//-------------------------------------------------------------
//作者：不用先生，2018.11.26
//自實現的圖像雙邊濾波算法
//bilateral.cpp
//-------------------------------------------------------------
#include?“opencv2/core/core.hpp“
#include?“opencv2/opencv.hpp“
#include?“opencv2/highgui.hpp“
#include?“opencv2/imgproc.hpp“
#include?
#include?

using?namespace?std;
using?namespace?cv;



//--------------------------------------------------------------
//函數名：my_Bilateral
//函數功能：自己編寫的可用于灰度圖及彩色圖的雙邊濾波函數；
//參數：Mat?&scr：輸入圖像，為單通道灰度圖或三通道彩色圖；
//參數：Mat?&dst：輸出圖像，尺寸與通道數與輸入圖像吻合；
//參數：int?d：濾波器大小，應該保證為奇數；
//參數：double?sigmaColor：顏色域/像素值域方差，sigmaColor越大，平滑效果越好，對邊緣的保護越弱；
//參數：double?sigmaSpace：空間域方差，sigmaSpace越大，結果越平滑；
//--------------------------------------------------------------
bool?my_Bilateral（Mat?&scr?Mat?&dst?int?d??double?sigmaColor?double?sigmaSpace）
{
	if?（!scr.data）
	{
		cerr?<		return?false;
	}
	if?（d?%?2?==?0）
	{
		cerr?<		return?false;
	}


	dst?=?scr.clone（）;

	int?row?=?dst.rows;???????//獲取圖像大小；
	int?col?=?dst.cols;

	int?copyBorderSize?=floor（0.5+?d?/?2）;
	Mat?copyBorder_dst;
	copyMakeBorder（dst?copyBorder_dst?copyBorderSize?copyBorderSize?copyBorderSize?copyBorderSize?BORDER_REFLECT）;

	int?channels?=?dst.channels（）;
	if?（channels?==?1）?????//如果是灰度圖像
	{
		
		for?（int?i?=?0;?i?		{
			for?（int?j?=?0;?j?			{
				double?weightSum?=?0;
				double?filterValue?=?0;
				for?（int?row_d?=?-（d?/?2）;?row_d?<=?（d?/?2）;?row_d++）???//以圖像中的一點為中心，d為邊長的方形區域內進行計算
				{
					for?（int?col_d?=?-（d?/?2）;?col_d?<=?（d?/?2）;?col_d++）
					{
						double?distance_Square?=?row_d*row_d?+?col_d*col_d;
						double?value_Square?=?pow（（scr.at（i?j）?-?copyBorder_dst.at（i?+?（d?/?2）?+?row_d?j?+?（d?/?2）?+?col_d））?2）;
						double?weight?=?exp（-1?*?（distance_Square?/?（2?*?sigmaSpace*sigmaSpace）?+?value_Square?/?（2?*?sigmaColor*sigmaColor）））;
						weightSum?+=?weight;???????????????//求濾波窗口內的權重和，用于歸一化；
						filterValue?+=?（weight*copyBorder_dst.at（i?+?（d?/?2）?+?row_d?j?+?（d?/?2）?+?col_d））;

					}
				}
				dst.at（i?j）?=?filterValue?/?weightSum;
				
			}
		}

		return?true;
	}
	else?if?（channels?==?3）?????//如果是RGB圖像
	{

		for?（int?c?=?0;?c?		{
			for?（int?i?=?0;?i?			{
				for?（int?j?=?0;?j?				{
					double?weightSum?=?0;
					double?filterValue?=?0;
					for?（int?row_d?=?-（d?/?2）;?row_d?<=?（d?/?2）;?row_d++）
					{
						for?（int?col_d?=?-（d?/?2）;?col_d?<=?（d?/?2）;?col_d++）
						{
							double?distance_Square?=?row_d*row_d?+?col_d*col_d;
							double?value_Square?=?pow（（scr.at（i?j）[c]?-?copyBorder_dst.at（i?+?（d?/?2）?+?row_d?j?+?（d?/?2）?+?col_d）[c]）?2）;
							double?weight?=?exp（-1?*?（distance_Square?/?（2?*?sigmaSpace*sigmaSpace）?+?value_Square?/?（2?*?sigmaColor*sigmaColor）））;
						

?屬性????????????大小?????日期????時間???名稱
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?????文件??????81718??2018-11-25?20:33??上傳資源\03.jpg

?????文件???????4677??2018-11-26?12:10??上傳資源\bilateral.cpp

?????文件??????53008??2015-10-22?11:12??上傳資源\lena.jpg

?????文件??????73585??2018-11-26?10:52??上傳資源\temp1.jpg

?????文件??????77633??2018-11-26?10:52??上傳資源\temp2.jpg

?????文件??????30440??2018-11-26?10:52??上傳資源\temp3.jpg

?????目錄??????????0??2018-11-26?12:11??上傳資源

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???????????????321061????????????????????7