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"""
The image module supports basic image loading, rescaling and display
operations.
"""
from __future__ import division
import os, warnings
import numpy as np
from numpy import ma
from matplotlib import rcParams
import matplotlib.artist as martist
from matplotlib.artist import allow_rasterization
import matplotlib.colors as mcolors
import matplotlib.cm as cm
import matplotlib.cbook as cbook
# For clarity, names from _image are given explicitly in this module:
import matplotlib._image as _image
import matplotlib._png as _png
# For user convenience, the names from _image are also imported into
# the image namespace:
from matplotlib._image import *
from matplotlib.transforms import BboxBase, Bbox
import matplotlib.transforms as mtransforms
class _AxesImageBase(martist.Artist, cm.ScalarMappable):
zorder = 0
# map interpolation strings to module constants
_interpd = {
'nearest' : _image.NEAREST,
'bilinear' : _image.BILINEAR,
'bicubic' : _image.BICUBIC,
'spline16' : _image.SPLINE16,
'spline36' : _image.SPLINE36,
'hanning' : _image.HANNING,
'hamming' : _image.HAMMING,
'hermite' : _image.HERMITE,
'kaiser' : _image.KAISER,
'quadric' : _image.QUADRIC,
'catrom' : _image.CATROM,
'gaussian' : _image.GAUSSIAN,
'bessel' : _image.BESSEL,
'mitchell' : _image.MITCHELL,
'sinc' : _image.SINC,
'lanczos' : _image.LANCZOS,
'blackman' : _image.BLACKMAN,
}
# reverse interp dict
_interpdr = dict([ (v,k) for k,v in _interpd.items()])
interpnames = _interpd.keys()
def __str__(self):
return "AxesImage(%g,%g;%gx%g)" % tuple(self.axes.bbox.bounds)
def __init__(self, ax,
cmap = None,
norm = None,
interpolation=None,
origin=None,
filternorm=1,
filterrad=4.0,
resample = False,
**kwargs
):
"""
interpolation and cmap default to their rc settings
cmap is a colors.Colormap instance
norm is a colors.Normalize instance to map luminance to 0-1
extent is data axes (left, right, bottom, top) for making image plots
registered with data plots. Default is to label the pixel
centers with the zero-based row and column indices.
Additional kwargs are matplotlib.artist properties
"""
martist.Artist.__init__(self)
cm.ScalarMappable.__init__(self, norm, cmap)
if origin is None: origin = rcParams['image.origin']
self.origin = origin
self.set_filternorm(filternorm)
self.set_filterrad(filterrad)
self._filterrad = filterrad
self.set_interpolation(interpolation)
self.set_resample(resample)
self.axes = ax
self._imcache = None
# this is an expetimental attribute, if True, unsampled image
# will be drawn using the affine transform that are
# appropriately skewed so that the given postition
# corresponds to the actual position in the coordinate. -JJL
self._image_skew_coordinate = None
self.update(kwargs)
def get_size(self):
'Get the numrows, numcols of the input image'
if self._A is None:
raise RuntimeError('You must first set the image array')
return self._A.shape[:2]
def set_alpha(self, alpha):
"""
Set the alpha value used for blending - not supported on
all backends
ACCEPTS: float
"""
martist.Artist.set_alpha(self, alpha)
self._imcache = None
def changed(self):
"""
Call this whenever the mappable is changed so observers can
update state
"""
self._imcache = None
self._rgbacache = None
cm.ScalarMappable.changed(self)
def make_image(self, magnification=1.0):
raise RuntimeError('The make_image method must be overridden.')
def _get_unsampled_image(self, A, image_extents, viewlim):
"""
convert numpy array A with given extents ([x1, x2, y1, y2] in
data coordinate) into the Image, given the vielim (should be a
bbox instance). Image will be clipped if the extents is
significantly larger than the viewlim.
