Difference between revisions of "BarraCUDA Boiz"

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==== EucideanDistance ====
 
==== EucideanDistance ====
 
Profiled the following project on github which finds the euclidean distance transformation on given chart formatted in a text file.  The project can be found here: [https://github.com/lanipse/Euclidean-Distance-Transform-CPP here]
 
Profiled the following project on github which finds the euclidean distance transformation on given chart formatted in a text file.  The project can be found here: [https://github.com/lanipse/Euclidean-Distance-Transform-CPP here]
[[File:TestImage2.jpg]]
 
  
 
The following is a example of the program running with an example input and the output afterwards.
 
The following is a example of the program running with an example input and the output afterwards.

Revision as of 13:23, 12 February 2017

BarraCUDA Boiz

Team Members

  1. Van Chau Bui
  2. Michael Michalski
  3. Agam Dogra


Progress

Assignment 1

EucideanDistance

Profiled the following project on github which finds the euclidean distance transformation on given chart formatted in a text file. The project can be found here: here

The following is a example of the program running with an example input and the output afterwards.

   Before:                                                          After:
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0   0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0
0 0 1 0 0 0 0 0 1 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0   0 0 1 0 0 0 0 0 1 0 0 0 0 1 1 2 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0
0 0 0 1 0 0 0 1 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 1 0 0 0 0   0 0 0 1 0 0 0 1 0 0 0 0 1 1 2 3 2 1 1 0 0 0 0 0 0 0 1 0 0 0 0
0 0 0 0 1 0 0 1 0 0 0 1 1 1 1 1 1 1 1 1 0 0 0 0 0 1 0 0 0 0 0   0 0 0 0 1 0 0 1 0 0 0 1 1 2 3 4 3 2 1 1 0 0 0 0 0 1 0 0 0 0 0
0 0 0 0 0 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 1 0 0 0 0   0 0 0 0 0 1 0 1 0 0 1 1 2 3 4 4 4 3 2 1 1 0 0 0 0 0 1 0 0 0 0
0 0 0 0 1 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 1 0 0 0   0 0 0 0 1 0 0 1 0 1 1 2 3 4 4 5 4 4 3 2 1 1 0 0 0 0 0 1 0 0 0
0 0 0 0 0 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 1 0 0 0 0   0 0 0 0 0 1 0 1 0 1 2 3 4 4 5 6 5 4 4 3 2 1 0 0 0 0 1 0 0 0 0
0 0 0 0 1 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 1 0 0 0   0 0 0 0 1 0 0 1 0 1 2 3 4 5 6 7 6 5 4 3 2 1 0 0 0 0 0 1 0 0 0
0 0 0 0 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 0 0   0 0 0 0 1 0 1 1 0 1 2 3 4 5 6 7 6 5 4 3 2 1 0 0 0 0 0 0 1 0 0
0 0 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0   0 0 1 1 1 0 1 0 0 1 2 3 4 5 5 6 5 5 4 3 2 1 1 1 1 1 1 1 0 0 0
0 0 1 0 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0   0 0 1 0 0 0 1 0 1 1 2 3 3 4 4 5 4 4 3 3 2 1 0 0 0 0 0 0 0 0 0
