feat: 切换后端至PaddleOCR-NCNN，切换工程为CMake

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

3rdparty/opencv-4.5.4/modules/features2d/perf/opencl/perf_brute_force_matcher.cpp

@@ -0,0 +1,129 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
+// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// @Authors
+//    Fangfang Bai, fangfang@multicorewareinc.com
+//    Jin Ma,       jin@multicorewareinc.com
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors as is and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+#include "../perf_precomp.hpp"
+#include "opencv2/ts/ocl_perf.hpp"
+
+#ifdef HAVE_OPENCL
+
+namespace opencv_test {
+namespace ocl {
+
+//////////////////// BruteForceMatch /////////////////
+
+typedef Size_MatType BruteForceMatcherFixture;
+
+OCL_PERF_TEST_P(BruteForceMatcherFixture, Match, ::testing::Combine(OCL_PERF_ENUM(OCL_SIZE_1, OCL_SIZE_2, OCL_SIZE_3), OCL_PERF_ENUM((MatType)CV_32FC1) ) )
+{
+    const Size_MatType_t params = GetParam();
+    const Size srcSize = get<0>(params);
+    const int type = get<1>(params);
+
+    checkDeviceMaxMemoryAllocSize(srcSize, type);
+
+    vector<DMatch> matches;
+    UMat uquery(srcSize, type), utrain(srcSize, type);
+
+    declare.in(uquery, utrain, WARMUP_RNG);
+
+    BFMatcher matcher(NORM_L2);
+
+    OCL_TEST_CYCLE()
+        matcher.match(uquery, utrain, matches);
+
+    SANITY_CHECK_MATCHES(matches, 1e-3);
+}
+
+OCL_PERF_TEST_P(BruteForceMatcherFixture, KnnMatch, ::testing::Combine(OCL_PERF_ENUM(OCL_SIZE_1, OCL_SIZE_2, OCL_SIZE_3), OCL_PERF_ENUM((MatType)CV_32FC1) ) )
+{
+    const Size_MatType_t params = GetParam();
+    const Size srcSize = get<0>(params);
+    const int type = get<1>(params);
+
+    checkDeviceMaxMemoryAllocSize(srcSize, type);
+
+    vector< vector<DMatch> > matches;
+    UMat uquery(srcSize, type), utrain(srcSize, type);
+
+    declare.in(uquery, utrain, WARMUP_RNG);
+
+    BFMatcher matcher(NORM_L2);
+
+    OCL_TEST_CYCLE()
+        matcher.knnMatch(uquery, utrain, matches, 2);
+
+    vector<DMatch> & matches0 = matches[0], & matches1 = matches[1];
+    SANITY_CHECK_MATCHES(matches0, 1e-3);
+    SANITY_CHECK_MATCHES(matches1, 1e-3);
+
+}
+
+OCL_PERF_TEST_P(BruteForceMatcherFixture, RadiusMatch, ::testing::Combine(OCL_PERF_ENUM(OCL_SIZE_1, OCL_SIZE_2, OCL_SIZE_3), OCL_PERF_ENUM((MatType)CV_32FC1) ) )
+{
+    const Size_MatType_t params = GetParam();
+    const Size srcSize = get<0>(params);
+    const int type = get<1>(params);
+
+    checkDeviceMaxMemoryAllocSize(srcSize, type);
+
+    vector< vector<DMatch> > matches;
+    UMat uquery(srcSize, type), utrain(srcSize, type);
+
+    declare.in(uquery, utrain, WARMUP_RNG);
+
+    BFMatcher matcher(NORM_L2);
+
+    OCL_TEST_CYCLE()
+        matcher.radiusMatch(uquery, utrain, matches, 2.0f);
+
+    vector<DMatch> & matches0 = matches[0], & matches1 = matches[1];
+    SANITY_CHECK_MATCHES(matches0, 1e-3);
+    SANITY_CHECK_MATCHES(matches1, 1e-3);
+}
+
+} // ocl
+} // cvtest
+
+#endif // HAVE_OPENCL

