Pillow/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.
#
from . import Image
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import operator, math
from functools import reduce
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##
# The <b>ImageStat</b> module calculates global statistics for an
# image, or a region of an image.
##
##
# Calculate statistics for the given image. If a mask is included,
# only the regions covered by that mask are included in the
# statistics.
class Stat:
"Get image or feature statistics"
##
# Create a statistics object.
#
# @def __init__(image, mask=None)
# @param image A PIL image, or a precalculate histogram.
# @param mask An optional mask.
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))
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def __getattr__(self, id):
"Calculate missing attribute"
if id[:4] == "_get":
raise AttributeError(id)
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# 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 = 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 = 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
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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