"""
xmin, xmax, ymin, ymax = image_extents
dxintv = xmax-xmin
dyintv = ymax-ymin
# the viewport scale factor
if viewlim.width == 0.0 and dxintv == 0.0:
sx = 1.0
else:
sx = dxintv/viewlim.width
if viewlim.height == 0.0 and dyintv == 0.0:
sy = 1.0
else:
sy = dyintv/viewlim.height
numrows, numcols = A.shape[:2]
if sx > 2:
x0 = (viewlim.x0-xmin)/dxintv * numcols
ix0 = max(0, int(x0 - self._filterrad))
x1 = (viewlim.x1-xmin)/dxintv * numcols
ix1 = min(numcols, int(x1 + self._filterrad))
xslice = slice(ix0, ix1)
xmin_old = xmin
xmin = xmin_old + ix0*dxintv/numcols
xmax = xmin_old + ix1*dxintv/numcols
dxintv = xmax - xmin
sx = dxintv/viewlim.width
else:
xslice = slice(0, numcols)
if sy > 2:
y0 = (viewlim.y0-ymin)/dyintv * numrows
iy0 = max(0, int(y0 - self._filterrad))
y1 = (viewlim.y1-ymin)/dyintv * numrows
iy1 = min(numrows, int(y1 + self._filterrad))
if self.origin == 'upper':
yslice = slice(numrows-iy1, numrows-iy0)
else:
yslice = slice(iy0, iy1)
ymin_old = ymin
ymin = ymin_old + iy0*dyintv/numrows
ymax = ymin_old + iy1*dyintv/numrows
dyintv = ymax - ymin
sy = dyintv/viewlim.height
else:
yslice = slice(0, numrows)
if xslice != self._oldxslice or yslice != self._oldyslice:
self._imcache = None
self._oldxslice = xslice
self._oldyslice = yslice
if self._imcache is None:
if self._A.dtype == np.uint8 and len(self._A.shape) == 3:
im = _image.frombyte(self._A[yslice,xslice,:], 0)
im.is_grayscale = False
else:
if self._rgbacache is None:
x = self.to_rgba(self._A, self._alpha)
self._rgbacache = x
else:
x = self._rgbacache
im = _image.fromarray(x[yslice,xslice], 0)
if len(self._A.shape) == 2:
im.is_grayscale = self.cmap.is_gray()
else:
im.is_grayscale = False
self._imcache = im
if self.origin=='upper':
im.flipud_in()
else:
im = self._imcache
return im, xmin, ymin, dxintv, dyintv, sx, sy
@staticmethod
def _get_rotate_and_skew_transform(x1, y1, x2, y2, x3, y3):
"""
Retuen a transform that does
(x1, y1) -> (x1, y1)
(x2, y2) -> (x2, y2)
(x2, y1) -> (x3, y3)
It was intended to derive a skew transform that preserve the
lower-left corner (x1, y1) and top-right corner(x2,y2), but
change the the lower-right-corner(x2, y1) to a new position
(x3, y3).
"""
tr1 = mtransforms.Affine2D()
tr1.translate(-x1, -y1)
x2a, y2a = tr1.transform_point((x2, y2))
x3a, y3a = tr1.transform_point((x3, y3))
inv_mat = 1./(x2a*y3a-y2a*x3a) * np.mat([[y3a, -y2a],[-x3a, x2a]])
a, b = (inv_mat * np.mat([[x2a], [x2a]])).flat
c, d = (inv_mat * np.mat([[y2a], [0]])).flat
tr2 = mtransforms.Affine2D.from_values(a, c, b, d, 0, 0)
tr = (tr1 + tr2 + mtransforms.Affine2D().translate(x1, y1)).inverted().get_affine()
return tr
def _draw_unsampled_image(self, renderer, gc):
"""
draw unsampled image. The renderer should support a draw_image method
with scale parameter.
"""
trans = self.get_transform() #axes.transData
# convert the coordinates to the intermediate coordinate (ic).
# The transformation from the ic to the canvas is a pure
# affine transfor.
# A straight-forward way is to use the non-affine part of the
# original transform for conversion to the ic.
# firs, convert the image extent to the ic
x_llc, x_trc, y_llc, y_trc = self.get_extent()
xy = trans.transform_non_affine(np.array([(x_llc, y_llc),
(x_trc, y_trc)]))
_xx1, _yy1 = xy[0]
_xx2, _yy2 = xy[1]
extent_in_ic = _xx1, _xx2, _yy1, _yy2
# define trans_ic_to_canvas : unless _image_skew_coordinate is
# set, it is simply a affine part of the original transform.
if self._image_skew_coordinate:
# skew the image when required.
x_lrc, y_lrc = self._image_skew_coordinate
xy2 = trans.transform_non_affine(np.array([(x_lrc, y_lrc)]))
_xx3, _yy3 = xy2[0]
tr_rotate_skew = self._get_rotate_and_skew_transform(_xx1, _yy1,
_xx2, _yy2,
_xx3, _yy3)
trans_ic_to_canvas = tr_rotate_skew+trans.get_affine()
else:
trans_ic_to_canvas = trans.get_affine()
# Now, viewLim in the ic. It can be roated and can be
# skewed. Make it big enough.
x1, y1, x2, y2 = self.axes.bbox.extents
trans_canvas_to_ic = trans_ic_to_canvas.inverted()
xy_ = trans_canvas_to_ic.transform(np.array([(x1, y1),
(x2, y1),
(x2, y2),
(x1, y2)]))
x1_, x2_ = min(xy_[:,0]), max(xy_[:,0])
y1_, y2_ = min(xy_[:,1]), max(xy_[:,1])
viewLim_in_ic = Bbox.from_extents(x1_, y1_, x2_, y2_)
