老板kinect去噪的任务下达已经有半个多月了,前期除了看了几天文献之外就打酱油了,好像每天都很忙,可是就是不知道在忙什么。这几天为了交差,就胡乱凑了几段代码,得到一个结果,也知道不行,先应付一下,再图打算。
程序思想很简单,先对静止的场景连续采样若干帧,然后对所有点在时间域取中值,对取完中值之后的无效点在空间域取最近邻,勉强将黑窟窿填上了。由于代码较长,现在奉上关键的几个片段:
#include<cv.h> #include<highgui.h> #include<iostream> using namespace std; #ifndef _DENOISE #define _DENOISE const int nFrames = 9; // number of consecutive frames const int width = 640; // frame width const int height = 480; // frame height class kinectDenoising { private: IplImage* denoisedImage; IplImage* frameSet[nFrames]; unsigned int numOfFrames; CvRect imageROI; public: kinectDenoising(); ~kinectDenoising(); void addFrame(IplImage* img); void setImageROI(bool isUpdate = true); void medianFiltering(); void nearestFiltering(); void updateFrameSet(IplImage* img); void showDenoiedImage(const char* window); void showCurrentImage(const char* window); }; void insertSort(unsigned short* data,int& len,unsigned short newData); #endif
这是定义的头文件,装模作样的写了一个类,在构造函数里面,除了对denoisedImage分配内存之外其他都置0,析构函数需要释放denoisedImage和frameSet数组的内存。numOfFrames本来设计为frameSet中的图像的帧数,结果由于偷懒就用了一个定长的数组。
void kinectDenoising::setImageROI(bool isUpdate) { if(!isUpdate) { imageROI = cvRect(22,44,591,434); } else { IplImage* image8u = cvCreateImage(cvSize(width,height),IPL_DEPTH_8U,1); IplImage* bitImage = cvCreateImage(cvSize(width,height),IPL_DEPTH_8U,1); // cvThreshold can only handle images of 8UC1 or 32FC1 cvConvertScale(frameSet[0],image8u,255.0/4096.0); cvThreshold(image8u,bitImage,0,1,CV_THRESH_BINARY); // the two mats rowReduced and colReduced have to be CV_32SC1 type // for function cvReduce() seems not to suitable for 16U type and // 8U type doesn't have enough room for the result. CvMat* rowReduced = cvCreateMat(1,bitImage->width,CV_32FC1); // bitImage->width represents number of cols, while bitImage->height stands for rows CvMat* colReduced = cvCreateMat(bitImage->height,1,CV_32FC1); cvReduce(bitImage,rowReduced,0,CV_REDUCE_SUM); cvReduce(bitImage,colReduced,1,CV_REDUCE_SUM); // compute imageROI.x for(int i=0;i<rowReduced->cols;i++) { float temp = CV_MAT_ELEM(*rowReduced,float,0,i); if(temp>bitImage->height/3) { imageROI.x = i; break; } } // computer imageROI.width for(int i=rowReduced->cols;i>0;i--) { float temp = CV_MAT_ELEM(*rowReduced,float,0,i-1); if(temp>bitImage->height/3) { imageROI.width = i-imageROI.x; break; } } // compute imageROI.y for(int i=0;i<colReduced->rows;i++) { float temp = CV_MAT_ELEM(*colReduced,float,i,0); if(temp>bitImage->height/3) { imageROI.y = i; break; } } // compute imageROI.height for(int i=colReduced->rows;i>0;i--) { float temp = CV_MAT_ELEM(*colReduced,float,i-1,0); if(temp>bitImage->height/3) { imageROI.height = i-imageROI.y; break; } } // set memory free cvReleaseImage(&bitImage); cvReleaseImage(&image8u); cvReleaseMat(&rowReduced); cvReleaseMat(&colReduced); } }
这是计算深度图像的滤波范围。由于深度图像和彩色图像的视点不一致,导致了将深度图像映射到彩色图像上时有效像素会缩小,典型的现象就是在深度图像的四周会出现黑色的区域。这个函数就是用来将四周的黑色框框去掉。用OpenCV的投影的方法。由于cvReduce()函数要进行累积和的计算,为了不使数据溢出,目标数组应该用32位的浮点型(此函数只支持8位unsigned char型和32位float型)。
void kinectDenoising::medianFiltering() { // set result image zero cvSetZero(denoisedImage); unsigned short data[nFrames]; int total; for(int i=imageROI.y;i<imageROI.y+imageROI.height;i++) { unsigned short* denoisedImageData = (unsigned short*)(denoisedImage->imageData+denoisedImage->widthStep*i); for(int j=imageROI.x;j<imageROI.x+imageROI.width;j++) { total = 0; for(int k=0;k<nFrames;k++) { insertSort(data,total,CV_IMAGE_ELEM(frameSet[k],unsigned short,i,j)); } if(total != 0) { denoisedImageData[j] = data[total/2]; } } } }
中值滤波,统计有效点并排序,然后取中值。insertSort()函数用来将值按从小到大的顺序进行插入,鉴于篇幅的关系,就不贴出来了。
void kinectDenoising::nearestFiltering() { CvPoint topLeft,downRight; IplImage* tempImage = cvCloneImage(denoisedImage); for(int i=imageROI.y;i<imageROI.y+imageROI.height;i++) { unsigned short* data = (unsigned short*)(denoisedImage->imageData+denoisedImage->widthStep*i); for(int j=imageROI.x;j<imageROI.x+imageROI.width;j++) { for(int k=1;data[j]==0;k++) { topLeft = cvPoint(j-k,i-k); // j为行数 i为列数 downRight = cvPoint(j+k,i+k); for(int m=topLeft.x;(m<=downRight.x) && (data[j]==0);m++) { if(m<0) continue; if(m>=width) break; if(topLeft.y>=0) { unsigned short temp = CV_IMAGE_ELEM(tempImage,unsigned short,topLeft.y,m); if(temp > 0) { data[j] = temp; break; } } if(downRight.y < height) { unsigned short temp = CV_IMAGE_ELEM(tempImage,unsigned short,downRight.y,m); if(temp > 0) { data[j] = temp; break; } } } for(int m=topLeft.y;(m<downRight.y) && (data[j]==0);m++) { if(m<0) continue; if(m>=height) break; if(topLeft.x>0) { unsigned short temp = CV_IMAGE_ELEM(tempImage,unsigned short,m,topLeft.x); if(temp > 0) { data[j] = temp; break; } } if(downRight.x<width) { unsigned short temp = CV_IMAGE_ELEM(tempImage,unsigned short,m,downRight.x); if(temp > 0) { data[j] = temp; break; } } } } } } cvReleaseImage(&tempImage); }
最后是中值滤波,从最内层开始,一层层往外扩,直到找到有效值为止。
运行结果:
源图像:
结果图像:
附注:本来这个程序是在8位图像上进行的。先取得16位的unsigned short型深度图像,然后通过cvConvertScale()函数将其转化为8位的unsigned char型,结果在进行去噪的时候怎么都不对,将unsigned char型的数据放到matlab中一看,发现在unsigned short型数据中为0值的像素莫名其妙的在unsigned char型里有了一个很小的值(比如说1, 2, 3, 4, 5什么的,就是不为0)。很奇怪,不知道OpenCV中是怎么搞的。看来还是源数据靠谱,于是将其改为16位的unsigned short型,结果形势一片大好。
http://blog.csdn.net/chenli2010/article/details/7006573