0 0 1 0 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0   0 0 1 0 1 1 1 0 0 1 1 2 2 3 4 4 4 3 2 2 1 1 0 0 0 0 0 0 0 0 0
0 0 1 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 0 0 0 0 0 0   0 0 1 0 0 0 0 0 0 0 1 1 1 2 3 4 3 2 1 1 1 0 1 0 0 0 0 0 0 0 0
0 0 0 1 0 0 0 0 0 1 0 0 1 1 1 1 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0   0 0 0 1 0 0 0 0 0 1 0 0 1 1 2 3 2 1 1 0 0 0 0 1 0 0 0 0 0 0 0
0 0 0 0 1 0 0 0 1 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0   0 0 0 0 1 0 0 0 1 0 0 0 0 1 1 2 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0
0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0   0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0
0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0   0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0
0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0   0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 2 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0
0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0   0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 2 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0
0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0   0 0 0 0 0 0 1 0 0 0 0 0 1 1 2 3 2 1 1 0 0 0 1 0 0 0 0 0 0 0 0
0 0 0 0 0 1 0 1 0 0 0 1 1 1 1 1 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0   0 0 0 0 0 1 0 1 0 0 0 1 1 2 3 4 3 2 1 1 0 1 0 0 0 0 0 0 0 0 0
0 0 0 0 1 0 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0   0 0 0 0 1 0 0 0 1 0 1 1 2 3 4 4 4 3 2 1 1 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0   0 0 0 0 0 0 0 0 0 1 1 2 3 4 4 5 4 4 3 2 1 1 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0   0 0 0 0 0 0 0 0 0 1 2 3 4 4 5 6 5 4 4 3 2 1 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0   0 0 0 0 0 0 0 0 0 1 2 3 4 5 6 7 6 5 4 3 2 1 0 0 0 0 0 0 0 0 0
0 0 0 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0   0 0 0 1 0 0 0 0 0 1 2 3 4 5 6 7 6 5 4 3 2 1 0 0 0 0 0 0 0 0 0
0 0 0 0 1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0   0 0 0 0 1 0 0 0 0 1 2 3 4 5 5 6 5 5 4 3 2 1 0 0 0 0 0 0 0 0 0
0 0 0 0 1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0   0 0 0 0 1 0 0 0 0 1 2 3 3 4 4 5 4 4 3 3 2 1 0 0 0 0 0 0 0 0 0
0 0 0 0 0 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0   0 0 0 0 0 1 0 0 0 1 1 2 2 3 4 4 4 3 2 2 1 1 0 0 0 0 0 0 0 0 0
0 0 0 1 0 0 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0   0 0 0 1 0 0 0 0 1 0 1 1 1 2 3 4 3 2 1 1 1 0 0 0 0 0 0 0 0 0 0
0 0 0 0 1 0 0 1 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0   0 0 0 0 1 0 0 1 0 0 0 0 1 1 2 3 2 1 1 0 0 0 0 0 0 0 0 0 0 0 0
0 0 1 0 0 1 1 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0   0 0 1 0 0 1 1 0 0 0 0 0 0 1 1 2 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0   0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0   0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


