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