Move trivial check to _get_peak_mask

skimage/feature/peak.py

@@ -26,6 +26,8 @@ def _get_peak_mask(image, min_distance, footprint, threshold_abs,
     Return the mask containing all peak candidates above thresholds.
     """
     if footprint is not None:
+        if footprint.size == 1 and min_distance == 1:
+            return np.ones_like(image, dtype=bool)
         image_max = ndi.maximum_filter(image, footprint=footprint,
                                        mode='constant')
     else:
@@ -37,6 +39,10 @@ def _get_peak_mask(image, min_distance, footprint, threshold_abs,
     else:
         threshold = threshold_abs
     mask &= image > threshold
+
+    # no peak for a trivial image
+    if np.count_nonzero(mask) == image.size:
+        mask[:] = False
     return mask
 
 
@@ -159,8 +165,6 @@ def peak_local_max(image, min_distance=1, threshold_abs=None,
     array([[10, 10, 10]])
 
     """
-    out = np.zeros_like(image, dtype=np.bool)
-
     threshold_abs = threshold_abs if threshold_abs is not None else image.min()
 
     if isinstance(exclude_border, bool):
@@ -188,13 +192,6 @@ def peak_local_max(image, min_distance=1, threshold_abs=None,
             "`exclude_border` must be bool, int, or tuple with the same "
             "length as the dimensionality of the image.")
 
-    # no peak for a trivial image
-    if np.all(image == image.flat[0]):
-        if indices is True:
-            return np.empty((0, image.ndim), np.int)
-        else:
-            return out
-
     # In the case of labels, call ndi on each label
     if labels is not None:
         label_values = np.unique(labels)
@@ -211,6 +208,8 @@ def peak_local_max(image, min_distance=1, threshold_abs=None,
         # For each label, extract a smaller image enclosing the object of
         # interest, identify num_peaks_per_label peaks and mark them in
         # variable out.
+        out = np.zeros_like(image, dtype=np.bool)
+
         for label_idx, obj in enumerate(ndi.find_objects(labels)):
             img_object = image[obj] * (labels[obj] == label_idx + 1)
             mask = _get_peak_mask(img_object, min_distance, footprint,
@@ -250,6 +249,7 @@ def peak_local_max(image, min_distance=1, threshold_abs=None,
         return coordinates
     else:
         nd_indices = tuple(coordinates.T)
+        out = np.zeros_like(image, dtype=np.bool)
         out[nd_indices] = True
         return out