diff --git a/demo_superpoint.py b/demo_superpoint.py
index 55e878f..f918de1 100755
--- a/demo_superpoint.py
+++ b/demo_superpoint.py
@@ -1,4 +1,4 @@
-#!/usr/bin/env python
+#!/usr/bin/env python3
 #
 # %BANNER_BEGIN%
 # ---------------------------------------------------------------------
@@ -152,19 +152,19 @@ def nms_fast(self, in_corners, H, W, dist_thresh):
     """
     Run a faster approximate Non-Max-Suppression on numpy corners shaped:
       3xN [x_i,y_i,conf_i]^T
-  
+
     Algo summary: Create a grid sized HxW. Assign each corner location a 1, rest
     are zeros. Iterate through all the 1's and convert them either to -1 or 0.
     Suppress points by setting nearby values to 0.
-  
+
     Grid Value Legend:
     -1 : Kept.
      0 : Empty or suppressed.
      1 : To be processed (converted to either kept or supressed).
-  
+
     NOTE: The NMS first rounds points to integers, so NMS distance might not
     be exactly dist_thresh. It also assumes points are within image boundaries.
-  
+
     Inputs
       in_corners - 3xN numpy array with corners [x_i, y_i, confidence_i]^T.
       H - Image height.
@@ -248,13 +248,13 @@ def run(self, img):
     heatmap = np.reshape(nodust, [Hc, Wc, self.cell, self.cell])
     heatmap = np.transpose(heatmap, [0, 2, 1, 3])
     heatmap = np.reshape(heatmap, [Hc*self.cell, Wc*self.cell])
-    xs, ys = np.where(heatmap >= self.conf_thresh) # Confidence threshold.
+    ys, xs = np.where(heatmap >= self.conf_thresh) # Confidence threshold.
     if len(xs) == 0:
       return np.zeros((3, 0)), None, None
     pts = np.zeros((3, len(xs))) # Populate point data sized 3xN.
-    pts[0, :] = ys
-    pts[1, :] = xs
-    pts[2, :] = heatmap[xs, ys]
+    pts[0, :] = xs
+    pts[1, :] = ys
+    pts[2, :] = heatmap[ys, xs]
     pts, _ = self.nms_fast(pts, H, W, dist_thresh=self.nms_dist) # Apply NMS.
     inds = np.argsort(pts[2,:])
     pts = pts[:,inds[::-1]] # Sort by confidence.