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d50445ff30
Similar to the recent adoption of Black. isort is a Python utility to sort imports alphabetically and automatically separate into sections. By using isort, contributors can quickly and automatically conform to the projects style without thinking. Just let the tool do it. Uses the configuration recommended by the Black to avoid conflicts of style. Rewrite TestImageQt.test_deprecated to no rely on import order.
148 lines
3.8 KiB
Python
148 lines
3.8 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 functools
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import math
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import operator
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class Stat(object):
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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 not isinstance(self.h, list):
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raise TypeError("first argument must be image or list")
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self.bands = list(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(functools.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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layerSum = 0.0
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for j in range(256):
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layerSum += j * self.h[i + j]
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v.append(layerSum)
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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 += (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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half = 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 > half:
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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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