#
# 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 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