# get the image, sliced if necessary. This is done in the ic.
im, xmin, ymin, dxintv, dyintv, sx, sy = \
self._get_unsampled_image(self._A, extent_in_ic, viewLim_in_ic)
if im is None: return # I'm not if this check is required. -JJL
fc = self.axes.patch.get_facecolor()
bg = mcolors.colorConverter.to_rgba(fc, 0)
im.set_bg( *bg)
# image input dimensions
im.reset_matrix()
numrows, numcols = im.get_size()
im.resize(numcols, numrows) # just to create im.bufOut that
# is required by backends. There
# may be better solution -JJL
im._url = self.get_url()
renderer.draw_image(gc, xmin, ymin, im, dxintv, dyintv,
trans_ic_to_canvas)
def _check_unsampled_image(self, renderer):
"""
return True if the image is better to be drawn unsampled.
The derived class needs to override it.
"""
return False
@allow_rasterization
def draw(self, renderer, *args, **kwargs):
if not self.get_visible(): return
if (self.axes.get_xscale() != 'linear' or
self.axes.get_yscale() != 'linear'):
warnings.warn("Images are not supported on non-linear axes.")
l, b, widthDisplay, heightDisplay = self.axes.bbox.bounds
gc = renderer.new_gc()
gc.set_clip_rectangle(self.axes.bbox.frozen())
gc.set_clip_path(self.get_clip_path())
if self._check_unsampled_image(renderer):
self._draw_unsampled_image(renderer, gc)
else:
if self._image_skew_coordinate is not None:
warnings.warn("Image will not be shown correctly with this backend.")
im = self.make_image(renderer.get_image_magnification())
if im is None:
return
im._url = self.get_url()
renderer.draw_image(gc, l, b, im)
gc.restore()
def contains(self, mouseevent):
"""
Test whether the mouse event occured within the image.
"""
if callable(self._contains): return self._contains(self,mouseevent)
# TODO: make sure this is consistent with patch and patch
# collection on nonlinear transformed coordinates.
# TODO: consider returning image coordinates (shouldn't
# be too difficult given that the image is rectilinear
x, y = mouseevent.xdata, mouseevent.ydata
xmin, xmax, ymin, ymax = self.get_extent()
if xmin > xmax:
xmin,xmax = xmax,xmin
if ymin > ymax:
ymin,ymax = ymax,ymin
#print x, y, xmin, xmax, ymin, ymax
if x is not None and y is not None:
inside = x>=xmin and x<=xmax and y>=ymin and y<=ymax
else:
inside = False
return inside,{}
def write_png(self, fname, noscale=False):
"""Write the image to png file with fname"""
im = self.make_image()
if im is None:
return
if noscale:
numrows, numcols = im.get_size()
im.reset_matrix()
im.set_interpolation(0)
im.resize(numcols, numrows)
im.flipud_out()
rows, cols, buffer = im.as_rgba_str()
_png.write_png(buffer, cols, rows, fname)
def set_data(self, A):
"""
Set the image array
ACCEPTS: numpy/PIL Image A
"""
# check if data is PIL Image without importing Image
if hasattr(A,'getpixel'):
self._A = pil_to_array(A)
else:
self._A = cbook.safe_masked_invalid(A)
if self._A.dtype != np.uint8 and not np.can_cast(self._A.dtype, np.float):
raise TypeError("Image data can not convert to float")
if (self._A.ndim not in (2, 3) or
(self._A.ndim == 3 and self._A.shape[-1] not in (3, 4))):
raise TypeError("Invalid dimensions for image data")
self._imcache =None
self._rgbacache = None
self._oldxslice = None
self._oldyslice = None
def set_array(self, A):
"""
retained for backwards compatibility - use set_data instead
ACCEPTS: numpy array A or PIL Image"""
# This also needs to be here to override the inherited
# cm.ScalarMappable.set_array method so it is not invoked
# by mistake.
self.set_data(A)
def get_interpolation(self):
"""
Return the interpolation method the image uses when resizing.
One of 'nearest', 'bilinear', 'bicubic', 'spline16', 'spline36', 'hanning',
'hamming', 'hermite', 'kaiser', 'quadric', 'catrom', 'gaussian',
'bessel', 'mitchell', 'sinc', 'lanczos',
"""
return self._interpolation
def set_interpolation(self, s):
"""
Set the interpolation method the image uses when resizing.
