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A collection of image utilities using the Python Imaging Library (PIL).
Note that PIL is not a dependency of SciPy and this module is not
available on systems that don't have PIL installed.
from __future__ import division, print_function, absolute_import
# Functions which need the PIL
import numpy
import tempfile
from numpy import (amin, amax, ravel, asarray, cast, arange, ones, newaxis,
transpose, iscomplexobj, uint8, issubdtype, array)
from PIL import Image, ImageFilter
except ImportError:
import Image
import ImageFilter
if not hasattr(Image, 'frombytes'):
Image.frombytes = Image.fromstring
__all__ = ['fromimage', 'toimage', 'imsave', 'imread', 'bytescale',
'imrotate', 'imresize', 'imshow', 'imfilter']
# Returns a byte-scaled image
def bytescale(data, cmin=None, cmax=None, high=255, low=0):
Byte scales an array (image).
Byte scaling means converting the input image to uint8 dtype and scaling
the range to ``(low, high)`` (default 0-255).
If the input image already has dtype uint8, no scaling is done.
data : ndarray
PIL image data array.
cmin : scalar, optional
Bias scaling of small values. Default is ``data.min()``.
cmax : scalar, optional
Bias scaling of large values. Default is ``data.max()``.
high : scalar, optional
Scale max value to `high`. Default is 255.
low : scalar, optional
Scale min value to `low`. Default is 0.
img_array : uint8 ndarray
The byte-scaled array.
>>> from scipy.misc import bytescale
>>> img = array([[ 91.06794177, 3.39058326, 84.4221549 ],
... [ 73.88003259, 80.91433048, 4.88878881],
... [ 51.53875334, 34.45808177, 27.5873488 ]])
>>> bytescale(img)
array([[255, 0, 236],
[205, 225, 4],
[140, 90, 70]], dtype=uint8)
>>> bytescale(img, high=200, low=100)
array([[200, 100, 192],
[180, 188, 102],
[155, 135, 128]], dtype=uint8)
>>> bytescale(img, cmin=0, cmax=255)
array([[91, 3, 84],
[74, 81, 5],
[52, 34, 28]], dtype=uint8)
if data.dtype == uint8:
return data
if high < low:
raise ValueError("`high` should be larger than `low`.")
if cmin is None:
cmin = data.min()
if cmax is None:
cmax = data.max()
cscale = cmax - cmin
if cscale < 0:
raise ValueError("`cmax` should be larger than `cmin`.")
elif cscale == 0:
cscale = 1
scale = float(high - low) / cscale
bytedata = (data * 1.0 - cmin) * scale + 0.4999
bytedata[bytedata > high] = high
bytedata[bytedata < 0] = 0
return cast[uint8](bytedata) + cast[uint8](low)
def imread(name, flatten=0):
Read an image from a file as an array.
name : str or file object
The file name or file object to be read.
flatten : bool, optional
If True, flattens the color layers into a single gray-scale layer.
imread : ndarray
The array obtained by reading image from file `imfile`.
The image is flattened by calling convert('F') on
the resulting image object.
im =
return fromimage(im, flatten=flatten)
def imsave(name, arr, format=None):
Save an array as an image.
name : str or file object
Output file name or file object.
arr : ndarray, MxN or MxNx3 or MxNx4
Array containing image values. If the shape is ``MxN``, the array
represents a grey-level image. Shape ``MxNx3`` stores the red, green
and blue bands along the last dimension. An alpha layer may be
included, specified as the last colour band of an ``MxNx4`` array.
format : str
Image format. If omitted, the format to use is determined from the
file name extension. If a file object was used instead of a file name,
this parameter should always be used.
