generic-library/opencv
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Revert "call resetDevice if gpu test fails"

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This reverts commit ab25fe9e37.
This commit is contained in:
Vladislav Vinogradov
2012-12-19 11:13:31 +04:00
parent 41c9377db0
commit 4ba33fa1ed
36 changed files with 5694 additions and 9450 deletions
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236
modules/gpu/test/test_hough.cpp
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@@ -81,36 +81,28 @@ PARAM_TEST_CASE(HoughLines, cv::gpu::DeviceInfo, cv::Size, UseRoi)
TEST_P(HoughLines, Accuracy)
{
try
{
const cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
const cv::Size size = GET_PARAM(1);
const bool useRoi = GET_PARAM(2);
const cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
const cv::Size size = GET_PARAM(1);
const bool useRoi = GET_PARAM(2);
const float rho = 1.0f;
const float theta = (float) (1.5 * CV_PI / 180.0);
const int threshold = 100;
const float rho = 1.0f;
const float theta = (float) (1.5 * CV_PI / 180.0);
onst int threshold = 100;
cv::Mat src(size, CV_8UC1);
generateLines(src);
cv::Mat src(size, CV_8UC1);
generateLines(src);
cv::gpu::GpuMat d_lines;
cv::gpu::HoughLines(loadMat(src, useRoi), d_lines, rho, theta, threshold);
cv::gpu::GpuMat d_lines;
cv::gpu::HoughLines(loadMat(src, useRoi), d_lines, rho, theta, threshold);
std::vector<cv::Vec2f> lines;
cv::gpu::HoughLinesDownload(d_lines, lines);
std::vector<cv::Vec2f> lines;
cv::gpu::HoughLinesDownload(d_lines, lines);
cv::Mat dst(size, CV_8UC1);
drawLines(dst, lines);
cv::Mat dst(size, CV_8UC1);
drawLines(dst, lines);
ASSERT_MAT_NEAR(src, dst, 0.0);
}
catch (...)
{
cv::gpu::resetDevice();
throw;
}
ASSERT_MAT_NEAR(src, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, HoughLines, testing::Combine(
@@ -134,61 +126,53 @@ PARAM_TEST_CASE(HoughCircles, cv::gpu::DeviceInfo, cv::Size, UseRoi)
TEST_P(HoughCircles, Accuracy)
{
try
const cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
const cv::Size size = GET_PARAM(1);
const bool useRoi = GET_PARAM(2);
const float dp = 2.0f;
const float minDist = 10.0f;
const int minRadius = 10;
const int maxRadius = 20;
const int cannyThreshold = 100;
const int votesThreshold = 20;
std::vector<cv::Vec3f> circles_gold(4);
circles_gold[0] = cv::Vec3i(20, 20, minRadius);
circles_gold[1] = cv::Vec3i(90, 87, minRadius + 3);
circles_gold[2] = cv::Vec3i(30, 70, minRadius + 8);
circles_gold[3] = cv::Vec3i(80, 10, maxRadius);
cv::Mat src(size, CV_8UC1);
drawCircles(src, circles_gold, true);
cv::gpu::GpuMat d_circles;
cv::gpu::HoughCircles(loadMat(src, useRoi), d_circles, CV_HOUGH_GRADIENT, dp, minDist, cannyThreshold, votesThreshold, minRadius, maxRadius);
std::vector<cv::Vec3f> circles;
cv::gpu::HoughCirclesDownload(d_circles, circles);
ASSERT_FALSE(circles.empty());
for (size_t i = 0; i < circles.size(); ++i)
{
const cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
const cv::Size size = GET_PARAM(1);
const bool useRoi = GET_PARAM(2);
cv::Vec3f cur = circles[i];
const float dp = 2.0f;
const float minDist = 10.0f;
const int minRadius = 10;
const int maxRadius = 20;
const int cannyThreshold = 100;
const int votesThreshold = 20;
bool found = false;
std::vector<cv::Vec3f> circles_gold(4);
circles_gold[0] = cv::Vec3i(20, 20, minRadius);
circles_gold[1] = cv::Vec3i(90, 87, minRadius + 3);
circles_gold[2] = cv::Vec3i(30, 70, minRadius + 8);
circles_gold[3] = cv::Vec3i(80, 10, maxRadius);
cv::Mat src(size, CV_8UC1);
drawCircles(src, circles_gold, true);
cv::gpu::GpuMat d_circles;
cv::gpu::HoughCircles(loadMat(src, useRoi), d_circles, CV_HOUGH_GRADIENT, dp, minDist, cannyThreshold, votesThreshold, minRadius, maxRadius);
std::vector<cv::Vec3f> circles;
cv::gpu::HoughCirclesDownload(d_circles, circles);
ASSERT_FALSE(circles.empty());
for (size_t i = 0; i < circles.size(); ++i)
for (size_t j = 0; j < circles_gold.size(); ++j)
{
cv::Vec3f cur = circles[i];
cv::Vec3f gold = circles_gold[j];
bool found = false;
for (size_t j = 0; j < circles_gold.size(); ++j)
if (std::fabs(cur[0] - gold[0]) < minDist && std::fabs(cur[1] - gold[1]) < minDist && std::fabs(cur[2] - gold[2]) < minDist)
{
cv::Vec3f gold = circles_gold[j];
if (std::fabs(cur[0] - gold[0]) < minDist && std::fabs(cur[1] - gold[1]) < minDist && std::fabs(cur[2] - gold[2]) < minDist)
{
found = true;
break;
}
found = true;
break;
}
ASSERT_TRUE(found);
}
}
catch (...)
