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使用liner、feather、multiband对已经拼接的数据进行融合

      所谓"blend",英文解释为“vt. 混合vi. 混合;协调n. 混合;掺合物”这里应该理解为是图像数据的融合。这是“识别->对准->融合”的最后一步。融合是决定拼接质量的关键一步,一方面它决定于图像对准的质量,一方面它本身的也直接对拼接的最终结果负责。

     最简单和便于理解的融合为liner,正好借这个例子来说明说明是融合,简单的说,就是在融合的区域……(这个地方引用相关资料)liner在opencv中没有实现,但是本身简单有效,对于要求不是很高的情况可以使用,这里给出函数(再做相关解释)
     #pragma region mulitStitch
/*----------------------------
 * 功|能 : 多图匹配
 *----------------------------
 * 函数y : MulitMatch
 * 访问 : private
 * 返回 : void
 *
 * 参数y : matinput      [in]     全部需要a匹配图的vector
 * 参数y : matloc1       [ot]     所有D匹配中D对应|于第一图的结果向量
 * 参数y : matloc1       [ot]     所有D匹配中D对应|于第二t图的结果向量
 * 参数y : match_method  [in]     匹配方法
 */
void MulitMatch(deque<Mat>& matinput,deque<Point>& matloc1,deque<Point>& matloc2, int match_method)
{
      Mat img_display1;Mat img_display2;
                  Point matchLoc1;Point matchLoc2;
                 for (int i =0;i<matinput.size()-1;i++)
                 {
 
                                 //拷贝副本
                                  img_display1 = matinput[i];
                                  img_display2 = matinput[i+1];
                                 //以中D心区域为aroi
                                 // Mat imagetmp (img_display1, Rect(img_display1.rows/2, img_display1.cols/2, 10, 10) );//11:44:02
                                  
                                 Mat imagetmp (img_display1, Rect(960,240, 10, 10) );//11:44:02
                                 int result_cols =  img_display1.cols - imagetmp.cols + 1;
                                 int result_rows = img_display1.rows - imagetmp.rows + 1;
                                 Mat imagematch;
                                 imagematch.create( result_cols, result_rows, CV_32FC1 );
                                 /// 进行D匹配和标准化
                                 //匹配1
                                 matchTemplate( img_display1, imagetmp, imagematch, match_method );
                                 normalize( imagematch, imagematch, 0, 1, NORM_MINMAX, -1, Mat() );
                                 double minVal; double maxVal;
                                 Point minLoc; Point maxLoc;
                                 minMaxLoc( imagematch, &minVal, &maxVal, &minLoc, &maxLoc, Mat() );
                                 //智能判D断,这a里的matchLoc就是最佳匹配点
                                 if( match_method  == CV_TM_SQDIFF || match_method == CV_TM_SQDIFF_NORMED )
                                 { matchLoc1 = minLoc; }
                                 else
                                 { matchLoc1 = maxLoc; }
                                matloc1.push_back(matchLoc1); //加入序列D
                                 //匹配2
                                 matchTemplate( img_display2, imagetmp, imagematch, match_method );
                                 normalize( imagematch, imagematch, 0, 1, NORM_MINMAX, -1, Mat() );
                                 minMaxLoc( imagematch, &minVal, &maxVal, &minLoc, &maxLoc, Mat() );
                                 //智能判D断,这a里的matchLoc就是最佳匹配点
                                 if( match_method  == CV_TM_SQDIFF || match_method == CV_TM_SQDIFF_NORMED )
                                 { matchLoc2 = minLoc; }
                                 else
                                 { matchLoc2 = maxLoc; }
                                 matloc2.push_back(matchLoc2); //加入序列D
                 }
}
/*----------------------------
 * 功|能 : 多图对准
 *----------------------------
 * 函数y : MulitAlign
 * 访问 : private
 * 返回 : Mat
 *
 * 参数y : matinput      [in]     全部需要a匹配图的vector
 * 参数y : matloc1       [ot]     所有D匹配中D对应|于第一图的结果向量
 * 参数y : matloc1       [ot]     所有D匹配中D对应|于第二t图的结果向量
 */
Mat MulitAlign(deque<Mat>& matinput,deque<Point>& matloc1,deque<Point>& matloc2)
{              
                Mat outImage; //待y输出图片
                 //计算图片大小   
                 int nr = matinput[0].rows;
                 int nl = matinput[0].cols*matinput[0].channels();
                 int ioffset = 0;int ioffsetdetail = 0;
                 //计算offset
                 for (int i =0;i<matloc1.size()-1;i++)
                {
                                ioffset = ioffset+matloc1[i].y- matloc2[i].y;
                }
                outImage.create( matinput[0].rows+ioffset, matinput[0].cols, matinput[0].type());
                 for (int i=0;i<matloc1.size()-1;i++)
                {
                                 if (i == 0)//如果第一弹
                                {
                                                 for (int a=0;a<nr;a++) //第一图
                                                {
                                                                 const uchar* inData=matinput[0].ptr<uchar>(a);
                                                                uchar* outData=outImage.ptr<uchar>(a); 
                                                                 for(int j=0;j<nl;j++)
                                                                {
                                                                                outData[j]=inData[j];           
                                                                }
                                                }
 
