Thursday, 30 June 2016

Speed up image hashing of opencv(img_hash) and introduce color moment hash

    In this post, I would like to show you two things.

1 : How could I accelerate the speed of the img_hash module(click me) from 1.5x~500x(roughly) from my last post(click me).

2 : A new image hash algorithms which works quite well under rotation attack.

Accelerate the speed of img_hash

    We only need one line to gain this huge performance gain, no more, no less.


cv::ocl::setUseOpenCL(false);

    What I do is close the optimization of openCL(I would not discuss why this speed things up dramatically on my laptop, if you are interesting about it, I would open another topic to discuss this phenomenon). Let us measure the performance after the change. Codes located at here(click me).

    Following comparison do not list the results of PHash about Average hash, PHash and Color hash algorithms, because I cannot find these algorithms in PHash library.

 
Computation time

Comparison time

 
Computation of img_hash with and without opencl

    As the results show, computation time of img_hash outperform PHash after I switch off opencl support(on you computer, switch it on may help you gain better performance) on my laptop(y410p). Whatever, the comparison performance do not change much with or without opencl support.


Benchmark of Color Moment Hash


    In this section, I would like to introduce an image hash algorithm which works quite well under rotation attack and provide a much better test results than my last post(click me).  This algorithm is introduced by this paper(click me), the class ColorMomentHash of img_hash module implement this algorithm.

    My last post only use one image--lena.png to do the experiment under different attack, in this post I will use the data set from phash to do the test(use miscellaneous data set(click me) as original image, apply different attack on it). These 3D bar charts are generated by Qt data visualization, I do not upload it to github yet because the codes are quite messy, if you need the source codes, please send the request to my email(thamngapwei@gmail.com), I would send you a copy of the codes, but do not expect I would refine the codes any time soon.

    The name of the images are quite long, it do not looks good when I draw it on chart, so I rename them to shorter form(001~023). Following are the mapping of those images. You can download the mapping of new name and old name from mega(click me).

    Threshold of the tests of color moment hash is 8, if the L2-Norm of two hash greater than 8, we treat it as fail, and draw it with red bars.

Contrast attack



Contrast attack on color moment hash
        
    
    Param is the gamma value of gamma correction.


Resize attack

Resize attack on color moment hash


     
    Param is the aspect ratio of horizontal and vertical site.

Gaussion noise attack

Gaussian noise attack on color moment hash
        
    

    Param is the standard deviation of gaussion.



Salt and pepper noise attack

Salt and pepper noise attack on color moment hash
    Param is the threshold of pepper and salt.

Rotation attack

Rotation attack on color moment hash

    Param is the angle of rotation.

Gaussian blur attack

Gaussian blur attack on color moment hash

    Param is the standard deviation of 3x3 gaussian filter.

Jpeg compression attack

 
Jpeg compression attack on color moment hash

    Param is the quality factor of jpeg compression, 100 means no compress.

Watermark attack

 
Watermark attack on color moment hash
    Param is the strength of watermark, 1.0 means the mark is 100% opaque. Image 017 and image 023 perform very poor because they are gray scale image.

    From these experiment data, we can say color moment hash perform very well under various attack except gaussion noise, salt and pepper noise and contrast attack.

Overall results of different algorithms


   Apparently, there are too many data to show for all of the algorithms, to make things more intuitive, I create the charts to help you measure the performance of these algorithms under different attacks.Their threshold are same as the last post(click me).

 
Average algorithm performance
PHash algorithm performance
Marr Hildreth algorithm performance
Radial hash algorithm performance
BMH zero algorithm performance
BMH one algorithm performance

Color moment algorithm performance
 
Overall reults
    These are the results of all of the algorithms, from the Overall results chart, it is easy to see that every algorithms have their pros and cons, you need to pick the one suit for your database. If speed is crucial, then average hash maybe is your best choices, because it is the fastest algorithms compare with other and perform very well under different attacks except of rotation and salt and pepper noise.If you need rotation resistance, color moment hash is you only choice because other algorithms suck on rotation attack. You can find the codes of these test cases from here(click me).

Compare with PHash library


    As this post show, img_hash module possess five advantages over the PHash library(click me).

1 : Processing speed of this module outperform PHash.

2 : This module adopt the same license as opencv(click me), which means you can do anything with it as you like without charging.

