pil-2009-raclette / PIL / ImageStat.py

#
# The Python Imaging Library.
# $Id: ImageStat.py 2134 2004-10-06 08:55:20Z fredrik $
#
# 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 Image
import operator, math

##
# 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 type(self.h) != type([]):
            raise TypeError, "first argument must be image or list"
        self.bands = 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 = 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
            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

if __name__ == "__main__":

    im = Image.open("Images/lena.ppm")

    st = Stat(im)

    print "extrema", st.extrema
    print "sum    ", st.sum
    print "mean   ", st.mean
    print "median ", st.median
    print "rms    ", st.rms
    print "sum2   ", st.sum2
    print "var    ", st.var
    print "stddev ", st.stddev