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opencv_contrib/modules/cudaimgproc/test/test_connectedcomponents.cpp

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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "test_precomp.hpp"
#ifdef HAVE_CUDA
namespace opencv_test {
namespace {
// This function force a row major order for the labels
template <typename LabelT>
void normalize_labels_impl(Mat& labels) {
std::map<LabelT, LabelT> map_new_labels;
LabelT i_max_new_label = 0;
for (int r = 0; r < labels.rows; ++r) {
LabelT* const mat_row = labels.ptr<LabelT>(r);
for (int c = 0; c < labels.cols; ++c) {
LabelT iCurLabel = mat_row[c];
if (iCurLabel > 0) {
if (map_new_labels.find(iCurLabel) == map_new_labels.end()) {
map_new_labels[iCurLabel] = ++i_max_new_label;
}
mat_row[c] = map_new_labels.at(iCurLabel);
}
}
}
}
void normalize_labels(Mat& labels) {
int type = labels.type();
int depth = type & CV_MAT_DEPTH_MASK;
int chans = 1 + (type >> CV_CN_SHIFT);
CV_Assert(chans == 1);
CV_Assert(depth == CV_16U || depth == CV_16S || depth == CV_32S);
switch (depth) {
case CV_16U: normalize_labels_impl<ushort>(labels); break;
case CV_16S: normalize_labels_impl<short>(labels); break;
case CV_32S: normalize_labels_impl<int>(labels); break;
default: CV_Assert(0);
}
}
////////////////////////////////////////////////////////
// ConnectedComponents
PARAM_TEST_CASE(ConnectedComponents, cv::cuda::DeviceInfo, int, int, cv::cuda::ConnectedComponentsAlgorithmsTypes)
{
cv::cuda::DeviceInfo devInfo;
int connectivity;
int ltype;
cv::cuda::ConnectedComponentsAlgorithmsTypes algo;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
connectivity = GET_PARAM(1);
ltype = GET_PARAM(2);
algo = GET_PARAM(3);
cv::cuda::setDevice(devInfo.deviceID());
}
};
CUDA_TEST_P(ConnectedComponents, Chessboard_Even)
{
std::initializer_list<int> sizes{ 16, 16 };
cv::Mat1b input;
cv::Mat1i correct_output_int;
cv::Mat correct_output;
// Chessboard image with even number of rows and cols
// Note that this is the maximum number of labels for 4-way connectivity
{
input = cv::Mat1b(sizes, {
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1
});
if (connectivity == 8) {
correct_output_int = cv::Mat1i(sizes, {
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1
});
}
else {
correct_output_int = cv::Mat1i(sizes, {
1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7, 0, 8, 0,
0, 9, 0, 10, 0, 11, 0, 12, 0, 13, 0, 14, 0, 15, 0, 16,
17, 0, 18, 0, 19, 0, 20, 0, 21, 0, 22, 0, 23, 0, 24, 0,
0, 25, 0, 26, 0, 27, 0, 28, 0, 29, 0, 30, 0, 31, 0, 32,
33, 0, 34, 0, 35, 0, 36, 0, 37, 0, 38, 0, 39, 0, 40, 0,
0, 41, 0, 42, 0, 43, 0, 44, 0, 45, 0, 46, 0, 47, 0, 48,
49, 0, 50, 0, 51, 0, 52, 0, 53, 0, 54, 0, 55, 0, 56, 0,
0, 57, 0, 58, 0, 59, 0, 60, 0, 61, 0, 62, 0, 63, 0, 64,
65, 0, 66, 0, 67, 0, 68, 0, 69, 0, 70, 0, 71, 0, 72, 0,
0, 73, 0, 74, 0, 75, 0, 76, 0, 77, 0, 78, 0, 79, 0, 80,
81, 0, 82, 0, 83, 0, 84, 0, 85, 0, 86, 0, 87, 0, 88, 0,
0, 89, 0, 90, 0, 91, 0, 92, 0, 93, 0, 94, 0, 95, 0, 96,
97, 0, 98, 0, 99, 0, 100, 0, 101, 0, 102, 0, 103, 0, 104, 0,
0, 105, 0, 106, 0, 107, 0, 108, 0, 109, 0, 110, 0, 111, 0, 112,
113, 0, 114, 0, 115, 0, 116, 0, 117, 0, 118, 0, 119, 0, 120, 0,
0, 121, 0, 122, 0, 123, 0, 124, 0, 125, 0, 126, 0, 127, 0, 128
});
}
}
correct_output_int.convertTo(correct_output, CV_MAT_DEPTH(ltype));
cv::Mat labels;
cv::Mat diff;
cv::cuda::GpuMat d_input;
cv::cuda::GpuMat d_labels;
d_input.upload(input);
EXPECT_NO_THROW(cv::cuda::connectedComponents(d_input, d_labels, connectivity, ltype, algo));
d_labels.download(labels);
normalize_labels(labels);
diff = labels != correct_output;
EXPECT_EQ(cv::countNonZero(diff), 0);
}
CUDA_TEST_P(ConnectedComponents, Chessboard_Odd)
{
std::initializer_list<int> sizes{ 15, 15 };
cv::Mat1b input;
cv::Mat1i correct_output_int;
