mirror of
https://github.com/opencv/opencv.git
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Agrim Rai
76 lines
2.8 KiB
Python
76 lines
2.8 KiB
Python
'''
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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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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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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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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 -> {args.output}")
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if __name__ == '__main__':
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main()
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