手势识别

Posted 2022-05-30 cv.exp

手势检测：

手势分割：
Selective search生成建议框

function [boxes blobIndIm blobBoxes hierarchy priority] = Image2HierarchicalGrouping(im, sigma, k, minSize, colourType, functionHandles)
% function [boxes blobIndIm blobBoxes hierarchy] = Image2HierarchicalGrouping
%                              (im, sigma, k, minSize, colourType, functionHandles)
%
% Creates hierarchical grouping from an image
%
% im:                   Image
% sigma (= 0.8):        Smoothing for initial segmentation (Felzenszwalb 2004)
% k (= 100):            Threshold for initial segmentation
% minSize (= 100):      Minimum size of segments for initial segmentation
% colourType:           ColourType in which to do grouping (see Image2ColourSpace)
% functionHandles:      Similarity functions which are called. Function
%                       creates as many hierarchies as there are functionHandles
%
% boxes:                N x 4 array with boxes of all hierarchical groupings
% blobIndIm:            Index image with the initial segmentation
% blobBoxes:            Boxes belonging to the indices in blobIndIm
% hierarchy:            M x 1 cell array with hierarchies. M =
%                       length(functionHandles)
%
%     Jasper Uijlings - 2013

% Change colour space
[colourIm imageToSegment] = Image2ColourSpace(im, colourType);

% Get initial segmentation, boxes, and neighbouring blobs
[blobIndIm blobBoxes neighbours] = mexFelzenSegmentIndex(imageToSegment, sigma, k, minSize);
numBlobs = size(blobBoxes,1);

% Skip hierarchical grouping if segmentation results in single region only
if numBlobs == 1
    warning('Oversegmentation results in a single region only');
    boxes = blobBoxes;
    hierarchy = [];
    priority = 1; % priority is legacy
    return;
end

%%% Calculate histograms and sizes as prerequisite for grouping procedure

% Get colour histogram
[colourHist blobSizes] = BlobStructColourHist(blobIndIm, colourIm);

% Get texture histogram
textureHist = BlobStructTextureHist(blobIndIm, colourIm);
% textureHist = BlobStructTextureHistLBP(blobIndIm, colourIm);

% Allocate memory for complete hierarchy.
blobStruct.colourHist = zeros(size(colourHist,2), numBlobs * 2 - 1);
blobStruct.textureHist = zeros(size(textureHist,2), numBlobs * 2 - 1);
blobStruct.size = zeros(numBlobs * 2 -1, 1);
blobStruct.boxes = zeros(numBlobs * 2 - 1, 4);

% Insert calculated histograms, sizes, and boxes
blobStruct.colourHist(:,1:numBlobs) = colourHist';
blobStruct.textureHist(:,1:numBlobs) = textureHist';
blobStruct.size(1:numBlobs) = blobSizes ./ 3;
blobStruct.boxes(1:numBlobs,:) = blobBoxes;

blobStruct.imSize = size(im,1) * size(im,2);

%%% If you want to use original blobs in similarity functions, uncomment
%%% these lines.
% blobStruct.blobs = cell(numBlobs * 2 - 1, 1);
% initialBlobs = SegmentIndices2Blobs(blobIndIm, blobBoxes);
% blobStruct.blobs(1:numBlobs) = initialBlobs;


% Loop over all merging strategies. Perform them one by one.
boxes = cell(1, length(functionHandles)+1);
priority = cell(1, length(functionHandles) + 1);
hierarchy = cell(1, length(functionHandles));
for i=1:length(functionHandles)
    [boxesi hierarchyi blobStructT mergeThreshold] = BlobStruct2HierarchicalGrouping(blobStruct, neighbours, numBlobs, functionHandlesi);
    boxesi = boxesi(numBlobs+1:end,:);
    priorityi = (size(boxesi, 1):-1:1)';
end

% Also save the initial boxes
i = i+1;
boxesi = blobBoxes;
priorityi = ones(size(boxesi, 1), 1) * (size(boxes1, 1)+1);

% Concatenate boxes and priorities resulting from the different merging
% strategies
boxes = cat(1, boxes:);
priority = cat(1, priority:);
[priority ids] = sort(priority, 'ascend');
boxes = boxes(ids,:);

手势识别：

深度学习与手势检测：

Fast rcnn训练

Optical Flow ConvNets(光流卷积网络)：

http://blog.csdn.net/forest_world/article/details/53965567 行为识别研究摘录

部分参考源码：

#include "stdafx.h"

