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tribta
8 years ago
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6 changed files with 411 additions and 139 deletions
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/**
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* file Smoothing.cpp |
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* brief Sample code for simple filters |
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* author OpenCV team |
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*/ |
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#include "opencv2/imgproc.hpp" |
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#include "opencv2/imgcodecs.hpp" |
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#include "opencv2/highgui.hpp" |
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using namespace std; |
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using namespace cv; |
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/// Global Variables
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int DELAY_CAPTION = 1500; |
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int DELAY_BLUR = 100; |
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int MAX_KERNEL_LENGTH = 31; |
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Mat src; Mat dst; |
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char window_name[] = "Smoothing Demo"; |
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/// Function headers
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int display_caption( const char* caption ); |
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int display_dst( int delay ); |
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/**
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* function main |
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*/ |
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int main( void ) |
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{ |
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namedWindow( window_name, WINDOW_AUTOSIZE ); |
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/// Load the source image
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src = imread( "../data/lena.jpg", IMREAD_COLOR ); |
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if( display_caption( "Original Image" ) != 0 ) { return 0; } |
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dst = src.clone(); |
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if( display_dst( DELAY_CAPTION ) != 0 ) { return 0; } |
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/// Applying Homogeneous blur
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if( display_caption( "Homogeneous Blur" ) != 0 ) { return 0; } |
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//![blur]
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for ( int i = 1; i < MAX_KERNEL_LENGTH; i = i + 2 ) |
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{ blur( src, dst, Size( i, i ), Point(-1,-1) ); |
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if( display_dst( DELAY_BLUR ) != 0 ) { return 0; } } |
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//![blur]
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/// Applying Gaussian blur
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if( display_caption( "Gaussian Blur" ) != 0 ) { return 0; } |
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//![gaussianblur]
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for ( int i = 1; i < MAX_KERNEL_LENGTH; i = i + 2 ) |
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{ GaussianBlur( src, dst, Size( i, i ), 0, 0 ); |
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if( display_dst( DELAY_BLUR ) != 0 ) { return 0; } } |
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//![gaussianblur]
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/// Applying Median blur
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if( display_caption( "Median Blur" ) != 0 ) { return 0; } |
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//![medianblur]
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for ( int i = 1; i < MAX_KERNEL_LENGTH; i = i + 2 ) |
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{ medianBlur ( src, dst, i ); |
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if( display_dst( DELAY_BLUR ) != 0 ) { return 0; } } |
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//![medianblur]
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/// Applying Bilateral Filter
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if( display_caption( "Bilateral Blur" ) != 0 ) { return 0; } |
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//![bilateralfilter]
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for ( int i = 1; i < MAX_KERNEL_LENGTH; i = i + 2 ) |
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{ bilateralFilter ( src, dst, i, i*2, i/2 ); |
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if( display_dst( DELAY_BLUR ) != 0 ) { return 0; } } |
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//![bilateralfilter]
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/// Wait until user press a key
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display_caption( "End: Press a key!" ); |
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waitKey(0); |
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return 0; |
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} |
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/**
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* @function display_caption |
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*/ |
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int display_caption( const char* caption ) |
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{ |
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dst = Mat::zeros( src.size(), src.type() ); |
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putText( dst, caption, |
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Point( src.cols/4, src.rows/2), |
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FONT_HERSHEY_COMPLEX, 1, Scalar(255, 255, 255) ); |
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imshow( window_name, dst ); |
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int c = waitKey( DELAY_CAPTION ); |
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if( c >= 0 ) { return -1; } |
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return 0; |
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} |
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/**
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* @function display_dst |
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*/ |
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int display_dst( int delay ) |
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{ |
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imshow( window_name, dst ); |
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int c = waitKey ( delay ); |
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if( c >= 0 ) { return -1; } |
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return 0; |
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} |
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@ -0,0 +1,115 @@ |
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/**
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* file Smoothing.cpp |
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* brief Sample code for simple filters |
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* author OpenCV team |
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*/ |
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#include <iostream> |
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#include "opencv2/imgproc.hpp" |
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#include "opencv2/imgcodecs.hpp" |
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#include "opencv2/highgui.hpp" |
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using namespace std; |
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using namespace cv; |
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/// Global Variables
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int DELAY_CAPTION = 1500; |
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int DELAY_BLUR = 100; |
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int MAX_KERNEL_LENGTH = 31; |
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Mat src; Mat dst; |
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char window_name[] = "Smoothing Demo"; |
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/// Function headers
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int display_caption( const char* caption ); |
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int display_dst( int delay ); |
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/**
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* function main |
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*/ |
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int main( int argc, char ** argv ) |
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{ |
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namedWindow( window_name, WINDOW_AUTOSIZE ); |
