Open Source Computer Vision Library
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182 lines
6.7 KiB
182 lines
6.7 KiB
// This file is part of OpenCV project. |
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// It is subject to the license terms in the LICENSE file found in the top-level directory |
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// of this distribution and at http://opencv.org/license.html. |
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#include "precomp.hpp" |
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#include "opencv2/dnn.hpp" |
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#include <algorithm> |
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namespace cv |
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{ |
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class FaceRecognizerSFImpl : public FaceRecognizerSF |
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{ |
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public: |
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FaceRecognizerSFImpl(const String& model, const String& config, int backend_id, int target_id) |
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{ |
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net = dnn::readNet(model, config); |
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CV_Assert(!net.empty()); |
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net.setPreferableBackend(backend_id); |
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net.setPreferableTarget(target_id); |
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}; |
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void alignCrop(InputArray _src_img, InputArray _face_mat, OutputArray _aligned_img) const override |
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{ |
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Mat face_mat = _face_mat.getMat(); |
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float src_point[5][2]; |
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for (int row = 0; row < 5; ++row) |
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{ |
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for(int col = 0; col < 2; ++col) |
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{ |
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src_point[row][col] = face_mat.at<float>(0, row*2+col+4); |
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} |
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} |
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Mat warp_mat = getSimilarityTransformMatrix(src_point); |
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warpAffine(_src_img, _aligned_img, warp_mat, Size(112, 112), INTER_LINEAR); |
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}; |
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void feature(InputArray _aligned_img, OutputArray _face_feature) override |
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{ |
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Mat inputBolb = dnn::blobFromImage(_aligned_img, 1, Size(112, 112), Scalar(0, 0, 0), true, false); |
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net.setInput(inputBolb); |
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net.forward(_face_feature); |
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}; |
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double match(InputArray _face_feature1, InputArray _face_feature2, int dis_type) const override |
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{ |
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Mat face_feature1 = _face_feature1.getMat(), face_feature2 = _face_feature2.getMat(); |
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normalize(face_feature1, face_feature1); |
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normalize(face_feature2, face_feature2); |
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if(dis_type == DisType::FR_COSINE){ |
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return sum(face_feature1.mul(face_feature2))[0]; |
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}else if(dis_type == DisType::FR_NORM_L2){ |
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return norm(face_feature1, face_feature2); |
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}else{ |
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throw std::invalid_argument("invalid parameter " + std::to_string(dis_type)); |
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} |
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}; |
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private: |
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Mat getSimilarityTransformMatrix(float src[5][2]) const { |
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float dst[5][2] = { {38.2946f, 51.6963f}, {73.5318f, 51.5014f}, {56.0252f, 71.7366f}, {41.5493f, 92.3655f}, {70.7299f, 92.2041f} }; |
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float avg0 = (src[0][0] + src[1][0] + src[2][0] + src[3][0] + src[4][0]) / 5; |
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float avg1 = (src[0][1] + src[1][1] + src[2][1] + src[3][1] + src[4][1]) / 5; |
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//Compute mean of src and dst. |
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float src_mean[2] = { avg0, avg1 }; |
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float dst_mean[2] = { 56.0262f, 71.9008f }; |
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//Subtract mean from src and dst. |
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float src_demean[5][2]; |
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for (int i = 0; i < 2; i++) |
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{ |
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for (int j = 0; j < 5; j++) |
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{ |
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src_demean[j][i] = src[j][i] - src_mean[i]; |
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} |
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} |
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float dst_demean[5][2]; |
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for (int i = 0; i < 2; i++) |
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{ |
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for (int j = 0; j < 5; j++) |
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{ |
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dst_demean[j][i] = dst[j][i] - dst_mean[i]; |
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} |
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} |
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double A00 = 0.0, A01 = 0.0, A10 = 0.0, A11 = 0.0; |
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for (int i = 0; i < 5; i++) |
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A00 += dst_demean[i][0] * src_demean[i][0]; |
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A00 = A00 / 5; |
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for (int i = 0; i < 5; i++) |
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A01 += dst_demean[i][0] * src_demean[i][1]; |
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A01 = A01 / 5; |
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for (int i = 0; i < 5; i++) |