cv::Mat correct_output;
// Chessboard image with even number of rows and cols
// Note that this is the maximum number of labels for 4-way connectivity
{
input = Mat1b(sizes, {
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1
});
if (connectivity == 8) {
correct_output_int = Mat1i(sizes, {
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1
});
}
else {
correct_output_int = Mat1i(sizes, {
1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7, 0, 8,
0, 9, 0, 10, 0, 11, 0, 12, 0, 13, 0, 14, 0, 15, 0,
16, 0, 17, 0, 18, 0, 19, 0, 20, 0, 21, 0, 22, 0, 23,
0, 24, 0, 25, 0, 26, 0, 27, 0, 28, 0, 29, 0, 30, 0,
31, 0, 32, 0, 33, 0, 34, 0, 35, 0, 36, 0, 37, 0, 38,
0, 39, 0, 40, 0, 41, 0, 42, 0, 43, 0, 44, 0, 45, 0,
46, 0, 47, 0, 48, 0, 49, 0, 50, 0, 51, 0, 52, 0, 53,
0, 54, 0, 55, 0, 56, 0, 57, 0, 58, 0, 59, 0, 60, 0,
61, 0, 62, 0, 63, 0, 64, 0, 65, 0, 66, 0, 67, 0, 68,
0, 69, 0, 70, 0, 71, 0, 72, 0, 73, 0, 74, 0, 75, 0,
76, 0, 77, 0, 78, 0, 79, 0, 80, 0, 81, 0, 82, 0, 83,
0, 84, 0, 85, 0, 86, 0, 87, 0, 88, 0, 89, 0, 90, 0,
91, 0, 92, 0, 93, 0, 94, 0, 95, 0, 96, 0, 97, 0, 98,
0, 99, 0, 100, 0, 101, 0, 102, 0, 103, 0, 104, 0, 105, 0,
106, 0, 107, 0, 108, 0, 109, 0, 110, 0, 111, 0, 112, 0, 113
});
}
}
correct_output_int.convertTo(correct_output, CV_MAT_DEPTH(ltype));
cv::Mat labels;
cv::Mat diff;
cv::cuda::GpuMat d_input;
cv::cuda::GpuMat d_labels;
d_input.upload(input);
EXPECT_NO_THROW(cv::cuda::connectedComponents(d_input, d_labels, connectivity, ltype, algo));
d_labels.download(labels);
normalize_labels(labels);
diff = labels != correct_output;
EXPECT_EQ(cv::countNonZero(diff), 0);
}
CUDA_TEST_P(ConnectedComponents, Maxlabels_8conn_Even)
{
std::initializer_list<int> sizes{ 16, 16 };
cv::Mat1b input;
cv::Mat1i correct_output_int;
cv::Mat correct_output;
// Chessboard image with even number of rows and cols
// Note that this is the maximum number of labels for 4-way connectivity
{
input = Mat1b(sizes, {
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
});
correct_output_int = Mat1i(sizes, {
1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7, 0, 8, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
9, 0, 10, 0, 11, 0, 12, 0, 13, 0, 14, 0, 15, 0, 16, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
17, 0, 18, 0, 19, 0, 20, 0, 21, 0, 22, 0, 23, 0, 24, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
25, 0, 26, 0, 27, 0, 28, 0, 29, 0, 30, 0, 31, 0, 32, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
33, 0, 34, 0, 35, 0, 36, 0, 37, 0, 38, 0, 39, 0, 40, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
41, 0, 42, 0, 43, 0, 44, 0, 45, 0, 46, 0, 47, 0, 48, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
49, 0, 50, 0, 51, 0, 52, 0, 53, 0, 54, 0, 55, 0, 56, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
57, 0, 58, 0, 59, 0, 60, 0, 61, 0, 62, 0, 63, 0, 64, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
});
}
correct_output_int.convertTo(correct_output, CV_MAT_DEPTH(ltype));
cv::Mat labels;
cv::Mat diff;
cv::cuda::GpuMat d_input;
cv::cuda::GpuMat d_labels;
d_input.upload(input);
EXPECT_NO_THROW(cv::cuda::connectedComponents(d_input, d_labels, connectivity, ltype, algo));
d_labels.download(labels);
normalize_labels(labels);
diff = labels != correct_output;
EXPECT_EQ(cv::countNonZero(diff), 0);
}
CUDA_TEST_P(ConnectedComponents, Maxlabels_8conn_Odd)
{
std::initializer_list<int> sizes{ 15, 15 };
cv::Mat1b input;
cv::Mat1i correct_output_int;
cv::Mat correct_output;
// Chessboard image with even number of rows and cols
// Note that this is the maximum number of labels for 4-way connectivity
{
input = Mat1b(sizes, {
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1
});
correct_output_int = Mat1i(sizes, {
1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7, 0, 8,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
