ENH: Automatically determine dynamic range in ssim if not specified.

skimage/measure/_ssim.py

@@ -5,6 +5,8 @@ __all__ = ['structural_similarity']
 import numpy as np
 from numpy.lib import stride_tricks
 
+from ..util.dtype import dtype_range
+
 def _as_windows(X, win_size=7, flatten_first_axis=True):
     """Re-stride an array to simulate a sliding window.
 
@@ -39,7 +41,7 @@ def _as_windows(X, win_size=7, flatten_first_axis=True):
     return windows
 
 
-def structural_similarity(X, Y, win_size=7, gradient=False, dynamic_range=255):
+def structural_similarity(X, Y, win_size=7, gradient=False, dynamic_range=None):
     """Compute the mean structural similarity index between two images.
 
     Parameters
@@ -49,12 +51,12 @@ def structural_similarity(X, Y, win_size=7, gradient=False, dynamic_range=255):
     win_size : int
         The side-length of the sliding window used in comparison.  Must
         be an odd value.
-    dynamic_range : int
-        Dynamic range of the input image (distance between minimum and
-        maximum possible values).  This should eventually be
-        auto-computed, but just specifying it manually for now.
     gradient : bool
         If True, also return the gradient.
+    dynamic_range : int
+        Dynamic range of the input image (distance between minimum and
+        maximum possible values).  By default, this is estimated from
+        the image data-type.
 
     Returns
     -------
@@ -81,6 +83,10 @@ def structural_similarity(X, Y, win_size=7, gradient=False, dynamic_range=255):
     if not (win_size % 2 == 1):
         raise ValueError('Window size must be odd.')
 
+    if dynamic_range is None:
+        dmin, dmax = dtype_range[X.dtype.type]
+        dynamic_range = dmax - dmin
+
     XW = _as_windows(X, win_size=win_size)
     YW = _as_windows(Y, win_size=win_size)
 

skimage/measure/tests/test_ssim.py

@@ -34,17 +34,32 @@ def test_ssim_image():
     assert(S1 < 0.3)
 
 def test_ssim_grad():
+    N = 30
+    X = np.random.random((N, N)) * 255
+    Y = np.random.random((N, N)) * 255
+
+    def func(Y):
+        return ssim(X, Y, dynamic_range=255)
+
+    def grad(Y):
+        return ssim(X, Y, dynamic_range=255, gradient=True)[1]
+
+    assert(np.all(opt.check_grad(func, grad, Y) < 0.05))
+
+def test_ssim_dtype():
     N = 30
     X = np.random.random((N, N))
     Y = np.random.random((N, N))
 
-    def func(Y):
-        return ssim(X, Y)
+    S1 = ssim(X, Y)
 
-    def grad(Y):
-        return ssim(X, Y, gradient=True)[1]
+    X = (X * 255).astype(np.uint8)
+    Y = (X * 255).astype(np.uint8)
 
-    assert(np.all(opt.check_grad(func, grad, Y) < 0.05))
+    S2 = ssim(X, Y)
+
+    assert S1 < 0.1
+    assert S2 < 0.1
 
 
 if __name__ == "__main__":