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/test/ocl/test_brute_force_matcher.cpp

@@ -0,0 +1,213 @@
+/*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, Institute Of Software Chinese Academy Of Science, all rights reserved.
+// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
+// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// @Authors
+//    Niko Li, newlife20080214@gmail.com
+//    Jia Haipeng, jiahaipeng95@gmail.com
+//    Zero Lin, Zero.Lin@amd.com
+//    Zhang Ying, zhangying913@gmail.com
+//    Yao Wang, bitwangyaoyao@gmail.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 "../test_precomp.hpp"
+#include "cvconfig.h"
+#include "opencv2/ts/ocl_test.hpp"
+
+#ifdef HAVE_OPENCL
+
+namespace opencv_test {
+namespace ocl {
+PARAM_TEST_CASE(BruteForceMatcher, int, int)
+{
+    int distType;
+    int dim;
+
+    int queryDescCount;
+    int countFactor;
+
+    Mat query, train;
+    UMat uquery, utrain;
+
+    virtual void SetUp()
+    {
+        distType = GET_PARAM(0);
+        dim = GET_PARAM(1);
+
+        queryDescCount = 300; // must be even number because we split train data in some cases in two
+        countFactor = 4; // do not change it
+
+        cv::Mat queryBuf, trainBuf;
+
+        // Generate query descriptors randomly.
+        // Descriptor vector elements are integer values.
+        queryBuf.create(queryDescCount, dim, CV_32SC1);
+        rng.fill(queryBuf, cv::RNG::UNIFORM, cv::Scalar::all(0), cv::Scalar::all(3));
+        queryBuf.convertTo(queryBuf, CV_32FC1);
+
+        // 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).
+        trainBuf.create(queryDescCount * countFactor, dim, CV_32FC1);
+        float step = 1.f / countFactor;
+        for (int qIdx = 0; qIdx < queryDescCount; qIdx++)
+        {
+            cv::Mat queryDescriptor = queryBuf.row(qIdx);
+            for (int c = 0; c < countFactor; c++)
+            {
+                int tIdx = qIdx * countFactor + c;
+                cv::Mat trainDescriptor = trainBuf.row(tIdx);
+                queryDescriptor.copyTo(trainDescriptor);
+                int elem = rng(dim);
+                float diff = rng.uniform(step * c, step * (c + 1));
+                trainDescriptor.at<float>(0, elem) += diff;
+            }
+        }
+
+        queryBuf.convertTo(query, CV_32F);
+        trainBuf.convertTo(train, CV_32F);
+        query.copyTo(uquery);
+        train.copyTo(utrain);
+    }
+};
+
+#ifdef __ANDROID__
+OCL_TEST_P(BruteForceMatcher, DISABLED_Match_Single)
+#else
+OCL_TEST_P(BruteForceMatcher, Match_Single)
+#endif
+{
+    BFMatcher matcher(distType);
+
+    std::vector<cv::DMatch> matches;
+    matcher.match(uquery, utrain,  matches);
+
+    ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
+
+    int badCount = 0;
+    for (size_t i = 0; i < matches.size(); i++)
+    {
+        cv::DMatch match = matches[i];
+        if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor) || (match.imgIdx != 0))
+            badCount++;
+    }
+
+    ASSERT_EQ(0, badCount);
+}
+
+#ifdef __ANDROID__
+OCL_TEST_P(BruteForceMatcher, DISABLED_KnnMatch_2_Single)
+#else
+OCL_TEST_P(BruteForceMatcher, KnnMatch_2_Single)
+#endif
+{
+    const int knn = 2;
+
+    BFMatcher matcher(distType);
+
+    std::vector< std::vector<cv::DMatch> > matches;
+    matcher.knnMatch(uquery, utrain, matches, knn);
+
+    ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
+
+    int badCount = 0;
+    for (size_t i = 0; i < matches.size(); i++)
+    {
+        if ((int)matches[i].size() != knn)
+            badCount++;
+        else
+        {
+            int localBadCount = 0;
+            for (int k = 0; k < knn; k++)
+            {
+                cv::DMatch match = matches[i][k];
+                if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k) || (match.imgIdx != 0))
+                    localBadCount++;
+            }
+            badCount += localBadCount > 0 ? 1 : 0;
+        }
+    }
+
+    ASSERT_EQ(0, badCount);
+}
+
+#ifdef __ANDROID__
+OCL_TEST_P(BruteForceMatcher, DISABLED_RadiusMatch_Single)
+#else
+OCL_TEST_P(BruteForceMatcher, RadiusMatch_Single)
+#endif
+{
+    float radius = 1.f / countFactor;
+
+    BFMatcher matcher(distType);
+
+    std::vector< std::vector<cv::DMatch> > matches;
+    matcher.radiusMatch(uquery, utrain, matches, radius);
+
+    ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
+
+    int badCount = 0;
+    for (size_t i = 0; i < matches.size(); i++)
+    {
+        if ((int)matches[i].size() != 1)
+        {
+            badCount++;
+        }
+        else
+        {
+            cv::DMatch match = matches[i][0];
+            if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor) || (match.imgIdx != 0))
+                badCount++;
+        }
+    }
+
+    ASSERT_EQ(0, badCount);
+}
+
+OCL_INSTANTIATE_TEST_CASE_P(Matcher, BruteForceMatcher, Combine( Values((int)NORM_L1, (int)NORM_L2),
+                                                                Values(57, 64, 83, 128, 179, 256, 304) ) );
+
+}//ocl
+}//cvtest
+
+#endif //HAVE_OPENCL

