Mat Histogram::drawHist(MatND _histMat)
{
if(_histMat.channels()>1)
cvError(1,__FUNCTION__,"Only for 1D histograms",__FILE__,__LINE__);
int w = 400;
int h = 400;
Mat histImage( h, w, CV_8UC3, Scalar( 0,0,0) );
Mat i2;
cv::normalize(_histMat,i2,0,255,CV_MINMAX);
for( int i = 0; i < _histSize[0]; i++ )
{
int bin_w = cvRound( (double) w/_histSize[0]);
rectangle( histImage, Point( i*bin_w, h ), Point( (i+1)*bin_w, h - cvRound( i2.at<float>(i)*h/255.0 ) ), Scalar( 0, 0, 255 ), -1 );
/*line( histImage, Point( bin_w*(i-1), hist_h - cvRound(1*i2.at<float>(i-1)) ) ,
Point( bin_w*(i), hist_h - cvRound(1*i2.at<float>(i)) ),
Scalar( 255, 0, 0), 2, 8, 0 );
/*line( histImage, Point( bin_w*(i-1), hist_h - cvRound(g_hist.at<float>(i-1)) ) ,
Point( bin_w*(i), hist_h - cvRound(g_hist.at<float>(i)) ),
Scalar( 0, 255, 0), 2, 8, 0 );
line( histImage, Point( bin_w*(i-1), hist_h - cvRound(r_hist.at<float>(i-1)) ) ,
Point( bin_w*(i), hist_h - cvRound(r_hist.at<float>(i)) ),
Scalar( 0, 0, 255), 2, 8, 0 );*/
}
return histImage;
}
void Analyser::compareToHistogram(Mat &image, Mat &histo, Mat &result) {
Mat img_blur(image.rows, image.cols, image.type());
GaussianBlur(image, img_blur, Size(0,0), 3);
Mat img_hsv(img_blur.rows, img_blur.cols, image.type());
cvtColor(img_blur, img_hsv, CV_BGR2HSV);
/// Use only the Hue value
Mat hue, saturation, brightness;
hue.create( img_hsv.size(), img_hsv.depth() );
saturation.create( img_hsv.size(), img_hsv.depth() );
brightness.create( img_hsv.size(), img_hsv.depth() );
int chH[] = { 0, 0 };
mixChannels( &img_hsv, 1, &hue, 1, chH, 1 );
int chS[] = { 1, 0 };
mixChannels( &img_hsv, 1, &saturation, 1, chS, 1 );
int chB[] = { 2, 0 };
mixChannels( &img_hsv, 1, &brightness, 1, chB, 1 );
Mat planes[] = {hue, saturation};
result = Mat(image.rows, image.cols, CV_8UC1, Scalar(0));
float h_ranges[] = {0,180};
float s_ranges[] = {0,255};
const float* ranges[] = {h_ranges, s_ranges};
MatND backproj;
calcBackProject( planes, 2, 0, histo, backproj, ranges, 1, true );
normalize( backproj, backproj, 0, 255, NORM_MINMAX, -1, Mat() );
// On enlève les valeurs trop sombres
for (int y=0 ; y<brightness.rows; y++) {
for (int x=0 ; x<brightness.cols; x++) {
if (brightness.data[brightness.step*y+x] < 50) {
backproj.data[backproj.step * y + x ] = 0;
}
}
}
backproj.copyTo(result);
}
Mat RadialHistogramClassifier::caracteristicVector(const Mat &segmentedHand) {
Mat direction = handDirection(segmentedHand).second;
float angle = atan(direction.at<float>(0,1)/direction.at<float>(0,0));
Mat rotatedHand;
rotateHand(segmentedHand, rotatedHand, angle);
Mat flippedHand = rotatedHand;
if (detectHandSide(rotatedHand, 1) == LEFT_HAND) {
horizontalSymmetry(rotatedHand, flippedHand);
}
MatND handRadialHistogram;
Point2f palmCenter = estimatePalmCenter(
flippedHand,
this->maxFingerWidth);
radialHistogramWithCenter(
flippedHand,
handRadialHistogram,
this->numberOfBins,
palmCenter);
return handRadialHistogram.t();
