void Na2DViewer::paintCrosshair(QPainter& painter)
{
float scale = defaultScale * cameraModel.scale();
QBrush brush1(Qt::black);
QBrush brush2(QColor(255, 255, 180));
QPen pen1(brush1, 2.0/scale);
QPen pen2(brush2, 1.0/scale);
// qDebug() << "paint crosshair";
// Q: Why all this complicated math instead of just [width()/2, height()/2]?
// A: This helps debug/document placement of image focus
qreal w2 = (pixmap.width() - 1.0) / 2.0; // origin at pixel center, not corner
qreal h2 = (pixmap.height() - 1.0) / 2.0; // origin at pixel center, not corner
qreal cx = w2 + flip_X * (cameraModel.focus().x() - w2) + 0.5;
qreal cy = h2 + flip_Y * (cameraModel.focus().y() - h2) + 0.5;
QPointF f(cx, cy);
QPointF dx1(4.0 / scale, 0);
QPointF dy1(0, 4.0 / scale);
QPointF dx2(10.0 / scale, 0); // crosshair size is ten pixels
QPointF dy2(0, 10.0 / scale);
painter.setPen(pen1);
painter.drawLine(f + dx1, f + dx2);
painter.drawLine(f - dx1, f - dx2);
painter.drawLine(f + dy1, f + dy2);
painter.drawLine(f - dy1, f - dy2);
painter.setPen(pen2);
painter.drawLine(f + dx1, f + dx2);
painter.drawLine(f - dx1, f - dx2);
painter.drawLine(f + dy1, f + dy2);
painter.drawLine(f - dy1, f - dy2);
}
void InterpolateBicubic::set_table( const std::vector<Value>& ff ){
plumed_assert( getNumberOfSplinePoints()==ff.size() );
plumed_assert( ff[0].getNumberOfDerivatives()==2 );
dcross=0.0; unsigned iplus, iminus;
for(unsigned i=1;i<np[0]-1;++i){
iplus=(i+1)*stride[0]; iminus=(i-1)*stride[0];
for(unsigned j=1;j<np[1]-1;++j){
dcross(i,j) = ( ff[iplus+j+1].get() + ff[iminus+j-1].get() - ff[iplus+j-1].get() - ff[iminus+j+1].get() ) /
getCrossTermDenominator( i, j );
}
}
double d1, d2; Matrix<double> tc(4,4);
std::vector<double> y(4), dy1(4), dy2(4), d2y12(4);
unsigned pij=0; unsigned ipos;
for (unsigned i=0;i<np[0]-1;++i){
ipos=i*stride[0]; d1 = getPointSpacing( 0, i );
for (unsigned j=0; j<np[1]-1;++j){
d2 = getPointSpacing( 1, j );
y[0] = ff[ipos+j].get(); y[1] = ff[ipos+stride[0]+j].get(); y[2] = ff[ipos+stride[0]+j+1].get(); y[3] = ff[ipos+j+1].get();
dy1[0] = ff[ipos+j].getDerivative(0); dy1[1] = ff[ipos+stride[0]+j].getDerivative(0);
dy1[2] = ff[ipos+stride[0]+j+1].getDerivative(0); dy1[3] = ff[ipos+j+1].getDerivative(0);
dy2[0] = ff[ipos+j].getDerivative(1); dy2[1] = ff[ipos+stride[0]+j].getDerivative(1);
dy2[2] = ff[ipos+stride[0]+j+1].getDerivative(1); dy2[3] = ff[ipos+j+1].getDerivative(1);
d2y12[0] = dcross( i, j ); d2y12[1] = dcross( i+1, j ); d2y12[2] = dcross( i+1, j+1 ); d2y12[3] = dcross( i, j+1 );
IBicCoeff( y, dy1, dy2, d2y12, d1, d2, tc);
pij=( ipos+j )*16;
for(unsigned k=0; k<4; ++k){ for(unsigned n=0; n<4; ++n){ clist[pij++]=tc(k,n); } }