                                                 for (int b=0;b<nr;b++) //第二t图
                                                {
                                                                 const uchar* inData=matinput[1].ptr<uchar>(b);
                                                                uchar* outData=outImage.ptr<uchar>(b+matloc1[0].y-matloc2[0].y); 
                                                                 for(int j=0;j<nl;j++)
                                                                {
                                                                                outData[j]=inData[j];           
                                                                }
                                                }
                                                ioffsetdetail += matloc1[0].y-matloc2[0].y;
                                }
                                 else//如果不是第一弹
                                {
                                                 for (int b=0;b<nr;b++) 
                                                {
                                                                 const uchar* inData=matinput[i+1].ptr<uchar>(b);
                                                                uchar* outData=outImage.ptr<uchar>(b+ioffsetdetail+matloc1[i].y-matloc2[i].y); 
                                                                 for(int j=0;j<nl;j++)
                                                                {
                                                                                outData[j]=inData[j];           
                                                                }
                                                }
                                                ioffsetdetail += matloc1[i+1].y-matloc2[i].y;
                                }              
                                                
                }
 
                 return outImage;
}
/*----------------------------
 * 功|能 : 多图融合
 *----------------------------
 * 函数y : MulitBlend
 * 访问 : private
 * 返回 : Mat&
 *
 * 参数y : matinput      [in]     图片输入序列D
 * 参数y : imagesrc      [in]     已经-对准的图片
 * 参数y : matloc1                            [in]     第一图匹配位置
 * 参数y : matloc2        [in]     第二t图匹配位置
 */
Mat MulitBlend(deque<Mat>& matinput, const Mat& imagesrc,deque<Point>& matloc1,deque<Point>& matloc2)
{              
                Mat outImage; //待y输出图片
                imagesrc.copyTo(outImage); //图像拷贝
                 int ioffsetdetail = 0;
                 double dblend = 0.0;
                 for (int i =0;i<matloc1.size()-1;i++)
                {
                                dblend = 0.0;
                                 int ioffset = matloc1[i].y - matloc2[i].y;//row的偏移
                                 for (int j = 0;j<100;j++)//这a个地方用i 和 j很不好
                                {              
                                                outImage.row(ioffsetdetail+ioffset+j) = matinput[i].row(ioffset+j)*(1-dblend)+ matinput[i+1].row(j)*(dblend);
                                                dblend = dblend +0.01;
                                }
                                                ioffsetdetail += ioffset;
                }
                 return outImage;
}
#pragma endregion mulitStitch
 
在最新版的opencv中(至少在2.4.5之后),提供了mulitband和feather函数。jsxyhelu认为,总体来说,mulitband是目前最好的融合算法,(paper),在(2007)这篇经典的图像拼接论文中得到引用,需要提及的一点是opencv的stitch函数主要就是基于2007这篇论文实现的,它的算法实现的第一篇引用论文就是2007。当然,好的算法可能用起来比较麻烦,简单的算法也有其适合使用的地方。
。。。。。。pipleline
mulitblend的主要思想是小频率事件大空间划分,大频率事件小空间划分,具体内容参考论文。featherblend就是常见的所谓“羽化”,这里主要考虑工程实现。
 
首先参考image blander函数
detail::Blender
class detail::Blender
Base class for all blenders.
class CV_EXPORTS Blender
{
public:
virtual ~Blender() {}
enum { NO, FEATHER, MULTI_blend };
static Ptr<Blender> createDefault(int type, bool try_gpu = false);
void prepare(const std::vector<Point> &corners, const std::vector<Size> &sizes);
virtual void prepare(Rect dst_roi);
virtual void feed(const Mat &img, const Mat &mask, Point tl);
virtual void blend(Mat &dst, Mat &dst_mask);
protected:
Mat dst_, dst_mask_;
Rect dst_roi_;
};
 
