Add convenience function for plotting matches

skimage/feature/__init__.py

@@ -13,6 +13,7 @@ from .brief import BRIEF
 from .censure import CenSurE
 from .orb import ORB
 from .match import match_descriptors
+from .util import plot_matches
 
 
 __all__ = ['daisy',
@@ -38,4 +39,5 @@ __all__ = ['daisy',
            'BRIEF',
            'CenSurE',
            'ORB',
-           'match_descriptors']
+           'match_descriptors',
+           'plot_matches']

skimage/feature/util.py

@@ -33,6 +33,93 @@ class DescriptorExtractor(object):
         raise NotImplementedError()
 
 
+def plot_matches(ax, image1, image2, keypoints1, keypoints2,
+                 indices1, indices2, keypoints_color='k', matches_color=None,
+                 only_matches=False):
+
+    """Plot matched features.
+
+    Parameters
+    ----------
+    ax : matplotlib.axes.Axes
+        Matches and image are drawn in this ax.
+    image1 : (N, M [, 3]) array
+        First grayscale or color image.
+    image2 : (N, M [, 3]) array
+        Second grayscale or color image.
+    keypoints : (K1, 2) array
+        First keypoint coordinates as ``(row, col)``.
+    keypoints : (K2, 2) array
+        Second keypoint coordinates as ``(row, col)``.
+    keypoints : (K1, 2) array
+        Keypoint coordinates as ``(row, col)``.
+    indices1 : (Q, ) array
+        Indices of corresponding matches for first set of keypoints.
+    indices2 : (Q, ) array
+        Indices of corresponding matches for second set of keypoints.
+    keypoints_color : matplotlib color
+        Color for keypoint locations.
+    matches_color : matplotlib color
+        Color for lines which connect keypoint matches. By default the
+        color is chosen randomly.
+    only_matches : bool
+        Whether to only plot matches and not plot the keypoint locations.
+
+    """
+
+    image1 = img_as_float(image1)
+    image2 = img_as_float(image2)
+
+    new_shape1 = image1.shape
+    new_shape2 = image2.shape
+
+    if image1.shape[0] < image2.shape[0]:
+        new_shape1[0] = image2.shape[0]
+    elif image1.shape[0] > image2.shape[0]:
+        new_shape2[0] = image1.shape[0]
+
+    if image1.shape[1] < image2.shape[1]:
+        new_shape1[1] = image2.shape[1]
+    elif image1.shape[1] > image2.shape[1]:
+        new_shape2[1] = image1.shape[1]
+
+    if new_shape1 != image1.shape:
+        new_image1 = np.zeros(new_shape1, dtype=image1.dtype)
+        new_image1[:image1.shape[0], :image1.shape[1]] = image1
+        image1 = new_image1
+
+    if new_shape2 != image2.shape:
+        new_image2 = np.zeros(new_shape2, dtype=image2.dtype)
+        new_image2[:image2.shape[0], :image2.shape[1]] = image2
+        image2 = new_image2
+
+    image = np.concatenate([image1, image2], axis=1)
+
+    offset = image1.shape
+
+    if not only_matches:
+        ax.scatter(keypoints1[:, 1], keypoints1[:, 0],
+                   facecolors='none', edgecolors=keypoints_color)
+        ax.scatter(keypoints2[:, 1] + offset[1], keypoints2[:, 0],
+                   facecolors='none', edgecolors=keypoints_color)
+
+    ax.imshow(image)
+    ax.axis((0, 2 * offset[1], offset[0], 0))
+
+    for i in range(len(indices1)):
+        idx1 = indices1[i]
+        idx2 = indices2[i]
+
+        if matches_color is None:
+            color = np.random.rand(3, 1)
+        else:
+            color = matches_color
+
+        ax.plot((keypoints1[idx1, 1], keypoints2[idx2, 1] + offset[1]),
+                (keypoints1[idx1, 0], keypoints2[idx2, 0]),
+                '-', color=color)
+
+
 def _prepare_grayscale_input_2D(image):
     image = np.squeeze(image)
     if image.ndim != 2: