OpenCV系列笔记十七：Adaptive thresholding

Overview

在介绍Adaptive thresholding之前，让我们来看看Integral Image的概念：

Integral images have been introduced as an efficient way of summing
pixels in image regions of interest. They are widely used in applications
that involve, for example, computations over sliding windows at multiple
scales.

可以看到，Integral Image是避免重复计算多个ROI像素值和的一种方法。接下来，我们实现的就是简单的矩形ROI，使得Integral Image上的每一个点的值为：该点和图像左上角形成的矩形区域像素值之和。

而Adaptive thresholding 是相对于fixed thresholding而言的。fixed就是我们之前常用的固定阀值，而Adaptive的阀值是由一个固定阀值和一个变化的均值决定的，这个均值，就是某像素点周围一些像素点的均值。

Code

integral.h

实际上，OpenCV已经提供了计算Integral Image的函数cv::integral，但是书上也自己实现了一边，我们这里也贴上。

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73

#define IINTEGRAL #include <opencv2/core.hpp> #include <opencv2/highgui.hpp> #include <opencv2/imgproc.hpp> #include <vector> template <typename T, int N> class IntegralImage { cv::Mat integralImage; public: IntegralImage(cv::Mat image) { cv::integral(image,integralImage,cv::DataType<T>::type); } // compute sum over sub-regions of any size from 4 pixel access cv::Vec<T,N> operator()(int xo, int yo, int width, int height) { // window at (xo,yo) of size width by height return (integralImage.at<cv::Vec<T,N> >(yo+height,xo+width) -integralImage.at<cv::Vec<T,N> >(yo+height,xo) -integralImage.at<cv::Vec<T,N> >(yo,xo+width) +integralImage.at<cv::Vec<T,N> >(yo,xo)); } // compute sum over sub-regions of any size from 4 pixel access cv::Vec<T,N> operator()(int x, int y, int radius) { // square window centered at (x,y) of size 2*radius+1 return (integralImage.at<cv::Vec<T,N> >(y+radius+1,x+radius+1) -integralImage.at<cv::Vec<T,N> >(y+radius+1,x-radius) -integralImage.at<cv::Vec<T,N> >(y-radius,x+radius+1) +integralImage.at<cv::Vec<T,N> >(y-radius,x-radius)); } }; // convert to a multi-channel image made of binary planes // nPlanes must be a power of 2 void (const cv::Mat& input, cv::Mat& output, int nPlanes) { // number of bits to mask out int n= 8-static_cast<int>(log(static_cast<double>(nPlanes))/log(2.0)); // mask used to eliminate least significant bits uchar mask= 0xFF<<n; // e.g. for div=16, mask= 0xF0 // create a vector of 16 binary images std::vector<cv::Mat> planes; // reduce to nBins bins by eliminating least significant bits cv::Mat reduced= input&mask; // compute each binary image plane for (int i=0; i<nPlanes; i++) { // 1 for each pixel equals to i<<shift planes.push_back((reduced==(i<<n))&0x1); // i<<n --> 16, 32, 48, ... } // create multi-channel image cv::merge(planes,output); } #endif

main.cpp

这里介绍了，OpenCV自带的fixed thresholding， Adaptive thresholding, boxFilter的用法，同时运用Integral Image重新实现了一遍Adaptive thresholding.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 大专栏  OpenCV系列笔记十七：Adaptive thresholding>91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133

#include <cv.h> #include <highgui.h> #include <opencv2/imgcodecs.hpp> #include <opencv/cv.hpp> #include <iostream> #include "integral.h" int main(){ cv::Mat image = cv::imread("/home/shensir/Documents/MyPrograming/Cpp/Clions/data/book.png",0); // display original image cv::namedWindow("Original Image"); cv::imshow("Original Image", image); // using a fixed threshold cv::Mat binaryFixed; cv::Mat binaryAdaptive; cv::threshold(image, binaryFixed,70,255,cv::THRESH_BINARY); //using as adaptive threshold int blockSize=21; //size of the neighborhood int threshold=10; // pixel will be compare to (mean-threshold) int64 time; time = cv::getTickCount(); cv::adaptiveThreshold( image, //input image binaryAdaptive, // output binary image 255, //max value for output cv::ADAPTIVE_THRESH_GAUSSIAN_C, // adaptive method cv::THRESH_BINARY, //threshold type blockSize, threshold ); time = cv::getTickCount() - time; std::cout<<"time (adaptiveThreshold) = "<<time<<endl; // compute integral image IntegralImage<int, 1>integral(image); // test integral image // cout<<"sum = "<<integral(18, 45,30, 50)<<endl; // cv::Mat test(image, cv::Rect(18,45,30,50)); // cv::Scalar t = cv::sum(test); // cout<<"sum test = "<<t[0]<<endl; cv::namedWindow("Fixed Threshold"); cv::imshow("Fixed Threshold", binaryFixed); cv::namedWindow("Adaptive Threshold"); cv::imshow("Adaptive Threshold", binaryAdaptive); cv::Mat binary = image.clone(); time = cv::getTickCount(); int nl = binary.rows; // number of lines int nc = binary.cols; // total number of elements per line // compute integral image cv::Mat iimage; cv::integral(image, iimage, CV_32S); //for each row int halfSize = blockSize/2; for(int j=halfSize; j<nl - halfSize -1;j++ ){ // get the address of row j uchar* data = binary.ptr<uchar>(j); int* idata1 = iimage.ptr<int>(j-halfSize); // 滑动窗口上边 int* idata2 = iimage.ptr<int>(j+halfSize+1); // 滑动窗口下边 //for pixel of a line for(int i=halfSize; i<nc-halfSize-1;i++){ //compute pix_mean int pix_mean = (idata2[i+halfSize+1]-idata2[i-halfSize]-idata1[i+halfSize+1] +idata1[i-halfSize])/(blockSize*blockSize); //apply adaptive threshold if(data[i]<(pix_mean-threshold)) data[i] = 0; else data[i] =255; } } // add white border for(int j=0;j<halfSize;j++){ uchar *data = binary.ptr<uchar>(j); for(int i=0; i<binary.cols;i++) data[i] = 255; } for(int j=binary.rows-halfSize-1;j<binary.rows;j++){ uchar * data = binary.ptr<uchar>(j); for(int i=0; i<binary.cols;i++){ data[i] = 255; } } for(int j=halfSize;j<nl-halfSize-1;j++){ uchar* data = binary.ptr<uchar>(j); for(int i=0; i<halfSize;i++) data[i] = 255; for(int i=binary.cols-halfSize-1;i<binary.cols;i++) data[i] = 255; } time = cv::getTickCount()-time; cout<<"time integral= "<<time<<endl; cv::namedWindow("Adaptive Threshold (integral)"); cv::imshow("Adaptive Threshold (integral)", binary); // adaptive threshold using image operators time = cv::getTickCount(); cv::Mat filtered; cv::Mat binaryFiltered; // box filter compute avg of pixels over a rectangle region cv::boxFilter(image, filtered, CV_8U, cv::Size(blockSize,blockSize)); // check if pixel greater than (mean+shreshold) binaryFiltered = image>=(filtered-threshold); time = cv::getTickCount()-time; cout<<"time filtered= "<<time<<endl; cv::namedWindow("Adaptive filtered"); cv::imshow("Adaptive filtered", binaryFiltered); cv::waitKey(); return 0; }

输出：

time (adaptiveThreshold) = 2305893
time integral= 7118664
time filtered= 1179760