在detail空间中存在blender函数,是所有blender的基函数。可以实现的包括feather和multiblend两种方法。
 
 blender = Blender::createDefault(blend_type, try_gpu);是创建函数,两个参数决定了采用哪一种blender方法,和是否采用gpu
 
 
detail::Blender::prepare
为blend准备相关数据
C++: void detail::Blender::prepare(const std::vector<Point>& corners, const std::vector<Size>&
sizes)
Parameters
corners – 原始图像文件的左上角点
sizes – 原始文件大小
注意这里的两点都是vector
 
detail::Blender::feed
处理图像
C++: void detail::Blender::feed(const Mat& img, const Mat& mask, Point tl)
Parameters
img –原始图像
mask – mask
tl topleft点
注意这里是一张一张处理图像的
 
detail::Blender::blend
处理blend操作,是最后最后输出操作 ,从blend结构体中返回pano全景图像了
C++: void detail::Blender::blend(Mat& dst, Mat& dst_mask)
Parameters
dst – Final pano
dst_mask – Final pano mask
 
detail::MultiBandBlender
class detail::MultiBandBlender : public detail::Blender
Blender which uses multi-band blending algorithm (see [BA83],就是前面提到的那篇论文).
class CV_EXPORTS MultiBandBlender : public Blender
{
public:
MultiBandBlender(int try_gpu = false, int num_bands = 5);
int numBands() const { return actual_num_bands_; }
void setNumBands(int val) { actual_num_bands_ = val; }
void prepare(Rect dst_roi);
void feed(const Mat &img, const Mat &mask, Point tl);
void blend(Mat &dst, Mat &dst_mask);
private:
/* hidden */
};
See also:
detail::Blender
 
detail::FeatherBlender
class detail::FeatherBlender : public detail::Blender
Simple blender which mixes images at its borders.
class CV_EXPORTS FeatherBlender : public Blender
{
public:
FeatherBlender(float sharpness = 0.02f) { setSharpness(sharpness); }
float sharpness() const { return sharpness_; }
void setSharpness(float val) { sharpness_ = val; }
void prepare(Rect dst_roi);
void feed(const Mat &img, const Mat &mask, Point tl);
void blend(Mat &dst, Mat &dst_mask);
// Creates weight maps for fixed set of source images by their masks and top-left corners.
// Final image can be obtained by simple weighting of the source images.
Rect createWeightMaps(const std::vector<Mat> &masks, const std::vector<Point> &corners,
std::vector<Mat> &weight_maps);
private:
/* hidden */
};
See also:
detail::Blender
这两个函数在文档中都没有详细的解释。这里进行补充说明。
基于cookbook第9章的estimateH.cpp继续前进
将其cv::Mat image1= cv::imread( "parliament1.bmp",1);
                cv::Mat image2= cv::imread( "parliament2.bmp",1);
结果没有问题,拼接的出来了,但是缝合线也非常明显
 
将不需要的代码注释掉,需要注意的是,书中的代码认为图片是从右向左移动的
//需要a注意a的一点是,原-始文件t的图片是按照从右至左边进行D移动的。
                cv::Point* p1 = new cv::Point(image1.cols,1);
                cv::Point* p2 = new cv::Point(image1.cols,image2.rows-1);
                cv::line(result,*p1,*p2,cv::Scalar(255,255,255),2);
                cv::namedWindow( "After warping0");
                cv::imshow( "After warping0",result);
画出一条白线,基本标注位置
 
 
cv::Mat result;
                cv::warpPerspective(image1, // input image
                                result,                                      // output image
                                homography,                         // homography
                                cv::Size(2*image1.cols,image1.rows)); // size of output image
                cv::Mat resultback;
                result.copyTo(resultback);
 
                 // Copy image 1 on the first half of full image
                cv::Mat half(result,cv::Rect(0,0,image2.cols,image2.rows));
                image2.copyTo(half);
    // Display the warp image
                cv::namedWindow( "After warping");
                cv::imshow( "After warping",result);
                 //需要a注意a的一点是,原-始文件t的图片是按照从右至左边进行D移动的。
//             cv::Point* p1 = new cv::Point(image1.cols,1);
//             cv::Point* p2 = new cv::Point(image1.cols,image2.rows-1);
//             cv::line(result,*p1,*p2,cv::Scalar(255,255,255),2);
//             cv::namedWindow("After warping0");
//             cv::imshow("After warping0",result);
                 //进行Dliner的融合
                Mat outImage; //待y输出图片
                result.copyTo(outImage); //图像拷贝
                 double dblend = 0.0;
                 int ioffset =image2.cols-100;//col的初始定位
                 for (int i = 0;i<100;i++)
                {              
                                outImage.col(ioffset+i) = image2.col(ioffset+i)*(1-dblend) + resultback.col(ioffset+i)*dblend;
                                dblend = dblend +0.01;
                }
需要注意的是这里不是将image1和image2进行融合,而是将原始的result和image进行融合。
由于背景比较单一,而且图片分辨率不是很高,所以这个结果融合的 结果非常不
 
目前方向:图像处理,人工智能
posted @ 2014-07-15 20:40  jsxyhelu  阅读(6286)  评论(0编辑  收藏  举报