ACCEPTS: ['nearest' | 'bilinear' | 'bicubic' | 'spline16' |
'spline36' | 'hanning' | 'hamming' | 'hermite' | 'kaiser' |
'quadric' | 'catrom' | 'gaussian' | 'bessel' | 'mitchell' |
'sinc' | 'lanczos' | ]
"""
if s is None: s = rcParams['image.interpolation']
s = s.lower()
if s not in self._interpd:
raise ValueError('Illegal interpolation string')
self._interpolation = s
def set_resample(self, v):
"""
set whether or not image resampling is used
ACCEPTS: True|False
"""
if v is None: v = rcParams['image.resample']
self._resample = v
def get_resample(self):
'return the image resample boolean'
return self._resample
def set_filternorm(self, filternorm):
"""
Set whether the resize filter norms the weights -- see
help for imshow
ACCEPTS: 0 or 1
"""
if filternorm:
self._filternorm = 1
else:
self._filternorm = 0
def get_filternorm(self):
'return the filternorm setting'
return self._filternorm
def set_filterrad(self, filterrad):
"""
Set the resize filter radius only applicable to some
interpolation schemes -- see help for imshow
ACCEPTS: positive float
"""
r = float(filterrad)
assert(r>0)
self._filterrad = r
def get_filterrad(self):
'return the filterrad setting'
return self._filterrad
class AxesImage(_AxesImageBase):
def __str__(self):
return "AxesImage(%g,%g;%gx%g)" % tuple(self.axes.bbox.bounds)
def __init__(self, ax,
cmap = None,
norm = None,
interpolation=None,
origin=None,
extent=None,
filternorm=1,
filterrad=4.0,
resample = False,
**kwargs
):
"""
interpolation and cmap default to their rc settings
cmap is a colors.Colormap instance
norm is a colors.Normalize instance to map luminance to 0-1
extent is data axes (left, right, bottom, top) for making image plots
registered with data plots. Default is to label the pixel
centers with the zero-based row and column indices.
Additional kwargs are matplotlib.artist properties
"""
self._extent = extent
_AxesImageBase.__init__(self, ax,
cmap = cmap,
norm = norm,
interpolation=interpolation,
origin=origin,
filternorm=filternorm,
filterrad=filterrad,
resample = resample,
**kwargs
)
def make_image(self, magnification=1.0):
if self._A is None:
raise RuntimeError('You must first set the image array or the image attribute')
# image is created in the canvas coordinate.
x1, x2, y1, y2 = self.get_extent()
trans = self.get_transform()
xy = trans.transform(np.array([(x1, y1),
(x2, y2),
]))
_x1, _y1 = xy[0]
_x2, _y2 = xy[1]
transformed_viewLim = mtransforms.TransformedBbox(self.axes.viewLim,
trans)
im, xmin, ymin, dxintv, dyintv, sx, sy = \
self._get_unsampled_image(self._A, [_x1, _x2, _y1, _y2],
transformed_viewLim)
fc = self.axes.patch.get_facecolor()
bg = mcolors.colorConverter.to_rgba(fc, 0)
im.set_bg( *bg)
# image input dimensions
im.reset_matrix()
numrows, numcols = im.get_size()
if numrows < 1 or numcols < 1: # out of range
return None
im.set_interpolation(self._interpd[self._interpolation])
im.set_resample(self._resample)
# the viewport translation
if dxintv == 0.0:
tx = 0.0
else:
tx = (xmin-transformed_viewLim.x0)/dxintv * numcols
if dyintv == 0.0:
ty = 0.0
else:
ty = (ymin-transformed_viewLim.y0)/dyintv * numrows
im.apply_translation(tx, ty)
l, b, r, t = self.axes.bbox.extents
widthDisplay = (round(r*magnification) + 0.5) - (round(l*magnification) - 0.5)
heightDisplay = (round(t*magnification) + 0.5) - (round(b*magnification) - 0.5)
# resize viewport to display
rx = widthDisplay / numcols
ry = heightDisplay / numrows
im.apply_scaling(rx*sx, ry*sy)
im.resize(int(widthDisplay+0.5), int(heightDisplay+0.5),
norm=self._filternorm, radius=self._filterrad)
return im
def _check_unsampled_image(self, renderer):
"""
return True if the image is better to be drawn unsampled.
"""
if renderer.option_scale_image() and self.get_interpolation() == "nearest":
return True
else:
return False
def set_extent(self, extent):
"""
extent is data axes (left, right, bottom, top) for making image plots
This updates ax.dataLim, and, if autoscaling, sets viewLim
to tightly fit the image, regardless of dataLim. Autoscaling
state is not changed, so following this with ax.autoscale_view
will redo the autoscaling in accord with dataLim.