}
}
}
gmMatrix3 Algebraic::hess(const gmVector3 & v)
{
double dfdxy = dxdy(v), dfdxz = dxdz(v), dfdyz = dydz(v);
double dfdxx = dx2(v), dfdyy = dy2(v), dfdzz = dz2(v);
gmMatrix3 hess(dfdxx, dfdxy, dfdxz,
dfdxy, dfdyy, dfdyz,
dfdxz, dfdyz, dfdzz);
return hess;
}
bool TextureBoostedSaturatedGradientDataTest(bool create, int width, int height, const Func1 & f)
{
bool result = true;
Data data(f.description);
TEST_LOG_SS(Info, (create ? "Create" : "Verify") << " test " << f.description << " [" << width << ", " << height << "].");
View src(width, height, View::Gray8, NULL, TEST_ALIGN(width));
View dx1(width, height, View::Gray8, NULL, TEST_ALIGN(width));
View dy1(width, height, View::Gray8, NULL, TEST_ALIGN(width));
View dx2(width, height, View::Gray8, NULL, TEST_ALIGN(width));
View dy2(width, height, View::Gray8, NULL, TEST_ALIGN(width));
const int saturation = 16, boost = 4;
if(create)
{
FillRandom(src);
TEST_SAVE(src);
f.Call(src, saturation, boost, dx1, dy1);
TEST_SAVE(dx1);
TEST_SAVE(dy1);
}
else
{
TEST_LOAD(src);
TEST_LOAD(dx1);
TEST_LOAD(dy1);
f.Call(src, saturation, boost, dx2, dy2);
TEST_SAVE(dx2);
TEST_SAVE(dy2);
result = result && Compare(dx1, dx2, 0, true, 32, 0, "dx");
result = result && Compare(dy1, dy2, 0, true, 32, 0, "dy");
}
return result;
}
bool TextureBoostedSaturatedGradientAutoTest(int width, int height, int saturation, int boost, const Func1 & f1, const Func1 & f2)
{
bool result = true;
TEST_LOG_SS(Info, "Test " << f1.description << " & " << f2.description << " [" << width << ", " << height << "] <" << saturation << ", " << boost << ">.");
View src(width, height, View::Gray8, NULL, TEST_ALIGN(width));
FillRandom(src);
View dx1(width, height, View::Gray8, NULL, TEST_ALIGN(width));
View dy1(width, height, View::Gray8, NULL, TEST_ALIGN(width));
View dx2(width, height, View::Gray8, NULL, TEST_ALIGN(width));
View dy2(width, height, View::Gray8, NULL, TEST_ALIGN(width));
TEST_EXECUTE_AT_LEAST_MIN_TIME(f1.Call(src, saturation, boost, dx1, dy1));
TEST_EXECUTE_AT_LEAST_MIN_TIME(f2.Call(src, saturation, boost, dx2, dy2));
result = result && Compare(dx1, dx2, 0, true, 32, 0, "dx");
result = result && Compare(dy1, dy2, 0, true, 32, 0, "dy");
return result;
}
//////////////////////////////////////////////////////////////////////
// Construction/Destruction
//////////////////////////////////////////////////////////////////////
Tensor::Tensor(const IplImage *cv_image, BOOL isComputeGradient)
{
//保存原图像的副本
m_img=cvCreateImage(cvSize(cv_image->width,cv_image->height),cv_image->depth,3);
cvCopyImage(cv_image,m_img);
//获取非线性多尺度结构张量的参数值
m_levels = 2;
