Add the perceptual difference metric to the rebaseline server

gm/rebaseline_server/imagediffdb.py

Index: gm/rebaseline_server/imagediffdb.py
diff --git a/gm/rebaseline_server/imagediffdb.py b/gm/rebaseline_server/imagediffdb.py
index 936301e1cdef393e736ad852283768482037a63c..70e03cb260ec6a7487867772441f075a81d431f0 100644
--- a/gm/rebaseline_server/imagediffdb.py
+++ b/gm/rebaseline_server/imagediffdb.py
@@ -10,10 +10,13 @@ Calulate differences between image pairs, and store them in a database.
 """
 
 import contextlib
+import csv
 import logging
 import os
 import re
 import shutil
+import sys
+import tempfile
 import urllib
 try:
   from PIL import Image, ImageChops
@@ -21,6 +24,15 @@ except ImportError:
   raise ImportError('Requires PIL to be installed; see '
                     + 'http://www.pythonware.com/products/pil/')
 
+# Set the PYTHONPATH to include the tools directory.
+sys.path.append(
+    os.path.join(
+        os.path.dirname(os.path.realpath(__file__)), os.pardir, os.pardir,
+                        'tools'))
+import find_run_binary
+
+SKPDIFF_BINARY_NAME = 'skpdiff'
+
 DEFAULT_IMAGE_SUFFIX = '.png'
 DEFAULT_IMAGES_SUBDIR = 'images'
 
@@ -99,6 +111,31 @@ class DiffRecord(object):
     whitediff_image = (graydiff_image.point(lambda p: p > 0 and VALUES_PER_BAND)
                                      .convert('1', dither=Image.NONE))
 
+    # Calculate the perceptual difference percentage.
+    skpdiff_csv_dir = tempfile.mkdtemp()
+    try:
+      skpdiff_csv_output = os.path.join(skpdiff_csv_dir, 'skpdiff-output.csv')
+      skpdiff_binary = find_run_binary.find_path_to_program(SKPDIFF_BINARY_NAME)
+      expected_img = os.path.join(storage_root, expected_images_subdir,
+                                  str(expected_image_locator) + image_suffix)
+      actual_img = os.path.join(storage_root, actual_images_subdir,
+                                str(actual_image_locator) + image_suffix)
+      find_run_binary.run_command(
+          [skpdiff_binary, '-p', expected_img, actual_img,
+           '--csv', skpdiff_csv_output, '-d', 'perceptual'])
+      with contextlib.closing(open(skpdiff_csv_output)) as csv_file:
+        for row in csv.DictReader(csv_file):
+          perceptual_similarity = float(row[' perceptual'].strip())
+          if perceptual_similarity == -1:

[epoger, 2014/02/11 17:48:50]

Do you think we should more broadly check for anything outside of normal range? 
(which I guess is from 0 to 1)

[rmistry, 2014/02/11 17:59:56]

SGTM. Done.

+            # skpdiff outputs -1 if the images are different sizes, treat them
+            # as having 0% perceptual similarity.
+            perceptual_similarity = 0
+          # skpdiff returns the perceptual similarity, convert it to get the
+          # perceptual difference percentage.
+          self._perceptual_difference = 100 - (perceptual_similarity * 100)
+    finally:
+      shutil.rmtree(skpdiff_csv_dir)
+
     # Final touches on diff_image: use whitediff_image as an alpha mask.
     # Unchanged pixels are transparent; differing pixels are opaque.
     diff_image.putalpha(whitediff_image)
@@ -128,6 +165,10 @@ class DiffRecord(object):
     return ((float(self._num_pixels_differing) * 100) /
             (self._width * self._height))
 
+  def get_perceptual_difference(self):
+    """Returns the perceptual difference percentage."""
+    return self._perceptual_difference
+
   def get_weighted_diff_measure(self):
     """Returns a weighted measure of image diffs, as a float between 0 and 100
     (inclusive)."""