}
void CV_ArrayOpTest::run( int /* start_from */)
{
int errcount = 0;
// dense matrix operations
{
int sz3[] = {5, 10, 15};
MatND A(3, sz3, CV_32F), B(3, sz3, CV_16SC4);
CvMatND matA = A, matB = B;
RNG rng;
rng.fill(A, CV_RAND_UNI, Scalar::all(-10), Scalar::all(10));
rng.fill(B, CV_RAND_UNI, Scalar::all(-10), Scalar::all(10));
int idx0[] = {3,4,5}, idx1[] = {0, 9, 7};
float val0 = 130;
Scalar val1(-1000, 30, 3, 8);
cvSetRealND(&matA, idx0, val0);
cvSetReal3D(&matA, idx1[0], idx1[1], idx1[2], -val0);
cvSetND(&matB, idx0, val1);
cvSet3D(&matB, idx1[0], idx1[1], idx1[2], -val1);
Ptr<CvMatND> matC = cvCloneMatND(&matB);
if( A.at<float>(idx0[0], idx0[1], idx0[2]) != val0 ||
A.at<float>(idx1[0], idx1[1], idx1[2]) != -val0 ||
cvGetReal3D(&matA, idx0[0], idx0[1], idx0[2]) != val0 ||
cvGetRealND(&matA, idx1) != -val0 ||
Scalar(B.at<Vec4s>(idx0[0], idx0[1], idx0[2])) != val1 ||
Scalar(B.at<Vec4s>(idx1[0], idx1[1], idx1[2])) != -val1 ||
Scalar(cvGet3D(matC, idx0[0], idx0[1], idx0[2])) != val1 ||
Scalar(cvGetND(matC, idx1)) != -val1 )
{
ts->printf(CvTS::LOG, "one of cvSetReal3D, cvSetRealND, cvSet3D, cvSetND "
"or the corresponding *Get* functions is not correct\n");
errcount++;
}
}
RNG rng;
const int MAX_DIM = 5, MAX_DIM_SZ = 10;
// sparse matrix operations
for( int si = 0; si < 10; si++ )
{
int depth = (unsigned)rng % 2 == 0 ? CV_32F : CV_64F;
int dims = ((unsigned)rng % MAX_DIM) + 1;
int i, k, size[MAX_DIM]= {0}, idx[MAX_DIM]= {0};
vector<string> all_idxs;
vector<double> all_vals;
vector<double> all_vals2;
string sidx, min_sidx, max_sidx;
double min_val=0, max_val=0;
int p = 1;
for( k = 0; k < dims; k++ )
{
size[k] = ((unsigned)rng % MAX_DIM_SZ) + 1;
p *= size[k];
}
SparseMat M( dims, size, depth );
map<string, double> M0;
int nz0 = (unsigned)rng % max(p/5,10);
nz0 = min(max(nz0, 1), p);
all_vals.resize(nz0);
all_vals2.resize(nz0);
Mat_<double> _all_vals(all_vals), _all_vals2(all_vals2);
rng.fill(_all_vals, CV_RAND_UNI, Scalar(-1000), Scalar(1000));
if( depth == CV_32F )
{
Mat _all_vals_f;
_all_vals.convertTo(_all_vals_f, CV_32F);
_all_vals_f.convertTo(_all_vals, CV_64F);
}
_all_vals.convertTo(_all_vals2, _all_vals2.type(), 2);
if( depth == CV_32F )
{
Mat _all_vals2_f;
_all_vals2.convertTo(_all_vals2_f, CV_32F);
_all_vals2_f.convertTo(_all_vals2, CV_64F);
}
minMaxLoc(_all_vals, &min_val, &max_val);
double _norm0 = norm(_all_vals, CV_C);
double _norm1 = norm(_all_vals, CV_L1);
double _norm2 = norm(_all_vals, CV_L2);
for( i = 0; i < nz0; i++ )
{
for(;;)
{
for( k = 0; k < dims; k++ )
idx[k] = (unsigned)rng % size[k];
sidx = idx2string(idx, dims);
if( M0.count(sidx) == 0 )
break;
}
all_idxs.push_back(sidx);
M0[sidx] = all_vals[i];
if( all_vals[i] == min_val )
min_sidx = sidx;
if( all_vals[i] == max_val )
max_sidx = sidx;
setValue(M, idx, all_vals[i], rng);
double v = getValue(M, idx, rng);
if( v != all_vals[i] )
{
ts->printf(CvTS::LOG, "%d. immediately after SparseMat[%s]=%.20g the current value is %.20g\n",