ASSERT(m_levels > 0 );
m_dim = m_levels * SiNGLE_TENSOR_DIM; //SiNGLE_TENSOR_DIM单一张量
//SiNGLE_TENSOR_DIM=n(n+1)/2;反解n=m_axes_cnt，m_axes_cnt为坐标抽的维数
m_axes_cnt = (unsigned int)(sqrt(2 * SiNGLE_TENSOR_DIM + 0.25) - 0.5); // 2
m_grad_dim = m_levels * m_axes_cnt; //m_grad_dim
////////////////////////////////////////////////////////////////////////////
//将多通道转化为单通道，默认为三个通道
unsigned int x,y,i,n;
m_w = cv_image->width;
m_h = cv_image->height;
IplImage *cv_channels[3];
for (n = 0;n < 3;n++)
{
cv_channels[n] = cvCreateImage( cvGetSize(cv_image), cv_image->depth, 1 );
}
cvSplit(cv_image, cv_channels[0], cv_channels[1], cv_channels[2], NULL);
////////////////////////////////////////////////////////////////////////////
//初始化m_tensor，CMatrix(m_h,m_w)创建一个矩阵，其元素全为0
m_tensor = new CMatrix *[m_dim];
for (i=0;i<m_dim;i++)
{
m_tensor[i] = new CMatrix(m_h,m_w);
}
////////////////////////////////////////////////////////////////////////////
//将每一尺度的张量转化为彩色图像存储起来，申请空间
m_pImageTensorRGB=new Image<Color_RGB> *[m_levels];
for (i=0;i<m_levels;i++)
{
m_pImageTensorRGB[i] = new Image<Color_RGB> (m_w,m_h);
}
//初始化m_gradient
if (isComputeGradient)
{
m_gradient = new CMatrix *[m_grad_dim];
for (i=0;i<m_grad_dim;i++)
{
m_gradient[i] = new CMatrix(m_h,m_w);
}
}
else
{
m_gradient = NULL;
}
//辅助矩阵
CMatrix image(m_h, m_w);
CMatrix dx(m_h,m_w);
CMatrix dy(m_h,m_w);
CMatrix dx2(m_h,m_w);
CMatrix dy2(m_h,m_w);
CMatrix dxdy(m_h,m_w);
//利用固定数据创建一个矩阵
CvMat cv_dx2 = cvMat(m_h, m_w, CV_64FC1, dx2.GetData());
CvMat cv_dy2 = cvMat(m_h, m_w, CV_64FC1, dy2.GetData());
CvMat cv_dxdy =cvMat(m_h, m_w, CV_64FC1, dxdy.GetData());
//完成IplImage向CMatrix类型的转换，对每一个颜色通道分别进行处理
for (n = 0;n <3;n++) //n表示通道数，默认为3
{
//将每一个通道的元素拷贝到image中
for (y = 0; y < m_h; y++)
{
for (x = 0; x < m_w; x++)
{
uchar* dst = &CV_IMAGE_ELEM( cv_channels[n], uchar, y, x );
image.SetElement(y, x, (double)(dst[0]));
}
}
//计算每一个颜色通道的梯度(x方向,y方向)并分别赋给dx，dy
image.centdiffX(dx);
image.centdiffY(dy);
//将dx，dy分别赋给cv_dx，cv_dy
CvMat cv_dx = cvMat(m_h, m_w, CV_64FC1, dx.GetData());
CvMat cv_dy = cvMat(m_h, m_w, CV_64FC1, dy.GetData());
//初始化cv_tensor0，cv_tensor1，cv_tensor2，此时m_tensor[0],m_tensor[1],m_tensor[2]均初始化0
CvMat cv_tensor0 = cvMat(m_h, m_w, CV_64FC1, (m_tensor[0])->GetData());
CvMat cv_tensor1 = cvMat(m_h, m_w, CV_64FC1, (m_tensor[1])->GetData());
CvMat cv_tensor2 = cvMat(m_h, m_w, CV_64FC1, (m_tensor[2])->GetData());
//计算图像的梯度，保存在cv_gradX，cv_gradY中，并赋值给m_gradient[0],m_gradient[1]
if (isComputeGradient)
{
//cv_gradX,cv_gradY初始化并计算
CvMat cv_gradX = cvMat(m_h, m_w, CV_64FC1, (m_gradient[0])->GetData());
CvMat cv_gradY = cvMat(m_h, m_w, CV_64FC1, (m_gradient[1])->GetData());
