vendor: OpenCV 5.0.0 snapshot at 40738fb16ceddb5fb3fea747585f7ce6abb0605b

samples/cpp/example_features_aliked_lightglue.cpp

@@ -0,0 +1,169 @@
+// 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.
+
+// ALIKED + LightGlueMatcher usage example
+// Demonstrates feature detection, extraction, and matching using ALIKED and LightGlue.
+
+#include <opencv2/features.hpp>
+#include <opencv2/imgcodecs.hpp>
+#include <opencv2/imgproc.hpp>
+#include <opencv2/highgui.hpp>
+#include <iostream>
+
+using namespace cv;
+using namespace std;
+
+int main(int argc, char** argv)
+{
+    // ---- Parse arguments ----
+    String alikedModel, lightglueModel, imgPath1, imgPath2;
+
+    if (argc >= 5)
+    {
+        imgPath1 = argv[1];
+        imgPath2 = argv[2];
+        alikedModel = argv[3];
+        lightglueModel = argv[4];
+    }
+else
+    {
+        cout << "Usage: " << argv[0] << " <image1> <image2> <aliked_model> <lightglue_model>" << endl;
+        cout << endl;
+        cout << "Example:" << endl;
+        cout << "  " << argv[0] << " img1.jpg img2.jpg aliked-n16rot-top1k-640.onnx aliked_lightglue.onnx" << endl;
+        return 0;
+    }
+
+    // ---- Load images ----
+    Mat img1 = imread(imgPath1);
+    Mat img2 = imread(imgPath2);
+    if (img1.empty() || img2.empty())
+    {
+        cerr << "Error: cannot load images." << endl;
+        return -1;
+    }
+
+    // ================================================================
+    //  1. Create ALIKED feature extractor
+    // ================================================================
+    // Method A: From ONNX model file
+    Ptr<ALIKED> aliked = ALIKED::create(alikedModel);
+
+    // Method B: Customize parameters
+    // ALIKED::Params params;
+    // params.inputSize = Size(640, 640);       // Network input resolution
+    // params.normalizeDescriptors = true;       // L2-normalize descriptors
+    // Ptr<ALIKED> aliked = ALIKED::create(alikedModel, params);
+
+    // Method C: From in-memory model data
+    // vector<uchar> modelData = readFile(alikedModel);
+    // Ptr<ALIKED> aliked = ALIKED::create(modelData);
+
+    cout << "Descriptor size: " << aliked->descriptorSize() << endl;  // 128
+    cout << "Descriptor type: " << aliked->descriptorType() << endl;  // CV_32F
+    cout << "Default norm: " << aliked->defaultNorm() << endl;        // NORM_L2
+
+    // ================================================================
+    //  2. Detect keypoints and compute descriptors
+    // ================================================================
+    vector<KeyPoint> kpts1, kpts2;
+    Mat descs1, descs2;
+
+    // Method A: detect + compute in one call (recommended)
+    aliked->detectAndCompute(img1, Mat(), kpts1, descs1);
+    aliked->detectAndCompute(img2, Mat(), kpts2, descs2);
+
+    // Method B: detect only (no descriptors)
+    // vector<KeyPoint> kpts;
+    // aliked->detect(img, kpts);
+
+    // Method C: compute only (from existing keypoints)
+    // Mat descs;
+    // aliked->compute(img, kpts, descs);
+
+    cout << "Image 1: " << kpts1.size() << " keypoints, descriptors " << descs1.rows << "x" << descs1.cols << endl;
+    cout << "Image 2: " << kpts2.size() << " keypoints, descriptors " << descs2.rows << "x" << descs2.cols << endl;
+
+    // ================================================================
+    //  3. Create LightGlueMatcher
+    // ================================================================
+    // Method A: From ONNX model file
+    Ptr<LightGlueMatcher> lg = LightGlueMatcher::create(lightglueModel);
+
+    // Method B: Customize parameters
+    // LightGlueMatcher::Params lgParams;
+    // lgParams.scoreThreshold = 0.1f;    // Filter low-confidence matches
+    // lgParams.disableWinograd = false;   // Keep Winograd convolution
+    // Ptr<LightGlueMatcher> lg = LightGlueMatcher::create(lightglueModel, lgParams);
+
+    // Method C: From in-memory model data
+    // vector<uchar> lgData = readFile(lightglueModel);
+    // Ptr<LightGlueMatcher> lg = LightGlueMatcher::create(lgData);
+
+    // ================================================================
+    //  4. Set keypoint context for LightGlue
+    // ================================================================
+    // LightGlue needs keypoint coordinates + image sizes for spatial reasoning.
+    // Build Nx2 float matrices with pixel coordinates.
+
+    Mat kpts1Mat((int)kpts1.size(), 2, CV_32F);
+    Mat kpts2Mat((int)kpts2.size(), 2, CV_32F);
+    for (size_t i = 0; i < kpts1.size(); i++)
+    {
+        kpts1Mat.at<float>((int)i, 0) = kpts1[i].pt.x;
+        kpts1Mat.at<float>((int)i, 1) = kpts1[i].pt.y;
+    }
+    for (size_t i = 0; i < kpts2.size(); i++)
+    {
+        kpts2Mat.at<float>((int)i, 0) = kpts2[i].pt.x;
+        kpts2Mat.at<float>((int)i, 1) = kpts2[i].pt.y;
+    }
+
+    // setPairInfo must be called before match()/knnMatch()
+    lg->setPairInfo(kpts1Mat, kpts2Mat, img1.size(), img2.size());
+
+    // ================================================================
+    //  5. Match descriptors
+    // ================================================================
+
+    // Method A: 1-to-1 matching (returns best match per query keypoint)
+    vector<DMatch> matches;
+    lg->match(descs1, descs2, matches);
+
+    cout << "1-to-1 matches: " << matches.size() << endl;
+
+    // Method B: kNN matching (k=1 only for LightGlue)
+    // vector<vector<DMatch>> knnMatches;
+    // lg->knnMatch(descs1, descs2, knnMatches, 1);
+    // // knnMatches[i] contains matches for query keypoint i
+
+    // ================================================================
+    //  6. Filter matches by confidence (optional)
+    // ================================================================
+    // DMatch distance = 1.0 - confidence_score
+    // Lower distance = better match
+
+    vector<DMatch> goodMatches;
+    float distanceThreshold = 0.9f;  // confidence > 0.1
+    for (const auto& m : matches)
+    {
+        if (m.distance < distanceThreshold)
+            goodMatches.push_back(m);
+    }
+    cout << "Good matches (distance < " << distanceThreshold << "): " << goodMatches.size() << endl;
+
+    // ================================================================
+    //  7. Visualize results
+    // ================================================================
+    Mat canvas;
+    cv::drawMatches(img1, kpts1, img2, kpts2, goodMatches, canvas,
+                    Scalar::all(-1), Scalar::all(-1), vector<char>(),
+                    DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);
+
+    imshow("ALIKED + LightGlue Matches", canvas);
+    cout << "Press any key to exit..." << endl;
+    waitKey(0);
+
+    return 0;
+}