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180 lines
6.3 KiB
C++
180 lines
6.3 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// Intel License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of Intel Corporation may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "test_precomp.hpp"
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#include <numeric>
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namespace opencv_test { namespace {
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BIGDATA_TEST(Imgproc_DistanceTransform, large_image_12218)
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{
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const int lls_maxcnt = 79992000; // labels's maximum count
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const int lls_mincnt = 1; // labels's minimum count
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int i, j, nz;
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Mat src(8000, 20000, CV_8UC1), dst, labels;
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for( i = 0; i < src.rows; i++ )
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for( j = 0; j < src.cols; j++ )
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src.at<uchar>(i, j) = (j > (src.cols / 2)) ? 0 : 255;
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distanceTransform(src, dst, labels, cv::DIST_L2, cv::DIST_MASK_3, DIST_LABEL_PIXEL);
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double scale = (double)lls_mincnt / (double)lls_maxcnt;
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labels.convertTo(labels, CV_32SC1, scale);
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Size size = labels.size();
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nz = cv::countNonZero(labels);
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EXPECT_EQ(nz, (size.height*size.width / 2));
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}
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TEST(Imgproc_DistanceTransform, wide_image_22732)
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{
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Mat src = Mat::zeros(1, 4099, CV_8U); // 4099 or larger used to be bad
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Mat dist(src.rows, src.cols, CV_32F);
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distanceTransform(src, dist, DIST_L2, DIST_MASK_PRECISE, CV_32F);
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int nz = countNonZero(dist);
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EXPECT_EQ(nz, 0);
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}
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TEST(Imgproc_DistanceTransform, large_square_22732)
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{
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Mat src = Mat::zeros(8000, 8005, CV_8U), dist;
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distanceTransform(src, dist, DIST_L2, DIST_MASK_PRECISE, CV_32F);
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int nz = countNonZero(dist);
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EXPECT_EQ(dist.size(), src.size());
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EXPECT_EQ(dist.type(), CV_32F);
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EXPECT_EQ(nz, 0);
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Point p0(src.cols-1, src.rows-1);
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src.setTo(1);
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src.at<uchar>(p0) = 0;
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distanceTransform(src, dist, DIST_L2, DIST_MASK_PRECISE, CV_32F);
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EXPECT_EQ(dist.size(), src.size());
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EXPECT_EQ(dist.type(), CV_32F);
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bool first = true;
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int nerrs = 0;
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for (int y = 0; y < dist.rows; y++)
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for (int x = 0; x < dist.cols; x++) {
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float d = dist.at<float>(y, x);
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double dx = (double)(x - p0.x), dy = (double)(y - p0.y);
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float d0 = (float)sqrt(dx*dx + dy*dy);
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if (std::abs(d0 - d) > 1) {
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if (first) {
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printf("y=%d, x=%d. dist_ref=%.2f, dist=%.2f\n", y, x, d0, d);
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first = false;
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}
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nerrs++;
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}
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}
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EXPECT_EQ(0, nerrs) << "reference distance map is different from computed one at " << nerrs << " pixels\n";
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}
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BIGDATA_TEST(Imgproc_DistanceTransform, issue_23895_3x3)
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{
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Mat src = Mat::zeros(50000, 50000, CV_8U), dist;
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distanceTransform(src.col(0), dist, DIST_L2, DIST_MASK_3);
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int nz = countNonZero(dist);
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EXPECT_EQ(nz, 0);
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}
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BIGDATA_TEST(Imgproc_DistanceTransform, issue_23895_5x5)
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{
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Mat src = Mat::zeros(50000, 50000, CV_8U), dist;
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distanceTransform(src.col(0), dist, DIST_L2, DIST_MASK_5);
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int nz = countNonZero(dist);
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EXPECT_EQ(nz, 0);
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}
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BIGDATA_TEST(Imgproc_DistanceTransform, issue_23895_5x5_labels)
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{
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Mat src = Mat::zeros(50000, 50000, CV_8U), dist, labels;
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distanceTransform(src.col(0), dist, labels, DIST_L2, DIST_MASK_5);
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int nz = countNonZero(dist);
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EXPECT_EQ(nz, 0);
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}
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TEST(Imgproc_DistanceTransform, max_distance_3x3)
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{
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Mat src = Mat::ones(1, 70000, CV_8U), dist;
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src.at<uint8_t>(0, 0) = 0;
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distanceTransform(src, dist, DIST_L2, DIST_MASK_3);
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double minVal, maxVal;
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minMaxLoc(dist, &minVal, &maxVal);
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EXPECT_GE(maxVal, 65533);
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}
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TEST(Imgproc_DistanceTransform, max_distance_5x5)
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{
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Mat src = Mat::ones(1, 70000, CV_8U), dist;
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src.at<uint8_t>(0, 0) = 0;
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distanceTransform(src, dist, DIST_L2, DIST_MASK_5);
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double minVal, maxVal;
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minMaxLoc(dist, &minVal, &maxVal);
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EXPECT_GE(maxVal, 65533);
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}
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TEST(Imgproc_DistanceTransform, max_distance_5x5_labels)
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{
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Mat src = Mat::ones(1, 70000, CV_8U), dist, labels;
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src.at<uint8_t>(0, 0) = 0;
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distanceTransform(src, dist, labels, DIST_L2, DIST_MASK_5);
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double minVal, maxVal;
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minMaxLoc(dist, &minVal, &maxVal);
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EXPECT_GE(maxVal, 65533);
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}
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TEST(Imgproc_DistanceTransform, precise_long_dist)
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{
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static const int maxDist = 1 << 16;
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Mat src = Mat::ones(1, 70000, CV_8U), dist;
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src.at<uint8_t>(0, 0) = 0;
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distanceTransform(src, dist, DIST_L2, DIST_MASK_PRECISE, CV_32F);
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Mat expected(src.size(), CV_32F);
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std::iota(expected.begin<float>(), expected.end<float>(), 0.f);
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expected.colRange(maxDist, expected.cols).setTo(maxDist);
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EXPECT_EQ(cv::norm(expected, dist, NORM_INF), 0);
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}
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}} // namespace
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