Construct an array of gradient intensity values and save to file:
>>> from scipy.misc import imsave
>>> x = np.zeros((255, 255))
>>> x = np.zeros((255, 255), dtype=np.uint8)
>>> x[:] = np.arange(255)
>>> imsave('gradient.png', x)
Construct an array with three colour bands (R, G, B) and store to file:
>>> rgb = np.zeros((255, 255, 3), dtype=np.uint8)
>>> rgb[..., 0] = np.arange(255)
>>> rgb[..., 1] = 55
>>> rgb[..., 2] = 1 - np.arange(255)
>>> imsave('rgb_gradient.png', rgb)
im = toimage(arr)
if format is None:
else:, format)
def fromimage(im, flatten=0):
Return a copy of a PIL image as a numpy array.
im : PIL image
Input image.
flatten : bool
If true, convert the output to grey-scale.
fromimage : ndarray
The different colour bands/channels are stored in the
third dimension, such that a grey-image is MxN, an
RGB-image MxNx3 and an RGBA-image MxNx4.
if not Image.isImageType(im):
raise TypeError("Input is not a PIL image.")
if flatten:
im = im.convert('F')
elif im.mode == '1':
# workaround for crash in PIL, see #1613.
return array(im)
_errstr = "Mode is unknown or incompatible with input array shape."
def toimage(arr, high=255, low=0, cmin=None, cmax=None, pal=None,
mode=None, channel_axis=None):
"""Takes a numpy array and returns a PIL image.
The mode of the PIL image depends on the array shape and the `pal` and
`mode` keywords.
For 2-D arrays, if `pal` is a valid (N,3) byte-array giving the RGB values
(from 0 to 255) then ``mode='P'``, otherwise ``mode='L'``, unless mode
is given as 'F' or 'I' in which case a float and/or integer array is made.
For 3-D arrays, the `channel_axis` argument tells which dimension of the
array holds the channel data.
For 3-D arrays if one of the dimensions is 3, the mode is 'RGB'
by default or 'YCbCr' if selected.
The numpy array must be either 2 dimensional or 3 dimensional.
data = asarray(arr)
if iscomplexobj(data):
raise ValueError("Cannot convert a complex-valued array.")
shape = list(data.shape)
valid = len(shape) == 2 or ((len(shape) == 3) and
((3 in shape) or (4 in shape)))
if not valid:
raise ValueError("'arr' does not have a suitable array shape for "
"any mode.")
if len(shape) == 2:
shape = (shape[1], shape[0]) # columns show up first
if mode == 'F':
data32 = data.astype(numpy.float32)
image = Image.frombytes(mode, shape, data32.tostring())
return image
if mode in [None, 'L', 'P']:
bytedata = bytescale(data, high=high, low=low,
cmin=cmin, cmax=cmax)
image = Image.frombytes('L', shape, bytedata.tostring())
if pal is not None:
image.putpalette(asarray(pal, dtype=uint8).tostring())
# Becomes a mode='P' automagically.
elif mode == 'P': # default gray-scale
pal = (arange(0, 256, 1, dtype=uint8)[:, newaxis] *
ones((3,), dtype=uint8)[newaxis, :])
image.putpalette(asarray(pal, dtype=uint8).tostring())
return image
if mode == '1': # high input gives threshold for 1
bytedata = (data > high)
image = Image.frombytes('1', shape, bytedata.tostring())
return image
if cmin is None:
cmin = amin(ravel(data))
if cmax is None:
cmax = amax(ravel(data))
data = (data*1.0 - cmin)*(high - low)/(cmax - cmin) + low
if mode == 'I':
data32 = data.astype(numpy.uint32)
image = Image.frombytes(mode, shape, data32.tostring())
raise ValueError(_errstr)
return image
# if here then 3-d array with a 3 or a 4 in the shape length.
# Check for 3 in datacube shape --- 'RGB' or 'YCbCr'
if channel_axis is None:
if (3 in shape):
ca = numpy.flatnonzero(asarray(shape) == 3)[0]
ca = numpy.flatnonzero(asarray(shape) == 4)
if len(ca):
ca = ca[0]
raise ValueError("Could not find channel dimension.")
ca = channel_axis
numch = shape[ca]
if numch not in [3, 4]:
raise ValueError("Channel axis dimension is not valid.")
bytedata = bytescale(data, high=high, low=low, cmin=cmin, cmax=cmax)
if ca == 2:
strdata = bytedata.tostring()
shape = (shape[1], shape[0])
elif ca == 1:
strdata = transpose(bytedata, (0, 2, 1)).tostring()
shape = (shape[2], shape[0])
elif ca == 0:
strdata = transpose(bytedata, (1, 2, 0)).tostring()
shape = (shape[2], shape[1])
if mode is None:
if numch == 3:
mode = 'RGB'
mode = 'RGBA'
if mode not in ['RGB', 'RGBA', 'YCbCr', 'CMYK']:
raise ValueError(_errstr)
if mode in ['RGB', 'YCbCr']:
if numch != 3:
raise ValueError("Invalid array shape for mode.")
if mode in ['RGBA', 'CMYK']:
if numch != 4:
raise ValueError("Invalid array shape for mode.")