i, sidx.c_str(), all_vals[i], v);
errcount++;
break;
}
}
Ptr<CvSparseMat> M2 = (CvSparseMat*)M;
MatND Md;
M.copyTo(Md);
SparseMat M3;
SparseMat(Md).convertTo(M3, Md.type(), 2);
int nz1 = (int)M.nzcount(), nz2 = (int)M3.nzcount();
double norm0 = norm(M, CV_C);
double norm1 = norm(M, CV_L1);
double norm2 = norm(M, CV_L2);
double eps = depth == CV_32F ? FLT_EPSILON*100 : DBL_EPSILON*1000;
if( nz1 != nz0 || nz2 != nz0)
{
errcount++;
ts->printf(CvTS::LOG, "%d: The number of non-zero elements before/after converting to/from dense matrix is not correct: %d/%d (while it should be %d)\n",
si, nz1, nz2, nz0 );
break;
}
if( fabs(norm0 - _norm0) > fabs(_norm0)*eps ||
fabs(norm1 - _norm1) > fabs(_norm1)*eps ||
fabs(norm2 - _norm2) > fabs(_norm2)*eps )
{
errcount++;
ts->printf(CvTS::LOG, "%d: The norms are different: %.20g/%.20g/%.20g vs %.20g/%.20g/%.20g\n",
si, norm0, norm1, norm2, _norm0, _norm1, _norm2 );
break;
}
int n = (unsigned)rng % max(p/5,10);
n = min(max(n, 1), p) + nz0;
for( i = 0; i < n; i++ )
{
double val1, val2, val3, val0;
if(i < nz0)
{
sidx = all_idxs[i];
string2idx(sidx, idx, dims);
val0 = all_vals[i];
}
else
{
for( k = 0; k < dims; k++ )
idx[k] = (unsigned)rng % size[k];
sidx = idx2string(idx, dims);
val0 = M0[sidx];
}
val1 = getValue(M, idx, rng);
val2 = getValue(M2, idx);
val3 = getValue(M3, idx, rng);
if( val1 != val0 || val2 != val0 || fabs(val3 - val0*2) > fabs(val0*2)*FLT_EPSILON )
{
errcount++;
ts->printf(CvTS::LOG, "SparseMat M[%s] = %g/%g/%g (while it should be %g)\n", sidx.c_str(), val1, val2, val3, val0 );
break;
}
}
for( i = 0; i < n; i++ )
{
double val1, val2;
if(i < nz0)
{
sidx = all_idxs[i];
string2idx(sidx, idx, dims);
}
else
{
for( k = 0; k < dims; k++ )
idx[k] = (unsigned)rng % size[k];
sidx = idx2string(idx, dims);
}
eraseValue(M, idx, rng);
eraseValue(M2, idx);
val1 = getValue(M, idx, rng);
val2 = getValue(M2, idx);
if( val1 != 0 || val2 != 0 )
{
errcount++;
ts->printf(CvTS::LOG, "SparseMat: after deleting M[%s], it is =%g/%g (while it should be 0)\n", sidx.c_str(), val1, val2 );
break;
}
}
int nz = (int)M.nzcount();
if( nz != 0 )
{
errcount++;
ts->printf(CvTS::LOG, "The number of non-zero elements after removing all the elements = %d (while it should be 0)\n", nz );
break;
}
int idx1[MAX_DIM], idx2[MAX_DIM];
double val1 = 0, val2 = 0;
M3 = SparseMat(Md);
minMaxLoc(M3, &val1, &val2, idx1, idx2);
string s1 = idx2string(idx1, dims), s2 = idx2string(idx2, dims);
if( val1 != min_val || val2 != max_val || s1 != min_sidx || s2 != max_sidx )
{
errcount++;
ts->printf(CvTS::LOG, "%d. Sparse: The value and positions of minimum/maximum elements are different from the reference values and positions:\n\t"
"(%g, %g, %s, %s) vs (%g, %g, %s, %s)\n", si, val1, val2, s1.c_str(), s2.c_str(),
min_val, max_val, min_sidx.c_str(), max_sidx.c_str());
break;
}
minMaxLoc(Md, &val1, &val2, idx1, idx2);
s1 = idx2string(idx1, dims), s2 = idx2string(idx2, dims);