3 : The codes are much more modern, easier to use, img_hash free you from memory management chores once and for all. A modern, good c++ library should not force their users take care the resources by themselves.

4 : Api of img_hash are consistent, much easier to use than PHash library. Do not believe it? Let us see some examples.

Case 1a : Compute Radial Hash by PHash library


Digest digests_0, digests_1;
digest_0.coeffs = 0;
digest_1.coeffs = 1;
ph_image_digest(img_0, 1.0, 1.0, digest_0);
ph_image_digest(img_1, 1.0, 1.0, digest_1);

double pcc = 0;
ph_crosscorr(digest_0, digest_1, pcc, 0.9);
//do something, remember to free your memory :(
free(digest_0.coeffs);
free(digest_1.coeffs);

Case 1b : Compare Radial Hash by img_hash

auto algo = RadialVarianceHash::create();
cv::Mat hash_0, hash_1;
algo->compute(img_0, hash_0);
algo->compute(img_1, hash_1);
double const value = algo->compare(hash_0, hash_1);
//do something
//you do not need to free anything by yourself


Case 2a : Compute Marr Hash by PHash library

int N = 0;
uint8_t *hash_0 = ph_mh_imagehash(img_0, N);
uint8_t *hash_1 = ph_mh_imagehash(img_1, N);
double const value = ph_hammingdistance2(hash_0 , 72, hash_1, 72);   
//do something, remember to free your memory :(
free(hash_0);
free(hash_1);

Case 2b : Compare Marr Hash by img_hash

auto algo = MarrHildrethHash::create();
cv::Mat hash_0, hash_1;
algo->compute(img_0, hash_0);
algo->compute(img_1, hash_1);
double const value = algo->compare(hash_0, hash_1);
//do something
//you do not need to free anything by yourself


Case 3a : Compute Block mean Hash by PHash library

BinHash *hash_0 = 0;
BinHash *hash_1 = 0;
ph_bmb_imagehash(imgs_0, 1, &hash_0);
ph_bmb_imagehash(imgs_1, 1, &hash_1);

double const value = ph_hammingdistance2(hash_0->hash,
                hash_0->bytelength,
                hash_1->hash,
                hash_1->bytelength); 
//do something, remember to free your memory :(
ph_bmb_free(hash_0);
ph_bmb_free(hash_1);

Case 3b : Compare Block mean Hash by img_hash

auto algo = BlockMeanHash::create(0);
cv::Mat hash_0, hash_1;
algo->compute(img_0, hash_0);
algo->compute(img_1, hash_1);
double const value = algo->compare(hash_0, hash_1);
//do something
//you do not need to free anything by yourself

    As you can see, img_hash not only faster, this module also provide you cleaner, more concise way to write your codes, you never need to remember different ways to find out your hash and how to compare them anymore, because the api of img_hash are consistent.


5 : This module only depend on opencv_core and opencv_imgproc, that means you should be able to compile it at ease on every major platform without scratching your heads.

Next move

    Develop an application--Similar Vision to show the capability of img_hash. Functions of this app are find out similar images from image set(of course, it will leverage the power of img_hash module) and similar video clips from videos. 

Sunday, 19 June 2016

Introduction to image hash module of opencv

    Anyone using the defacto standard computer vision library--opencv, have you ever hope opencv provide us ready to use, image hash algorithms like average hash, perceptual hash, block mean hash, radial variance hash, marr hildreth hash like PHash does? PHash sound like a robust solution and run quite fast, but prefer PHash mean you need to add more dependencies into your project and open your source codes, open source is not a viable option in most of the commercial products. Do you, like me, do not want to add more dependencies into your codes? Have a royalty free, robust and high performance image hash algorithms for your project?Let us admit it, we do not like to solve dependencies issues related to programming, beyond that, many of the commercial project need to remain close source, it would be much better if opencv provide us an image hash module.

    If opencv do not have one, why not just create one for it?

1 :  The algorithms of image hash are not too complicated.
2 :  PHash library already implement many of image hash algorithms, we could port them to opencv and use it as golden model.
3 :  opencv is an open source computer vision library. If we ever found any bugs, missing features, poor performance, we can do something to make it better.

    The good news is I have implement all of the algorithms I mentioned above, refine the codes(ex : block mean hash able to process single channel image, codes become cleaner and safer), do some bug fixes(please check the pull request if you want to know the details), free you from memory management chores and open a pull request. The bad news is this pull request hasn't merged yet when I write this post, so you need to clone/pull it down and build by yourself. Fear not, this module only depend on the core and imgproc of opencv, it should be fairly easy to build(opencv is quite easy to build from the beginning :)).