At n = 1302:

Flat profile:

Each sample counts as 0.01 seconds.
no time accumulated

 %   cumulative   self              self     total
time   seconds   seconds    calls  Ts/call  Ts/call  name
 0.00      0.00     0.00      810     0.00     0.00  EuclideanDistanceTransform::loadNeighbors(int, int, int)
 0.00      0.00     0.00        1     0.00     0.00  _GLOBAL__sub_I__ZN26EuclideanDistanceTransformC2ERSt14basic_ifstreamIcSt11char_traitsIcEERSt14basic_ofstreamIcS2_ES7_
 0.00      0.00     0.00        1     0.00     0.00  EuclideanDistanceTransform::zeroFramed()
 0.00      0.00     0.00        1     0.00     0.00  EuclideanDistanceTransform::firstPassEuclideanDistance(std::basic_ofstream<char, std::char_traits<char> >&)
 0.00      0.00     0.00        1     0.00     0.00  EuclideanDistanceTransform::secondPassEuclideanDistance(std::basic_ofstream<char, std::char_traits<char> >&)
 0.00      0.00     0.00        1     0.00     0.00  EuclideanDistanceTransform::loadImage(std::basic_ifstream<char, std::char_traits<char> >&)


At n = 1000000:

Flat profile:

Each sample counts as 0.01 seconds.
 %   cumulative   self              self     total
time   seconds   seconds    calls  ms/call  ms/call  name
33.33      0.03     0.03        1    30.00    30.00  EuclideanDistanceTransform::loadImage(std::basic_ifstream<char, std::char_traits<char> >&)
22.22      0.05     0.02  1001696     0.00     0.00  EuclideanDistanceTransform::loadNeighbors(int, int, int)
22.22      0.07     0.02                             EuclideanDistanceTransform::EuclideanDistanceTransform(std::basic_ifstream<char, std::char_traits<char> >&, std::basic_ofstream<char, std::char_traits<char> >&, std::basic_ofstream<char, std::char_traits<char> >&)
11.11      0.08     0.01        1    10.00    20.00  EuclideanDistanceTransform::firstPassEuclideanDistance(std::basic_ofstream<char, std::char_traits<char> >&)
11.11      0.09     0.01        1    10.00    20.00  EuclideanDistanceTransform::secondPassEuclideanDistance(std::basic_ofstream<char, std::char_traits<char> >&)
 0.00      0.09     0.00        1     0.00     0.00  _GLOBAL__sub_I__ZN26EuclideanDistanceTransformC2ERSt14basic_ifstreamIcSt11char_traitsIcEERSt14basic_ofstreamIcS2_ES7_
 0.00      0.09     0.00        1     0.00     0.00  EuclideanDistanceTransform::zeroFramed()

At n = 10000000:

Flat profile:

Each sample counts as 0.01 seconds.
 %   cumulative   self              self     total
time   seconds   seconds    calls  ms/call  ms/call  name
35.14      0.26     0.26                             EuclideanDistanceTransform::EuclideanDistanceTransform(std::basic_ifstream<char, std::char_traits<char> >&, std::basic_ofstream<char, std::char_traits<char> >&, std::basic_ofstream<char, std::char_traits<char> >&)
27.03      0.46     0.20        1   200.00   255.00  EuclideanDistanceTransform::secondPassEuclideanDistance(std::basic_ofstream<char, std::char_traits<char> >&)
20.27      0.61     0.15        1   150.00   205.00  EuclideanDistanceTransform::firstPassEuclideanDistance(std::basic_ofstream<char, std::char_traits<char> >&)
14.86      0.72     0.11  9998998     0.00     0.00  EuclideanDistanceTransform::loadNeighbors(int, int, int)
 2.70      0.74     0.02        1    20.00    20.00  EuclideanDistanceTransform::loadImage(std::basic_ifstream<char, std::char_traits<char> >&)
 0.00      0.74     0.00        1     0.00     0.00  _GLOBAL__sub_I__ZN26EuclideanDistanceTransformC2ERSt14basic_ifstreamIcSt11char_traitsIcEERSt14basic_ofstreamIcS2_ES7_
 0.00      0.74     0.00        1     0.00     0.00  EuclideanDistanceTransform::zeroFramed()

SeamCarving

Seam carving (or liquid re-scaling) is an algorithm for content-aware image resizing. It functions by establishing a number of seams (paths of least importance) in an image and automatically removes seams to reduce image size or inserts seams to extend it. The profiled project can be found on Github using this link: here

Here is an example of a test case:

I shrunk the image by 1000 pixels. 

Before:

TestImage.jpg 

After:

1000output.png


On shrinking by 100 pixels.

Flat profile:

Each sample counts as 0.01 seconds.
 %   cumulative   self              self     total           
time   seconds   seconds    calls  ms/call  ms/call  name    
36.84      5.87     5.87 2549733700     0.00     0.00  unsigned char& cv::Mat::at<unsigned char>(int, int)
24.50      9.77     3.90      100    39.02    71.54  computeSeam(cv::_InputArray const&, std::vector<int, std::allocator<int> >&)
11.31     11.57     1.80      100    18.01    46.39  void carveSeam<unsigned char>(cv::Mat&, std::vector<int, std::allocator<int> >&)
 7.76     12.81     1.24 558300205     0.00     0.00  int& cv::Mat::at<int>(int, int)
 6.28     13.81     1.00      100    10.01    37.36  detectEdge(cv::_InputArray const&, cv::_OutputArray const&)
 3.58     14.38     0.57 186060000     0.00     0.00  cvRound(double)
 2.89     14.84     0.46 186060000     0.00     0.00  unsigned char cv::saturate_cast<unsigned char>(double)
 2.64     15.26     0.42 186060000     0.00     0.00  unsigned char cv::saturate_cast<unsigned char>(int)
 2.14     15.60     0.34                             cv::Size_<int>::Size_(int, int)
 1.70     15.87     0.27 186060000     0.00     0.00  std::vector<int, std::allocator<int> >::operator[](unsigned long)
 0.44     15.94     0.07                             frame_dummy
 0.00     15.94     0.00     1003     0.00     0.00  cv::Mat::release()


On shrinking by 500 pixels.

