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https://github.com/opencv/opencv.git
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better vo samples
This commit is contained in:
Agrim Rai
@@ -2,7 +2,6 @@
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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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// Copyright (C) 2026, BigVision LLC, all rights reserved.
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// Third party copyrights are property of their respective owners.
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#include <opencv2/slam.hpp>
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#include <opencv2/features.hpp>
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@@ -12,52 +11,73 @@
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using namespace cv;
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static const char* ALIKED_MODEL = "/media/user/path/to/models/aliked-n16rot-top1k-640.onnx";
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static const char* LIGHTGLUE_MODEL = "/media/user/path/to/models/aliked_lightglue.onnx";
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static const char* IMAGES_DIR = "/media/user/path/to/dataset";
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static const char* OUTPUT_DIR = "vo_out";
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static const char* keys =
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"{ help h | | Print help message }"
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"{ aliked | <none> | Path to ALIKED ONNX model }"
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"{ lightglue | <none> | Path to LightGlue ONNX model }"
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"{ images | <none> | Path to directory with input images }"
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"{ output | vo_out | Output directory for trajectory and map }"
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"{ fx | 718.856 | Camera focal length X }"
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"{ fy | 718.856 | Camera focal length Y }"
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"{ cx | 607.1928 | Camera principal point X }"
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"{ cy | 185.2157 | Camera principal point Y }"
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"{ min-parallax | 1.5 | Minimum initialisation parallax in degrees }"
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"{ min-points | 50 | Minimum initialisation map points }";
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// KITTI-00: fx, fy, cx, cy
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static const Matx33d K(718.856, 0., 607.1928,
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0., 718.856, 185.2157,
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0., 0., 1.);
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// k1, k2, p1, p2, k3
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static const std::vector<double> DIST = { -0.2811, 0.0723, -0.0003, 0.0001, 0.0 };
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static Ptr<Feature2D> makeDetector()
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int main(int argc, char** argv)
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{
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ALIKED::Params p;
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p.inputSize = Size(640, 640);
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p.engine = dnn::ENGINE_NEW;
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return ALIKED::create(ALIKED_MODEL, p);
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}
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CommandLineParser parser(argc, argv, keys);
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parser.about("Monocular visual odometry using ALIKED + LightGlue\n"
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" Example: visual_odometry --aliked=aliked.onnx --lightglue=lg.onnx --images=./seq\n");
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static Ptr<DescriptorMatcher> makeMatcher()
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{
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return LightGlueMatcher::create(LIGHTGLUE_MODEL, 0.0f,
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dnn::DNN_BACKEND_DEFAULT,
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dnn::DNN_TARGET_CPU);
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}
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if (parser.has("help"))
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{
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parser.printMessage();
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return 0;
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}
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int main()
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{
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slam::OdometryParams params;
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params.minInitParallaxDeg = 1.5;
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params.minInitPoints = 50;
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const String alikedPath = parser.get<String>("aliked");
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const String lightgluePath = parser.get<String>("lightglue");
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const String imagesDir = parser.get<String>("images");
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if (!parser.check() || alikedPath == "<none>" || lightgluePath == "<none>" || imagesDir == "<none>")
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{
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parser.printErrors();
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parser.printMessage();
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return 1;
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}
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const String outputDir = parser.get<String>("output");
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const Matx33d K(parser.get<double>("fx"), 0., parser.get<double>("cx"),
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0., parser.get<double>("fy"), parser.get<double>("cy"),
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0., 0., 1.);
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ALIKED::Params detParams;
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detParams.inputSize = Size(640, 640);
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detParams.engine = dnn::ENGINE_NEW;
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auto detector = ALIKED::create(alikedPath, detParams);
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auto matcher = LightGlueMatcher::create(lightgluePath, 0.0f,
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dnn::DNN_BACKEND_DEFAULT,
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dnn::DNN_TARGET_CPU);
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slam::OdometryParams voParams;
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voParams.minInitParallaxDeg = parser.get<double>("min-parallax");
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voParams.minInitPoints = parser.get<int>("min-points");
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auto vo = slam::VisualOdometry::create(
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makeDetector(), makeMatcher(),
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IMAGES_DIR, OUTPUT_DIR,
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Mat(K), Mat(DIST), params);
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detector, matcher,
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imagesDir, outputDir,
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Mat(K), Mat(), voParams);
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const int64 t0 = getTickCount();
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const bool ok = vo->run();
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const int64 t0 = getTickCount();
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const bool ok = vo->run();
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const double elapsed = (getTickCount() - t0) / getTickFrequency();
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std::cout << "run=" << (ok ? "ok" : "FAILED")
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std::cout << "run=" << (ok ? "ok" : "FAILED")
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<< " frames=" << vo->getTrajectory().size()
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<< " elapsed=" << elapsed << "s\n"
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<< "output -> " << OUTPUT_DIR << "\n";
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<< "output -> " << outputDir << "\n";
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return ok ? 0 : 1;
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}
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@@ -1,55 +1,74 @@
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'''
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Monocular visual odometry with cv.slam.VisualOdometry (ALIKED + LightGlue).
