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Kevin Keraudren
41 lines
1.3 KiB
Python
41 lines
1.3 KiB
Python
"""
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============================================
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Robust 3D line model estimation using RANSAC
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============================================
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In this example we see how to robustly fit a 3D line model to faulty data using
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the RANSAC algorithm.
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"""
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import numpy as np
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from matplotlib import pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D
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from skimage.measure import LineModelND, ransac
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np.random.seed(seed=1)
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# generate coordinates of line
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point = np.array([0, 0, 0], dtype='float')
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direction = np.array([1, 1, 1], dtype='float') / np.sqrt(3)
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xyz = point + 10 * np.arange(-100, 100)[..., np.newaxis] * direction
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# add gaussian noise to coordinates
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noise = np.random.normal(size=xyz.shape)
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xyz += 0.5 * noise
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xyz[::2] += 20 * noise[::2]
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xyz[::4] += 100 * noise[::4]
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# robustly fit line only using inlier data with RANSAC algorithm
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model_robust, inliers = ransac(xyz, LineModelND, min_samples=2,
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residual_threshold=1, max_trials=1000)
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outliers = inliers == False
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fig = plt.figure()
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ax = fig.add_subplot(111, projection='3d')
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ax.scatter(xyz[inliers][:, 0], xyz[inliers][:, 1], xyz[inliers][:, 2], c='b',
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marker='o', label='Inlier data')
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ax.scatter(xyz[outliers][:, 0], xyz[outliers][:, 1], xyz[outliers][:, 2], c='r',
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marker='o', label='Outlier data')
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ax.legend(loc='lower left')
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plt.show()
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