718c41634f
1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试 2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程 3.重整权利声明文件，重整代码工程，确保最小化侵权风险 Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
183 lines
7.9 KiB
C++
183 lines
7.9 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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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., 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 the copyright holders 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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namespace opencv_test { namespace {
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#undef RENDER_MSERS
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#define RENDER_MSERS 0
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#if defined RENDER_MSERS && RENDER_MSERS
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static void renderMSERs(const Mat& gray, Mat& img, const vector<vector<Point> >& msers)
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{
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cvtColor(gray, img, COLOR_GRAY2BGR);
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RNG rng((uint64)1749583);
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for( int i = 0; i < (int)msers.size(); i++ )
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{
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uchar b = rng.uniform(0, 256);
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uchar g = rng.uniform(0, 256);
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uchar r = rng.uniform(0, 256);
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Vec3b color(b, g, r);
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const Point* pt = &msers[i][0];
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size_t j, n = msers[i].size();
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for( j = 0; j < n; j++ )
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img.at<Vec3b>(pt[j]) = color;
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}
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}
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#endif
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TEST(Features2d_MSER, cases)
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{
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uchar buf[] =
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{
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255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,
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255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,
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255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,
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255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,
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255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,
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255, 255, 255, 255, 255, 0, 0, 0, 0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 0, 0, 0, 255, 255, 255, 255,
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255, 255, 255, 255, 255, 0, 0, 0, 0, 0, 255, 255, 255, 255, 255, 255, 255, 255, 0, 0, 0, 0, 255, 255, 255, 255,
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255, 255, 255, 255, 255, 0, 0, 0, 0, 0, 255, 255, 255, 255, 255, 255, 255, 255, 0, 0, 0, 0, 255, 255, 255, 255,
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255, 255, 255, 255, 255, 0, 0, 0, 0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 0, 0, 0, 255, 255, 255, 255,
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255, 255, 255, 255, 255, 255, 0, 0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 0, 255, 255, 255, 255, 255,
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255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,
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255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,
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255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,
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255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255
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};
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Mat big_image = imread(cvtest::TS::ptr()->get_data_path() + "mser/puzzle.png", 0);
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Mat small_image(14, 26, CV_8U, buf);
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static const int thresharr[] = { 0, 70, 120, 180, 255 };
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const int kDelta = 5;
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Ptr<MSER> mserExtractor = MSER::create( kDelta );
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vector<vector<Point> > msers;
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vector<Rect> boxes;
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RNG rng((uint64)123456);
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for( int i = 0; i < 100; i++ )
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{
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bool use_big_image = rng.uniform(0, 7) != 0;
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bool invert = rng.uniform(0, 2) != 0;
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bool binarize = use_big_image ? rng.uniform(0, 5) != 0 : false;
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bool blur = rng.uniform(0, 2) != 0;
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int thresh = thresharr[rng.uniform(0, 5)];
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/*if( i == 0 )
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{
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use_big_image = true;
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invert = binarize = blur = false;
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}*/
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const Mat& src0 = use_big_image ? big_image : small_image;
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Mat src = src0.clone();
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int kMinArea = use_big_image ? 256 : 10;
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int kMaxArea = (int)src.total()/4;
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mserExtractor->setMinArea(kMinArea);
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mserExtractor->setMaxArea(kMaxArea);
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if( invert )
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bitwise_not(src, src);
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if( binarize )
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cv::threshold(src, src, thresh, 255, THRESH_BINARY);
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if( blur )
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GaussianBlur(src, src, Size(5, 5), 1.5, 1.5);
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int minRegs = use_big_image ? 7 : 2;
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int maxRegs = use_big_image ? 1000 : 20;
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if( binarize && (thresh == 0 || thresh == 255) )
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minRegs = maxRegs = 0;
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mserExtractor->detectRegions( src, msers, boxes );
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int nmsers = (int)msers.size();
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ASSERT_EQ(nmsers, (int)boxes.size());
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if( maxRegs < nmsers || minRegs > nmsers )
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{
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printf("%d. minArea=%d, maxArea=%d, nmsers=%d, minRegs=%d, maxRegs=%d, "
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"image=%s, invert=%d, binarize=%d, thresh=%d, blur=%d\n",
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i, kMinArea, kMaxArea, nmsers, minRegs, maxRegs, use_big_image ? "big" : "small",
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(int)invert, (int)binarize, thresh, (int)blur);
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#if defined RENDER_MSERS && RENDER_MSERS
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Mat image;
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imshow("source", src);
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renderMSERs(src, image, msers);
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imshow("result", image);
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waitKey();
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#endif
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}
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ASSERT_LE(minRegs, nmsers);
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ASSERT_GE(maxRegs, nmsers);
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}
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}
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TEST(Features2d_MSER, history_update_regression)
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{
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String dataPath = cvtest::TS::ptr()->get_data_path() + "mser/";
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vector<Mat> tstImages;
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tstImages.push_back(imread(dataPath + "mser_test.png", IMREAD_GRAYSCALE));
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tstImages.push_back(imread(dataPath + "mser_test2.png", IMREAD_GRAYSCALE));
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for(size_t j = 0; j < tstImages.size(); j++)
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{
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size_t previous_size = 0;
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for(int minArea = 100; minArea > 10; minArea--)
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{
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Ptr<MSER> mser = MSER::create(1, minArea, (int)(tstImages[j].cols * tstImages[j].rows * 0.2));
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mser->setPass2Only(true);
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vector<vector<Point> > mserContours;
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vector<Rect> boxRects;
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mser->detectRegions(tstImages[j], mserContours, boxRects);
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ASSERT_LE(previous_size, mserContours.size());
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previous_size = mserContours.size();
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}
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}
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}
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}} // namespace
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