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Example:
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python visual_odometry.py --aliked aliked.onnx --lightglue lg.onnx --images ./seq
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'''
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import argparse
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import time
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import numpy as np
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import cv2 as cv
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ALIKED_MODEL = '/media/user/path/to/models/aliked-n16rot-top1k-640.onnx'
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LIGHTGLUE_MODEL = '/media/user/path/to/models/aliked_lightglue.onnx'
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IMAGES_DIR = '/media/user/path/to/dataset'
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OUTPUT_DIR = 'vo_out'
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# KITTI-00: fx, fy, cx, cy
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K = np.array([[718.856, 0., 607.1928],
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[0., 718.856, 185.2157],
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[0., 0., 1. ]], dtype=np.float64)
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# k1, k2, p1, p2, k3
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DIST = np.array([-0.2811, 0.0723, -0.0003, 0.0001, 0.0], dtype=np.float64)
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def make_detector():
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p = cv.ALIKED.Params()
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p.inputSize = (640, 640)
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p.engine = cv.dnn.ENGINE_NEW
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return cv.ALIKED.create(ALIKED_MODEL, p)
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def make_matcher():
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return cv.LightGlueMatcher.create(
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LIGHTGLUE_MODEL, 0.0,
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cv.dnn.DNN_BACKEND_DEFAULT,
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cv.dnn.DNN_TARGET_CPU)
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def build_K(fx, fy, cx, cy):
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return np.array([[fx, 0., cx],
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[0., fy, cy],
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[0., 0., 1.]], dtype=np.float64)
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def main():
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params = cv.slam.OdometryParams()
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params.minInitParallaxDeg = 1.5
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params.minInitPoints = 50
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parser = argparse.ArgumentParser(
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description='Monocular visual odometry using ALIKED + LightGlue')
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parser.add_argument('--aliked', required=True,
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help='Path to ALIKED ONNX model')
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parser.add_argument('--lightglue', required=True,
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help='Path to LightGlue ONNX model')
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parser.add_argument('--images', required=True,
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help='Path to directory with input images')
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parser.add_argument('--output', default='vo_out',
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help='Output directory for trajectory and map (default: vo_out)')
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parser.add_argument('--fx', type=float, default=718.856,
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help='Camera focal length X (default: KITTI-00)')
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parser.add_argument('--fy', type=float, default=718.856,
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help='Camera focal length Y (default: KITTI-00)')
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parser.add_argument('--cx', type=float, default=607.1928,
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help='Camera principal point X (default: KITTI-00)')
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parser.add_argument('--cy', type=float, default=185.2157,
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help='Camera principal point Y (default: KITTI-00)')
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parser.add_argument('--min-parallax', type=float, default=1.5,
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help='Minimum initialisation parallax in degrees (default: 1.5)')
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parser.add_argument('--min-points', type=int, default=50,
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help='Minimum initialisation map points (default: 50)')
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args = parser.parse_args()
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det_params = cv.ALIKED.Params()
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det_params.inputSize = (640, 640)
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det_params.engine = cv.dnn.ENGINE_NEW
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detector = cv.ALIKED.create(args.aliked, det_params)
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matcher = cv.LightGlueMatcher.create(
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args.lightglue, 0.0,
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cv.dnn.DNN_BACKEND_DEFAULT,
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cv.dnn.DNN_TARGET_CPU)
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vo_params = cv.slam.OdometryParams()
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vo_params.minInitParallaxDeg = args.min_parallax
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vo_params.minInitPoints = args.min_points
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K = build_K(args.fx, args.fy, args.cx, args.cy)
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vo = cv.slam.VisualOdometry.create(
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make_detector(), make_matcher(),
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IMAGES_DIR, OUTPUT_DIR,
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K, DIST, params)
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detector, matcher,
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args.images, args.output,
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K, np.array([]), vo_params)
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t0 = time.perf_counter()
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ok = vo.run()
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elapsed = time.perf_counter() - t0
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print(f"run={'ok' if ok else 'FAILED'} frames={len(vo.getTrajectory())} elapsed={elapsed:.2f}s")
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print(f"output -> {OUTPUT_DIR}")
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print(f"output -> {args.output}")
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if __name__ == '__main__':
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