3rdparty/opencv-4.5.4/modules/features2d/perf/opencl/perf_feature2d.cpp

@@ -0,0 +1,81 @@
+#include "../perf_precomp.hpp"
+#include "opencv2/ts/ocl_perf.hpp"
+#include "../perf_feature2d.hpp"
+
+#ifdef HAVE_OPENCL
+
+namespace opencv_test {
+namespace ocl {
+
+OCL_PERF_TEST_P(feature2d, detect, testing::Combine(Feature2DType::all(), TEST_IMAGES))
+{
+    Ptr<Feature2D> detector = getFeature2D(get<0>(GetParam()));
+    std::string filename = getDataPath(get<1>(GetParam()));
+    Mat mimg = imread(filename, IMREAD_GRAYSCALE);
+
+    ASSERT_FALSE(mimg.empty());
+    ASSERT_TRUE(detector);
+
+    UMat img, mask;
+    mimg.copyTo(img);
+    declare.in(img);
+    vector<KeyPoint> points;
+
+    OCL_TEST_CYCLE() detector->detect(img, points, mask);
+
+    EXPECT_GT(points.size(), 20u);
+    SANITY_CHECK_NOTHING();
+}
+
+OCL_PERF_TEST_P(feature2d, extract, testing::Combine(testing::Values(DETECTORS_EXTRACTORS), TEST_IMAGES))
+{
+    Ptr<Feature2D> detector = AKAZE::create();
+    Ptr<Feature2D> extractor = getFeature2D(get<0>(GetParam()));
+    std::string filename = getDataPath(get<1>(GetParam()));
+    Mat mimg = imread(filename, IMREAD_GRAYSCALE);
+
+    ASSERT_FALSE(mimg.empty());
+    ASSERT_TRUE(extractor);
+
+    UMat img, mask;
+    mimg.copyTo(img);
+    declare.in(img);
+    vector<KeyPoint> points;
+    detector->detect(img, points, mask);
+
+    EXPECT_GT(points.size(), 20u);
+
+    UMat descriptors;
+
+    OCL_TEST_CYCLE() extractor->compute(img, points, descriptors);
+
+    EXPECT_EQ((size_t)descriptors.rows, points.size());
+    SANITY_CHECK_NOTHING();
+}
+
+OCL_PERF_TEST_P(feature2d, detectAndExtract, testing::Combine(testing::Values(DETECTORS_EXTRACTORS), TEST_IMAGES))
+{
+    Ptr<Feature2D> detector = getFeature2D(get<0>(GetParam()));
+    std::string filename = getDataPath(get<1>(GetParam()));
+    Mat mimg = imread(filename, IMREAD_GRAYSCALE);
+
+    ASSERT_FALSE(mimg.empty());
+    ASSERT_TRUE(detector);
+
+    UMat img, mask;
+    mimg.copyTo(img);
+    declare.in(img);
+    vector<KeyPoint> points;
+    UMat descriptors;
+
+    OCL_TEST_CYCLE() detector->detectAndCompute(img, mask, points, descriptors, false);
+
+    EXPECT_GT(points.size(), 20u);
+    EXPECT_EQ((size_t)descriptors.rows, points.size());
+    SANITY_CHECK_NOTHING();
+}
+
+} // ocl
+} // cvtest
+
+#endif // HAVE_OPENCL