3rdparty/opencv-4.5.4/modules/features2d/test/ocl/test_feature2d.cpp

@@ -0,0 +1,72 @@
+// 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 "../test_precomp.hpp"
+#include "cvconfig.h"
+#include "opencv2/ts/ocl_test.hpp"
+
+#ifdef HAVE_OPENCL
+
+namespace opencv_test {
+namespace ocl {
+
+#define TEST_IMAGES testing::Values(\
+    "detectors_descriptors_evaluation/images_datasets/leuven/img1.png",\
+    "../stitching/a3.png", \
+    "../stitching/s2.jpg")
+
+PARAM_TEST_CASE(Feature2DFixture, Ptr<Feature2D>, std::string)
+{
+    std::string filename;
+    Mat image, descriptors;
+    vector<KeyPoint> keypoints;
+    UMat uimage, udescriptors;
+    vector<KeyPoint> ukeypoints;
+    Ptr<Feature2D> feature;
+
+    virtual void SetUp()
+    {
+        feature = GET_PARAM(0);
+        filename = GET_PARAM(1);
+
+        image = readImage(filename);
+
+        ASSERT_FALSE(image.empty());
+
+        image.copyTo(uimage);
+
+        OCL_OFF(feature->detect(image, keypoints));
+        OCL_ON(feature->detect(uimage, ukeypoints));
+        // note: we use keypoints from CPU for GPU too, to test descriptors separately
+        OCL_OFF(feature->compute(image, keypoints, descriptors));
+        OCL_ON(feature->compute(uimage, keypoints, udescriptors));
+    }
+};
+
+OCL_TEST_P(Feature2DFixture, KeypointsSame)
+{
+    EXPECT_EQ(keypoints.size(), ukeypoints.size());
+
+    for (size_t i = 0; i < keypoints.size(); ++i)
+    {
+        EXPECT_GE(KeyPoint::overlap(keypoints[i], ukeypoints[i]), 0.95);
+        EXPECT_NEAR(keypoints[i].angle, ukeypoints[i].angle, 0.05);
+    }
+}
+
+OCL_TEST_P(Feature2DFixture, DescriptorsSame)
+{
+    EXPECT_MAT_NEAR(descriptors, udescriptors, 0.001);
+}
+
+OCL_INSTANTIATE_TEST_CASE_P(AKAZE, Feature2DFixture,
+    testing::Combine(testing::Values(AKAZE::create()), TEST_IMAGES));
+
+OCL_INSTANTIATE_TEST_CASE_P(AKAZE_DESCRIPTOR_KAZE, Feature2DFixture,
+    testing::Combine(testing::Values(AKAZE::create(AKAZE::DESCRIPTOR_KAZE)), TEST_IMAGES));
+
+}//ocl
+}//cvtest
+
+#endif //HAVE_OPENCL