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opencv/modules/imgproc/perf/perf_contours.cpp
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Anshu fe160f3eed Merge pull request #28548 from 0AnshuAditya0:fix-minEnclosingCircle-welzl-28546
imgproc: fix minEnclosingCircle O(n^3) worst case by adding Welzl shuffle #28548

Welzl's algorithm requires random permutation of input points to
achieve expected O(n) time. Without shuffling, sorted inputs such
as those produced by findContours() trigger O(n^3) worst case.

Fix: copy input to std::vector<PT> and apply cv::randShuffle()
before processing. Uses OpenCV's RNG so cv::setRNGSeed() ensures
reproducible behavior.

Original benchmark (5088 contour points, Release, AVX2):
findContours output: 3.93 ms → 0.033 ms (119x speedup)
Random points: 0.051 ms → 0.049 ms (no regression)

Perf test results (this PR, Release, AVX2):
| Input | N | Time |
|-------|---|------|
| Sequential circle points | 10000 | 0.03 ms |
| Sequential circle points | 5000 | 0.01 ms |
| Random points (CV_32F) | 100000 | 1.87 ms |
| Random points (CV_32S) | 100000 | 2.81 ms |

Fixes #28546

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-03-04 15:16:07 +03:00

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4.3 KiB
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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 "perf_precomp.hpp"
namespace opencv_test { namespace {
CV_ENUM(RetrMode, RETR_EXTERNAL, RETR_LIST, RETR_CCOMP, RETR_TREE)
CV_ENUM(ApproxMode, CHAIN_APPROX_NONE, CHAIN_APPROX_SIMPLE, CHAIN_APPROX_TC89_L1, CHAIN_APPROX_TC89_KCOS)
typedef TestBaseWithParam< tuple<Size, RetrMode, ApproxMode, int> > TestFindContours;
PERF_TEST_P(TestFindContours, findContours,
Combine(
Values( szVGA, sz1080p ), // image size
RetrMode::all(), // retrieval mode
ApproxMode::all(), // approximation method
Values( 32, 128 ) // blob count
)
)
{
Size img_size = get<0>(GetParam());
int retr_mode = get<1>(GetParam());
int approx_method = get<2>(GetParam());
int blob_count = get<3>(GetParam());
RNG rng;
Mat img = Mat::zeros(img_size, CV_8UC1);
for(int i = 0; i < blob_count; i++ )
{
Point center;
center.x = (unsigned)rng % (img.cols-2);
center.y = (unsigned)rng % (img.rows-2);
Size axes;
axes.width = ((unsigned)rng % 49 + 2)/2;
axes.height = ((unsigned)rng % 49 + 2)/2;
double angle = (unsigned)rng % 180;
int brightness = (unsigned)rng % 2;
// keep the border clear
ellipse( img(Rect(1,1,img.cols-2,img.rows-2)), Point(center), Size(axes), angle, 0., 360., Scalar(brightness), -1);
}
vector< vector<Point> > contours;
TEST_CYCLE() findContours( img, contours, retr_mode, approx_method );
SANITY_CHECK_NOTHING();
}
typedef TestBaseWithParam< tuple<Size, ApproxMode, int> > TestFindContoursFF;
PERF_TEST_P(TestFindContoursFF, findContours,
Combine(
Values(szVGA, sz1080p), // image size
ApproxMode::all(), // approximation method
Values(32, 128) // blob count
)
)
{
Size img_size = get<0>(GetParam());
int approx_method = get<1>(GetParam());
int blob_count = get<2>(GetParam());
RNG rng;
Mat img = Mat::zeros(img_size, CV_32SC1);
for (int i = 0; i < blob_count; i++)
{
Point center;
center.x = (unsigned)rng % (img.cols - 2);
center.y = (unsigned)rng % (img.rows - 2);
Size axes;
axes.width = ((unsigned)rng % 49 + 2) / 2;
axes.height = ((unsigned)rng % 49 + 2) / 2;
double angle = (unsigned)rng % 180;
int brightness = (unsigned)rng % 2;
// keep the border clear
ellipse(img(Rect(1, 1, img.cols - 2, img.rows - 2)), Point(center), Size(axes), angle, 0., 360., Scalar(brightness), -1);
}
vector< vector<Point> > contours;
TEST_CYCLE() findContours(img, contours, RETR_FLOODFILL, approx_method);
SANITY_CHECK_NOTHING();
}
typedef TestBaseWithParam< tuple<MatDepth, int> > TestBoundingRect;
PERF_TEST_P(TestBoundingRect, BoundingRect,
Combine(
testing::Values(CV_32S, CV_32F), // points type
Values(400, 511, 1000, 10000, 100000) // points count
)
)
{
int ptType = get<0>(GetParam());
int n = get<1>(GetParam());
Mat pts(n, 2, ptType);
declare.in(pts, WARMUP_RNG);
cv::Rect rect;
TEST_CYCLE() rect = boundingRect(pts);
SANITY_CHECK_NOTHING();
}
typedef TestBaseWithParam< tuple<MatDepth, int> > TestMinEnclosingCircle;
PERF_TEST_P(TestMinEnclosingCircle, minEnclosingCircle,
Combine(
testing::Values(CV_32S, CV_32F),
Values(400, 1000, 10000, 100000)
))
{
int ptType = get<0>(GetParam());
int n = get<1>(GetParam());
Mat pts(n, 2, ptType);
declare.in(pts, WARMUP_RNG);
Point2f center;
float radius;
TEST_CYCLE() minEnclosingCircle(pts, center, radius);
SANITY_CHECK_NOTHING();
}
typedef TestBaseWithParam<int> TestMinEnclosingCircleWorstCase;
PERF_TEST_P(TestMinEnclosingCircleWorstCase, minEnclosingCircle_sequential,
Values(400, 1000, 5000, 10000))
{
int n = GetParam();
vector<Point2f> contour;
for(int i = 0; i < n; ++i) {
float angle = (float)(i * 2 * CV_PI / n);
contour.push_back(Point2f(cos(angle) * 100, sin(angle) * 100));
}
Point2f center;
float radius;
TEST_CYCLE() minEnclosingCircle(contour, center, radius);
SANITY_CHECK_NOTHING();
}
} } // namespace