Add DAISY visualization + example plot.

doc/examples/plot_daisy.py

@@ -0,0 +1,28 @@
+"""
+===============================
+Dense DAISY feature description
+===============================
+
+The DAISY local image descriptor is based on gradient orientation histograms
+similar to the SIFT descriptor. It is formulated in a way that allows for fast
+dense extraction which is useful for e.g. bag-of-features image
+representations.
+
+In this example a limited number of DAISY descriptors are extracted at a large
+scale for illustrative purposes.
+"""
+
+from skimage.feature import daisy
+from skimage import data
+import matplotlib.pyplot as plt
+
+
+img = data.camera()
+descs, descs_img = daisy(img, step=180, radius=58, rings=2, histograms=6,
+                         orientations=8, visualize=True)
+
+plt.axis('off')
+plt.imshow(descs_img)
+descs_num = descs.shape[0] * descs.shape[1]
+plt.title('%i DAISY descriptors extracted:' % descs_num)
+plt.show()

doc/examples/plot_shapes.py

@@ -13,7 +13,7 @@ This example shows how to fill several different shapes:
 
 import matplotlib.pyplot as plt
 
-from skimage.draw import line, polygon, circle, ellipse
+from skimage.draw import line, polygon, circle, circle_perimeter, ellipse
 import numpy as np
 
 
@@ -42,5 +42,9 @@ img[rr,cc,:] = (255, 255, 0)
 rr, cc = ellipse(300, 300, 100, 200, img.shape)
 img[rr,cc,2] = 255
 
+# circle
+rr, cc = circle_perimeter(120, 400, 50)
+img[rr, cc, :] = (255, 0, 255)
+
 plt.imshow(img)
-plt.show()
+plt.show()

skimage/feature/_daisy.py

@@ -6,7 +6,7 @@ from skimage import img_as_float
 
 
 def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8,
-          normalization='l1', sigmas=None, ring_radii=None):
+          normalization='l1', sigmas=None, ring_radii=None, visualize=False):
     '''Extract DAISY feature descriptors densely for the given image.
 
     DAISY is a feature descriptor similar to SIFT formulated in a way that
@@ -65,6 +65,8 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8,
         following predicate since no radius is needed for the center
         histogram.
             ``len(ring_radii) == len(sigmas)+1``
+    visualize : bool, optional
+        Generate a visualization of the DAISY descriptors
 
 
     Returns
@@ -75,10 +77,12 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8,
           | ``P = ceil((M-radius*2)/step)``
           | ``Q = ceil((N-radius*2)/step)``
           | ``R = (rings*histograms + 1)*orientations``
+    descs_img : (M, N, 3) array (only if visualize==True)
+        Visualization of the DAISY descriptors.
 
     References
     ----------
-    .. [1] Tola et al. "Daisy\: An efficient dense descriptor applied to wide-
+    .. [1] Tola et al. "Daisy: An efficient dense descriptor applied to wide-
            baseline stereo." Pattern Analysis and Machine Intelligence, IEEE
            Transactions on 32.5 (2010): 815-830.
     .. [2] http://cvlab.epfl.ch/alumni/tola/daisy.html
@@ -120,10 +124,11 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8,
     kappa = 1. / hist_sigma
     bessel = iv(0, kappa)
     hist = np.empty((orientations,) + img.shape, dtype=float)
-    for i in range(orientations):
-        mu = 2 * i * pi / orientations - pi
+    orientation_angles = [2 * o * pi / orientations - pi
+                          for o in range(orientations)]
+    for i, o in enumerate(orientation_angles):
         # Weigh bin contribution by the circular normal distribution
-        hist[i, :, :] = exp(kappa * cos(grad_ori - mu)) / (2 * pi * bessel)
+        hist[i, :, :] = exp(kappa * cos(grad_ori - o)) / (2 * pi * bessel)
         # Weigh bin contribution by the gradient magnitude
         hist[i, :, :] = np.multiply(hist[i, :, :], grad_mag)
 
@@ -169,4 +174,57 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8,
                                     axis=2))
                 descs[:, :, i:i + orientations] /= norms[:, :, np.newaxis]
 
-    return descs
+    if visualize:
+        from skimage import draw
+        from skimage import color
+        descs_img = color.gray2rgb(img)
+        for i in range(descs.shape[0]):
+            for j in range(descs.shape[1]):
+                # Draw center histogram sigma
+                color = (1, 0, 0)
+                desc_y = i * step + radius
+                desc_x = j * step + radius
+                coords = draw.circle_perimeter(desc_y, desc_x, sigmas[0])
+                set_color(descs_img, coords, color)
+                max_bin = np.max(descs[i, j, :])
+                for o_num, o in enumerate(orientation_angles):
+                    # Draw center histogram bins
+                    bin_size = descs[i, j, o_num] / max_bin
+                    dy = sigmas[0] * bin_size * sin(o)
+                    dx = sigmas[0] * bin_size * cos(o)
+                    coords = draw.line(desc_y, desc_x, desc_y + dy,
+                                       desc_x + dx)
+                    set_color(descs_img, coords, color)
+                for r_num, r in enumerate(ring_radii):
+                    color_offset = float(1 + r_num) / rings
+                    color = (1 - color_offset, 1, color_offset)
+                    for t_num, t in enumerate(theta):
+                        # Draw ring histogram sigmas
+                        hist_y = desc_y + int(round(r * sin(t)))
+                        hist_x = desc_x + int(round(r * cos(t)))
+                        coords = draw.circle_perimeter(hist_y, hist_x,
+                                                       sigmas[r_num + 1])
+                        set_color(descs_img, coords, color)
+                        for o_num, o in enumerate(orientation_angles):
+                            # Draw histogram bins
+                            bin_size = descs[i, j, orientations + r_num *
+                                             histograms * orientations +
+                                             t_num * orientations + o_num]
+                            bin_size /= max_bin
+                            dy = sigmas[r_num + 1] * bin_size * sin(o)
+                            dx = sigmas[r_num + 1] * bin_size * cos(o)
+                            coords = draw.line(hist_y, hist_x, hist_y + dy,
+                                               hist_x + dx)
+                            set_color(descs_img, coords, color)
+        return descs, descs_img
+    else:
+        return descs
+
+
+def set_color(img, coords, color):
+    ''' Set pixel color at the given coordiantes in the image.'''
+    coords = filter(lambda (y, x): y >= 0 and y < img.shape[0] and x >= 0
+                    and x < img.shape[1], zip(*coords))
+    if coords:
+        rr, cc = zip(*coords)
+        img[rr, cc, :] = color

skimage/feature/tests/test_daisy.py

@@ -85,6 +85,11 @@ def test_daisy_normalization():
     assert_raises(ValueError, daisy, img, normalization='does_not_exist')
 
 
+def test_daisy_visualization():
+    img = img_as_float(data.lena()[:128, :128].mean(axis=2))
+    descs, descs_img = daisy(img, visualize=True)
+    assert(descs_img.shape == (128, 128, 3))
+
 if __name__ == '__main__':
     from numpy import testing
     testing.run_module_suite()