if( (min_val < 0 && (val1 != min_val || s1 != min_sidx)) ||
(max_val > 0 && (val2 != max_val || s2 != max_sidx)) )
{
errcount++;
ts->printf(CvTS::LOG, "%d. Dense: The value and positions of minimum/maximum elements are different from the reference values and positions:\n\t"
"(%g, %g, %s, %s) vs (%g, %g, %s, %s)\n", si, val1, val2, s1.c_str(), s2.c_str(),
min_val, max_val, min_sidx.c_str(), max_sidx.c_str());
break;
}
}
ts->set_failed_test_info(errcount == 0 ? CvTS::OK : CvTS::FAIL_INVALID_OUTPUT);
}
void CHumanTracker::generateRegionHistogram(Mat& region, MatND &hist, bool vis)
{
MatND prior = hist.clone();
Mat hsv;
cvtColor(region, hsv, CV_BGR2HSV);
Mat mask = Mat::zeros(region.rows, region.cols, CV_8UC1);
for (int r = 0; r < region.rows; r++)
{
for (int c = 0; c < region.cols; c++)
{
unsigned int v = hsv.at<Vec3b>(r,c)[2];
// TODO: Make me parameters
if (( v > 10) && (v < 240))
{
mask.at<uchar>(r,c) = 1;
}
}
}
// namedWindow( "Face Mask", 1 );
// imshow( "Face Mask", mask * 255 );
int histSize[] = {hbins, sbins};
float hranges[] = {0, 180};
float sranges[] = {0, 256};
const float* ranges[] = { hranges, sranges};
int channels[] = {0, 1};
calcHist(&hsv, 1, channels, mask, hist, 2, histSize, ranges, true, false);
for( int h = 0; h < hbins; h++ )
{
for( int s = 0; s < sbins; s++ )
{
hist.at<float>(h,s) = (0.99 * prior.at<float>(h,s)) + (0.01 * hist.at<float>(h,s));
}
}
// We should make it a probability dist.
normalize(hist, hist, 1.0, 0.0, NORM_L1);
if (vis)
{
// For vis
Mat visHist;
normalize(hist, visHist, 255.0, 0.00, NORM_MINMAX);
int scale = 10;
Mat histImg = Mat::zeros(sbins * scale, hbins* scale, CV_8UC3);
for( int h = 0; h < hbins; h++ )
{
for( int s = 0; s < sbins; s++ )
{
float binVal = visHist.at<float>(h, s);
int intensity = cvRound(binVal);///maxVal);
rectangle( histImg, Point(h*scale, s*scale),
Point( (h+1)*scale - 1, (s+1)*scale - 1),
Scalar::all(intensity),
CV_FILLED );
}
}
if ((debugLevel & 0x08) == 0x08)
{
namedWindow( "H-S Histogram", 1 );
imshow( "H-S Histogram", histImg );
waitKey(1);
}
}
}
/*
* 功能：直方图相似度计算和匹配
* 参数：
* 状态：
*/
int histMatch()
{
Mat frame,img;
Mat staRoiHist;
MatND RoiHist;
int DRAW_H=400,DRAW_W=400;
Mat draw(DRAW_W,DRAW_H,CV_8UC3,Scalar(0,0,0));//建立一个显示直方图的图片，背景为纯黑色
int DRAW_BIN_W=cvRound(DRAW_W/histsize1);
/****打开摄像头****/
VideoCapture cam(0);
if(!cam.isOpened())
return -1;
/****鼠标捕捉****/
namedWindow("camera",1);
namedWindow("rgb_hist",1);
setMouseCallback("camera",onMouse,0);//这里用的是面向对象的思想，只要有鼠标动作就会调用鼠标响应函数
while(1)
{
if(!pause)//暂停按钮只需控制视频的读取
{
cam>>frame;
if(frame.empty())
break; //break此处跳出的是while语句，一般是跳出for或while语句，不要理解为跳出if语句
}
if(tracking)
{
Mat RoiImage(frame,selectROI);
/*********************************************************************************************/
/* calcHist():计算图像块的直方图矩阵
/*calcHist(),第1个参数为原数组区域列表；第二个参数为有计算几个原数组；参数3为需要统计的通道索引数；参数4为操作掩码
/*第5个参数为存放目标直方图矩阵；参数6为需要计算的直方图的维数；参数7为每一维的bin的个数；参数8为每一维数值的取值范围