    Following examples will show you how to use img_hash, you will find out it is much easier to use than PHash library because the api are more consistent + you do not need to manage the memory by yourself.

How to use it


#include <opencv2/core.hpp>
#include <opencv2/core/ocl.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/img_hash.hpp>
#include <opencv2/imgproc.hpp>

void computeHash(cv::Ptr<cv::img_hash::ImgHashBase> algo)
{
    cv::Mat const input = cv::imread("lena.png");
    cv::Mat const target = cv::imread("lena_blur.png");
    
    cv::Mat inHash; //hash of input image
    cv::Mat targetHash; //hash of target image

    //comupte hash of input and target
    algo->compute(input, inHash);
    algo->compute(target, targetHash);
    //Compare the similarity of inHash and targetHash
    //recommended thresholds are written in the header files
    //of every classes
    double const mismatch = algo->compare(inHash, targetHash);
    std::cout<<mismatch<<std::endl;
}

int main()
{
    //disable opencl acceleration may boost up speed of img_hash
    //however, in this post I do not disable the optimization of opencl    
    //cv::ocl::setUseOpenCL(false);

    computeHash(img_hash::AverageHash::create());
    computeHash(img_hash::PHash::create());
    computeHash(img_hash::MarrHildrethHash::create());
    computeHash(img_hash::RadialVarianceHash::create());
    //BlockMeanHash support mode 0 and mode 1, they associate to 
    //mode 1 and mode 2 of PHash library
    computeHash(img_hash::BlockMeanHash::create(0));
    computeHash(img_hash::BlockMeanHash::create(1));
    computeHash(img_hash::ColorMomentHash::create());
}


    With these functions, we can measure the performance of our algorithms under different "attack", like resize, contrast, noise and rotation. Before we start the test, let me define the thresholds of "pass" and "fail".One thing to remember is, to make thing simple, I only use lena to show the results, different data set may need different thresholds/algorithms to get best results.

Threshold


     After we determine our threshold, we could use our beloved lena to do the test :).

lena.png

Resize attack

Resize attack


    Every algorithms(BMH mean block mean hash) work very well on different size and aspect ratio except of radial variance hash, this algorithms work on different size, but we need to keep the aspect ratio.


Contrast Attack

Contrast Attack

    Every algorithms works quite well under different contrast, although Radical variance hash, BMH zero and BMH one do not works well under very low contrast.

 Gaussian Noise Attack

Gaussian noise attack
    Very fortunate, every algorithms survive under the attack of gaussian nose.

Salt And Pepper Noise Attack

Salt and pepper noise attack
      As we can see, only Radical hash and BMH perform well under the attack of pepper and salt.


Rotation Attack

Rotation attack
    Apparently, all of the algorithms can not survive under rotation attack. But is this really matter?I guess not(do you always need to search the image after rotation by google?). If you really need to deal with rotation attack, I suggest you give BOVW(bag of visual words) a try, I use it to construct robust CBIR system before, the defects of robust BOVW based CBIR are long computation time, consume a lot of memory and much harder to scale to large data set(you will need to build up distributed system in that case).

    We have go through all of the tests, now let us measure the performance of hash computation time and comparison time of different algorithms(my laptop is Y410P, os is windows 10 64bits, compiler is vc2015 64bits with update 2 install).

    You can find all the details of different attacks at here(click me).

Computation Performance Test--img_hash vs PHash library

  I use different algorithms to compute the hash of 100 images from ukbench(ukbench03000.jpg~ukbench03099.jpg). The source codes of opencv comparison is located at here(check the function measure_computation_time and measure_comparison_time, I am using img_hash_1_0 when I am writing this post),  source codes of PHash performance test(version 0.94 since I am on windows) is located at here.


Computation performance test

Comparison performance test


    In most cases, img_hash is faster than PHash, but the speed of BMH zero and BMH one are slower than PHash version almost 30% or 40%.  The bottleneck is cv::resize(over 95% of times spend on it), to speed things up, we need a faster resize function.