9, 0, 10, 0, 11, 0, 12, 0, 13, 0, 14, 0, 15, 0, 16,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
17, 0, 18, 0, 19, 0, 20, 0, 21, 0, 22, 0, 23, 0, 24,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
25, 0, 26, 0, 27, 0, 28, 0, 29, 0, 30, 0, 31, 0, 32,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
33, 0, 34, 0, 35, 0, 36, 0, 37, 0, 38, 0, 39, 0, 40,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
41, 0, 42, 0, 43, 0, 44, 0, 45, 0, 46, 0, 47, 0, 48,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
49, 0, 50, 0, 51, 0, 52, 0, 53, 0, 54, 0, 55, 0, 56,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
57, 0, 58, 0, 59, 0, 60, 0, 61, 0, 62, 0, 63, 0, 64
});
}
correct_output_int.convertTo(correct_output, CV_MAT_DEPTH(ltype));
cv::Mat labels;
cv::Mat diff;
cv::cuda::GpuMat d_input;
cv::cuda::GpuMat d_labels;
d_input.upload(input);
EXPECT_NO_THROW(cv::cuda::connectedComponents(d_input, d_labels, connectivity, ltype, algo));
d_labels.download(labels);
normalize_labels(labels);
diff = labels != correct_output;
EXPECT_EQ(cv::countNonZero(diff), 0);
}
CUDA_TEST_P(ConnectedComponents, Single_Row)
{
std::initializer_list<int> sizes{ 1, 15 };
cv::Mat1b input;
cv::Mat1i correct_output_int;
cv::Mat correct_output;
// Chessboard image with even number of rows and cols
// Note that this is the maximum number of labels for 4-way connectivity
{
input = Mat1b(sizes, { 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1 });
correct_output_int = Mat1i(sizes, { 1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7, 0, 8 });
}
correct_output_int.convertTo(correct_output, CV_MAT_DEPTH(ltype));
cv::Mat labels;
cv::Mat diff;
cv::cuda::GpuMat d_input;
cv::cuda::GpuMat d_labels;
d_input.upload(input);
EXPECT_NO_THROW(cv::cuda::connectedComponents(d_input, d_labels, connectivity, ltype, algo));
d_labels.download(labels);
normalize_labels(labels);
diff = labels != correct_output;
EXPECT_EQ(cv::countNonZero(diff), 0);
}
CUDA_TEST_P(ConnectedComponents, Single_Column)
{
std::initializer_list<int> sizes{ 15, 1 };
cv::Mat1b input;
cv::Mat1i correct_output_int;
cv::Mat correct_output;
// Chessboard image with even number of rows and cols
// Note that this is the maximum number of labels for 4-way connectivity
{
input = Mat1b(sizes, { 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1 });
correct_output_int = Mat1i(sizes, { 1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7, 0, 8 });
}
correct_output_int.convertTo(correct_output, CV_MAT_DEPTH(ltype));
cv::Mat labels;
cv::Mat diff;
cv::cuda::GpuMat d_input;
cv::cuda::GpuMat d_labels;
d_input.upload(input);
EXPECT_NO_THROW(cv::cuda::connectedComponents(d_input, d_labels, connectivity, ltype, algo));
d_labels.download(labels);
normalize_labels(labels);
diff = labels != correct_output;
EXPECT_EQ(cv::countNonZero(diff), 0);
}
CUDA_TEST_P(ConnectedComponents, Concentric_Circles)
{
string img_path = cvtest::TS::ptr()->get_data_path() + "connectedcomponents/concentric_circles.png";
string exp_path = cvtest::TS::ptr()->get_data_path() + "connectedcomponents/ccomp_exp.png";
Mat img = imread(img_path, 0);
EXPECT_FALSE(img.empty());
Mat exp = imread(exp_path, 0);
EXPECT_FALSE(exp.empty());
Mat labels;
exp.convertTo(exp, ltype);
GpuMat d_img;
GpuMat d_labels;
d_img.upload(img);
EXPECT_NO_THROW(cv::cuda::connectedComponents(d_img, d_labels, connectivity, ltype, algo));
d_labels.download(labels);
normalize_labels(labels);
Mat diff = labels != exp;
EXPECT_EQ(cv::countNonZero(diff), 0);
}
INSTANTIATE_TEST_CASE_P(CUDA_ImgProc, ConnectedComponents, testing::Combine(
ALL_DEVICES,
testing::Values(8),
testing::Values(CV_32S),
testing::Values(cv::cuda::CCL_DEFAULT, cv::cuda::CCL_BKE)
));
}
} // namespace
#endif // HAVE_CUDA