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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.
"""
# Functions which need the PIL
transpose, iscomplexobj, uint8, issubdtype, array)
except ImportError: import Image import ImageFilter
Image.frombytes = Image.fromstring
'imrotate', 'imresize', 'imshow', 'imfilter']
"and will be removed in 1.2.0.") """ 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.
This function is only available if Python Imaging Library (PIL) is installed.
Parameters ---------- 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.
Returns ------- img_array : uint8 ndarray The byte-scaled array.
Examples -------- >>> from scipy.misc import bytescale >>> img = np.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 > 255: raise ValueError("`high` should be less than or equal to 255.") if low < 0: raise ValueError("`low` should be greater than or equal to 0.") if high < low: raise ValueError("`high` should be greater than or equal to `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 - cmin) * scale + low return (bytedata.clip(low, high) + 0.5).astype(uint8)
"and will be removed in 1.2.0.\n" "Use ``imageio.imread`` instead.") """ Read an image from a file as an array.
This function is only available if Python Imaging Library (PIL) is installed.
Parameters ---------- 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. mode : str, optional Mode to convert image to, e.g. ``'RGB'``. See the Notes for more details.
Returns ------- imread : ndarray The array obtained by reading the image.
Notes ----- `imread` uses the Python Imaging Library (PIL) to read an image. The following notes are from the PIL documentation.
`mode` can be one of the following strings:
* 'L' (8-bit pixels, black and white) * 'P' (8-bit pixels, mapped to any other mode using a color palette) * 'RGB' (3x8-bit pixels, true color) * 'RGBA' (4x8-bit pixels, true color with transparency mask) * 'CMYK' (4x8-bit pixels, color separation) * 'YCbCr' (3x8-bit pixels, color video format) * 'I' (32-bit signed integer pixels) * 'F' (32-bit floating point pixels)
PIL also provides limited support for a few special modes, including 'LA' ('L' with alpha), 'RGBX' (true color with padding) and 'RGBa' (true color with premultiplied alpha).
When translating a color image to black and white (mode 'L', 'I' or 'F'), the library uses the ITU-R 601-2 luma transform::
L = R * 299/1000 + G * 587/1000 + B * 114/1000
When `flatten` is True, the image is converted using mode 'F'. When `mode` is not None and `flatten` is True, the image is first converted according to `mode`, and the result is then flattened using mode 'F'.
"""
im = Image.open(name) return fromimage(im, flatten=flatten, mode=mode)
"and will be removed in 1.2.0.\n" "Use ``imageio.imwrite`` instead.") """ Save an array as an image.
This function is only available if Python Imaging Library (PIL) is installed.
.. warning::
This function uses `bytescale` under the hood to rescale images to use the full (0, 255) range if ``mode`` is one of ``None, 'L', 'P', 'l'``. It will also cast data for 2-D images to ``uint32`` for ``mode=None`` (which is the default).
Parameters ---------- 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.
Examples -------- 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, channel_axis=2) if format is None: im.save(name) else: im.save(name, format) return
"and will be removed in 1.2.0.\n" "Use ``np.asarray(im)`` instead.") """ Return a copy of a PIL image as a numpy array.
This function is only available if Python Imaging Library (PIL) is installed.
Parameters ---------- im : PIL image Input image. flatten : bool If true, convert the output to grey-scale. mode : str, optional Mode to convert image to, e.g. ``'RGB'``. See the Notes of the `imread` docstring for more details.
Returns ------- 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 mode is not None: if mode != im.mode: im = im.convert(mode) elif im.mode == 'P': # Mode 'P' means there is an indexed "palette". If we leave the mode # as 'P', then when we do `a = array(im)` below, `a` will be a 2-D # containing the indices into the palette, and not a 3-D array # containing the RGB or RGBA values. if 'transparency' in im.info: im = im.convert('RGBA') else: im = im.convert('RGB')
if flatten: im = im.convert('F') elif im.mode == '1': # Workaround for crash in PIL. When im is 1-bit, the call array(im) # can cause a seg. fault, or generate garbage. See # https://github.com/scipy/scipy/issues/2138 and # https://github.com/python-pillow/Pillow/issues/350. # # This converts im from a 1-bit image to an 8-bit image. im = im.convert('L')
a = array(im) return a
"and will be removed in 1.2.0.\n" "Use Pillow's ``Image.fromarray`` directly instead.") mode=None, channel_axis=None): """Takes a numpy array and returns a PIL image.
This function is only available if Python Imaging Library (PIL) is installed.
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.