"""
self._extent = extent
xmin, xmax, ymin, ymax = extent
corners = (xmin, ymin), (xmax, ymax)
self.axes.update_datalim(corners)
if self.axes._autoscaleXon:
self.axes.set_xlim((xmin, xmax), auto=None)
if self.axes._autoscaleYon:
self.axes.set_ylim((ymin, ymax), auto=None)
def get_extent(self):
'get the image extent: left, right, bottom, top'
if self._extent is not None:
return self._extent
else:
sz = self.get_size()
#print 'sz', sz
numrows, numcols = sz
if self.origin == 'upper':
return (-0.5, numcols-0.5, numrows-0.5, -0.5)
else:
return (-0.5, numcols-0.5, -0.5, numrows-0.5)
class NonUniformImage(AxesImage):
def __init__(self, ax, **kwargs):
"""
kwargs are identical to those for AxesImage, except
that 'interpolation' defaults to 'nearest'
"""
interp = kwargs.pop('interpolation', 'nearest')
AxesImage.__init__(self, ax,
**kwargs)
self.set_interpolation(interp)
def _check_unsampled_image(self, renderer):
"""
return False. Do not use unsampled image.
"""
return False
def make_image(self, magnification=1.0):
if self._A is None:
raise RuntimeError('You must first set the image array')
x0, y0, v_width, v_height = self.axes.viewLim.bounds
l, b, r, t = self.axes.bbox.extents
width = (round(r) + 0.5) - (round(l) - 0.5)
height = (round(t) + 0.5) - (round(b) - 0.5)
width *= magnification
height *= magnification
im = _image.pcolor(self._Ax, self._Ay, self._A,
height, width,
(x0, x0+v_width, y0, y0+v_height),
self._interpd[self._interpolation])
fc = self.axes.patch.get_facecolor()
bg = mcolors.colorConverter.to_rgba(fc, 0)
im.set_bg(*bg)
im.is_grayscale = self.is_grayscale
return im
def set_data(self, x, y, A):
"""
Set the grid for the pixel centers, and the pixel values.
*x* and *y* are 1-D ndarrays of lengths N and M, respectively,
specifying pixel centers
*A* is an (M,N) ndarray or masked array of values to be
colormapped, or a (M,N,3) RGB array, or a (M,N,4) RGBA
array.
"""
x = np.asarray(x,np.float32)
y = np.asarray(y,np.float32)
A = cbook.safe_masked_invalid(A)
if len(x.shape) != 1 or len(y.shape) != 1\
or A.shape[0:2] != (y.shape[0], x.shape[0]):
raise TypeError("Axes don't match array shape")
if len(A.shape) not in [2, 3]:
raise TypeError("Can only plot 2D or 3D data")
if len(A.shape) == 3 and A.shape[2] not in [1, 3, 4]:
raise TypeError("3D arrays must have three (RGB) or four (RGBA) color components")
if len(A.shape) == 3 and A.shape[2] == 1:
A.shape = A.shape[0:2]
if len(A.shape) == 2:
if A.dtype != np.uint8:
A = (self.cmap(self.norm(A))*255).astype(np.uint8)
self.is_grayscale = self.cmap.is_gray()
else:
A = np.repeat(A[:,:,np.newaxis], 4, 2)
A[:,:,3] = 255
self.is_grayscale = True
else:
if A.dtype != np.uint8:
A = (255*A).astype(np.uint8)
if A.shape[2] == 3:
B = zeros(tuple(list(A.shape[0:2]) + [4]), np.uint8)
B[:,:,0:3] = A
B[:,:,3] = 255
A = B
self.is_grayscale = False
self._A = A
self._Ax = x
self._Ay = y
self._imcache = None
# I am adding this in accor with _AxesImageBase.set_data --
# examples/pylab_examples/image_nonuniform.py was breaking on
# the call to _get_unsampled_image when the oldxslice attr was
# accessed - JDH 3/3/2010
self._oldxslice = None
self._oldyslice = None
def set_array(self, *args):
raise NotImplementedError('Method not supported')
def set_interpolation(self, s):
if s != None and not s in ('nearest','bilinear'):
raise NotImplementedError('Only nearest neighbor and bilinear interpolations are supported')
AxesImage.set_interpolation(self, s)
def get_extent(self):
if self._A is None:
raise RuntimeError('Must set data first')
return self._Ax[0], self._Ax[-1], self._Ay[0], self._Ay[-1]
def set_filternorm(self, s):
pass
def set_filterrad(self, s):
pass
def set_norm(self, norm):
if self._A is not None:
raise RuntimeError('Cannot change colors after loading data')
cm.ScalarMappable.set_norm(self, norm)
def set_cmap(self, cmap):
if self._A is not None:
raise RuntimeError('Cannot change colors after loading data')
cm.ScalarMappable.set_cmap(self, cmap)
class PcolorImage(martist.Artist, cm.ScalarMappable):
'''
Make a pcolor-style plot with an irregular rectangular grid.
This uses a variation of the original irregular image code,
and it is used by pcolorfast for the corresponding grid type.
