Merge pull request #749 from ahojnnes/gsoc-experimental

Hide experimental GSoC functions for 0.9 release

doc/examples/plot_censure_keypoints.py

@@ -1,54 +0,0 @@
-"""
-=========================
-CenSurE Feature Detection
-=========================
-
-In this example we detect and plot the CenSurE (Center Surround Extrema)
-features at various scales using Difference of Boxes, Octagon and Star shaped
-bi-level filters.
-
-"""
-
-from skimage.feature import keypoints_censure
-from skimage.data import lena
-from skimage.color import rgb2gray
-import matplotlib.pyplot as plt
-
-# Initializing the parameters for Censure keypoints
-img = lena()
-gray_img = rgb2gray(img)
-min_scale = 2
-max_scale = 6
-non_max_threshold = 0.15
-line_threshold = 10
-
-
-_, ax = plt.subplots(nrows=(max_scale - min_scale - 1), ncols=3,
-                     figsize=(6, 6))
-plt.subplots_adjust(wspace=0.02, hspace=0.02, top=0.94,
-                    bottom=0.02, left=0.06, right=0.98)
-
-# Detecting Censure keypoints for the following filters
-for col, mode in enumerate(['dob', 'octagon', 'star']):
-
-    ax[0, col].set_title(mode.upper(), fontsize=12)
-
-    keypoints, scales = keypoints_censure(gray_img, min_scale, max_scale,
-                                          mode, non_max_threshold,
-                                          line_threshold)
-
-    # Plotting Censure features at all the scales
-    for row, scale in enumerate(range(min_scale + 1, max_scale)):
-        mask = scales == scale
-        x = keypoints[mask, 1]
-        y = keypoints[mask, 0]
-        s = 0.5 * 2 ** (scale + min_scale + 1)
-        ax[row, col].imshow(img)
-        ax[row, col].scatter(x, y, s, facecolors='none', edgecolors='b')
-        ax[row, col].set_xticks([])
-        ax[row, col].set_yticks([])
-        ax[row, col].axis((0, img.shape[1], img.shape[0], 0))
-        if col == 0:
-            ax[row, col].set_ylabel('Scale %d' % scale, fontsize=12)
-
-plt.show()

skimage/feature/__init__.py

@@ -7,9 +7,7 @@ from .corner import (corner_kitchen_rosenfeld, corner_harris,
                      corner_peaks)
 from .corner_cy import corner_moravec
 from .template import match_template
-from ._brief import brief, match_keypoints_brief
-from .util import pairwise_hamming_distance
-from .censure import keypoints_censure
+
 
 __all__ = ['daisy',
            'hog',
@@ -24,8 +22,4 @@ __all__ = ['daisy',
            'corner_subpix',
            'corner_peaks',
            'corner_moravec',
-           'match_template',
-           'brief',
-           'pairwise_hamming_distance',
-           'match_keypoints_brief',
-           'keypoints_censure']
+           'match_template']

skimage/feature/_brief.py

@@ -9,7 +9,9 @@ from ._brief_cy import _brief_loop
 
 def brief(image, keypoints, descriptor_size=256, mode='normal', patch_size=49,
           sample_seed=1, variance=2):
-    """Extract BRIEF Descriptor about given keypoints for a given image.
+    """**Experimental function**.
+
+    Extract BRIEF Descriptor about given keypoints for a given image.
 
     Parameters
     ----------
@@ -178,7 +180,9 @@ def brief(image, keypoints, descriptor_size=256, mode='normal', patch_size=49,
 
 def match_keypoints_brief(keypoints1, descriptors1, keypoints2,
                           descriptors2, threshold=0.15):
-    """Match keypoints described using BRIEF descriptors in one image to
+    """**Experimental function**.
+
+    Match keypoints described using BRIEF descriptors in one image to
     those in second image.
 
     Parameters

skimage/feature/censure.py

@@ -111,7 +111,8 @@ def _suppress_lines(feature_mask, image, sigma, line_threshold):
 
 def keypoints_censure(image, min_scale=1, max_scale=7, mode='DoB',
                       non_max_threshold=0.15, line_threshold=10):
-    """
+    """**Experimental function**.
+
     Extracts CenSurE keypoints along with the corresponding scale using
     either Difference of Boxes, Octagon or STAR bi-level filter.
 

skimage/feature/util.py

@@ -14,7 +14,9 @@ def _mask_border_keypoints(image, keypoints, dist):
 
 
 def pairwise_hamming_distance(array1, array2):
-    """Calculate hamming dissimilarity measure between two sets of
+    """**Experimental function**.
+
+    Calculate hamming dissimilarity measure between two sets of
     vectors.
 
     Parameters