#
# 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.
#
from __future__ import annotations
import math
from functools import cached_property
from . import Image
[docs]
class Stat:
[docs]
def __init__(
self, image_or_list: Image.Image | list[int], mask: Image.Image | None = None
) -> None:
"""
Calculate statistics for the given image. If a mask is included,
only the regions covered by that mask are included in the
statistics. You can also pass in a previously calculated histogram.
:param image: A PIL image, or a precalculated histogram.
.. note::
For a PIL image, calculations rely on the
:py:meth:`~PIL.Image.Image.histogram` method. The pixel counts are
grouped into 256 bins, even if the image has more than 8 bits per
channel. So ``I`` and ``F`` mode images have a maximum ``mean``,
``median`` and ``rms`` of 255, and cannot have an ``extrema`` maximum
of more than 255.
:param mask: An optional mask.
"""
if isinstance(image_or_list, Image.Image):
self.h = image_or_list.histogram(mask)
elif isinstance(image_or_list, list):
self.h = image_or_list
else:
msg = "first argument must be image or list" # type: ignore[unreachable]
raise TypeError(msg)
self.bands = list(range(len(self.h) // 256))
@cached_property
def extrema(self) -> list[tuple[int, int]]:
"""
Min/max values for each band in the image.
.. note::
This relies on the :py:meth:`~PIL.Image.Image.histogram` method, and
simply returns the low and high bins used. This is correct for
images with 8 bits per channel, but fails for other modes such as
``I`` or ``F``. Instead, use :py:meth:`~PIL.Image.Image.getextrema` to
return per-band extrema for the image. This is more correct and
efficient because, for non-8-bit modes, the histogram method uses
:py:meth:`~PIL.Image.Image.getextrema` to determine the bins used.
"""
def minmax(histogram: list[int]) -> tuple[int, int]:
res_min, res_max = 255, 0
for i in range(256):
if histogram[i]:
res_min = i
break
for i in range(255, -1, -1):
if histogram[i]:
res_max = i
break
return res_min, res_max
return [minmax(self.h[i:]) for i in range(0, len(self.h), 256)]
@cached_property
def count(self) -> list[int]:
"""Total number of pixels for each band in the image."""
return [sum(self.h[i : i + 256]) for i in range(0, len(self.h), 256)]
@cached_property
def sum(self) -> list[float]:
"""Sum of all pixels for each band in the image."""
v = []
for i in range(0, len(self.h), 256):
layer_sum = 0.0
for j in range(256):
layer_sum += j * self.h[i + j]
v.append(layer_sum)
return v
@cached_property
def sum2(self) -> list[float]:
"""Squared sum of all pixels for each band in the image."""
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
@cached_property
def mean(self) -> list[float]:
"""Average (arithmetic mean) pixel level for each band in the image."""
return [self.sum[i] / self.count[i] for i in self.bands]
@cached_property
def median(self) -> list[int]:
"""Median pixel level for each band in the image."""
v = []
for i in self.bands:
s = 0
half = self.count[i] // 2
b = i * 256
for j in range(256):
s = s + self.h[b + j]
if s > half:
break
v.append(j)
return v
@cached_property
def rms(self) -> list[float]:
"""RMS (root-mean-square) for each band in the image."""
return [math.sqrt(self.sum2[i] / self.count[i]) for i in self.bands]
@cached_property
def var(self) -> list[float]:
"""Variance for each band in the image."""
return [
(self.sum2[i] - (self.sum[i] ** 2.0) / self.count[i]) / self.count[i]
for i in self.bands
]
@cached_property
def stddev(self) -> list[float]:
"""Standard deviation for each band in the image."""
return [math.sqrt(self.var[i]) for i in self.bands]
Global = Stat # compatibility