Add short description to ORB example

doc/examples/plot_orb.py

@@ -0,0 +1,77 @@
+"""
+==========================================
+ORB feature detector and binary descriptor
+==========================================
+
+This example demonstrates the ORB feature detection and binary description
+algorithm. It uses an oriented FAST detection method and the rotated BRIEF
+descriptors.
+
+ORB is comparatively scale- and rotation-invariant. As a binary descriptor it
+allows to employ the very efficient Hamming distance metric for matching and
+is thus preferred for real-time applications.
+
+"""
+import numpy as np
+from skimage import data
+from skimage import transform as tf
+from skimage.feature import (match_descriptors, corner_harris,
+                             corner_peaks, ORB)
+from skimage.color import rgb2gray
+from skimage import img_as_float
+import matplotlib.pyplot as plt
+
+
+img1_color = data.lena()
+img2_color = tf.rotate(img1_color, 180)
+tform = tf.AffineTransform(scale=(1.3, 1.1), rotation=0.5,
+                           translation=(0, -200))
+img3_color = tf.warp(img1_color, tform)
+img1 = rgb2gray(img1_color)
+img2 = rgb2gray(img2_color)
+img3 = rgb2gray(img3_color)
+
+descriptor_extractor = ORB(n_keypoints=200)
+keypoints1, descriptors1 = descriptor_extractor.detect_and_extract(img1)
+keypoints2, descriptors2 = descriptor_extractor.detect_and_extract(img2)
+keypoints3, descriptors3 = descriptor_extractor.detect_and_extract(img3)
+
+idxs1, idxs2 = match_descriptors(descriptors1, descriptors2, cross_check=True)
+src12 = keypoints1[idxs1]
+dst12 = keypoints2[idxs2]
+
+idxs1, idxs3 = match_descriptors(descriptors1, descriptors3, cross_check=True)
+src13 = keypoints1[idxs1]
+dst13 = keypoints3[idxs3]
+
+img12 = np.concatenate((img_as_float(img1_color),
+                        img_as_float(img2_color)), axis=1)
+img13 = np.concatenate((img_as_float(img1_color),
+                        img_as_float(img3_color)), axis=1)
+
+imgs = (img12, img13)
+srcs = (src12, src13)
+dsts = (dst12, dst13)
+
+offset = img1.shape
+
+fig, ax = plt.subplots(nrows=2, ncols=1)
+
+for i in range(2):
+
+    ax[i].imshow(imgs[i], interpolation='nearest')
+    ax[i].axis('off')
+    ax[i].axis((0, 2 * offset[1], offset[0], 0))
+
+    src = srcs[i]
+    dst = dsts[i]
+
+    for m in range(len(src)):
+        color = np.random.rand(3, 1)
+        ax[i].plot((src[m, 1], dst[m, 1] + offset[1]), (src[m, 0], dst[m, 0]),
+                '-', color=color)
+        ax[i].scatter(src[m, 1], src[m, 0], facecolors='none', edgecolors=color)
+        ax[i].scatter(dst[m, 1] + offset[1], dst[m, 0], facecolors='none',
+                   edgecolors=color)
+
+plt.show()