cvAdd(&cv_gradX, &cv_dx, &cv_gradX);//对于三个通道进行累加
cvAdd(&cv_gradY, &cv_dy, &cv_gradY);
}
//计算结构张量，cv_tensor0=dx*dx,cv_tensor1=dy*dy,cv_tensor2=dx*dy
cvMul(&cv_dx, &cv_dx, &cv_dx2);
cvAdd(&cv_tensor0, &cv_dx2, &cv_tensor0);
cvMul(&cv_dy, &cv_dy, &cv_dy2);
cvAdd(&cv_tensor1, &cv_dy2, &cv_tensor1);
cvMul(&cv_dx, &cv_dy, &cv_dxdy);
cvAdd(&cv_tensor2, &cv_dxdy, &cv_tensor2);
//单尺度计算完毕，以下为多尺度非线性结构张量的计算方法
if (m_levels > 1)
{
unsigned int wavelet_levels = m_levels - 1; //-1的原因是因为之前没有if (m_levels==1)的判断语句
double dMaxValue,dMinValue;
cvMinMaxLoc(cv_channels[n], &dMinValue, &dMaxValue);//Finds global minimum, maximum
//将图像的像素值归一化到[0,1]
Wavelet *wave = new Wavelet(&image, dMinValue, dMaxValue, wavelet_levels); //调用Wavelet的构造函数
//新建WaveletDetailImages结构体的数组
WaveletDetailImages *D_images = new WaveletDetailImages[wavelet_levels];
for (i = 0; i < wavelet_levels; i++)
{
D_images[i].Detail_1 = new CMatrix(m_h, m_w);
D_images[i].Detail_2 = new CMatrix(m_h, m_w);
}
wave->execute(D_images);//得到D(s,x),D(s,y)
for (i = 0; i < wavelet_levels; i++)
{
//默认多尺度结构张量的比例因子a=2
double scale = pow((float)0.25, (int)(i + 1)); //见公式(2-15)
CvMat cv_dx = cvMat(m_h, m_w, CV_64FC1, D_images[i].Detail_1->GetData());
CvMat cv_dy = cvMat(m_h, m_w, CV_64FC1, D_images[i].Detail_2->GetData());
CvMat cv_tensor0 = cvMat(m_h, m_w, CV_64FC1, (m_tensor[(i+1) * SiNGLE_TENSOR_DIM])->GetData());
CvMat cv_tensor1 = cvMat(m_h, m_w, CV_64FC1, (m_tensor[(i+1) * SiNGLE_TENSOR_DIM + 1])->GetData());
CvMat cv_tensor2 = cvMat(m_h, m_w, CV_64FC1, (m_tensor[(i+1) * SiNGLE_TENSOR_DIM + 2])->GetData());
//计算梯度
if (isComputeGradient)
{
CvMat cv_gradX = cvMat(m_h, m_w, CV_64FC1, (m_gradient[(i+1) * m_axes_cnt])->GetData());
CvMat cv_gradY = cvMat(m_h, m_w, CV_64FC1, (m_gradient[(i+1) * m_axes_cnt + 1])->GetData());
cvAdd(&cv_gradX, &cv_dx, &cv_gradX);
cvAdd(&cv_gradY, &cv_dy, &cv_gradY);
}
//计算张量
cvMul(&cv_dx, &cv_dx, &cv_dx2, scale);
cvAdd(&cv_tensor0, &cv_dx2, &cv_tensor0);
cvMul(&cv_dy, &cv_dy, &cv_dy2, scale);
cvAdd(&cv_tensor1, &cv_dy2, &cv_tensor1);
cvMul(&cv_dx, &cv_dy, &cv_dxdy, scale);
cvAdd(&cv_tensor2, &cv_dxdy, &cv_tensor2);
}
for (i = 0; i < wavelet_levels; i++)
{
delete D_images[i].Detail_1;
delete D_images[i].Detail_2;
}
delete [] D_images;
delete wave;
}
cvReleaseImage(&cv_channels[n]);
}
//将每一尺度的结构张量转换为彩色图像存储起来
for (i=0;i<m_levels;i++)
{
for (y=0;y<m_h;y++)
{
for (x=0;x<m_w;x++)
{
(*m_pImageTensorRGB[i])(x,y).r=(m_tensor[i*SiNGLE_TENSOR_DIM])->GetElement(y,x);
(*m_pImageTensorRGB[i])(x,y).g=(m_tensor[i*SiNGLE_TENSOR_DIM+1])->GetElement(y,x);
(*m_pImageTensorRGB[i])(x,y).b=(m_tensor[i*SiNGLE_TENSOR_DIM+2])->GetElement(y,x);
}
}
}
m_tensors = NULL;
}
