Use boolean mask for inlier return value of RANSAC

doc/examples/plot_matching.py

@@ -105,6 +105,7 @@ model.estimate(src, dst)
 # robustly estimate affine transform model with RANSAC
 model_robust, inliers = ransac((src, dst), AffineTransform, min_samples=3,
                                residual_threshold=2, max_trials=100)
+outliers = inliers == False
 
 
 # compare "true" and estimated transform parameters
@@ -128,16 +129,11 @@ ax[1].axis('off')
 ax[1].axis((0, 400, 200, 0))
 ax[1].set_title('Faulty correspondencies')
 
-for i in range(len(src)):
-    if i in inliers:
-        ax_idx = 0
-        color = 'g'
-    else:
-        ax_idx = 1
-        color = 'r'
-    ax[ax_idx].plot((src[i, 1], dst[i, 1] + 200), (src[i, 0], dst[i, 0]), '-',
+
+for ax_idx, (m, color) in enumerate(((inliers, 'g'), (outliers, 'r'))):
+    ax[ax_idx].plot((src[m, 1], dst[m, 1] + 200), (src[m, 0], dst[m, 0]), '-',
                     color=color)
-    ax[ax_idx].plot(src[i, 1], src[i, 0], '.', markersize=10, color=color)
-    ax[ax_idx].plot(dst[i, 1] + 200, dst[i, 0], '.', markersize=10, color=color)
+    ax[ax_idx].plot(src[m, 1], src[m, 0], '.', markersize=10, color=color)
+    ax[ax_idx].plot(dst[m, 1] + 200, dst[m, 0], '.', markersize=10, color=color)
 
 plt.show()

skimage/measure/fit.py

@@ -517,7 +517,7 @@ def ransac(data, model_class, min_samples, residual_threshold,
     model : object
         Best model with largest consensus set.
     inliers : (N, ) array
-        Indices of inliers.
+        Boolean mask of inliers classified as ``True``.
 
     References
     ----------
@@ -598,8 +598,6 @@ def ransac(data, model_class, min_samples, residual_threshold,
     # number of samples
     N = data[0].shape[0]
 
-    data_idxs = np.arange(N)
-
     for _ in range(max_trials):
 
         # choose random sample set
@@ -623,12 +621,11 @@ def ransac(data, model_class, min_samples, residual_threshold,
 
         sample_model_residuals = np.abs(sample_model.residuals(*data))
         # consensus set / inliers
-        sample_model_inliers = data_idxs[sample_model_residuals
-                                         < residual_threshold]
+        sample_model_inliers = sample_model_residuals < residual_threshold
         sample_model_residuals_sum = np.sum(sample_model_residuals**2)
 
         # choose as new best model if number of inliers is maximal
-        sample_inlier_num = sample_model_inliers.shape[0]
+        sample_inlier_num = np.sum(sample_model_inliers)
         if (
             # more inliers
             sample_inlier_num > best_inlier_num

skimage/measure/tests/test_fit.py

@@ -167,17 +167,16 @@ def test_ransac_geometric():
 
     # add some faulty data
     outliers = (0, 5, 20)
-    dst[0] = (10000, 10000)
-    dst[1] = (-100, 100)
-    dst[2] = (50, 50)
+    dst[outliers[0]] = (10000, 10000)
+    dst[outliers[1]] = (-100, 100)
+    dst[outliers[2]] = (50, 50)
 
     # estimate parameters of corrupted data
-    model_est, inliers = ransac((src, dst), AffineTransform, 2, 10)
+    model_est, inliers = ransac((src, dst), AffineTransform, 2, 20)
 
     # test whether estimated parameters equal original parameters
     assert_almost_equal(model0._matrix, model_est._matrix)
-    for outlier in outliers:
-        assert outlier not in inliers
+    assert np.all(np.nonzero(inliers == False)[0] == outliers)
 
 
 def test_ransac_is_data_valid():