Add simple RANSAC example script

doc/examples/plot_ransac.py

@@ -0,0 +1,55 @@
+"""
+=========================================
+Robust line model estimation using RANSAC
+=========================================
+
+In this example we see how to robustly fit a line model to faulty data using
+the RANSAC algorithm.
+
+"""
+import numpy as np
+from matplotlib import pyplot as plt
+
+from skimage.measure import LineModel, ransac
+
+
+np.random.seed(seed=1)
+
+# generate coordinates of line
+x = np.arange(-200, 200)
+y = 0.2 * x + 20
+data = np.column_stack([x, y])
+
+# add faulty data
+faulty = np.array(30 * [(180, -100)])
+faulty += 5 * np.random.normal(size=faulty.shape)
+data[:faulty.shape[0]] = faulty
+
+# add gaussian noise to coordinates
+noise = np.random.normal(size=data.shape)
+data += 0.5 * noise
+data[::2] += 5 * noise[::2]
+data[::4] += 20 * noise[::4]
+
+# fit line using all data
+model = LineModel()
+model.estimate(data)
+
+# robustly fit line only using inlier data with RANSAC algorithm
+model_robust, inliers = ransac(data, LineModel, min_samples=2,
+                               residual_threshold=1, max_trials=1000)
+outliers = inliers == False
+
+# generate coordinates of estimated models
+line_x = np.arange(-250, 250)
+line_y = model.predict_y(line_x)
+line_y_robust = model_robust.predict_y(line_x)
+
+plt.plot(data[inliers, 0], data[inliers, 1], '.b', alpha=0.6,
+         label='Inlier data')
+plt.plot(data[outliers, 0], data[outliers, 1], '.r', alpha=0.6,
+         label='Outlier data')
+plt.plot(line_x, line_y, '-k', label='Line model from all data')
+plt.plot(line_x, line_y_robust, '-b', label='Robust line model')
+plt.legend(loc='lower left')
+plt.show()