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| 3 | PointedEar | 1 | /*------------------------------------------------------------------------------------------*\ |
| 2 | This file contains material supporting chapter 6 of the cookbook: |
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| 3 | Computer Vision Programming using the OpenCV Library. |
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| 4 | by Robert Laganiere, Packt Publishing, 2011. |
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| 5 | |||
| 6 | This program is free software; permission is hereby granted to use, copy, modify, |
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| 7 | and distribute this source code, or portions thereof, for any purpose, without fee, |
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| 8 | subject to the restriction that the copyright notice may not be removed |
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| 9 | or altered from any source or altered source distribution. |
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| 10 | The software is released on an as-is basis and without any warranties of any kind. |
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| 11 | In particular, the software is not guaranteed to be fault-tolerant or free from failure. |
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| 12 | The author disclaims all warranties with regard to this software, any use, |
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| 13 | and any consequent failure, is purely the responsibility of the user. |
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| 14 | |||
| 15 | Copyright (C) 2010-2011 Robert Laganiere, www.laganiere.name |
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| 16 | \*------------------------------------------------------------------------------------------*/ |
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| 17 | |||
| 18 | #include <iostream> |
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| 19 | #include <opencv2/core/core.hpp> |
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| 20 | #include <opencv2/imgproc/imgproc.hpp> |
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| 21 | #include <opencv2/highgui/highgui.hpp> |
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| 22 | |||
| 23 | int main() |
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| 24 | { |
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| 25 | // Read input image |
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| 26 | cv::Mat image= cv::imread("../boldt.jpg",0); |
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| 27 | if (!image.data) |
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| 28 | return 0; |
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| 29 | |||
| 30 | // Display the image |
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| 31 | cv::namedWindow("Original Image"); |
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| 32 | cv::imshow("Original Image",image); |
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| 33 | |||
| 34 | // Blur the image |
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| 35 | cv::Mat result; |
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| 36 | cv::GaussianBlur(image,result,cv::Size(5,5),1.5); |
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| 37 | |||
| 38 | // Display the blurred image |
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| 39 | cv::namedWindow("Gaussian filtered Image"); |
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| 40 | cv::imshow("Gaussian filtered Image",result); |
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| 41 | |||
| 42 | // Get the gaussian kernel (1.5) |
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| 43 | cv::Mat gauss= cv::getGaussianKernel(9,1.5,CV_32F); |
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| 44 | |||
| 45 | // Display kernel values |
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| 46 | cv::Mat_<float>::const_iterator it= gauss.begin<float>(); |
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| 47 | cv::Mat_<float>::const_iterator itend= gauss.end<float>(); |
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| 48 | std::cout << "["; |
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| 49 | for ( ; it!= itend; ++it) { |
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| 50 | std::cout << *it << " "; |
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| 51 | } |
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| 52 | std::cout << "]" << std::endl; |
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| 53 | |||
| 54 | // Get the gaussian kernel (0.5) |
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| 55 | gauss= cv::getGaussianKernel(9,0.5,CV_32F); |
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| 56 | |||
| 57 | // Display kernel values |
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| 58 | it= gauss.begin<float>(); |
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| 59 | itend= gauss.end<float>(); |
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| 60 | std::cout << "["; |
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| 61 | for ( ; it!= itend; ++it) { |
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| 62 | std::cout << *it << " "; |
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| 63 | } |
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| 64 | std::cout << "]" << std::endl; |
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| 65 | |||
| 66 | // Get the gaussian kernel (2.5) |
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| 67 | gauss= cv::getGaussianKernel(9,2.5,CV_32F); |
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| 68 | |||
| 69 | // Display kernel values |
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| 70 | it= gauss.begin<float>(); |
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| 71 | itend= gauss.end<float>(); |
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| 72 | std::cout << "["; |
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| 73 | for ( ; it!= itend; ++it) { |
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| 74 | std::cout << *it << " "; |
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| 75 | } |
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| 76 | std::cout << "]" << std::endl; |
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| 77 | |||
| 78 | // Get the Deriv kernel (2.5) |
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| 79 | cv::Mat kx, ky; |
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| 80 | cv::getDerivKernels(kx,ky,2,2,7,true); |
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| 81 | |||
| 82 | // Display kernel values |
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| 83 | cv::Mat_<float>::const_iterator kit= kx.begin<float>(); |
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| 84 | cv::Mat_<float>::const_iterator kitend= kx.end<float>(); |
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| 85 | std::cout << "["; |
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| 86 | for ( ; kit!= kitend; ++kit) { |
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| 87 | std::cout << *kit << " "; |
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| 88 | } |
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| 89 | std::cout << "]" << std::endl; |
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| 90 | |||
| 91 | // Blur the image with a mean filter |
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| 92 | cv::blur(image,result,cv::Size(5,5)); |
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| 93 | |||
| 94 | // Display the blurred image |
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| 95 | cv::namedWindow("Mean filtered Image"); |
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| 96 | cv::imshow("Mean filtered Image",result); |
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| 97 | |||
| 98 | // Read input image with salt&pepper noise |
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| 99 | image= cv::imread("../salted.bmp",0); |
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| 100 | if (!image.data) |
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| 101 | return 0; |
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| 102 | |||
| 103 | // Display the S&P image |
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| 104 | cv::namedWindow("S&P Image"); |
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| 105 | cv::imshow("S&P Image",image); |
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| 106 | |||
| 107 | // Blur the image with a mean filter |
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| 108 | cv::blur(image,result,cv::Size(5,5)); |
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| 109 | |||
| 110 | // Display the blurred image |
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| 111 | cv::namedWindow("Mean filtered S&P Image"); |
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| 112 | cv::imshow("Mean filtered S&P Image",result); |
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| 113 | |||
| 114 | // Applying a median filter |
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| 115 | cv::medianBlur(image,result,5); |
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| 116 | |||
| 117 | // Display the blurred image |
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| 118 | cv::namedWindow("Median filtered S&P Image"); |
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| 119 | cv::imshow("Median filtered S&P Image",result); |
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| 120 | |||
| 121 | // Reduce by 4 the size of the image (the wrong way) |
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| 122 | image= cv::imread("../boldt.jpg",0); |
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| 123 | cv::Mat reduced(image.rows/2,image.cols/2,CV_8U); |
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| 124 | |||
| 125 | for (int i=0; i<reduced.rows; i++) |
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| 126 | for (int j=0; j<reduced.cols; j++) |
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| 127 | reduced.at<uchar>(i,j)= image.at<uchar>(i*2,j*2); |
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| 128 | |||
| 129 | // Display the reduced image |
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| 130 | cv::namedWindow("Badly reduced Image"); |
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| 131 | cv::imshow("Badly reduced Image",reduced); |
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| 132 | |||
| 133 | cv::waitKey(); |
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| 134 | return 0; |
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| 135 | } |