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f77c3574af
Use YuNet of fixed input shape to fix not-supported-dynamic-zero-shape for FaceDetectorYN * use yunet with input of fixed shape * update yunet used in face recognition regression
220 lines
7.5 KiB
C++
220 lines
7.5 KiB
C++
// 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 "test_precomp.hpp"
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namespace opencv_test { namespace {
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// label format:
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// image_name
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// num_face
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// face_1
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// face_..
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// face_num
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std::map<std::string, Mat> blobFromTXT(const std::string& path, int numCoords)
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{
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std::ifstream ifs(path.c_str());
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CV_Assert(ifs.is_open());
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std::map<std::string, Mat> gt;
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Mat faces;
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int faceNum = -1;
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int faceCount = 0;
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for (std::string line, key; getline(ifs, line); )
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{
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std::istringstream iss(line);
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if (line.find(".png") != std::string::npos)
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{
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// Get filename
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iss >> key;
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}
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else if (line.find(" ") == std::string::npos)
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{
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// Get the number of faces
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iss >> faceNum;
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}
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else
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{
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// Get faces
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Mat face(1, numCoords, CV_32FC1);
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for (int j = 0; j < numCoords; j++)
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{
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iss >> face.at<float>(0, j);
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}
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faces.push_back(face);
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faceCount++;
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}
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if (faceCount == faceNum)
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{
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// Store faces
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gt[key] = faces;
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faces.release();
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faceNum = -1;
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faceCount = 0;
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}
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}
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return gt;
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}
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TEST(Objdetect_face_detection, regression)
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{
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// Pre-set params
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float scoreThreshold = 0.7f;
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float matchThreshold = 0.9f;
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float l2disThreshold = 5.0f;
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int numLM = 5;
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int numCoords = 4 + 2 * numLM;
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// Load ground truth labels
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std::map<std::string, Mat> gt = blobFromTXT(findDataFile("dnn_face/detection/cascades_labels.txt"), numCoords);
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// for (auto item: gt)
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// {
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// std::cout << item.first << " " << item.second.size() << std::endl;
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// }
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// Initialize detector
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std::string model = findDataFile("dnn/onnx/models/yunet-202202.onnx", false);
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Ptr<FaceDetectorYN> faceDetector = FaceDetectorYN::create(model, "", Size(300, 300));
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faceDetector->setScoreThreshold(0.7f);
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// Detect and match
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for (auto item: gt)
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{
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std::string imagePath = findDataFile("cascadeandhog/images/" + item.first);
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Mat image = imread(imagePath);
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// Set input size
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faceDetector->setInputSize(image.size());
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// Run detection
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Mat faces;
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faceDetector->detect(image, faces);
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// std::cout << item.first << " " << item.second.rows << " " << faces.rows << std::endl;
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// Match bboxes and landmarks
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std::vector<bool> matchedItem(item.second.rows, false);
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for (int i = 0; i < faces.rows; i++)
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{
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if (faces.at<float>(i, numCoords) < scoreThreshold)
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continue;
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bool boxMatched = false;
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std::vector<bool> lmMatched(numLM, false);
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cv::Rect2f resBox(faces.at<float>(i, 0), faces.at<float>(i, 1), faces.at<float>(i, 2), faces.at<float>(i, 3));
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for (int j = 0; j < item.second.rows && !boxMatched; j++)
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{
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if (matchedItem[j])
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continue;
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// Retrieve bbox and compare IoU
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cv::Rect2f gtBox(item.second.at<float>(j, 0), item.second.at<float>(j, 1), item.second.at<float>(j, 2), item.second.at<float>(j, 3));
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double interArea = (resBox & gtBox).area();
