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Tutorial Smoothing Images
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tribta
@@ -1,112 +0,0 @@
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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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