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https://github.com/introlab/rtabmap.git
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45 lines
1.3 KiB
Python
45 lines
1.3 KiB
Python
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#! /usr/bin/env python3
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#
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# Drop this file in the "demo" folder of OANet git: https://github.com/zjhthu/OANet
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# To use with rtabmap:
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# --Vis/CorNNType 6 --PyMatcher/Path ~/OANet/demo/rtabmap_oanet.py --PyMatcher/Model ~/OANet/model/gl3d/sift-4000/model_best.pth
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#
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import sys
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import os
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sys.path.append(os.path.dirname(os.path.realpath(__file__))+'/../core')
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if not hasattr(sys, 'argv'):
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sys.argv = ['']
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#print(os.sys.path)
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#print(sys.version)
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import numpy as np
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from learnedmatcher import LearnedMatcher
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lm = None
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def init(descriptorDim, matchThreshold, iterations, cuda, model_path):
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print("OANet python init()")
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global lm
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lm = LearnedMatcher(model_path, inlier_threshold=1, use_ratio=0, use_mutual=0)
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def match(kptsFrom, kptsTo, scoresFrom, scoresTo, descriptorsFrom, descriptorsTo, imageWidth, imageHeight):
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#print("OANet python match()")
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kpt1 = np.asarray(kptsFrom)
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kpt2 = np.asarray(kptsTo)
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desc1 = np.asarray(descriptorsFrom)
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desc2 = np.asarray(descriptorsTo)
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global lm
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matches, _, _ = lm.infer([kpt1, kpt2], [desc1, desc2])
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return matches
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if __name__ == '__main__':
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#test
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init(128, 0.2, 20, False, True)
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match([[1, 2], [1,3], [4,6]], [[1, 3], [1,2], [16,2]], [1, 3,6], [1,3,5], np.full((3, 128), 1), np.full((3, 128), 1), 640, 480)
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