'''
def __init__(self, ax,
x=None,
y=None,
A=None,
cmap = None,
norm = None,
**kwargs
):
"""
cmap defaults to its rc setting
cmap is a colors.Colormap instance
norm is a colors.Normalize instance to map luminance to 0-1
Additional kwargs are matplotlib.artist properties
"""
martist.Artist.__init__(self)
cm.ScalarMappable.__init__(self, norm, cmap)
self.axes = ax
self._rgbacache = None
self.update(kwargs)
self.set_data(x, y, A)
def make_image(self, magnification=1.0):
if self._A is None:
raise RuntimeError('You must first set the image array')
fc = self.axes.patch.get_facecolor()
bg = mcolors.colorConverter.to_rgba(fc, 0)
bg = (np.array(bg)*255).astype(np.uint8)
l, b, r, t = self.axes.bbox.extents
width = (round(r) + 0.5) - (round(l) - 0.5)
height = (round(t) + 0.5) - (round(b) - 0.5)
width = width * magnification
height = height * magnification
if self.check_update('array'):
A = self.to_rgba(self._A, alpha=self._alpha, bytes=True)
self._rgbacache = A
if self._A.ndim == 2:
self.is_grayscale = self.cmap.is_gray()
else:
A = self._rgbacache
vl = self.axes.viewLim
im = _image.pcolor2(self._Ax, self._Ay, A,
height,
width,
(vl.x0, vl.x1, vl.y0, vl.y1),
bg)
im.is_grayscale = self.is_grayscale
return im
@allow_rasterization
def draw(self, renderer, *args, **kwargs):
if not self.get_visible(): return
im = self.make_image(renderer.get_image_magnification())
gc = renderer.new_gc()
gc.set_clip_rectangle(self.axes.bbox.frozen())
gc.set_clip_path(self.get_clip_path())
renderer.draw_image(gc,
round(self.axes.bbox.xmin),
round(self.axes.bbox.ymin),
im)
gc.restore()
def set_data(self, x, y, A):
A = cbook.safe_masked_invalid(A)
if x is None:
x = np.arange(0, A.shape[1]+1, dtype=np.float64)
else:
x = np.asarray(x, np.float64).ravel()
if y is None:
y = np.arange(0, A.shape[0]+1, dtype=np.float64)
else:
y = np.asarray(y, np.float64).ravel()
if A.shape[:2] != (y.size-1, x.size-1):
print A.shape
print y.size
print x.size
raise ValueError("Axes don't match array shape")
if A.ndim not in [2, 3]:
raise ValueError("A must be 2D or 3D")
if A.ndim == 3 and A.shape[2] == 1:
A.shape = A.shape[:2]
self.is_grayscale = False
if A.ndim == 3:
if A.shape[2] in [3, 4]:
if (A[:,:,0] == A[:,:,1]).all() and (A[:,:,0] == A[:,:,2]).all():
self.is_grayscale = True
else:
raise ValueError("3D arrays must have RGB or RGBA as last dim")
self._A = A
self._Ax = x
self._Ay = y
self.update_dict['array'] = True
def set_array(self, *args):
raise NotImplementedError('Method not supported')
def set_alpha(self, alpha):
"""
Set the alpha value used for blending - not supported on
all backends
ACCEPTS: float
"""
martist.Artist.set_alpha(self, alpha)
self.update_dict['array'] = True
class FigureImage(martist.Artist, cm.ScalarMappable):
zorder = 0
def __init__(self, fig,
cmap = None,
norm = None,
offsetx = 0,
offsety = 0,
origin=None,
**kwargs
):
"""
cmap is a colors.Colormap instance
norm is a colors.Normalize instance to map luminance to 0-1
kwargs are an optional list of Artist keyword args
"""
martist.Artist.__init__(self)
cm.ScalarMappable.__init__(self, norm, cmap)
if origin is None: origin = rcParams['image.origin']
self.origin = origin
self.figure = fig
self.ox = offsetx
self.oy = offsety
self.update(kwargs)
self.magnification = 1.0
def contains(self, mouseevent):
"""Test whether the mouse event occured within the image.
"""
if callable(self._contains): return self._contains(self,mouseevent)
xmin, xmax, ymin, ymax = self.get_extent()
xdata, ydata = mouseevent.x, mouseevent.y
#print xdata, ydata, xmin, xmax, ymin, ymax
if xdata is not None and ydata is not None:
inside = xdata>=xmin and xdata<=xmax and ydata>=ymin and ydata<=ymax
else:
inside = False
return inside,{}
def get_size(self):
'Get the numrows, numcols of the input image'
if self._A is None:
raise RuntimeError('You must first set the image array')
return self._A.shape[:2]
def get_extent(self):
'get the image extent: left, right, bottom, top'
numrows, numcols = self.get_size()
return (-0.5+self.ox, numcols-0.5+self.ox,
-0.5+self.oy, numrows-0.5+self.oy)
def set_data(self, A):
"""
Set the image array
"""
cm.ScalarMappable.set_array(self, cbook.safe_masked_invalid(A))
def set_array(self, A):
"""
Deprecated; use set_data for consistency with other image types.
