fix for compute_mean_level method (#58)

added code to convert float to int from the return value of min and max method

image_match/goldberg.py

@@ -392,11 +392,11 @@ def compute_mean_level(image, x_coords, y_coords, P=None):
         avg_grey = np.zeros((x_coords.shape[0], y_coords.shape[0]))
 
         for i, x in enumerate(x_coords):        # not the fastest implementation
-            lower_x_lim = max([x - P/2, 0])
-            upper_x_lim = min([lower_x_lim + P, image.shape[0]])
+            lower_x_lim = int(max([x - P/2, 0]))
+            upper_x_lim = int(min([lower_x_lim + P, image.shape[0]]))
             for j, y in enumerate(y_coords):
-                lower_y_lim = max([y - P/2, 0])
-                upper_y_lim = min([lower_y_lim + P, image.shape[1]])
+                lower_y_lim = int(max([y - P/2, 0]))
+                upper_y_lim = int(min([lower_y_lim + P, image.shape[1]]))
 
                 avg_grey[i, j] = np.mean(image[lower_x_lim:upper_x_lim,
                                         lower_y_lim:upper_y_lim])  # no smoothing here as in the paper