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/samples/gpu/multi.cpp

@@ -0,0 +1,95 @@
+/* This sample demonstrates the way you can perform independent tasks
+   on the different GPUs */
+
+// Disable some warnings which are caused with CUDA headers
+#if defined(_MSC_VER)
+#pragma warning(disable: 4201 4408 4100)
+#endif
+
+#include <iostream>
+#include "opencv2/core.hpp"
+#include "opencv2/cudaarithm.hpp"
+
+#if !defined(HAVE_CUDA)
+
+int main()
+{
+    std::cout << "CUDA support is required (OpenCV CMake parameter 'WITH_CUDA' must be true)." << std::endl;
+    return 0;
+}
+
+#else
+
+using namespace std;
+using namespace cv;
+using namespace cv::cuda;
+
+struct Worker : public cv::ParallelLoopBody
+{
+    void operator()(const Range& r) const CV_OVERRIDE
+    {
+        for (int i = r.start; i < r.end; ++i) { this->operator()(i); }
+    }
+    void operator()(int device_id) const;
+};
+
+int main()
+{
+    int num_devices = getCudaEnabledDeviceCount();
+    if (num_devices < 2)
+    {
+        std::cout << "Two or more GPUs are required\n";
+        return -1;
+    }
+    for (int i = 0; i < num_devices; ++i)
+    {
+        cv::cuda::printShortCudaDeviceInfo(i);
+
+        DeviceInfo dev_info(i);
+        if (!dev_info.isCompatible())
+        {
+            std::cout << "CUDA module isn't built for GPU #" << i << " ("
+                 << dev_info.name() << ", CC " << dev_info.majorVersion()
+                 << dev_info.minorVersion() << "\n";
+            return -1;
+        }
+    }
+
+    // Execute calculation in two threads using two GPUs
+    cv::Range devices(0, 2);
+    cv::parallel_for_(devices, Worker(), devices.size());
+
+    return 0;
+}
+
+
+void Worker::operator()(int device_id) const
+{
+    setDevice(device_id);
+
+    Mat src(1000, 1000, CV_32F);
+    Mat dst;
+
+    RNG rng(0);
+    rng.fill(src, RNG::UNIFORM, 0, 1);
+
+    // CPU works
+    cv::transpose(src, dst);
+
+    // GPU works
+    GpuMat d_src(src);
+    GpuMat d_dst;
+    cuda::transpose(d_src, d_dst);
+
+    // Check results
+    bool passed = cv::norm(dst - Mat(d_dst), NORM_INF) < 1e-3;
+    std::cout << "GPU #" << device_id << " (" << DeviceInfo().name() << "): "
+        << (passed ? "passed" : "FAILED") << endl;
+
+    // Deallocate data here, otherwise deallocation will be performed
+    // after context is extracted from the stack
+    d_src.release();
+    d_dst.release();
+}
+
+#endif