3rdparty/opencv-4.5.4/modules/features2d/perf/perf_batchDistance.cpp

@@ -0,0 +1,167 @@
+#include "perf_precomp.hpp"
+
+namespace opencv_test
+{
+using namespace perf;
+
+CV_ENUM(NormType, NORM_L1, NORM_L2, NORM_L2SQR, NORM_HAMMING, NORM_HAMMING2)
+
+typedef tuple<NormType, MatType, bool> Norm_Destination_CrossCheck_t;
+typedef perf::TestBaseWithParam<Norm_Destination_CrossCheck_t> Norm_Destination_CrossCheck;
+
+typedef tuple<NormType, bool> Norm_CrossCheck_t;
+typedef perf::TestBaseWithParam<Norm_CrossCheck_t> Norm_CrossCheck;
+
+typedef tuple<MatType, bool> Source_CrossCheck_t;
+typedef perf::TestBaseWithParam<Source_CrossCheck_t> Source_CrossCheck;
+
+void generateData( Mat& query, Mat& train, const int sourceType );
+
+PERF_TEST_P(Norm_Destination_CrossCheck, batchDistance_8U,
+            testing::Combine(testing::Values((int)NORM_L1, (int)NORM_L2SQR),
+                             testing::Values(CV_32S, CV_32F),
+                             testing::Bool()
+                             )
+            )
+{
+    NormType normType = get<0>(GetParam());
+    int destinationType = get<1>(GetParam());
+    bool isCrossCheck = get<2>(GetParam());
+    int knn = isCrossCheck ? 1 : 0;
+
+    Mat queryDescriptors;
+    Mat trainDescriptors;
+    Mat dist;
+    Mat ndix;
+
+    generateData(queryDescriptors, trainDescriptors, CV_8U);
+
+    TEST_CYCLE()
+    {
+        batchDistance(queryDescriptors, trainDescriptors, dist, destinationType, (isCrossCheck) ? ndix : noArray(),
+                      normType, knn, Mat(), 0, isCrossCheck);
+    }
+
+    SANITY_CHECK(dist);
+    if (isCrossCheck) SANITY_CHECK(ndix);
+}
+
+PERF_TEST_P(Norm_CrossCheck, batchDistance_Dest_32S,
+            testing::Combine(testing::Values((int)NORM_HAMMING, (int)NORM_HAMMING2),
+                             testing::Bool()
+                             )
+            )
+{
+    NormType normType = get<0>(GetParam());
+    bool isCrossCheck = get<1>(GetParam());
+    int knn = isCrossCheck ? 1 : 0;
+
+    Mat queryDescriptors;
+    Mat trainDescriptors;
+    Mat dist;
+    Mat ndix;
+
+    generateData(queryDescriptors, trainDescriptors, CV_8U);
+
+    TEST_CYCLE()
+    {
+        batchDistance(queryDescriptors, trainDescriptors, dist, CV_32S, (isCrossCheck) ? ndix : noArray(),
+                      normType, knn, Mat(), 0, isCrossCheck);
+    }
+
+    SANITY_CHECK(dist);
+    if (isCrossCheck) SANITY_CHECK(ndix);
+}
+
+PERF_TEST_P(Source_CrossCheck, batchDistance_L2,
+            testing::Combine(testing::Values(CV_8U, CV_32F),
+                             testing::Bool()
+                             )
+            )
+{
+    int sourceType = get<0>(GetParam());
+    bool isCrossCheck = get<1>(GetParam());
+    int knn = isCrossCheck ? 1 : 0;
+
+    Mat queryDescriptors;
+    Mat trainDescriptors;
+    Mat dist;
+    Mat ndix;
+
+    generateData(queryDescriptors, trainDescriptors, sourceType);
+
+    declare.time(50);
+    TEST_CYCLE()
+    {
+        batchDistance(queryDescriptors, trainDescriptors, dist, CV_32F, (isCrossCheck) ? ndix : noArray(),
+                      NORM_L2, knn, Mat(), 0, isCrossCheck);
+    }
+
+    SANITY_CHECK(dist);
+    if (isCrossCheck) SANITY_CHECK(ndix);
+}
+
+PERF_TEST_P(Norm_CrossCheck, batchDistance_32F,
+            testing::Combine(testing::Values((int)NORM_L1, (int)NORM_L2SQR),
+                             testing::Bool()
+                             )
+            )
+{
+    NormType normType = get<0>(GetParam());
+    bool isCrossCheck = get<1>(GetParam());
+    int knn = isCrossCheck ? 1 : 0;
+
+    Mat queryDescriptors;
+    Mat trainDescriptors;
+    Mat dist;
+    Mat ndix;
+
+    generateData(queryDescriptors, trainDescriptors, CV_32F);
+    declare.time(100);
+
+    TEST_CYCLE()
+    {
+        batchDistance(queryDescriptors, trainDescriptors, dist, CV_32F, (isCrossCheck) ? ndix : noArray(),
+                      normType, knn, Mat(), 0, isCrossCheck);
+    }
+
+    SANITY_CHECK(dist, 1e-4);
+    if (isCrossCheck) SANITY_CHECK(ndix);
+}
+
+void generateData( Mat& query, Mat& train, const int sourceType )
+{
+    const int dim = 500;
+    const int queryDescCount = 300; // must be even number because we split train data in some cases in two
+    const int countFactor = 4; // do not change it
+    RNG& rng = theRNG();
+
+    // Generate query descriptors randomly.
+    // Descriptor vector elements are integer values.
+    Mat buf( queryDescCount, dim, CV_32SC1 );
+    rng.fill( buf, RNG::UNIFORM, Scalar::all(0), Scalar(3) );
+    buf.convertTo( query, sourceType );
+
+    // Generate train descriptors as follows:
+    // copy each query descriptor to train set countFactor times
+    // and perturb some one element of the copied descriptors in
+    // in ascending order. General boundaries of the perturbation
+    // are (0.f, 1.f).
+    train.create( query.rows*countFactor, query.cols, sourceType );
+    float step = (sourceType == CV_8U ? 256.f : 1.f) / countFactor;
+    for( int qIdx = 0; qIdx < query.rows; qIdx++ )
+    {
+        Mat queryDescriptor = query.row(qIdx);
+        for( int c = 0; c < countFactor; c++ )
+        {
+            int tIdx = qIdx * countFactor + c;
+            Mat trainDescriptor = train.row(tIdx);
+            queryDescriptor.copyTo( trainDescriptor );
+            int elem = rng(dim);
+            float diff = rng.uniform( step*c, step*(c+1) );
+            trainDescriptor.col(elem) += diff;
+        }
+    }
+}
+
+} // namespace

