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曾与蒿藜同雨露,한때 잡초와 쑥과 함께 비와 이슬을 나누던 곳이 이제는 소나무와 삼나무와 함께 서리와 눈을 견뎌내고 있다.终随松柏到冰霜.かつては雑草やヨモギと共に雨や露を分かち合っていたが、今では松やヒノキと共に霜や雪に耐えている。曾与蒿藜同雨露,Once sharing rain and dew with weeds and wormwood, now enduring frost and snow with pines and cypresses.终随松柏到冰霜.曾与蒿藜同雨露한때 잡초와 쑥과 함께 비와 이슬을 나누던 곳이 이제는 소나무와 삼나무와 함께 서리와 눈을 견뎌내고 있다.,终随松柏到冰霜.譖セ荳手珍阯懷酔髮ィ髴イ�檎サ磯囂譚セ譟丞芦蜀ー髴�曾与蒿藜同雨露,鏇句笌钂胯棞鍚岄洦闇诧紝缁堥殢鏉炬煆鍒板啺闇�终随松柏到冰霜.曾与蒿藜同雨露,한때 잡초와 쑥과 함께 비와 이슬을 나누던 곳이 이제는 소나무와 삼나무와 함께 서리와 눈을 견뎌내고 있다.终随松柏到冰霜.曾与蒿藜同雨露,终随松柏到冰霜.
#
# 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
import functools
class Stat(object):
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(functools.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):
layerSum = 0.0
for j in range(256):
layerSum += j * self.h[i + j]
v.append(layerSum)
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