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/// Load the source image
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const char* filename = argc >=2 ? argv[1] : "../data/lena.jpg"; |
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src = imread( filename, IMREAD_COLOR ); |
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if(src.empty()){ |
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printf(" Error opening image\n"); |
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printf(" Usage: ./Smoothing [image_name -- default ../data/lena.jpg] \n"); |
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return -1; |
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} |
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if( display_caption( "Original Image" ) != 0 ) { return 0; } |
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dst = src.clone(); |
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if( display_dst( DELAY_CAPTION ) != 0 ) { return 0; } |
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/// Applying Homogeneous blur
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if( display_caption( "Homogeneous Blur" ) != 0 ) { return 0; } |
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//![blur]
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for ( int i = 1; i < MAX_KERNEL_LENGTH; i = i + 2 ) |
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{ blur( src, dst, Size( i, i ), Point(-1,-1) ); |
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if( display_dst( DELAY_BLUR ) != 0 ) { return 0; } } |
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//![blur]
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/// Applying Gaussian blur
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if( display_caption( "Gaussian Blur" ) != 0 ) { return 0; } |
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//![gaussianblur]
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for ( int i = 1; i < MAX_KERNEL_LENGTH; i = i + 2 ) |
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{ GaussianBlur( src, dst, Size( i, i ), 0, 0 ); |
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if( display_dst( DELAY_BLUR ) != 0 ) { return 0; } } |
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//![gaussianblur]
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/// Applying Median blur
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if( display_caption( "Median Blur" ) != 0 ) { return 0; } |
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//![medianblur]
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for ( int i = 1; i < MAX_KERNEL_LENGTH; i = i + 2 ) |
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{ medianBlur ( src, dst, i ); |
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if( display_dst( DELAY_BLUR ) != 0 ) { return 0; } } |
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//![medianblur]
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/// Applying Bilateral Filter
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if( display_caption( "Bilateral Blur" ) != 0 ) { return 0; } |
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//![bilateralfilter]
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for ( int i = 1; i < MAX_KERNEL_LENGTH; i = i + 2 ) |
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{ bilateralFilter ( src, dst, i, i*2, i/2 ); |
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if( display_dst( DELAY_BLUR ) != 0 ) { return 0; } } |
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//![bilateralfilter]
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/// Done
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display_caption( "Done!" ); |
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return 0; |
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} |
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/**
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* @function display_caption |
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*/ |
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int display_caption( const char* caption ) |
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{ |
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dst = Mat::zeros( src.size(), src.type() ); |
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putText( dst, caption, |
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Point( src.cols/4, src.rows/2), |
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FONT_HERSHEY_COMPLEX, 1, Scalar(255, 255, 255) ); |
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return display_dst(DELAY_CAPTION); |
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} |
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/**
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* @function display_dst |
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*/ |
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int display_dst( int delay ) |
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{ |
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imshow( window_name, dst ); |
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int c = waitKey ( delay ); |
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if( c >= 0 ) { return -1; } |
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return 0; |
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} |
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@ -0,0 +1,101 @@ |
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import org.opencv.core.*; |
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import org.opencv.highgui.HighGui; |
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import org.opencv.imgcodecs.Imgcodecs; |
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import org.opencv.imgproc.Imgproc; |
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class SmoothingRun { |
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/// Global Variables
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int DELAY_CAPTION = 1500; |
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int DELAY_BLUR = 100; |
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int MAX_KERNEL_LENGTH = 31; |
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Mat src = new Mat(), dst = new Mat(); |
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String windowName = "Filter Demo 1"; |
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public void run(String[] args) { |
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String filename = ((args.length > 0) ? args[0] : "../data/lena.jpg"); |
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src = Imgcodecs.imread(filename, Imgcodecs.IMREAD_COLOR); |
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if( src.empty() ) { |
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System.out.println("Error opening image"); |
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System.out.println("Usage: ./Smoothing [image_name -- default ../data/lena.jpg] \n"); |
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System.exit(-1); |
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} |
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if( displayCaption( "Original Image" ) != 0 ) { System.exit(0); } |
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dst = src.clone(); |
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if( displayDst( DELAY_CAPTION ) != 0 ) { System.exit(0); } |
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/// Applying Homogeneous blur
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if( displayCaption( "Homogeneous Blur" ) != 0 ) { System.exit(0); } |
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//! [blur]
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for (int i = 1; i < MAX_KERNEL_LENGTH; i = i + 2) { |
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Imgproc.blur(src, dst, new Size(i, i), new Point(-1, -1)); |
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displayDst(DELAY_BLUR); |
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} |
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//! [blur]
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/// Applying Gaussian blur
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if( displayCaption( "Gaussian Blur" ) != 0 ) { System.exit(0); } |
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//! [gaussianblur]
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for (int i = 1; i < MAX_KERNEL_LENGTH; i = i + 2) { |
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Imgproc.GaussianBlur(src, dst, new Size(i, i), 0, 0); |
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displayDst(DELAY_BLUR); |
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} |
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//! [gaussianblur]
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/// Applying Median blur
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if( displayCaption( "Median Blur" ) != 0 ) { System.exit(0); } |
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//! [medianblur]
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for (int i = 1; i < MAX_KERNEL_LENGTH; i = i + 2) { |
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Imgproc.medianBlur(src, dst, i); |
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displayDst(DELAY_BLUR); |