Flat profile:

Each sample counts as 0.01 seconds.
  %   cumulative   self              self     total           
 time   seconds   seconds    calls  ms/call  ms/call  name    
 37.44     30.05    30.05 11103057554     0.00     0.00  unsigned char& cv::Mat::at<unsigned char>(int, int)
 24.22     49.49    19.44      500    38.88    71.23  computeSeam(cv::_InputArray const&, std::vector<int, std::allocator<int> >&)
 12.09     59.19     9.71      500    19.41    48.18  void carveSeam<unsigned char>(cv::Mat&, std::vector<int, std::allocator<int> >&)
  7.57     65.27     6.07      500    12.15    37.01  detectEdge(cv::_InputArray const&, cv::_OutputArray const&)
  7.17     71.02     5.75 2431501402     0.00     0.00  int& cv::Mat::at<int>(int, int)
  2.94     73.38     2.36 810300000     0.00     0.00  cvRound(double)
  2.46     75.36     1.98 810300000     0.00     0.00  unsigned char cv::saturate_cast<unsigned char>(double)
  2.23     77.15     1.79                             cv::Size_<int>::Size_(int, int)
  1.89     78.66     1.52 810300000     0.00     0.00  unsigned char cv::saturate_cast<unsigned char>(int)
  1.55     79.90     1.24 810300000     0.00     0.00  std::vector<int, std::allocator<int> >::operator[](unsigned long)
  0.36     80.19     0.29                             frame_dummy
  0.12     80.29     0.10      500     0.20     0.20  std::vector<int, std::allocator<int> >::resize(unsigned long, int)
  0.00     80.29     0.00     5003     0.00     0.00  cv::Mat::release()

On shrinking by 1000 pixels.

Flat profile:

Each sample counts as 0.01 seconds.
  %   cumulative   self              self     total           
 time   seconds   seconds    calls  ms/call  ms/call  name    
 38.13     40.06    40.06 18093467576     0.00     0.00  unsigned char& cv::Mat::at<unsigned char>(int, int)
 24.02     65.30    25.23     1000    25.23    47.05  computeSeam(cv::_InputArray const&, std::vector<int, std::allocator<int> >&)
 11.98     77.88    12.59     1000    12.59    32.07  void carveSeam<unsigned char>(cv::Mat&, std::vector<int, std::allocator<int> >&)
  7.63     85.90     8.01 3963002390     0.00     0.00  int& cv::Mat::at<int>(int, int)
  6.77     93.01     7.11     1000     7.11    23.07  detectEdge(cv::_InputArray const&, cv::_OutputArray const&)
  2.56     95.70     2.69 1320600000     0.00     0.00  cvRound(double)
  2.55     98.38     2.68                             cv::Size_<int>::Size_(int, int)
  2.25    100.75     2.37 1320600000     0.00     0.00  unsigned char cv::saturate_cast<unsigned char>(double)
  2.02    102.88     2.13 1320600000     0.00     0.00  unsigned char cv::saturate_cast<unsigned char>(int)
  1.87    104.84     1.96 1320600000     0.00     0.00  std::vector<int, std::allocator<int> >::operator[](unsigned long)
  0.23    105.08     0.24                             frame_dummy
  0.04    105.12     0.04     1000     0.04     0.04  std::vector<int, std::allocator<int> >::resize(unsigned long, int)


KmeansPlusPlus

Kmeansplusplus is a clustering method that determins which in this case takes an image and splits it into k number of clusters. For an image it selects k number of pixels and uses those pixels as a reference point to compare all the other pixels to change their colors based on which reference pixel they are closest to. The first integer is k (the number of reference points), the second integer is the number of times to iterate through the image.


Original source code can be found here


Opencv setup instructions (linux):

Required : opencv, cmake, g++, make, gprof


1) sudo apt-get install build-essential cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev

2) Downoad opencv version 2.4.13 here and extract it

3) cd opencv-2.4.13

4) mkdir build

5) cd build

6) cmake -D CMAKE_BUILD_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local ..

