Add normalization test and remove unnecessary clipping.

skimage/feature/_template.pyx

@@ -120,16 +120,10 @@ def match_template(np.ndarray[float, ndim=2, mode="c"] image,
             window_sqr_sum = sum_integral(integral_sqr, i, j, i_end, j_end)
             den = sqrt((window_sqr_sum - window_mean_sqr) * template_ssd)
 
-            # enforce some limits
-            if fabs(num) < den:
-                num /= den
-            elif fabs(num) < den * 1.125:
-                if num > 0:
-                    num = 1
-                else:
-                    num = -1
-            else:
+            if den == 0:
                 num = 0
+            else:
+                num /= den
             result[i, j] = num
     return result
 

skimage/feature/tests/test_template.py

@@ -34,6 +34,42 @@ def test_template():
         yield assert_close, xy, xy_target
 
 
+def test_normalization():
+    """Test that `match_template` gives the correct normalization.
+
+    Normalization gives 1 for a perfect match and -1 for an inverted-match.
+    This test adds positive and negative squares to a zero-array and matches
+    the array with a positive template.
+    """
+    n = 5
+    N = 20
+    ipos, jpos = (2, 3)
+    ineg, jneg = (12, 11)
+    image = np.zeros((N, N))
+    image[ipos:ipos + n, jpos:jpos + n] = 10
+    image[ineg:ineg + n, jneg:jneg + n] = -10
+
+    # white square with a black border
+    template = np.zeros((n+2, n+2))
+    template[1:1+n, 1:1+n] = 1
+
+    result = match_template(image, template)
+
+    # get the max and min results.
+    sorted_result = np.argsort(result.flat)
+    iflat_min = sorted_result[0]
+    iflat_max = sorted_result[-1]
+    min_result = np.unravel_index(iflat_min, (N, N))
+    max_result = np.unravel_index(iflat_max, (N, N))
+
+    # shift result by 1 because of template border
+    assert np.all((np.array(min_result) + 1) == (ineg, jneg))
+    assert np.all((np.array(max_result) + 1) == (ipos, jpos))
+
+    assert np.allclose(result.flat[iflat_min], -1)
+    assert np.allclose(result.flat[iflat_max], 1)
+
+
 if __name__ == "__main__":
     from numpy import testing
     testing.run_module_suite()