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diff --git a/lib/Python/Lib/PIL/ImageStat.py b/lib/Python/Lib/PIL/ImageStat.py
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+++ b/lib/Python/Lib/PIL/ImageStat.py
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+#
+# The Python Imaging Library.
+# $Id$
+#
+# global image statistics
+#
+# History:
+# 1996-04-05 fl   Created
+# 1997-05-21 fl   Added mask; added rms, var, stddev attributes
+# 1997-08-05 fl   Added median
+# 1998-07-05 hk   Fixed integer overflow error
+#
+# Notes:
+# This class shows how to implement delayed evaluation of attributes.
+# To get a certain value, simply access the corresponding attribute.
+# The __getattr__ dispatcher takes care of the rest.
+#
+# Copyright (c) Secret Labs AB 1997.
+# Copyright (c) Fredrik Lundh 1996-97.
+#
+# See the README file for information on usage and redistribution.
+#
+
+import math
+import operator
+from functools import reduce
+
+
+class Stat:
+
+    def __init__(self, image_or_list, mask=None):
+        try:
+            if mask:
+                self.h = image_or_list.histogram(mask)
+            else:
+                self.h = image_or_list.histogram()
+        except AttributeError:
+            self.h = image_or_list  # assume it to be a histogram list
+        if not isinstance(self.h, list):
+            raise TypeError("first argument must be image or list")
+        self.bands = list(range(len(self.h) // 256))
+
+    def __getattr__(self, id):
+        "Calculate missing attribute"
+        if id[:4] == "_get":
+            raise AttributeError(id)
+        # calculate missing attribute
+        v = getattr(self, "_get" + id)()
+        setattr(self, id, v)
+        return v
+
+    def _getextrema(self):
+        "Get min/max values for each band in the image"
+
+        def minmax(histogram):
+            n = 255
+            x = 0
+            for i in range(256):
+                if histogram[i]:
+                    n = min(n, i)
+                    x = max(x, i)
+            return n, x  # returns (255, 0) if there's no data in the histogram
+
+        v = []
+        for i in range(0, len(self.h), 256):
+            v.append(minmax(self.h[i:]))
+        return v
+
+    def _getcount(self):
+        "Get total number of pixels in each layer"
+
+        v = []
+        for i in range(0, len(self.h), 256):
+            v.append(reduce(operator.add, self.h[i:i+256]))
+        return v
+
+    def _getsum(self):
+        "Get sum of all pixels in each layer"
+
+        v = []
+        for i in range(0, len(self.h), 256):
+            sum = 0.0
+            for j in range(256):
+                sum += j * self.h[i + j]
+            v.append(sum)
+        return v
+
+    def _getsum2(self):
+        "Get squared sum of all pixels in each layer"
+
+        v = []
+        for i in range(0, len(self.h), 256):
+            sum2 = 0.0
+            for j in range(256):
+                sum2 += (j ** 2) * float(self.h[i + j])
+            v.append(sum2)
+        return v
+
+    def _getmean(self):
+        "Get average pixel level for each layer"
+
+        v = []
+        for i in self.bands:
+            v.append(self.sum[i] / self.count[i])
+        return v
+
+    def _getmedian(self):
+        "Get median pixel level for each layer"
+
+        v = []
+        for i in self.bands:
+            s = 0
+            l = self.count[i]//2
+            b = i * 256
+            for j in range(256):
+                s = s + self.h[b+j]
+                if s > l:
+                    break
+            v.append(j)
+        return v
+
+    def _getrms(self):
+        "Get RMS for each layer"
+
+        v = []
+        for i in self.bands:
+            v.append(math.sqrt(self.sum2[i] / self.count[i]))
+        return v
+
+    def _getvar(self):
+        "Get variance for each layer"
+
+        v = []
+        for i in self.bands:
+            n = self.count[i]
+            v.append((self.sum2[i]-(self.sum[i]**2.0)/n)/n)
+        return v
+
+    def _getstddev(self):
+        "Get standard deviation for each layer"
+
+        v = []
+        for i in self.bands:
+            v.append(math.sqrt(self.var[i]))
+        return v
+
+Global = Stat  # compatibility