/*参数10为每个bin的大小是否相同的标志，默认为1，即bin的大小都相同；参数11为直方图建立时清除内存痕迹标志，默认为0，即清除
/**********************************************************************************************/
calcHist(&RoiImage,1,channels,Mat(),RoiHist,3,histsize,ranges);
//原数组区域RoiImage,1个源，需要统计的通道索引为{0,1,2},
//目标直方图RoiHist，3维，每一维的bin数histsize,取值范围为
//ranges,实际上计算出的目标矩阵类似一维矩阵。
/****************************************************************************************/
/* normalize()：根据某种范数或者数值范围归一化数组
/* normalize(),参数1表示需要归一化的数组；参数2为归一化后的目的数组;参数3表示输出数值的最小值/最大值
/* 或者输出数值的范数;参数4表示输出数值的最小值/最大值;参数5表示归一化数组使用的归一化类型，默认值为使用
/* L2范数;参数6为对应元素的掩膜矩阵，默认值为空，即不采用掩膜操作
/*****************************************************************************************/
normalize(RoiHist,RoiHist);//使用L2范数将RoiHist直方图原地归一化
vector<Mat> rgb_planes;//这里的vector为向量，向量的数据类型为Mat结构体，向量的长度为3
split(RoiImage,rgb_planes);//将rgb图分解到rgb_planes各个分量中
calcHist(&rgb_planes[0],1,0,Mat(),rhist,1,&histsize1,&ranges1);
normalize(rhist,rhist,0,DRAW_H,NORM_MINMAX);//进行最大最小值归一化
calcHist(&rgb_planes[1],1,0,Mat(),ghist,1,&histsize1,&ranges1);
normalize(ghist,ghist,0,DRAW_H,NORM_MINMAX);
calcHist(&rgb_planes[2],1,0,Mat(),bhist,1,&histsize1,&ranges1);
normalize(bhist,bhist,0,DRAW_H,NORM_MINMAX);
if(nFrame_num==1)
{
// preselectROI=selectROI;
preselectROI.x=selectROI.x;
preselectROI.y=selectROI.y;
preselectROI.width=selectROI.width;
preselectROI.height=selectROI.height;
staRoiHist=RoiHist.clone();//第一次选定目标，作为标准模板目标
}
else if(nFrame_num>1&&comp==true)
{
/**************************************************************************************/
/* compareHist():比较2个直方图的相似度
/* compareHist()，参数1为比较相似度的直方图1；参数2为比较相似度的直方图2；参数3为相似度的计算方式。有四种，
/* 分别为CV_COMP_CORREL,CV_COMP_CHISQR,CV_COMP_INTERSECT,CV_COMP_BHATTACHARYYA
/*************************************************************************************/
double distence=compareHist(staRoiHist,RoiHist,CV_COMP_INTERSECT);
//计算后面选定的与这次选定的相似度，使用INTERSECT，值越大越相似
printf("与第1次选定的图像区域相似度为：%f\n",distence);//数组越大，相似度越大
//显示直方图
for(int i=1;i<histsize1;i++)
{
//画直线中要注意2点，因为图片的原点在左上角，而直方图坐标系的原点在左下角，
//所以高度值都需要被直方图图纸高度减掉，另外取一维直方图时只能用at运算符
line(draw,Point(DRAW_BIN_W*(i-1),DRAW_H-cvRound(rhist.at<float>((i-1)))),
Point(DRAW_BIN_W*(i),DRAW_H-cvRound(rhist.at<float>(i))),Scalar(255,0,0),2,8,0);
line(draw,Point(DRAW_BIN_W*(i-1),DRAW_H-cvRound(ghist.at<float>((i-1)))),
Point(DRAW_BIN_W*(i),DRAW_H-cvRound(ghist.at<float>(i))),Scalar(0,255,0),2,8,0);
line(draw,Point(DRAW_BIN_W*(i-1),DRAW_H-cvRound(bhist.at<float>((i-1)))),
Point(DRAW_BIN_W*(i),DRAW_H-cvRound(bhist.at<float>(i))),Scalar(0,0,255),2,8,0);
}
imshow("rgb_hist",draw);
draw=Mat::zeros(DRAW_W,DRAW_H,CV_8UC3);//每画完一次直方图后都进行一次清0操作
comp=false;
}
rectangle(frame,selectROI,Scalar(0,255,0),2,8);//手动选定一次就显示一次
}//end tracking
rectangle(frame,preselectROI,Scalar(0,0,255),5,8);//初始的选定目标一直不变
imshow("camera",frame);
//键盘响应
char c = (char)waitKey(10);
if( c == 27 )
break;
switch(c)
{
case 'p'://暂停键
pause = !pause;
break;
default:
;
}
}//end while;