Use ndimage.maximum_filter to simplify peak detection.

This change should give identitical results to the previous implementation.

skimage/feature/peak.py

@@ -26,44 +26,24 @@ def peak_min_dist(image, min_distance=10, threshold=0.1):
         (row, column) coordinates of peaks.
     """
     image = image.copy()
-    # Non maximum filter of size 3
-    image_max = ndimage.maximum_filter(image, 3, mode='constant')
+    # Non maximum filter
+    size = 2 * min_distance + 1
+    image_max = ndimage.maximum_filter(image, size=size, mode='constant')
     mask = (image == image_max)
     image *= mask
 
     # Remove the image borders
-    image[:3] = 0
-    image[-3:] = 0
-    image[:, :3] = 0
-    image[:, -3:] = 0
+    image[:min_distance] = 0
+    image[-min_distance:] = 0
+    image[:, :min_distance] = 0
+    image[:, -min_distance:] = 0
 
     # find top corner candidates above a threshold
     corner_threshold = np.max(image.ravel()) * threshold
     image_t = (image >= corner_threshold) * 1
 
-    # get coordinates of candidates
-    candidates = image_t.nonzero()
-    coords = np.transpose(candidates)
+    # get coordinates of peaks
+    coordinates = np.transpose(image_t.nonzero())
 
-    # ...and their values
-    candidate_values = image[candidates]
-
-    # sort candidates
-    index = np.argsort(candidate_values)[::-1]
-
-    # store allowed point locations in array
-    allowed_locations = np.zeros(image.shape)
-    allowed_locations[min_distance:-min_distance,
-                      min_distance:-min_distance] = 1
-
-    # select the best points taking min_distance into account
-    filtered_coords = []
-    for i in index:
-        if allowed_locations[tuple(coords[i])] == 1:
-            filtered_coords.append(coords[i])
-            allowed_locations[
-              (coords[i][0] - min_distance):(coords[i][0] + min_distance),
-              (coords[i][1] - min_distance):(coords[i][1] + min_distance)] = 0
-
-    return np.array(filtered_coords)
+    return coordinates