{
cv::gpu::resetDevice();
throw;
ASSERT_TRUE(found);
}
}
@@ -206,68 +190,60 @@ PARAM_TEST_CASE(GeneralizedHough, cv::gpu::DeviceInfo, UseRoi)
TEST_P(GeneralizedHough, POSITION)
{
try
const cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
const bool useRoi = GET_PARAM(1);
cv::Mat templ = readImage("../cv/shared/templ.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(templ.empty());
cv::Point templCenter(templ.cols / 2, templ.rows / 2);
const size_t gold_count = 3;
cv::Point pos_gold[gold_count];
pos_gold[0] = cv::Point(templCenter.x + 10, templCenter.y + 10);
pos_gold[1] = cv::Point(2 * templCenter.x + 40, templCenter.y + 10);
pos_gold[2] = cv::Point(2 * templCenter.x + 40, 2 * templCenter.y + 40);
cv::Mat image(templ.rows * 3, templ.cols * 3, CV_8UC1, cv::Scalar::all(0));
for (size_t i = 0; i < gold_count; ++i)
{
const cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
const bool useRoi = GET_PARAM(1);
cv::Mat templ = readImage("../cv/shared/templ.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(templ.empty());
cv::Point templCenter(templ.cols / 2, templ.rows / 2);
const size_t gold_count = 3;
cv::Point pos_gold[gold_count];
pos_gold[0] = cv::Point(templCenter.x + 10, templCenter.y + 10);
pos_gold[1] = cv::Point(2 * templCenter.x + 40, templCenter.y + 10);
pos_gold[2] = cv::Point(2 * templCenter.x + 40, 2 * templCenter.y + 40);
cv::Mat image(templ.rows * 3, templ.cols * 3, CV_8UC1, cv::Scalar::all(0));
for (size_t i = 0; i < gold_count; ++i)
{
cv::Rect rec(pos_gold[i].x - templCenter.x, pos_gold[i].y - templCenter.y, templ.cols, templ.rows);
cv::Mat imageROI = image(rec);
templ.copyTo(imageROI);
}
cv::Ptr<cv::gpu::GeneralizedHough_GPU> hough = cv::gpu::GeneralizedHough_GPU::create(cv::GHT_POSITION);
hough->set("votesThreshold", 200);
hough->setTemplate(loadMat(templ, useRoi));
cv::gpu::GpuMat d_pos;
hough->detect(loadMat(image, useRoi), d_pos);
std::vector<cv::Vec4f> pos;
hough->download(d_pos, pos);
ASSERT_EQ(gold_count, pos.size());
for (size_t i = 0; i < gold_count; ++i)
{
cv::Point gold = pos_gold[i];
bool found = false;
for (size_t j = 0; j < pos.size(); ++j)
{
cv::Point2f p(pos[j][0], pos[j][1]);
if (::fabs(p.x - gold.x) < 2 && ::fabs(p.y - gold.y) < 2)
{
found = true;
break;
}
}
ASSERT_TRUE(found);
}
cv::Rect rec(pos_gold[i].x - templCenter.x, pos_gold[i].y - templCenter.y, templ.cols, templ.rows);
cv::Mat imageROI = image(rec);
templ.copyTo(imageROI);
}
catch (...)
cv::Ptr<cv::gpu::GeneralizedHough_GPU> hough = cv::gpu::GeneralizedHough_GPU::create(cv::GHT_POSITION);
hough->set("votesThreshold", 200);
hough->setTemplate(loadMat(templ, useRoi));
cv::gpu::GpuMat d_pos;
hough->detect(loadMat(image, useRoi), d_pos);
std::vector<cv::Vec4f> pos;
hough->download(d_pos, pos);
ASSERT_EQ(gold_count, pos.size());
for (size_t i = 0; i < gold_count; ++i)
{
cv::gpu::resetDevice();
throw;
cv::Point gold = pos_gold[i];
bool found = false;
for (size_t j = 0; j < pos.size(); ++j)
{
cv::Point2f p(pos[j][0], pos[j][1]);
if (::fabs(p.x - gold.x) < 2 && ::fabs(p.y - gold.y) < 2)
{
found = true;
break;
}
}
ASSERT_TRUE(found);
}
}