Open Source Computer Vision Library https://opencv.org/
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/*M///////////////////////////////////////////////////////////////////////////////////////
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#include "test_precomp.hpp"
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#ifdef HAVE_CUDA
namespace
{
struct GreedyLabeling
{
struct dot
{
int x;
int y;
static dot make(int i, int j)
{
dot d; d.x = i; d.y = j;
return d;
}
};
struct InInterval
{
InInterval(const int& _lo, const int& _hi) : lo(-_lo), hi(_hi) {}
const int lo, hi;
bool operator() (const unsigned char a, const unsigned char b) const
{
int d = a - b;
return lo <= d && d <= hi;
}
};
GreedyLabeling(cv::Mat img)
: image(img), _labels(image.size(), CV_32SC1, cv::Scalar::all(-1)) {}
void operator() (cv::Mat labels) const
{
InInterval inInt(0, 2);
dot* stack = new dot[image.cols * image.rows];
int cc = -1;
int* dist_labels = (int*)labels.data;
int pitch = (int) labels.step1();
unsigned char* source = (unsigned char*)image.data;
int width = image.cols;
int height = image.rows;
int step1 = (int)image.step1();
for (int j = 0; j < image.rows; ++j)
for (int i = 0; i < image.cols; ++i)
{
if (dist_labels[j * pitch + i] != -1) continue;
dot* top = stack;
dot p = dot::make(i, j);
cc++;
dist_labels[j * pitch + i] = cc;
while (top >= stack)
{
int* dl = &dist_labels[p.y * pitch + p.x];
unsigned char* sp = &source[p.y * step1 + p.x];
dl[0] = cc;
//right
if( p.x < (width - 1) && dl[ +1] == -1 && inInt(sp[0], sp[+1]))
*top++ = dot::make(p.x + 1, p.y);
//left
if( p.x > 0 && dl[-1] == -1 && inInt(sp[0], sp[-1]))
*top++ = dot::make(p.x - 1, p.y);
//bottom
if( p.y < (height - 1) && dl[+pitch] == -1 && inInt(sp[0], sp[+step1]))
*top++ = dot::make(p.x, p.y + 1);
//top
if( p.y > 0 && dl[-pitch] == -1 && inInt(sp[0], sp[-step1]))
*top++ = dot::make(p.x, p.y - 1);
p = *--top;
}
}
delete[] stack;
}
void checkCorrectness(cv::Mat gpu)
{
cv::Mat diff = gpu - _labels;
int outliers = 0;
for (int j = 0; j < image.rows; ++j)
for (int i = 0; i < image.cols - 1; ++i)
{
if ( (_labels.at<int>(j,i) == gpu.at<int>(j,i + 1)) && (diff.at<int>(j, i) != diff.at<int>(j,i + 1)))
{
outliers++;
}
}
ASSERT_TRUE(outliers < gpu.cols + gpu.rows);
}
cv::Mat image;
cv::Mat _labels;
};
}
struct Labeling : testing::TestWithParam<cv::cuda::DeviceInfo>
{
cv::cuda::DeviceInfo devInfo;
virtual void SetUp()
{
devInfo = GetParam();
cv::cuda::setDevice(devInfo.deviceID());
}
cv::Mat loat_image()
{
return cv::imread(std::string( cvtest::TS::ptr()->get_data_path() ) + "labeling/label.png");
}
};
CUDA_TEST_P(Labeling, DISABLED_ConnectedComponents)
{
cv::Mat image;
cvtColor(loat_image(), image, cv::COLOR_BGR2GRAY);
cv::threshold(image, image, 150, 255, cv::THRESH_BINARY);
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ASSERT_TRUE(image.type() == CV_8UC1);
GreedyLabeling host(image);
host(host._labels);
cv::cuda::GpuMat mask;
mask.create(image.rows, image.cols, CV_8UC1);
cv::cuda::GpuMat components;
components.create(image.rows, image.cols, CV_32SC1);
cv::cuda::connectivityMask(cv::cuda::GpuMat(image), mask, cv::Scalar::all(0), cv::Scalar::all(2));
cv::cuda::labelComponents(mask, components);
host.checkCorrectness(cv::Mat(components));
}
INSTANTIATE_TEST_CASE_P(CUDA_ConnectedComponents, Labeling, ALL_DEVICES);
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#endif // HAVE_CUDA