# Here we know data and mode is correct
image = Image.frombytes(mode, shape, strdata)
return image
def imrotate(arr, angle, interp='bilinear'):
Rotate an image counter-clockwise by angle degrees.
arr : ndarray
Input array of image to be rotated.
angle : float
The angle of rotation.
interp : str, optional
- 'nearest' : for nearest neighbor
- 'bilinear' : for bilinear
- 'cubic' : cubic
- 'bicubic' : for bicubic
imrotate : ndarray
The rotated array of image.
arr = asarray(arr)
func = {'nearest': 0, 'bilinear': 2, 'bicubic': 3, 'cubic': 3}
im = toimage(arr)
im = im.rotate(angle, resample=func[interp])
return fromimage(im)
def imshow(arr):
Simple showing of an image through an external viewer.
Uses the image viewer specified by the environment variable
SCIPY_PIL_IMAGE_VIEWER, or if that is not defined then `see`,
to view a temporary file generated from array data.
arr : ndarray
Array of image data to show.
>>> a = np.tile(np.arange(255), (255,1))
>>> from scipy import misc
>>> misc.imshow(a)
im = toimage(arr)
fnum, fname = tempfile.mkstemp('.png')
raise RuntimeError("Error saving temporary image data.")
import os
cmd = os.environ.get('SCIPY_PIL_IMAGE_VIEWER', 'see')
status = os.system("%s %s" % (cmd, fname))
if status != 0:
raise RuntimeError('Could not execute image viewer.')
def imresize(arr, size, interp='bilinear', mode=None):
Resize an image.
arr : ndarray
The array of image to be resized.
size : int, float or tuple
* int - Percentage of current size.
* float - Fraction of current size.
* tuple - Size of the output image.
interp : str, optional
Interpolation to use for re-sizing ('nearest', 'bilinear', 'bicubic'
or 'cubic').
mode : str, optional
The PIL image mode ('P', 'L', etc.) to convert `arr` before resizing.
imresize : ndarray
The resized array of image.
See Also
toimage : Implicitly used to convert `arr` according to `mode`.
scipy.ndimage.zoom : More generic implementation that does not use PIL.
im = toimage(arr, mode=mode)
ts = type(size)
if issubdtype(ts, int):
percent = size / 100.0
size = tuple((array(im.size)*percent).astype(int))
elif issubdtype(type(size), float):
size = tuple((array(im.size)*size).astype(int))
size = (size[1], size[0])
func = {'nearest': 0, 'bilinear': 2, 'bicubic': 3, 'cubic': 3}
imnew = im.resize(size, resample=func[interp])
return fromimage(imnew)
def imfilter(arr, ftype):
Simple filtering of an image.
arr : ndarray
The array of Image in which the filter is to be applied.
ftype : str
The filter that has to be applied. Legal values are:
'blur', 'contour', 'detail', 'edge_enhance', 'edge_enhance_more',
'emboss', 'find_edges', 'smooth', 'smooth_more', 'sharpen'.
imfilter : ndarray
The array with filter applied.
*Unknown filter type.* If the filter you are trying
to apply is unsupported.
_tdict = {'blur': ImageFilter.BLUR,
'contour': ImageFilter.CONTOUR,
'detail': ImageFilter.DETAIL,
'edge_enhance': ImageFilter.EDGE_ENHANCE,
'edge_enhance_more': ImageFilter.EDGE_ENHANCE_MORE,
'emboss': ImageFilter.EMBOSS,
'find_edges': ImageFilter.FIND_EDGES,
'smooth': ImageFilter.SMOOTH,
'smooth_more': ImageFilter.SMOOTH_MORE,
'sharpen': ImageFilter.SHARPEN
im = toimage(arr)
if ftype not in _tdict:
raise ValueError("Unknown filter type.")
return fromimage(im.filter(_tdict[ftype]))
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