图像稳定

Question

嘿， 我正在做一个使用光流法稳定视频序列的项目。 到目前为止，我在光流方面做得很好。但我面前有 2 个分支来处理它。 1-得到光流后，我找到了图像位移的平均值，然后我从第二帧的特征中减去了平均值，我的问题是接下来要做什么？

2- 或者我可以使用 openCV 函数来稳定图像，我计算了变换矩阵，然后我使用 cvPerspectiveTransform 和 cvWarpPerspective，但我收到错误，即“错误标志”

你可以看到代码，我想要的是如何稳定图像？我想要你能提供的任何解决方案吗？

enter code here
#include <stdio.h>
#include <stdlib.h>    
//#include "/usr/include/opencv/cv.h"    
#include <cv.h>    
#include <cvaux.h>    
#include <highgui.h>    
#include <math.h>    
#include <iostream>

#define PI 3.1415926535898

double rads(double degs)
{
    return (PI/180 * degs);
}

CvCapture *cap;

IplImage *img;    
IplImage *frame;     
IplImage *frame1;    
IplImage *frame3;    
IplImage *frame2;    
IplImage *temp_image1;    
IplImage *temp_image2;    
IplImage *frame1_1C;     
IplImage *frame2_1C;     
IplImage *eig_image;     
IplImage *temp_image;     
IplImage *pyramid1 = NULL;    
IplImage *pyramid2 = NULL;

char * mapx;
char * mapy;

int h;
int corner_count;
CvMat* M = cvCreateMat(3,3,CV_32FC1);
CvPoint p,q,l,s;
double hypotenuse;
double angle;

int line_thickness = 1, line_valid = 1, pos = 0;
CvScalar line_color;
CvScalar target_color[4] = { // in BGR order
        {{   0,   0, 255,   0 }},  // red    
        {{   0, 255,   0,   0 }},  // green    
        {{ 255,   0,   0,   0 }}, // blue    
        {{   0, 255, 255,   0 }}   // yellow    
};

inline static double square(int a)    
{
return a * a;  
}

char* IntToChar(int num){return NULL;}

/*{
    char* retstr = static_cast<char*>(calloc(12, sizeof(char)));

    if (sprintf(retstr, "%i", num) > 0)
    {
        return retstr;
    }
    else
    {
        return NULL;
    }
}*/

inline static void allocateOnDemand( IplImage **img, CvSize size, int depth, int channels )
{
    if ( *img != NULL ) 
         return;

    *img = cvCreateImage( size, depth, channels );

    if ( *img == NULL )
    {
        fprintf(stderr, "Error: Couldn't allocate image.  Out of memory?\n");
        exit(-1);
    }
}

void clearImage (IplImage *img)
{ 
    for (int i=0; i<img->imageSize; i++)    
        img->imageData[i] = (char) 0;    
}

int main()
{
    cap = cvCaptureFromCAM(0);    
    //cap = cvCaptureFromAVI("/home/saif/Desktop/NAO.. the project/jj/Test3.avi");

    CvSize frame_size;

    // Reading the video's frame size
    frame_size.height = (int) cvGetCaptureProperty( cap, CV_CAP_PROP_FRAME_HEIGHT );
    frame_size.width  = (int) cvGetCaptureProperty( cap, CV_CAP_PROP_FRAME_WIDTH );    
    cvNamedWindow("Optical Flow", CV_WINDOW_AUTOSIZE);

    while(true)    
    {
    frame = cvQueryFrame( cap );

        if (frame == NULL)
        {    
            fprintf(stderr, "Error: Hmm. The end came sooner than we thought.\n");
            return -1;    
        }

        // Allocating another image if it is not allocated already.     
        allocateOnDemand( &frame1_1C, frame_size, IPL_DEPTH_8U, 1 );    
        cvConvertImage(frame, frame1_1C, 0);    
        allocateOnDemand( &frame1, frame_size, IPL_DEPTH_8U, 3 );    
        cvConvertImage(frame, frame1, 0);

        //Get the second frame of video.    
        frame = cvQueryFrame( cap );

        if (frame == NULL)    
        {
            fprintf(stderr, "Error: Hmm. The end came sooner than we thought.\n");
            return -1;
        }

        if(!frame) 
        {    
            printf("bad video \n");    
            exit(0);
        }

        allocateOnDemand( &frame2_1C, frame_size, IPL_DEPTH_8U, 1 );   
        cvConvertImage(frame, frame2_1C, 0);    
        allocateOnDemand( &frame2, frame_size, IPL_DEPTH_8U, 3 );    
        cvConvertImage(frame, frame2, 0);