.. warning::
This function uses `bytescale` under the hood to rescale images to use the full (0, 255) range if ``mode`` is one of ``None, 'L', 'P', 'l'``. It will also cast data for 2-D images to ``uint32`` for ``mode=None`` (which is the default).
Notes ----- 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()) else: 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] else: ca = numpy.flatnonzero(asarray(shape) == 4) if len(ca): ca = ca[0] else: raise ValueError("Could not find channel dimension.") else: 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' else: 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
"and will be removed in 1.2.0.\n" "Use ``skimage.transform.rotate`` instead.") """ Rotate an image counter-clockwise by angle degrees.
This function is only available if Python Imaging Library (PIL) is installed.
.. warning::
This function uses `bytescale` under the hood to rescale images to use the full (0, 255) range if ``mode`` is one of ``None, 'L', 'P', 'l'``. It will also cast data for 2-D images to ``uint32`` for ``mode=None`` (which is the default).
Parameters ---------- arr : ndarray Input array of image to be rotated. angle : float The angle of rotation. interp : str, optional Interpolation
- 'nearest' : for nearest neighbor - 'bilinear' : for bilinear - 'lanczos' : for lanczos - 'cubic' : for bicubic - 'bicubic' : for bicubic
Returns ------- imrotate : ndarray The rotated array of image.
""" arr = asarray(arr) func = {'nearest': 0, 'lanczos': 1, 'bilinear': 2, 'bicubic': 3, 'cubic': 3} im = toimage(arr) im = im.rotate(angle, resample=func[interp]) return fromimage(im)
"and will be removed in 1.2.0.\n" "Use ``matplotlib.pyplot.imshow`` instead.") def imshow(arr): """ Simple showing of an image through an external viewer.
This function is only available if Python Imaging Library (PIL) is installed.
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.
.. warning::
This function uses `bytescale` under the hood to rescale images to use the full (0, 255) range if ``mode`` is one of ``None, 'L', 'P', 'l'``. It will also cast data for 2-D images to ``uint32`` for ``mode=None`` (which is the default).
Parameters ---------- arr : ndarray Array of image data to show.
Returns ------- None
Examples -------- >>> a = np.tile(np.arange(255), (255,1)) >>> from scipy import misc >>> misc.imshow(a)
""" im = toimage(arr) fnum, fname = tempfile.mkstemp('.png') try: im.save(fname) except: raise RuntimeError("Error saving temporary image data.")
import os os.close(fnum)
cmd = os.environ.get('SCIPY_PIL_IMAGE_VIEWER', 'see') status = os.system("%s %s" % (cmd, fname))
os.unlink(fname) if status != 0: raise RuntimeError('Could not execute image viewer.')
"and will be removed in 1.2.0.\n" "Use ``skimage.transform.resize`` instead.") """ Resize an image.
This function is only available if Python Imaging Library (PIL) is installed.
.. warning::
This function uses `bytescale` under the hood to rescale images to use the full (0, 255) range if ``mode`` is one of ``None, 'L', 'P', 'l'``. It will also cast data for 2-D images to ``uint32`` for ``mode=None`` (which is the default).
Parameters ---------- 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 (height, width).
interp : str, optional Interpolation to use for re-sizing ('nearest', 'lanczos', 'bilinear', 'bicubic' or 'cubic'). mode : str, optional The PIL image mode ('P', 'L', etc.) to convert `arr` before resizing. If ``mode=None`` (the default), 2-D images will be treated like ``mode='L'``, i.e. casting to long integer. For 3-D and 4-D arrays, `mode` will be set to ``'RGB'`` and ``'RGBA'`` respectively.
Returns ------- 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, numpy.signedinteger): percent = size / 100.0 size = tuple((array(im.size)*percent).astype(int)) elif issubdtype(type(size), numpy.floating): size = tuple((array(im.size)*size).astype(int)) else: size = (size[1], size[0]) func = {'nearest': 0, 'lanczos': 1, 'bilinear': 2, 'bicubic': 3, 'cubic': 3} imnew = im.resize(size, resample=func[interp]) return fromimage(imnew)
"and will be removed in 1.2.0.\n" "Use Pillow filtering functionality directly.") def imfilter(arr, ftype): """ Simple filtering of an image.
This function is only available if Python Imaging Library (PIL) is installed.
.. warning::
This function uses `bytescale` under the hood to rescale images to use the full (0, 255) range if ``mode`` is one of ``None, 'L', 'P', 'l'``. It will also cast data for 2-D images to ``uint32`` for ``mode=None`` (which is the default).
Parameters ---------- 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'.
Returns ------- imfilter : ndarray The array with filter applied.
Raises ------ ValueError *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])) |