7) make -j7

8) sudo make install


At this point we recomend you test to see if opencv is working correctly by following this


Build KmeansPlusPlus :

1) create "CMakeLists.txt"

   cmake_minimum_required(VERSION 2.8)
   project( Kmeans++ )
   find_package( OpenCV REQUIRED )
   add_executable( Kmeans++ main.cpp mt19937ar.c )
   target_link_libraries( Kmeans++ ${OpenCV_LIBS} )

2) cmake -DCMAKE_CXX_FLAGS=-pg -DCMAKE_EXE_LINKER_FLAGS=-pg -DCMAKE_SHARED_LINKER_FLAGS=-pg .

3) make

  Note possible errors (if compiling from original source):
      1) Error : "opencv2\opencv.hpp: No such file or directory #include <opencv2\opencv.hpp>"
         Solution : Change '<opencv2\opencv.hpp>' to '<opencv2/opencv.hpp>'
      2) Error : " 'printf' was not declared in this scope"
         Solution : add "#include <stdio.h>"

4) ./Kmeans++ <source_img> <output_img> <#clusters> <#iterations>

5) gprof -p -b ./Kmeans++ > Kmeans++.flt


Original Image :

Baboon.jpg


Test Cases:


Command line: ./Kmeans++ baboon.jpg baboon_out_5x100.jpg 5 100

Flat profile:

Each sample counts as 0.01 seconds.

 %   cumulative   self              self     total           
time   seconds   seconds    calls   s/call   s/call  name    
50.51      1.60     1.60 951584265     0.00     0.00  float& cv::Mat::at<float>(int, int)
42.62      2.95     1.35        1     1.35     3.15  kmeanspp(cv::Mat&, cv::Mat&, cv::Mat&, cv::Mat&, int, int)
 4.26      3.09     0.14 78906844     0.00     0.00  int& cv::Mat::at<int>(int, int)
 1.89      3.15     0.06        1     0.06     0.06  std::vector<double, std::allocator<double> >::~vector()
 0.32      3.16     0.01  1572864     0.00     0.00  unsigned char& cv::Mat::at<unsigned char>(int, int)
 0.32      3.17     0.01                             main
 0.16      3.17     0.01  3053814     0.00     0.00  std::vector<double, std::allocator<double> >::operator[](unsigned long)

Baboon out 5x100.jpg


Command line: ./Kmeans++ baboon.jpg baboon_out_5x500.jpg 5 500

Flat profile:

Each sample counts as 0.01 seconds.

 %   cumulative   self              self     total           
time   seconds   seconds    calls   s/call   s/call  name    
45.62      7.34     7.34        1     7.34    16.11  kmeanspp(cv::Mat&, cv::Mat&, cv::Mat&, cv::Mat&, int, int)
43.88     14.41     7.06 4726463865     0.00     0.00  float& cv::Mat::at<float>(int, int)
 6.90     15.52     1.11 393485644     0.00     0.00  int& cv::Mat::at<int>(int, int)
 3.17     16.03     0.51        1     0.51     0.51  std::vector<double, std::allocator<double> >::~vector()
 0.44     16.10     0.07  3273980     0.00     0.00  std::vector<double, std::allocator<double> >::operator[](unsigned long)
 0.06     16.11     0.01        1     0.01     0.01  std::vector<double, std::allocator<double> >::vector(unsigned long, double const&, std::allocator<double> const&)


Command line: ./Kmeans++ baboon.jpg baboon_out_100x5.jpg 100 5

Flat profile:

Each sample counts as 0.01 seconds.

