Adding LineModel3D for RANSAC, unit test and example.

doc/examples/plot_ransac3D.py

@@ -0,0 +1,38 @@
+"""
+=========================================
+Robust line model estimation using RANSAC
+=========================================
+
+In this example we see how to robustly fit a 3D line model to faulty data using
+the RANSAC algorithm.
+
+"""
+import numpy as np
+from matplotlib import pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+from skimage.measure import LineModel3D, ransac
+
+np.random.seed(seed=1)
+
+# generate coordinates of line
+point = np.array([0, 0, 0], dtype='float')
+direction = np.array([1, 1, 1], dtype='float') / np.sqrt(3)
+xyz = point + 10 * np.arange(-100, 100)[..., np.newaxis] * direction
+
+# add gaussian noise to coordinates
+noise = np.random.normal(size=xyz.shape)
+xyz += 0.5 * noise
+xyz[::2] += 20 * noise[::2]
+xyz[::4] += 100 * noise[::4]
+
+# robustly fit line only using inlier data with RANSAC algorithm
+model_robust, inliers = ransac(xyz, LineModel3D, min_samples=2,
+                               residual_threshold=1, max_trials=1000)
+outliers = inliers == False
+
+fig = plt.figure()
+ax = fig.add_subplot(111, projection='3d')
+ax.scatter(xyz[inliers][:, 0], xyz[inliers][:, 1], xyz[inliers][:, 2], c='b', marker='o', label='Inlier data')
+ax.scatter(xyz[outliers][:, 0], xyz[outliers][:, 1], xyz[outliers][:, 2], c='r', marker='o', label='Outlier data')
+ax.legend(loc='lower left')
+plt.show()