ORB matching example

doc/examples/plot_orb_matching.py

@@ -0,0 +1,60 @@
+import numpy as np
+from skimage import data
+from skimage import transform as tf
+from skimage.feature import pairwise_hamming_distance, brief, match_binary_descriptors, corner_harris, corner_peaks, keypoints_orb, descriptor_orb
+from skimage.color import rgb2gray
+from skimage import img_as_float
+import matplotlib.pyplot as plt
+
+rotate = 0.5
+translate = (-100, -200)
+scaling = (1.5, 1.5)
+match_threshold = 0.40
+match_cross_check = True
+
+img_color = data.lena()
+tform = tf.AffineTransform(scale = scaling, rotation=rotate, translation=translate)
+transformed_img_color = tf.warp(img_color, tform)
+img = rgb2gray(img_color)
+transformed_img = rgb2gray(transformed_img_color)
+
+keypoints1, orientations1, scales1 = keypoints_orb(img, n_keypoints=250)
+keypoints1.shape
+descriptors1, keypoints1 = descriptor_orb(img, keypoints1, orientations1, scales1)
+keypoints1.shape
+descriptors1.shape
+
+keypoints2, orientations2, scales2 = keypoints_orb(transformed_img, n_keypoints=250)
+keypoints2.shape
+descriptors2, keypoints2 = descriptor_orb(transformed_img, keypoints2, orientations2, scales2)
+keypoints2.shape
+descriptors2.shape
+
+pairwise_hamming_distance(descriptors1, descriptors2)
+matched_keypoints, mask1, mask2 = match_binary_descriptors(keypoints1, descriptors1, keypoints2, descriptors2, cross_check=match_cross_check, threshold=match_threshold)
+
+matched_keypoints.shape
+
+# Plotting the matched correspondences in both the images using matplotlib
+src = matched_keypoints[:, 0, :]
+dst = matched_keypoints[:, 1, :]
+src_scale = 10 * (scales1[mask1] + 1) ** 2
+dst_scale = 10 * (scales2[mask2] + 1) ** 2
+
+img_combined = np.concatenate((img_as_float(img_color), img_as_float(transformed_img_color)), axis=1)
+offset = img.shape
+
+fig, ax = plt.subplots(nrows=1, ncols=1)
+plt.gray()
+
+ax.imshow(img_combined, interpolation='nearest')
+ax.axis('off')
+ax.axis((0, 2 * offset[1], offset[0], 0))
+ax.set_title('Matched correspondences : Rotation = %f; Scale = %s; Translation = %s; threshold = %f; cross_check = %r' % (rotate, scaling, translate, match_threshold, match_cross_check))
+
+for m in range(len(src)):
+    ax.plot((src[m, 1], dst[m, 1] + offset[1]), (src[m, 0], dst[m, 0]), '-', color='g')
+    ax.scatter(src[m, 1], src[m, 0], src_scale[m], facecolors='none', edgecolors='b')
+    ax.scatter(dst[m, 1] + offset[1], dst[m, 0], dst_scale[m], facecolors='none', edgecolors='b')
+
+plt.show()

skimage/feature/__init__.py

@@ -9,7 +9,8 @@ from .corner import (corner_kitchen_rosenfeld, corner_harris,
                      hessian_matrix_eigvals)
 from .corner_cy import corner_moravec, corner_orientations
 from .template import match_template
-from ._brief import brief, match_keypoints_brief
+from ._brief import brief
+from .match import match_binary_descriptors
 from .util import pairwise_hamming_distance
 from .censure import keypoints_censure
 from .orb import keypoints_orb, descriptor_orb
@@ -30,7 +31,7 @@ __all__ = ['daisy',
            'match_template',
            'brief',
            'pairwise_hamming_distance',
-           'match_keypoints_brief',
+           'match_binary_descriptors',
            'keypoints_censure',
            'corner_fast',
            'corner_orientations',

skimage/feature/orb.py

@@ -19,7 +19,7 @@ OFAST_MASK = np.array([[0, 0, 1, 1, 1, 0, 0],
 
 
 def keypoints_orb(image, n_keypoints=200, fast_n=9, fast_threshold=0.20,
-                  harris_k=0.05,  downscale=np.sqrt(2), n_scales=3):
+                  harris_k=0.05,  downscale=np.sqrt(2), n_scales=4):
 
     """Detect Oriented Fast keypoints.
 
@@ -134,7 +134,7 @@ def keypoints_orb(image, n_keypoints=200, fast_n=9, fast_threshold=0.20,
 
 
 def descriptor_orb(image, keypoints, orientations, scales,
-                   downscale=np.sqrt(2), n_scales=5):
+                   downscale=np.sqrt(2), n_scales=4):
     """Compute rBRIEF descriptors of input keypoints.
 
     Parameters
@@ -206,7 +206,7 @@ def descriptor_orb(image, keypoints, orientations, scales,
         curr_scale_orientation = orientations[curr_scale_mask]
 
         border_mask = _mask_border_keypoints(curr_image, curr_scale_kpts,
-                                             dist=13)
+                                             dist=14)
         curr_scale_kpts = curr_scale_kpts[border_mask]
         curr_scale_orientation = curr_scale_orientation[border_mask]
 

skimage/feature/orb_cy.pyx

@@ -9,7 +9,7 @@ import numpy as np
 from libc.math cimport sin, cos, M_PI, round
 
 
-pos = np.loadtxt("orb_descriptor_positions.txt", dtype=np.int8)
+pos = np.loadtxt("skimage/feature/orb_descriptor_positions.txt", dtype=np.int8)
 pos0 = np.ascontiguousarray(pos[:, :2])
 pos1 = np.ascontiguousarray(pos[:, 2:])