#include "cv.h" 
#include "highgui.h"

#include <stdio.h> 
#include <stdlib.h> 
#include <string.h> 
#include <assert.h> 
#include <math.h> 
#include <float.h> 
#include <limits.h> 
#include <time.h> 
#include <ctype.h>

#ifdef _EiC 
#define WIN32 
#endif

static CvMemStorage* storage = 0; 
static CvHaarClassifierCascade* cascade = 0;

void detect_and_draw( IplImage* image );

const char* cascade_name;

int main( int argc, char** argv ) 
 
    cascade_name = "palm.xml";
    cascade = (CvHaarClassifierCascade*)cvLoad( cascade_name, 0, 0, 0 ); 

    if( !cascade ) 
     
        fprintf( stderr, "ERROR: Could not load classifier cascade\\n" ); 
        return -1; 
     
    storage = cvCreateMemStorage(0); 
    cvNamedWindow( "result", 1 ); 

    const char* filename = "step3.JPG"; 
    IplImage* image = cvLoadImage( filename, 1 );

    if( image ) 
     
        detect_and_draw( image ); 
        cvWaitKey(0); 
        cvReleaseImage( &image );   
    

    cvDestroyWindow("result"); 

    return 0; 



void detect_and_draw(IplImage* img ) 
 
    double scale=1.2; 
    static CvScalar colors[] =  
        0,0,255,0,128,255,0,255,255,0,255,0, 
        255,128,0,255,255,0,255,0,0,255,0,255 
    ;//Just some pretty colors to draw with

    IplImage* gray = cvCreateImage(cvSize(img->width,img->height),8,1); 
    IplImage* small_img=cvCreateImage(cvSize(cvRound(img->width/scale),cvRound(img->height/scale)),8,1); 
    cvCvtColor(img,gray, CV_BGR2GRAY); 
    cvResize(gray, small_img, CV_INTER_LINEAR);

    cvEqualizeHist(small_img,small_img); //直方图均衡

    //Detect objects if any 
    cvClearMemStorage(storage); 
    double t = (double)cvGetTickCount(); 
    CvSeq* objects = cvHaarDetectObjects(small_img, 
                                                                        cascade, 
                                                                        storage, 
                                                                        1.1, 
                                                                        2, 
                                                                        0/*CV_HAAR_DO_CANNY_PRUNING*/, 
                                                                        cvSize(30,30));

    t = (double)cvGetTickCount() - t; 
    printf( "detection time = %gms\\n", t/((double)cvGetTickFrequency()*1000.) );

    //Loop through found objects and draw boxes around them 
    for(int i=0;i<(objects? objects->total:0);++i) 
     
        CvRect* r=(CvRect*)cvGetSeqElem(objects,i); 
        cvRectangle(img, cvPoint(r->x*scale,r->y*scale), cvPoint((r->x+r->width)*scale,(r->y+r->height)*scale), colors[i%8]); 
     
    for( int i = 0; i < (objects? objects->total : 0); i++ ) 
     
        CvRect* r = (CvRect*)cvGetSeqElem( objects, i ); 
        CvPoint center; 
        int radius; 
        center.x = cvRound((r->x + r->width*0.5)*scale); 
        center.y = cvRound((r->y + r->height*0.5)*scale); 
        radius = cvRound((r->width + r->height)*0.25*scale); 
        cvCircle( img, center, radius, colors[i%8], 3, 8, 0 ); 
    

    cvShowImage( "result", img ); 
    cvReleaseImage(&gray); 
    cvReleaseImage(&small_img); 

Hand segmentation with structured convolutional learning

肤色检测：

int isSkin(int R,int G,int B)
  
if(R>95&&G>40&&B>20&&R>G&&R>B&&
    (MAX(R,G,B)-MIN(R,G,B)>15)&&abs(R-G)>15)
  
    return 1;  

else
  
    return 0;  
  
  

手势样本库下载
http://download.csdn.net/detail/forest_world/9666342
http://download.csdn.net/detail/forest_world/9666349

Adaboost算法中xml文件数组化

参考资料：
http://www.zhihu.com/question/20131478

http://www.cnblogs.com/CVArt/archive/2011/07/20/2111676.html 抗遮挡手势跟踪算法研究

http://blog.sina.com.cn/s/blog_5d3402010100s0tr.html camshift结合kalman预测对特定颜色的跟踪

http://blog.csdn.net/myarrow/article/details/51933651 [置顶] 手势估计- Hand Pose Estimation