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 12:21, 12 February 2017
Contents
BarraCUDA Boiz
Team Members
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 File:TestImage2.jpg
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 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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:After:
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 :
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)
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&)
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.