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double iou = interArea / (resBox.area() + gtBox.area() - interArea);
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if (iou >= matchThreshold)
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{
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boxMatched = true;
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matchedItem[j] = true;
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}
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// Match landmarks if bbox is matched
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if (!boxMatched)
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continue;
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for (int lmIdx = 0; lmIdx < numLM; lmIdx++)
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{
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float gtX = item.second.at<float>(j, 4 + 2 * lmIdx);
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float gtY = item.second.at<float>(j, 4 + 2 * lmIdx + 1);
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float resX = faces.at<float>(i, 4 + 2 * lmIdx);
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float resY = faces.at<float>(i, 4 + 2 * lmIdx + 1);
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float l2dis = cv::sqrt((gtX - resX) * (gtX - resX) + (gtY - resY) * (gtY - resY));
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if (l2dis <= l2disThreshold)
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{
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lmMatched[lmIdx] = true;
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}
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}
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}
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EXPECT_TRUE(boxMatched) << "In image " << item.first << ", cannot match resBox " << resBox << " with any ground truth.";
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if (boxMatched)
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{
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EXPECT_TRUE(std::all_of(lmMatched.begin(), lmMatched.end(), [](bool v) { return v; })) << "In image " << item.first << ", resBox " << resBox << " matched but its landmarks failed to match.";
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}
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}
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}
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}
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TEST(Objdetect_face_recognition, regression)
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{
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// Pre-set params
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float score_thresh = 0.9f;
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float nms_thresh = 0.3f;
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double cosine_similar_thresh = 0.363;
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double l2norm_similar_thresh = 1.128;
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// Load ground truth labels
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std::ifstream ifs(findDataFile("dnn_face/recognition/cascades_label.txt").c_str());
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CV_Assert(ifs.is_open());
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std::set<std::string> fSet;
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std::map<std::string, Mat> featureMap;
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std::map<std::pair<std::string, std::string>, int> gtMap;
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for (std::string line, key; getline(ifs, line);)
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{
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std::string fname1, fname2;
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int label;
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std::istringstream iss(line);
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iss>>fname1>>fname2>>label;
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// std::cout<<fname1<<" "<<fname2<<" "<<label<<std::endl;
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fSet.insert(fname1);
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fSet.insert(fname2);
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gtMap[std::make_pair(fname1, fname2)] = label;
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}
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// Initialize detector
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std::string detect_model = findDataFile("dnn/onnx/models/yunet-202202.onnx", false);
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Ptr<FaceDetectorYN> faceDetector = FaceDetectorYN::create(detect_model, "", Size(150, 150), score_thresh, nms_thresh);
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std::string recog_model = findDataFile("dnn/onnx/models/face_recognizer_fast.onnx", false);
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Ptr<FaceRecognizerSF> faceRecognizer = FaceRecognizerSF::create(recog_model, "");
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// Detect and match
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for (auto fname: fSet)
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{
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std::string imagePath = findDataFile("dnn_face/recognition/" + fname);
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Mat image = imread(imagePath);
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Mat faces;
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faceDetector->detect(image, faces);
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Mat aligned_face;
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faceRecognizer->alignCrop(image, faces.row(0), aligned_face);
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Mat feature;
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faceRecognizer->feature(aligned_face, feature);
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featureMap[fname] = feature.clone();
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}
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for (auto item: gtMap)
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{
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Mat feature1 = featureMap[item.first.first];
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Mat feature2 = featureMap[item.first.second];
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int label = item.second;
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double cos_score = faceRecognizer->match(feature1, feature2, FaceRecognizerSF::DisType::FR_COSINE);
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double L2_score = faceRecognizer->match(feature1, feature2, FaceRecognizerSF::DisType::FR_NORM_L2);
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EXPECT_TRUE(label == 0 ? cos_score <= cosine_similar_thresh : cos_score > cosine_similar_thresh) << "Cosine match result of images " << item.first.first << " and " << item.first.second << " is different from ground truth (score: "<< cos_score <<";Thresh: "<< cosine_similar_thresh <<").";
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EXPECT_TRUE(label == 0 ? L2_score > l2norm_similar_thresh : L2_score <= l2norm_similar_thresh) << "L2norm match result of images " << item.first.first << " and " << item.first.second << " is different from ground truth (score: "<< L2_score <<";Thresh: "<< l2norm_similar_thresh <<").";
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}
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}
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}} // namespace
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