 %   cumulative   self              self     total           
time   seconds   seconds    calls   s/call   s/call  name    
46.29      1.30     1.30        1     1.30     2.79  kmeanspp(cv::Mat&, cv::Mat&, cv::Mat&, cv::Mat&, int, int)
45.94      2.59     1.29 951585120     0.00     0.00  float& cv::Mat::at<float>(int, int)
 4.27      2.71     0.12 66348332     0.00     0.00  std::vector<double, std::allocator<double> >::operator[](unsigned long)
 2.49      2.78     0.07        1     0.07     0.07  std::vector<double, std::allocator<double> >::~vector()
 0.71      2.80     0.02                             main
 0.36      2.81     0.01        1     0.01     0.01  std::vector<double, std::allocator<double> >::vector(unsigned long, double const&, std::allocator<double> const&)


Command line: ./Kmeans++ baboon.jpg baboon_out_500x5.jpg 500 5

Flat profile:

Each sample counts as 0.01 seconds.

 %   cumulative   self              self     total           
time   seconds   seconds    calls   s/call   s/call  name    
49.65      7.07     7.07 4726468320     0.00     0.00  float& cv::Mat::at<float>(int, int)
44.97     13.47     6.40        1     6.40    14.22  kmeanspp(cv::Mat&, cv::Mat&, cv::Mat&, cv::Mat&, int, int)
 2.53     13.83     0.36 327813766     0.00     0.00  std::vector<double, std::allocator<double> >::operator[](unsigned long)
 2.28     14.16     0.33        1     0.33     0.33  std::vector<double, std::allocator<double> >::~vector()
 0.42     14.22     0.06        1     0.06     0.06  std::vector<double, std::allocator<double> >::vector(unsigned long, double const&, std::allocator<double> const&)
 0.14     14.24     0.02                             main
 0.07     14.25     0.01  1572864     0.00     0.00  unsigned char& cv::Mat::at<unsigned char>(int, int)


Command line: ./Kmeans++ baboon.jpg baboon_out_100x100.jpg 100 100

Flat profile:

Each sample counts as 0.01 seconds.

 %   cumulative   self              self     total           
time   seconds   seconds    calls   s/call   s/call  name    
69.03     60.68    60.68        1    60.68    87.95  kmeanspp(cv::Mat&, cv::Mat&, cv::Mat&, cv::Mat&, int, int)
28.80     86.00    25.32 7453309108     0.00     0.00  float& cv::Mat::at<float>(int, int)
 1.95     87.71     1.71        1     1.71     1.71  std::vector<double, std::allocator<double> >::~vector()
 0.15     87.84     0.14 78935344     0.00     0.00  int& cv::Mat::at<int>(int, int)
 0.11     87.94     0.10 66396782     0.00     0.00  std::vector<double, std::allocator<double> >::operator[](unsigned long)
 0.02     87.96     0.02        1     0.02     0.02  std::vector<double, std::allocator<double> >::vector(unsigned long, double const&, std::allocator<double> const&)


Command line: ./Kmeans++ baboon.jpg baboon_out_500x500.jpg 500 500

Flat profile:

Each sample counts as 0.01 seconds.

 %   cumulative   self              self     total           
time   seconds   seconds    calls  Ks/call  Ks/call  name    
54.00    919.48   919.48        1     0.92     1.70  kmeanspp(cv::Mat&, cv::Mat&, cv::Mat&, cv::Mat&, int, int)
44.16   1671.44   751.95 8242561860     0.00     0.00  float& cv::Mat::at<float>(int, int)
 1.82   1702.36    30.93        1     0.03     0.03  std::vector<double, std::allocator<double> >::~vector()
 0.06   1703.31     0.95 394228144     0.00     0.00  int& cv::Mat::at<int>(int, int)
 0.03   1703.75     0.44 329563076     0.00     0.00  std::vector<double, std::allocator<double> >::operator[](unsigned long)
 0.00   1703.78     0.03        1     0.00     0.00  std::vector<double, std::allocator<double> >::vector(unsigned long, double const&, std::allocator<double> const&)
 0.00   1703.79     0.01  1572864     0.00     0.00  unsigned char& cv::Mat::at<unsigned char>(int, int)
 0.00   1703.80     0.01        1     0.00     0.00  __gnu_cxx::__enable_if<std::__is_scalar<double>::__value, double*>::__type std::__fill_n_a<double*, unsigned long, double>(double*, unsigned long, double const&)

Baboon out 500x500.jpg


As you can see if you select a lot of clusters the image will appear very similar to the original but if you select a small number of clusters most of the detail is gone.