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} |
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//! [medianblur]
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/// Applying Bilateral Filter
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if( displayCaption( "Bilateral Blur" ) != 0 ) { System.exit(0); } |
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//![bilateralfilter]
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for (int i = 1; i < MAX_KERNEL_LENGTH; i = i + 2) { |
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Imgproc.bilateralFilter(src, dst, i, i * 2, i / 2); |
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displayDst(DELAY_BLUR); |
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} |
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//![bilateralfilter]
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/// Done
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displayCaption( "Done!" ); |
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System.exit(0); |
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} |
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int displayCaption(String caption) { |
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dst = Mat.zeros(src.size(), src.type()); |
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Imgproc.putText(dst, caption, |
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new Point(src.cols() / 4, src.rows() / 2), |
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Core.FONT_HERSHEY_COMPLEX, 1, new Scalar(255, 255, 255)); |
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return displayDst(DELAY_CAPTION); |
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} |
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int displayDst(int delay) { |
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HighGui.imshow( windowName, dst ); |
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int c = HighGui.waitKey( delay ); |
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if (c >= 0) { return -1; } |
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return 0; |
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} |
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} |
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public class Smoothing { |
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public static void main(String[] args) { |
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// Load the native library.
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System.loadLibrary(Core.NATIVE_LIBRARY_NAME); |
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new SmoothingRun().run(args); |
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} |
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} |
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@ -0,0 +1,107 @@ |
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import sys |
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import cv2 |
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import numpy as np |
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# Global Variables |
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DELAY_CAPTION = 1500 |
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DELAY_BLUR = 100 |
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MAX_KERNEL_LENGTH = 31 |
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src = None |
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dst = None |
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window_name = 'Smoothing Demo' |
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def main(argv): |
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cv2.namedWindow(window_name, cv2.WINDOW_AUTOSIZE) |
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# Load the source image |
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imageName = argv[0] if len(argv) > 0 else "../data/lena.jpg" |
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global src |
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src = cv2.imread(imageName, 1) |
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if src is None: |
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print ('Error opening image') |
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print ('Usage: smoothing.py [image_name -- default ../data/lena.jpg] \n') |
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return -1 |
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if display_caption('Original Image') != 0: |
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return 0 |
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global dst |
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dst = np.copy(src) |
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if display_dst(DELAY_CAPTION) != 0: |
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return 0 |
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# Applying Homogeneous blur |
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if display_caption('Homogeneous Blur') != 0: |
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return 0 |
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## [blur] |
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for i in range(1, MAX_KERNEL_LENGTH, 2): |
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dst = cv2.blur(src, (i, i)) |
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if display_dst(DELAY_BLUR) != 0: |
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return 0 |
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## [blur] |
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# Applying Gaussian blur |
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if display_caption('Gaussian Blur') != 0: |
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return 0 |
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## [gaussianblur] |
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for i in range(1, MAX_KERNEL_LENGTH, 2): |
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dst = cv2.GaussianBlur(src, (i, i), 0) |
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if display_dst(DELAY_BLUR) != 0: |
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return 0 |
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## [gaussianblur] |
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# Applying Median blur |
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if display_caption('Median Blur') != 0: |
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return 0 |
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## [medianblur] |
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for i in range(1, MAX_KERNEL_LENGTH, 2): |
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dst = cv2.medianBlur(src, i) |
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if display_dst(DELAY_BLUR) != 0: |
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return 0 |
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## [medianblur] |
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# Applying Bilateral Filter |
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if display_caption('Bilateral Blur') != 0: |
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return 0 |
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## [bilateralfilter] |
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# Remember, bilateral is a bit slow, so as value go higher, it takes long time |
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for i in range(1, MAX_KERNEL_LENGTH, 2): |
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dst = cv2.bilateralFilter(src, i, i * 2, i / 2) |
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if display_dst(DELAY_BLUR) != 0: |
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return 0 |
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## [bilateralfilter] |
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# Done |
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display_caption('Done!') |
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return 0 |
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def display_caption(caption): |
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global dst |
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dst = np.zeros(src.shape, src.dtype) |
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rows, cols, ch = src.shape |
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cv2.putText(dst, caption, |
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(int(cols / 4), int(rows / 2)), |
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cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255)) |
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return display_dst(DELAY_CAPTION) |
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def display_dst(delay): |
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cv2.imshow(window_name, dst) |
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c = cv2.waitKey(delay) |
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if c >= 0 : return -1 |
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return 0 |
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if __name__ == "__main__": |
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main(sys.argv[1:]) |
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