3rdparty/opencv-4.5.4/modules/features2d/perf/perf_feature2d.cpp

@@ -0,0 +1,72 @@
+#include "perf_feature2d.hpp"
+
+namespace opencv_test
+{
+using namespace perf;
+
+PERF_TEST_P(feature2d, detect, testing::Combine(Feature2DType::all(), TEST_IMAGES))
+{
+    Ptr<Feature2D> detector = getFeature2D(get<0>(GetParam()));
+    std::string filename = getDataPath(get<1>(GetParam()));
+    Mat img = imread(filename, IMREAD_GRAYSCALE);
+
+    ASSERT_FALSE(img.empty());
+    ASSERT_TRUE(detector);
+
+    declare.in(img);
+    Mat mask;
+    vector<KeyPoint> points;
+
+    TEST_CYCLE() detector->detect(img, points, mask);
+
+    EXPECT_GT(points.size(), 20u);
+    SANITY_CHECK_NOTHING();
+}
+
+PERF_TEST_P(feature2d, extract, testing::Combine(testing::Values(DETECTORS_EXTRACTORS), TEST_IMAGES))
+{
+    Ptr<Feature2D> detector = AKAZE::create();
+    Ptr<Feature2D> extractor = getFeature2D(get<0>(GetParam()));
+    std::string filename = getDataPath(get<1>(GetParam()));
+    Mat img = imread(filename, IMREAD_GRAYSCALE);
+
+    ASSERT_FALSE(img.empty());
+    ASSERT_TRUE(extractor);
+
+    declare.in(img);
+    Mat mask;
+    vector<KeyPoint> points;
+    detector->detect(img, points, mask);
+
+    EXPECT_GT(points.size(), 20u);
+
+    Mat descriptors;
+
+    TEST_CYCLE() extractor->compute(img, points, descriptors);
+
+    EXPECT_EQ((size_t)descriptors.rows, points.size());
+    SANITY_CHECK_NOTHING();
+}
+
+PERF_TEST_P(feature2d, detectAndExtract, testing::Combine(testing::Values(DETECTORS_EXTRACTORS), TEST_IMAGES))
+{
+    Ptr<Feature2D> detector = getFeature2D(get<0>(GetParam()));
+    std::string filename = getDataPath(get<1>(GetParam()));
+    Mat img = imread(filename, IMREAD_GRAYSCALE);
+
+    ASSERT_FALSE(img.empty());
+    ASSERT_TRUE(detector);
+
+    declare.in(img);
+    Mat mask;
+    vector<KeyPoint> points;
+    Mat descriptors;
+
+    TEST_CYCLE() detector->detectAndCompute(img, mask, points, descriptors, false);
+
+    EXPECT_GT(points.size(), 20u);
+    EXPECT_EQ((size_t)descriptors.rows, points.size());
+    SANITY_CHECK_NOTHING();
+}
+
+} // namespace

