图像滤波

#include <iostream>
#include <opencv2/core.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>

/////////////////////////////////////////
//求积分图-优化方法
//由上方negral(i-1,j)加上当前行的和即可
//对于W*H图像：2*(W-1)*(H-1)次加减法
//比常规方法快1.5倍左右
/////////////////////////////////////////
void Fast_integral(cv::Mat& src, cv::Mat& dst) {
	int nr = src.rows;
	int nc = src.cols;
	int sum_r = 0;
	dst = cv::Mat::zeros(nr + 1, nc + 1, CV_64F);
	for (int i = 1; i < dst.rows; ++i) {
		for (int j = 1, sum_r = 0; j < dst.cols; ++j) {
			//行累加，因为积分图相当于在原图上方加一行，左边加一列，所以积分图的(1,1)对应原图(0,0),(i,j)对应(i-1,j-1)
			sum_r = src.at<uchar>(i - 1, j - 1) + sum_r; //行累加
			dst.at<double>(i, j) = dst.at<double>(i - 1, j) + sum_r;
		}
	}
}

//////////////////////////////////
//盒子滤波-均值滤波是其特殊情况
/////////////////////////////////
void BoxFilter(cv::Mat& src, cv::Mat& dst, cv::Size wsize, bool normalize) {

	//图像边界扩充
	if (wsize.height % 2 == 0 || wsize.width % 2 == 0) {
		fprintf(stderr, "Please enter odd size!");
		exit(-1);
	}
	int hh = (wsize.height - 1) / 2;
	int hw = (wsize.width - 1) / 2;
	cv::Mat Newsrc;
	cv::copyMakeBorder(src, Newsrc, hh, hh, hw, hw, cv::BORDER_REFLECT);//以边缘为轴，对称
	src.copyTo(dst);

	//计算积分图
	cv::Mat inte;
	Fast_integral(Newsrc, inte);

	//BoxFilter
	double mean = 0;
	for (int i = hh + 1; i < src.rows + hh + 1; ++i) {  //积分图图像比原图（边界扩充后的）多一行和一列 
		for (int j = hw + 1; j < src.cols + hw + 1; ++j) {
			double top_left = inte.at<double>(i - hh - 1, j - hw - 1);
			double top_right = inte.at<double>(i - hh - 1, j + hw);
			double buttom_left = inte.at<double>(i + hh, j - hw - 1);
			double buttom_right = inte.at<double>(i + hh, j + hw);
			if (normalize == true)
				mean = (buttom_right - top_right - buttom_left + top_left) / wsize.area();
			else
				mean = buttom_right - top_right - buttom_left + top_left;

			//一定要进行判断和数据类型转换
			if (mean < 0)
				mean = 0;
			else if (mean>255)
				mean = 255;
			dst.at<uchar>(i - hh - 1, j - hw - 1) = static_cast<uchar>(mean);

		}
	}
}

int main() {
	cv::Mat src = cv::imread("E:\\VS2015Opencv\\vs2015\\project\\picture\\06.jpg");
	if (src.empty()) {
		return -1;
	}

	//自编BoxFilter测试
	cv::Mat dst1;
	double t2 = (double)cv::getTickCount(); //测时间
	if (src.channels() > 1) {
		std::vector<cv::Mat> channel;
		cv::split(src, channel);
		BoxFilter(channel[0], channel[0], cv::Size(7, 7), true);//盒子滤波
		BoxFilter(channel[1], channel[1], cv::Size(7, 7), true);//盒子滤波
		BoxFilter(channel[2], channel[2], cv::Size(7, 7), true);//盒子滤波
		cv::merge(channel, dst1);
	}
	else
		BoxFilter(src, dst1, cv::Size(7, 7), true);//盒子滤波
	t2 = (double)cv::getTickCount() - t2;
	double time2 = (t2 *1000.) / ((double)cv::getTickFrequency());
	std::cout << "FASTmy_process=" << time2 << " ms. " << std::endl << std::endl;

	//opencv自带BoxFilter测试
	cv::Mat dst2;
	double t1 = (double)cv::getTickCount(); //测时间
	cv::boxFilter(src, dst2, -1, cv::Size(7, 7), cv::Point(-1, -1), true, cv::BORDER_CONSTANT);//盒子滤波
	t1 = (double)cv::getTickCount() - t1;
	double time1 = (t1 *1000.) / ((double)cv::getTickFrequency());
	std::cout << "Opencvbox_process=" << time1 << " ms. " << std::endl << std::endl;

	cv::namedWindow("src");
	cv::imshow("src", src);
	cv::namedWindow("ourdst", CV_WINDOW_NORMAL);
	cv::imshow("ourdst", dst1);
	cv::namedWindow("opencvdst", CV_WINDOW_NORMAL);
	cv::imshow("opencvdst", dst2);
	cv::waitKey(0);

}