"""
self.set_data(A)
def make_image(self, magnification=1.0):
if self._A is None:
raise RuntimeError('You must first set the image array')
x = self.to_rgba(self._A, self._alpha)
self.magnification = magnification
# if magnification is not one, we need to resize
ismag = magnification!=1
#if ismag: raise RuntimeError
if ismag:
isoutput = 0
else:
isoutput = 1
im = _image.fromarray(x, isoutput)
fc = self.figure.get_facecolor()
im.set_bg( *mcolors.colorConverter.to_rgba(fc, 0) )
im.is_grayscale = (self.cmap.name == "gray" and
len(self._A.shape) == 2)
if ismag:
numrows, numcols = self.get_size()
numrows *= magnification
numcols *= magnification
im.set_interpolation(_image.NEAREST)
im.resize(numcols, numrows)
if self.origin=='upper':
im.flipud_out()
return im
@allow_rasterization
def draw(self, renderer, *args, **kwargs):
if not self.get_visible(): return
# todo: we should be able to do some cacheing here
im = self.make_image(renderer.get_image_magnification())
gc = renderer.new_gc()
gc.set_clip_rectangle(self.figure.bbox)
gc.set_clip_path(self.get_clip_path())
renderer.draw_image(gc, round(self.ox), round(self.oy), im)
gc.restore()
def write_png(self, fname):
"""Write the image to png file with fname"""
im = self.make_image()
rows, cols, buffer = im.as_rgba_str()
_png.write_png(buffer, cols, rows, fname)
class BboxImage(_AxesImageBase):
"""
The Image class whose size is determined by the given bbox.
"""
def __init__(self, bbox,
cmap = None,
norm = None,
interpolation=None,
origin=None,
filternorm=1,
filterrad=4.0,
resample = False,
**kwargs
):
"""
cmap is a colors.Colormap instance
norm is a colors.Normalize instance to map luminance to 0-1
kwargs are an optional list of Artist keyword args
"""
_AxesImageBase.__init__(self, ax=None,
cmap = cmap,
norm = norm,
interpolation=interpolation,
origin=origin,
filternorm=filternorm,
filterrad=filterrad,
resample = resample,
**kwargs
)
self.bbox = bbox
def get_window_extent(self, renderer=None):
if renderer is None:
renderer = self.get_figure()._cachedRenderer
if isinstance(self.bbox, BboxBase):
return self.bbox
elif callable(self.bbox):
return self.bbox(renderer)
else:
raise ValueError("unknown type of bbox")
def contains(self, mouseevent):
"""Test whether the mouse event occured within the image.
"""
if callable(self._contains): return self._contains(self,mouseevent)
if not self.get_visible():# or self.get_figure()._renderer is None:
return False,{}
x, y = mouseevent.x, mouseevent.y
inside = self.get_window_extent().contains(x, y)
return inside,{}
def get_size(self):
'Get the numrows, numcols of the input image'
if self._A is None:
raise RuntimeError('You must first set the image array')
return self._A.shape[:2]
def make_image(self, renderer, magnification=1.0):
if self._A is None:
raise RuntimeError('You must first set the image array or the image attribute')
if self._imcache is None:
if self._A.dtype == np.uint8 and len(self._A.shape) == 3:
im = _image.frombyte(self._A, 0)
im.is_grayscale = False
else:
if self._rgbacache is None:
x = self.to_rgba(self._A, self._alpha)
self._rgbacache = x
else:
x = self._rgbacache
im = _image.fromarray(x, 0)
if len(self._A.shape) == 2:
im.is_grayscale = self.cmap.is_gray()
else:
im.is_grayscale = False
self._imcache = im
if self.origin=='upper':
im.flipud_in()
else:
im = self._imcache
# image input dimensions
im.reset_matrix()
im.set_interpolation(self._interpd[self._interpolation])
im.set_resample(self._resample)
l, b, r, t = self.get_window_extent(renderer).extents #bbox.extents
widthDisplay = (round(r) + 0.5) - (round(l) - 0.5)
heightDisplay = (round(t) + 0.5) - (round(b) - 0.5)
widthDisplay *= magnification
heightDisplay *= magnification
numrows, numcols = self._A.shape[:2]
# resize viewport to display
rx = widthDisplay / numcols
ry = heightDisplay / numrows
#im.apply_scaling(rx*sx, ry*sy)
im.apply_scaling(rx, ry)
#im.resize(int(widthDisplay+0.5), int(heightDisplay+0.5),
# norm=self._filternorm, radius=self._filterrad)
im.resize(int(widthDisplay), int(heightDisplay),
norm=self._filternorm, radius=self._filterrad)
return im
@allow_rasterization
def draw(self, renderer, *args, **kwargs):
if not self.get_visible(): return
# todo: we should be able to do some cacheing here
image_mag = renderer.get_image_magnification()
im = self.make_image(renderer, image_mag)
l, b, r, t = self.get_window_extent(renderer).extents
gc = renderer.new_gc()
self._set_gc_clip(gc)
#gc.set_clip_path(self.get_clip_path())
renderer.draw_image(gc, round(l), round(b), im)
gc.restore()
def imread(fname, format=None):
"""
Return image file in *fname* as :class:`numpy.array`. *fname* may
be a string path or a Python file-like object.
