is to compile the code. I’ve set up a small CMakeLists.txt file below to compile the tests. This file locates the google test library and links it with the test application. Note that we also have to link to the pthread library or the application won’t compile.

cmake_minimum_required(VERSION 2.6)
 
# Locate GTest
find_package(GTest REQUIRED)
include_directories(${GTEST_INCLUDE_DIRS})
 
# Link runTests with what we want to test and the GTest and pthread library
add_executable(runTests tests.cpp)
target_link_libraries(runTests ${GTEST_LIBRARIES} pthread)

Compile and run the tests:

cmake CMakeLists.txt
make
./runTests

Have fun testing! You can download all of the code above at my Github page: https://github.com/smistad/GTest

References

http://code.google.com/p/googletest/wiki/Documentation
http://www.ibm.com/developerworks/aix/library/au-googletestingframework.html

Simple Image Processing Library

2
1 year ago
by Erik Smistad in


I do a lot image processing both on images and 3D images / volumes. There exist many image processing libraries out there. Some are big and some are small, but none seems to fit my taste. ITK is one of the major image processing libraries used in my field of research, but this library is, in my opinion, extremly cumbersome. And I can’t be the only one who think so since there has been made an alternative called Simple ITK. There exists many other image processing libraries that tries to be simple to use, but most of them don’t allow you to do volume processing, which I do a lot of. I want a library that allows me to quickly go from an algorithm concept to getting actual pictures on the screen so that I can quickly verify the results. So far I’ve been using Matlab for prototyping image processing algoritmhs, and it have worked quite well, but as I see it Matlab has two major problem: speed and computation and GUI in one thread. A long story short, I’ve made my own Simple Image Processing Library (SIPL) which I now use in my research. I’ve added a short guide here on how to use and install it in case anybody else feel the same as I do and thinks this library could be of any use to them as well. Also, this small little library is still in development so if you have any feedback, suggestions, comments or bug reports please let me know.

Main goals of the library:

  • Simple and condensed – Easy to get from an algorithm concept to pictures on the screen
  • GUI in seperat thread – Display and explore images interactively while computation is still going on
  • Cross-platform – Linux, Windows and Mac compatible

Press more below to see code examples, download and read install instruction.
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Marching Cubes implementation using OpenCL and OpenGL

11
1 year ago
by Erik Smistad in , ,

In a school project I recently created a fast implementation of Marching Cubes that uses OpenCL to extract surfaces from volumetric datasets and OpenGL to render the surfaces on screen. I wrote a paper together with my two supervisors about the implementation and presented it at the Joint Workshop on High Performance and Distributed Computing for Medical Imaging at the MICCAI 2011 conference. Our implementation achieved real-time speeds for volumes of sizes up to 512x512x512 on a standard GPU with 1GB memory. The paper entitled “Real-Time Surface Extraction and Visualization of Medical Images using OpenCL and GPUs” describing the implementation can be downloaded here. The source code of the implementation can be downloaded from my GitHub page.

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OpenCL C++ Utilities

11
1 year ago
by Erik Smistad in

I recently created a small utility library for OpenCL with C++. It consists of a set of function based on the OpenCL C++ bindings to help set up an OpenCL context, compiling OpenCL code and viewing error functions. I hope these functions can be useful for others and I’m planning on adding more utility functions in the future. Note that I haven’t tested it on all platforms yet. Feedback and comments are most welcome.

The source code can be downloaded from my GitHub page.

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3D Gradient Vector Flow Matlab Implementation

16
2 years ago
by Erik Smistad in

Gradient Vector Flow (to the right) calculated on the volume to the left.

Gradient Vector Flow (GVF) is a feature-preserving diffusion of gradient information. It was originally introduced by Xu and Prince to drive snakes, or active contours, towards edges of interest in image segmentation. But GVF is also used for detection of tubular structures and skeletonization. In this post I present a simple Matlab implementation of GVF for 3D images which I made because I could not find any online. The implementation is a simple extension of Xu and Prince original 2D implementation found at their website.

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Using the C++ bindings for OpenCL

24
2 years ago
by Erik Smistad in

While the OpenCL API is written in C, the OpenCL 1.1 specification also comes with a specification for C++ bindings. In this post I go through how to use the C++ bindings instead of C for the simple example of vector addition from my previous post Getting started with OpenCL and GPU computing.

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