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A10 += dst_demean[i][1] * src_demean[i][0]; |
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A10 = A10 / 5; |
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for (int i = 0; i < 5; i++) |
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A11 += dst_demean[i][1] * src_demean[i][1]; |
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A11 = A11 / 5; |
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Mat A = (Mat_<double>(2, 2) << A00, A01, A10, A11); |
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double d[2] = { 1.0, 1.0 }; |
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double detA = A00 * A11 - A01 * A10; |
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if (detA < 0) |
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d[1] = -1; |
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double T[3][3] = { {1.0, 0.0, 0.0}, {0.0, 1.0, 0.0}, {0.0, 0.0, 1.0} }; |
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Mat s, u, vt, v; |
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SVD::compute(A, s, u, vt); |
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double smax = s.ptr<double>(0)[0]>s.ptr<double>(1)[0] ? s.ptr<double>(0)[0] : s.ptr<double>(1)[0]; |
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double tol = smax * 2 * FLT_MIN; |
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int rank = 0; |
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if (s.ptr<double>(0)[0]>tol) |
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rank += 1; |
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if (s.ptr<double>(1)[0]>tol) |
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rank += 1; |
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double arr_u[2][2] = { {u.ptr<double>(0)[0], u.ptr<double>(0)[1]}, {u.ptr<double>(1)[0], u.ptr<double>(1)[1]} }; |
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double arr_vt[2][2] = { {vt.ptr<double>(0)[0], vt.ptr<double>(0)[1]}, {vt.ptr<double>(1)[0], vt.ptr<double>(1)[1]} }; |
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double det_u = arr_u[0][0] * arr_u[1][1] - arr_u[0][1] * arr_u[1][0]; |
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double det_vt = arr_vt[0][0] * arr_vt[1][1] - arr_vt[0][1] * arr_vt[1][0]; |
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if (rank == 1) |
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{ |
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if ((det_u*det_vt) > 0) |
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{ |
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Mat uvt = u*vt; |
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T[0][0] = uvt.ptr<double>(0)[0]; |
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T[0][1] = uvt.ptr<double>(0)[1]; |
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T[1][0] = uvt.ptr<double>(1)[0]; |
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T[1][1] = uvt.ptr<double>(1)[1]; |
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} |
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else |
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{ |
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double temp = d[1]; |
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d[1] = -1; |
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Mat D = (Mat_<double>(2, 2) << d[0], 0.0, 0.0, d[1]); |
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Mat Dvt = D*vt; |
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Mat uDvt = u*Dvt; |
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T[0][0] = uDvt.ptr<double>(0)[0]; |
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T[0][1] = uDvt.ptr<double>(0)[1]; |
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T[1][0] = uDvt.ptr<double>(1)[0]; |
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T[1][1] = uDvt.ptr<double>(1)[1]; |
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d[1] = temp; |
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} |
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} |
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else |
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{ |
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Mat D = (Mat_<double>(2, 2) << d[0], 0.0, 0.0, d[1]); |
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Mat Dvt = D*vt; |
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Mat uDvt = u*Dvt; |
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T[0][0] = uDvt.ptr<double>(0)[0]; |
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T[0][1] = uDvt.ptr<double>(0)[1]; |
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T[1][0] = uDvt.ptr<double>(1)[0]; |
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T[1][1] = uDvt.ptr<double>(1)[1]; |
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} |
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double var1 = 0.0; |
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for (int i = 0; i < 5; i++) |
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var1 += src_demean[i][0] * src_demean[i][0]; |
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var1 = var1 / 5; |
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double var2 = 0.0; |
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for (int i = 0; i < 5; i++) |
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var2 += src_demean[i][1] * src_demean[i][1]; |
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var2 = var2 / 5; |
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double scale = 1.0 / (var1 + var2)* (s.ptr<double>(0)[0] * d[0] + s.ptr<double>(1)[0] * d[1]); |
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double TS[2]; |
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TS[0] = T[0][0] * src_mean[0] + T[0][1] * src_mean[1]; |
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TS[1] = T[1][0] * src_mean[0] + T[1][1] * src_mean[1]; |
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T[0][2] = dst_mean[0] - scale*TS[0]; |
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T[1][2] = dst_mean[1] - scale*TS[1]; |
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T[0][0] *= scale; |
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T[0][1] *= scale; |
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T[1][0] *= scale; |
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T[1][1] *= scale; |
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Mat transform_mat = (Mat_<double>(2, 3) << T[0][0], T[0][1], T[0][2], T[1][0], T[1][1], T[1][2]); |
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return transform_mat; |
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} |
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private: |
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dnn::Net net; |
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}; |
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Ptr<FaceRecognizerSF> FaceRecognizerSF::create(const String& model, const String& config, int backend_id, int target_id) |
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{ |
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return makePtr<FaceRecognizerSFImpl>(model, config, backend_id, target_id); |
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} |
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} // namespace cv
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