        CvSize optical_flow_window = cvSize(5,5);    
        eig_image = cvCreateImage( frame_size, IPL_DEPTH_32F, 1 );    
        temp_image = cvCreateImage( frame_size, IPL_DEPTH_32F, 1 );

        CvTermCriteria optical_flow_termination_criteria = cvTermCriteria( CV_TERMCRIT_ITER | CV_TERMCRIT_EPS, 20, .3 );

        // Feature tracking 
        CvPoint2D32f frame1_features[4];
        CvPoint2D32f frame2_features[4];

        //cvCornerEigenValsAndVecs(eig_image, temp_image, 1 );    
        corner_count = 4;

        cvGoodFeaturesToTrack(frame1_1C,eig_image , temp_image, frame1_features, &corner_count, 0.1, .01, NULL, 5, 1);    
        cvFindCornerSubPix( frame1_1C, frame1_features, corner_count,cvSize(5, 5) ,optical_flow_window , optical_flow_termination_criteria);

        if ( corner_count <= 0 )    
            printf( "\nNo features detected.\n" );    
        else    
            printf( "\nNumber of features found = %d\n", corner_count );

        //Locus Kande method.     
        char optical_flow_found_feature[20];    
        float optical_flow_feature_error[20];

        allocateOnDemand( &pyramid1, frame_size, IPL_DEPTH_8U, 1 );    
        allocateOnDemand( &pyramid2, frame_size, IPL_DEPTH_8U, 1 );

        cvCalcOpticalFlowPyrLK(frame1_1C, frame2_1C, pyramid1, pyramid2, frame1_features, frame2_features, corner_count, optical_flow_window, 5, optical_flow_found_feature, NULL, optical_flow_termination_criteria, NULL);

    /*
    double sumOfDistancesX = 0;    
    double sumOfDistancesY = 0;

    int debug = 0;

     CvFont font1, font2;    
     CvScalar red, green, blue;    
     IplImage* seg_in = NULL;    
     IplImage *seg_out = NULL;

     allocateOnDemand( &seg_in,  frame_size, IPL_DEPTH_8U, 3 );    
     allocateOnDemand( &seg_out, frame_size, IPL_DEPTH_8U, 3 );

     clearImage(seg_in);    
     clearImage(seg_in);    

     for( int i=0; i <corner_count; i++ )
     {

         if ( optical_flow_found_feature[i] == 0 )  
             continue;    
         p.x = (int) frame1_features[i].x;    
         p.y = (int) frame1_features[i].y;    
         q.x = (int) frame2_features[i].x;    
         q.y = (int) frame2_features[i].y;
         angle = atan2( (double) p.y - q.y, (double) p.x - q.x );

          sumOfDistancesX += q.x - p.x;     
          sumOfDistancesY += q.y - p.y;

          //cvRemap(frame2,frame1,averageDistanceX , averageDistanceY,CV_INTER_LINEAR | CV_WARP_FILL_OUTLIERS, cvScalarAll(0));    
      }
      */

      /*    
      int averageDistanceX = sumOfDistancesX / corner_count;    
      int averageDistanceY = sumOfDistancesY / corner_count;    
      l.x = averageDistanceX - q.x;    
      s.y = averageDistanceY - q.y;
      */

#define cvWarpPerspectiveQMatrix cvGetPerspectiveTransform

       //CvMat* N = cvCreateMat(3,3,CV_32FC1);

       cvGetPerspectiveTransform(frame2_features, frame1_features, M);
       cvPerspectiveTransform(frame1_features, frame2_features, M);    
       cvWarpPerspective( frame2_features, frame1_features, M,CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS,cvScalarAll(0) );

        cvShowImage("Optical Flow", frame1);    
        cvWaitKey(50);
    }

    cvReleaseCapture(&cap);    
    cvReleaseMat(&M);    

    return 0;    
}

Answer 1

您不想从第二张图像中减去平均位移，而是希望通过平均位移来变换（移动）第二张图像，使其与第一张图像“匹配”。您使用的“位移”取决于您的情况。

• 如果您的相机在晃动但静止不动，则您希望使用两个连续帧之间的平均位移作为第二帧的变换向量。对于每个新帧，您都会计算转换后的第一帧和新帧之间的位移，然后转换新帧。
• 如果您的相机移动和抖动（即山地自行车上的头盔式相机），您需要首先找到几帧帧之间的平均位移，然后通过该平均位移与它与前一帧之间的位移。

编辑 对于选项 2，您基本上需要做的是计算最后几帧中帧之间平均移动的平均值。您可以通过多种方式做到这一点，但我建议使用卡尔曼滤波器之类的东西。然后，对于一个新帧，您计算该帧与（校正的）前一帧之间的移动。从得到的移动中减去到该点的平均移动，然后将新帧移动该差异。