testing/compare: Fix image comparison RMS calculation.

mgiuca-google · mgiuca-google · commit 82ecfcff4bcf · 2013-02-27T11:49:00.000+11:00
The previous implementation did not compute the RMS error. It computed the RMS
in the difference of the number of colour components of each value. While this
computes 0 for equal images, it is incorrect in general. In particular, it does
not detect differences in images with the same pixels in different places. It
also cannot distinguish small changes in the colour of a pixel from large ones.
diff --git a/lib/matplotlib/testing/compare.py b/lib/matplotlib/testing/compare.py
@@ -251,37 +251,21 @@ def crop_to_same(actual_path, actual_image, expected_path, expected_image):
    return actual_image, expected_image
 
 def calculate_rms(expectedImage, actualImage):
-   # compare the resulting image histogram functions
-   expected_version = version.LooseVersion("1.6")
-   found_version = version.LooseVersion(np.__version__)
+   # calculate the per-pixel errors, then compute the root mean square error
+   num_values = reduce(operator.mul, expectedImage.shape)
+   absDiffImage = abs(expectedImage - actualImage)
 
    # On Numpy 1.6, we can use bincount with minlength, which is much faster than
    # using histogram
+   expected_version = version.LooseVersion("1.6")
+   found_version = version.LooseVersion(np.__version__)
    if found_version >= expected_version:
-      rms = 0
-
-      for i in xrange(0, 3):
-         h1p = expectedImage[:,:,i]
-         h2p = actualImage[:,:,i]
-
-         h1h = np.bincount(h1p.ravel(), minlength=256)
-         h2h = np.bincount(h2p.ravel(), minlength=256)
-
-         rms += np.sum(np.power((h1h-h2h), 2))
+      histogram = np.bincount(absDiffImage.ravel(), minlength=256)
    else:
-      rms = 0
-      bins = np.arange(257)
-
-      for i in xrange(0, 3):
-         h1p = expectedImage[:,:,i]
-         h2p = actualImage[:,:,i]
-
-         h1h = np.histogram(h1p, bins=bins)[0]
-         h2h = np.histogram(h2p, bins=bins)[0]
+      histogram = np.histogram(absDiffImage, bins=np.arange(257))[0]
 
-         rms += np.sum(np.power((h1h-h2h), 2))
-
-   rms = np.sqrt(rms / (256 * 3))
+   sumOfSquares = sum(count*(i**2) for i, count in enumerate(histogram))
+   rms = np.sqrt(float(sumOfSquares) / num_values)
 
    return rms
 
@@ -321,9 +305,10 @@ def compare_images( expected, actual, tol, in_decorator=False ):
 
    actualImage, expectedImage = crop_to_same(actual, actualImage, expected, expectedImage)
 
-   # compare the resulting image histogram functions
-   expected_version = version.LooseVersion("1.6")
-   found_version = version.LooseVersion(np.__version__)
+   # convert to signed integers, so that the images can be subtracted without
+   # overflow
+   expectedImage = expectedImage.astype(np.int32)
+   actualImage = actualImage.astype(np.int32)
 
    rms = calculate_rms(expectedImage, actualImage)
 