3rdparty/opencv-4.5.4/modules/features2d/perf/perf_feature2d.hpp

@@ -0,0 +1,90 @@
+#ifndef __OPENCV_PERF_FEATURE2D_HPP__
+#define __OPENCV_PERF_FEATURE2D_HPP__
+
+#include "perf_precomp.hpp"
+
+namespace opencv_test
+{
+
+/* configuration for tests of detectors/descriptors. shared between ocl and cpu tests. */
+
+// detectors/descriptors configurations to test
+#define DETECTORS_ONLY                                                                  \
+    FAST_DEFAULT, FAST_20_TRUE_TYPE5_8, FAST_20_TRUE_TYPE7_12, FAST_20_TRUE_TYPE9_16,   \
+    FAST_20_FALSE_TYPE5_8, FAST_20_FALSE_TYPE7_12, FAST_20_FALSE_TYPE9_16,              \
+                                                                                        \
+    AGAST_DEFAULT, AGAST_5_8, AGAST_7_12d, AGAST_7_12s, AGAST_OAST_9_16,                \
+                                                                                        \
+    MSER_DEFAULT
+
+#define DETECTORS_EXTRACTORS                                                            \
+    ORB_DEFAULT, ORB_1500_13_1,                                                         \
+    AKAZE_DEFAULT, AKAZE_DESCRIPTOR_KAZE,                                               \
+    BRISK_DEFAULT,                                                                      \
+    KAZE_DEFAULT,                                                                       \
+    SIFT_DEFAULT
+
+#define CV_ENUM_EXPAND(name, ...) CV_ENUM(name, __VA_ARGS__)
+
+enum Feature2DVals { DETECTORS_ONLY, DETECTORS_EXTRACTORS };
+CV_ENUM_EXPAND(Feature2DType, DETECTORS_ONLY, DETECTORS_EXTRACTORS)
+
+typedef tuple<Feature2DType, string> Feature2DType_String_t;
+typedef perf::TestBaseWithParam<Feature2DType_String_t> feature2d;
+
+#define TEST_IMAGES testing::Values(\
+    "cv/detectors_descriptors_evaluation/images_datasets/leuven/img1.png",\
+    "stitching/a3.png", \
+    "stitching/s2.jpg")
+
+static inline Ptr<Feature2D> getFeature2D(Feature2DType type)
+{
+    switch(type) {
+    case ORB_DEFAULT:
+        return ORB::create();
+    case ORB_1500_13_1:
+        return ORB::create(1500, 1.3f, 1);
+    case FAST_DEFAULT:
+        return FastFeatureDetector::create();
+    case FAST_20_TRUE_TYPE5_8:
+        return FastFeatureDetector::create(20, true, FastFeatureDetector::TYPE_5_8);
+    case FAST_20_TRUE_TYPE7_12:
+        return FastFeatureDetector::create(20, true, FastFeatureDetector::TYPE_7_12);
+    case FAST_20_TRUE_TYPE9_16:
+        return FastFeatureDetector::create(20, true, FastFeatureDetector::TYPE_9_16);
+    case FAST_20_FALSE_TYPE5_8:
+        return FastFeatureDetector::create(20, false, FastFeatureDetector::TYPE_5_8);
+    case FAST_20_FALSE_TYPE7_12:
+        return FastFeatureDetector::create(20, false, FastFeatureDetector::TYPE_7_12);
+    case FAST_20_FALSE_TYPE9_16:
+        return FastFeatureDetector::create(20, false, FastFeatureDetector::TYPE_9_16);
+    case AGAST_DEFAULT:
+        return AgastFeatureDetector::create();
+    case AGAST_5_8:
+        return AgastFeatureDetector::create(70, true, AgastFeatureDetector::AGAST_5_8);
+    case AGAST_7_12d:
+        return AgastFeatureDetector::create(70, true, AgastFeatureDetector::AGAST_7_12d);
+    case AGAST_7_12s:
+        return AgastFeatureDetector::create(70, true, AgastFeatureDetector::AGAST_7_12s);
+    case AGAST_OAST_9_16:
+        return AgastFeatureDetector::create(70, true, AgastFeatureDetector::OAST_9_16);
+    case AKAZE_DEFAULT:
+        return AKAZE::create();
+    case AKAZE_DESCRIPTOR_KAZE:
+        return AKAZE::create(AKAZE::DESCRIPTOR_KAZE);
+    case BRISK_DEFAULT:
+        return BRISK::create();
+    case KAZE_DEFAULT:
+        return KAZE::create();
+    case MSER_DEFAULT:
+        return MSER::create();
+    case SIFT_DEFAULT:
+        return SIFT::create();
+    default:
+        return Ptr<Feature2D>();
+    }
+}
+
+} // namespace
+
+#endif // __OPENCV_PERF_FEATURE2D_HPP__

3rdparty/opencv-4.5.4/modules/features2d/perf/perf_main.cpp

@@ -0,0 +1,7 @@
+#include "perf_precomp.hpp"
+
+#if defined(HAVE_HPX)
+    #include <hpx/hpx_main.hpp>
+#endif
+
+CV_PERF_TEST_MAIN(features2d)

3rdparty/opencv-4.5.4/modules/features2d/perf/perf_precomp.hpp

@@ -0,0 +1,7 @@
+#ifndef __OPENCV_PERF_PRECOMP_HPP__
+#define __OPENCV_PERF_PRECOMP_HPP__
+
+#include "opencv2/ts.hpp"
+#include "opencv2/features2d.hpp"
+
+#endif