void recurGraph(byte *g) {
int res,i,j;
byte buf[W*H];
res=complete(g);
if(res==-1) return;
if(res==1) {
if(isMin(g)) {
outGraph(g);
testGraph(g);
printf("ok\n");
return;
}//if
}//if
for(i=0; i<H*W; i++) {
for(j=0; j<4; j++) {
if((g[i]&(1<<j))&&(g[i]&(16<<j))) {
copy(g,buf);
buf[i]-=(1<<j);
recurGraph(buf);
copy(g,buf);
buf[i]-=(16<<j);
recurGraph(buf);
return;
}//if
}//for
}//for
}//recurGraph
void HessianDetector::findLevelKeypoints(float curScale, float pixelDistance)
{
assert(par.border >= 2);
const int rows = cur.rows;
const int cols = cur.cols;
for (int r = par.border; r < (rows - par.border); r++)
{
for (int c = par.border; c < (cols - par.border); c++)
{
const float val = cur.at<float>(r,c);
if ( (val > positiveThreshold && (isMax(val, cur, r, c) && isMax(val, low, r, c) && isMax(val, high, r, c))) ||
(val < negativeThreshold && (isMin(val, cur, r, c) && isMin(val, low, r, c) && isMin(val, high, r, c))) )
// either positive -> local max. or negative -> local min.
localizeKeypoint(r, c, curScale, pixelDistance);
}
}
}
int main(int argc, const char * argv[]) {
int num[] = {34,56,67,23,567,67,98,87};
int len = sizeof(num) / sizeof(num[0]);
//最大值
int res = isMax(num,len);
printf("%d \n",res);
//最小值
res = isMin(num,len);
printf("%d \n",res);
//累加和
res = sum(num,len);
printf("%d \n",res);
//平均数
res = avg(num,len);
printf("%d \n",res);
//找
int key = 23;
res = number(num,len,key);
printf("%d \n",res);
}
bool Normalization::algorithm() {
KstVectorPtr vectorIn = inputVector(VECTOR_IN);
KstVectorPtr vectorOut = outputVector(VECTOR_OUT);
double *arr;
double *Yi;
int iLength = vectorIn->length();
int w = 1;
arr = new double[iLength];
Yi = new double[iLength];
for(int i=0; i<iLength; i++)
{
Yi[i] = vectorIn->value()[i];
}
//
// exclude peak values
//
for(int loop=0; loop<2; loop++)
{
for(int i=0; i<iLength; i++)
{
arr[i] = Yi[i];
}
for(int i=0; i<iLength; i++)
{
if(isMin(Yi, i, iLength) || isMax(Yi, i, iLength))
{
excludePts(arr, i, w, iLength);
}
}
searchHighPts(arr, iLength);
interpolate(Yi, arr, iLength);
}
//
// do a piecewise linear fit
//
vectorOut->resize(iLength, false);
int L = 3;
double cof[2] = { 0.0, 0.0 };
for(int i=0; i<iLength; i=i+L)
{
fit(i, L, iLength, Yi, cof, vectorOut);
}
//
// normalize
//
for(int i=0; i<iLength; i++)
{
vectorOut->value()[i] = vectorIn->value()[i] / vectorOut->value()[i];
}
//
// exclude off points
//
for(int i=0; i<iLength; i++)
{
if(vectorOut->value()[i] < 0.0 || vectorOut->value()[i] > 1.2)
{
vectorOut->value()[i] = NOPOINT;
}
}
delete[] arr;
delete[] Yi;
return true;
}