Find similar image from ukbench

    The results looks good, but could it find similar images? Of course dude, let me show you how could we measure the hash values of our target from ukbench(for simplicity, I only pick 100 images from ukbench).

target

 
void find_target(cv::Ptr<cv::img_hash::ImgHashBase> algo, bool smaller)
{
    using namespace cv::img_hash;

    cv::Mat input = cv::imread("ukbench/ukbench03037.jpg");
    //not a good way to reuse the codes by calling
    //measure comparision time, please bear with me
    std::vector<cv::Mat> targets = measure_comparison_time(algo, "");

    double idealValue;
    if(smaller)
    {
        idealValue = std::numeric_limits<double>::max();
    }
    else
    {
        idealValue = std::numeric_limits<double>::min();
    }
    size_t targetIndex = 0;
    cv::Mat inputHash;
    algo->compute(input, inputHash);
    for(size_t i = 0; i != targets.size(); ++i)
    {
        double const value = algo->compare(inputHash, targets[i]);
        if(smaller)
        {
            if(value < idealValue)
            {
                idealValue = value;
                targetIndex = i;
            }
        }
        else
        {
            if(value > idealValue)
            {
                idealValue = value;
                targetIndex = i;
            }
        }
    }
    std::cout<<"mismatch value : "<<idealValue<<std::endl;
    cv::Mat result = cv::imread("ukbench/ukbench0" +
                                std::to_string(targetIndex + 3000) +
                                ".jpg");
    cv::imshow("input", input);
    cv::imshow("found img " + std::to_string(targetIndex + 3000), result);
    cv::waitKey();
    cv::destroyAllWindows();
}

void find_target()
{
    using namespace cv::img_hash;

    find_target(AverageHash::create());
    find_target(PHash::create());
    find_target(MarrHildrethHash::create());
    find_target(RadialVarianceHash::create(), false);
    find_target(BlockMeanHash::create(0));
    find_target(BlockMeanHash::create(1));
}


    You will find out every algorithms give you back the same image you are looking for.

Conclusion

    Average hash and PHash are the fastest algorithms, but if you want a more robust one, pick BMH zero, BMH zero and BMH give similar resutls, but BMH one is slower since it need to spend more computation power. Hash comparision of Radial hash are much slower than other's, because it need to find out peak cross-correlation values from 40 combinations. If you want to know how to speed things up and know more about rotation invariant image hash algorithm, give this link(click me) a try.

    You can find the test cases at here. If you think this post helpful, please give my repositories(blogCodes2 and my img_hash of opencv_contrib) a star :). If you want to join the developments, please open a pull request, thanks.

Friday, 17 June 2016

Remove annoying trailing white space by c++

  If you ever try to commit something to opencv(I am porting/implementing various image hash algorithms to opencv_contrib when I writing this post, you can find my branch at here), you would likely to find out some extremely annoying messages as

modules/tracking/include/opencv2/tracking/tracker.hpp:857: trailing whitespace.
+  
modules/tracking/include/opencv2/tracking/tracker.hpp:880: trailing whitespace.
+ 
modules/tracking/include/opencv2/tracking/tracker.hpp:890: trailing whitespace.
+ 
modules/tracking/include/opencv2/tracking/tracker.hpp:1433: trailing whitespace.
+        Params(); 
modules/tracking/include/opencv2/tracking/tracker.hpp:1434: trailing whitespace.
+        
modules/tracking/include/opencv2/tracking/tracker.hpp:1444: trailing whitespace.


blablabla. They pop out in your files time to time, cost you more times to fix them, pollute your commit history, not only that, those trailing white spaces, they are hard to spot by human eyes.
Apparently, eliminate those trailing white space is not a job suit for humans, we would better leave those tedious tasks to our friends--computer.

    To teach our friend know what do I want to do, I write a small program to help us(source codes located at here), you should be able to compile and run it if you familiar with c++ and boost.

    Enough of talk, let me show you an example

Example 00


    As you can see, Example 00 contains a lot of tabs and trailing white space, not only that, there are a tab we should not removed(tab of std::string("\t")), this is the time my small tool--kill_trailing_white_space come in. All you need to do is specify you want to remove the tab and trailing white space of a file, or the files inside the folder(will scan the folders recursively). Example

"kill_trailing_white_space --input_file main.cpp"
"kill_trailing_white_space --input_folder img_hash"

    After the process, we could have a clean file as Example 01.


Example 01

    You can see the help menu if you enter --help.By now this small tool only support the files with extension ".hpp" and ".cpp". Feel free to modify the codes to suit your needs.