mirror of
https://github.com/wassname/scikit-image.git
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Johannes Schönberger
57 lines
1.6 KiB
Python
57 lines
1.6 KiB
Python
"""
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=========================================
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Robust line model estimation using RANSAC
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=========================================
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In this example we see how to robustly fit a 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 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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x = np.arange(-200, 200)
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y = 0.2 * x + 20
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data = np.column_stack([x, y])
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# add faulty data
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faulty = np.array(30 * [(180., -100)])
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faulty += 5 * np.random.normal(size=faulty.shape)
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data[:faulty.shape[0]] = faulty
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# add gaussian noise to coordinates
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noise = np.random.normal(size=data.shape)
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data += 0.5 * noise
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data[::2] += 5 * noise[::2]
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data[::4] += 20 * noise[::4]
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# fit line using all data
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model = LineModelND()
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model.estimate(data)
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# robustly fit line only using inlier data with RANSAC algorithm
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model_robust, inliers = ransac(data, 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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# generate coordinates of estimated models
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line_x = np.arange(-250, 250)
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line_y = model.predict_y(line_x)
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line_y_robust = model_robust.predict_y(line_x)
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fig, ax = plt.subplots()
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ax.plot(data[inliers, 0], data[inliers, 1], '.b', alpha=0.6,
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label='Inlier data')
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ax.plot(data[outliers, 0], data[outliers, 1], '.r', alpha=0.6,
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label='Outlier data')
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ax.plot(line_x, line_y, '-k', label='Line model from all data')
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ax.plot(line_x, line_y_robust, '-b', label='Robust line model')
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ax.legend(loc='lower left')
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plt.show()
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