本文整理汇总了C++中DImage::cols方法的典型用法代码示例。如果您正苦于以下问题：C++ DImage::cols方法的具体用法？C++ DImage::cols怎么用？C++ DImage::cols使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类DImage的用法示例。
在下文中一共展示了DImage::cols方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: apply_clustering
DPlane DColorCluster::apply_clustering(const DImage &input)
{
DPlane result(input.rows(), input.cols());
for(int i=0; i<input.rows(); i++)
for(int j=0; j<input.cols(); j++)
{
int closest_cluster=0;
double min_dist=1000000000;
DTriple sample(input[0][i][j], input[1][i][j], input[2][i][j]);
for(int c=0; c<clusters.size(); c++)
if(clusters[c].distance_to(sample) < min_dist)
{
min_dist = clusters[c].distance_to(sample);
closest_cluster = c;
}
result[i][j] = closest_cluster+1;
}
return result;
}示例2: WritePPMImage
void WritePPMImage(const DImage &img, const char *filename)
{
FILE *fp = fopen(filename, "wb");
char temp[1024];
// write magic number
fprintf(fp, "P6\n");
// write dimensions
fprintf(fp, "%d %d\n", img.cols(), img.rows());
// write max pixel value
fprintf(fp, "255\n");
for(int i=0; i<img.rows(); i++)
for(int j=0; j<img.cols(); j++)
for(int k=0; k<3; k++)
fputc(int(img[k][i][j]), fp);
fclose(fp);
return;
}示例3: do_clustering
// k clusters per channel = k^3 clusters
DPlane DColorCluster::do_clustering(DImage &input, int k, bool ignore_black)
{
int cluster_count = k*k*k;
for(int i=0; i<cluster_count; i++)
clusters.push_back(DRGBCluster());
DPlane result(input.rows(), input.cols());
int delta = 256/(k*2);
for(int a=0, cl=0; a<k; a++)
for(int b=0; b<k; b++)
for(int c=0; c<k; c++, cl++)
clusters[cl].set_mean(
DTriple((a+1)*delta, (b+1)*delta, (c+1)*delta));
int changes=1000000000;
bool done=false;
while(!done) {
changes=0;
for(int i=0; i<input.rows(); i++)
for(int j=0; j<input.cols(); j++)
{
if(ignore_black && input[0][i][j] == 0 && input[1][i][j] == 0 &&
input[2][i][j] == 0)
{
result[i][j] = 0;
continue;
}
int closest_cluster=0;
double min_dist=1000000000;
DTriple sample(input[0][i][j], input[1][i][j], input[2][i][j]);
for(int c=0; c<cluster_count; c++)
if(clusters[c].distance_to(sample) < min_dist)
{
min_dist = clusters[c].distance_to(sample);
closest_cluster = c;
}
clusters[closest_cluster].add_sample(sample);
if(closest_cluster+1 != result[i][j])
{
result[i][j] = closest_cluster+1;
changes++;
}
}
if( changes < input.rows() * input.cols() * 0.001)
done=true;
else
for(int i=0; i<cluster_count; i++)
{
clusters[i].update_mean();
clusters[i].remove_all();
}
printf("there were %d changes\n",changes);
}
return result;
}