If *format* is provided, will try to read file of that type,
otherwise the format is deduced from the filename. If nothing can
be deduced, PNG is tried.
Return value is a :class:`numpy.array`. For grayscale images, the
return array is MxN. For RGB images, the return value is MxNx3.
For RGBA images the return value is MxNx4.
matplotlib can only read PNGs natively, but if `PIL
<http://www.pythonware.com/products/pil/>`_ is installed, it will
use it to load the image and return an array (if possible) which
can be used with :func:`~matplotlib.pyplot.imshow`.
"""
def pilread():
'try to load the image with PIL or return None'
try: import Image
except ImportError: return None
image = Image.open( fname )
return pil_to_array(image)
handlers = {'png' :_png.read_png, }
if format is None:
if cbook.is_string_like(fname):
basename, ext = os.path.splitext(fname)
ext = ext.lower()[1:]
else:
ext = 'png'
else:
ext = format
if ext not in handlers.keys():
im = pilread()
if im is None:
raise ValueError('Only know how to handle extensions: %s; with PIL installed matplotlib can handle more images' % handlers.keys())
return im
handler = handlers[ext]
# To handle Unicode filenames, we pass a file object to the PNG
# reader extension, since Python handles them quite well, but it's
# tricky in C.
if cbook.is_string_like(fname):
fname = open(fname, 'rb')
return handler(fname)
def imsave(fname, arr, vmin=None, vmax=None, cmap=None, format=None,
origin=None, dpi=100):
"""
Saves a 2D :class:`numpy.array` as an image with one pixel per element.
The output formats available depend on the backend being used.
Arguments:
*fname*:
A string containing a path to a filename, or a Python file-like object.
If *format* is *None* and *fname* is a string, the output
format is deduced from the extension of the filename.
*arr*:
A 2D array.
Keyword arguments:
*vmin*/*vmax*: [ None | scalar ]
*vmin* and *vmax* set the color scaling for the image by fixing the
values that map to the colormap color limits. If either *vmin* or *vmax*
is None, that limit is determined from the *arr* min/max value.
*cmap*:
cmap is a colors.Colormap instance, eg cm.jet.
If None, default to the rc image.cmap value.
*format*:
One of the file extensions supported by the active
backend. Most backends support png, pdf, ps, eps and svg.
*origin*
[ 'upper' | 'lower' ] Indicates where the [0,0] index of
the array is in the upper left or lower left corner of
the axes. Defaults to the rc image.origin value.
*dpi*
The DPI to store in the metadata of the file. This does not affect the
resolution of the output image.
"""
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
figsize = [x / float(dpi) for x in arr.shape[::-1]]
fig = Figure(figsize=figsize, dpi=dpi, frameon=False)
canvas = FigureCanvas(fig)
im = fig.figimage(arr, cmap=cmap, vmin=vmin, vmax=vmax, origin=origin)
fig.savefig(fname, dpi=dpi, format=format)
def pil_to_array( pilImage ):
"""
load a PIL image and return it as a numpy array of uint8. For
grayscale images, the return array is MxN. For RGB images, the
return value is MxNx3. For RGBA images the return value is MxNx4
"""
def toarray(im):
'return a 1D array of floats'
x_str = im.tostring('raw',im.mode,0,-1)
x = np.fromstring(x_str,np.uint8)
return x
if pilImage.mode in ('RGBA', 'RGBX'):
im = pilImage # no need to convert images
elif pilImage.mode=='L':
im = pilImage # no need to luminance images
# return MxN luminance array
x = toarray(im)
x.shape = im.size[1], im.size[0]
return x
elif pilImage.mode=='RGB':
#return MxNx3 RGB array
im = pilImage # no need to RGB images
x = toarray(im)
x.shape = im.size[1], im.size[0], 3
return x
else: # try to convert to an rgba image
try:
im = pilImage.convert('RGBA')
except ValueError:
raise RuntimeError('Unknown image mode')
# return MxNx4 RGBA array
x = toarray(im)
x.shape = im.size[1], im.size[0], 4
return x
def thumbnail(infile, thumbfile, scale=0.1, interpolation='bilinear',
preview=False):
"""
make a thumbnail of image in *infile* with output filename
*thumbfile*.
*infile* the image file -- must be PNG or PIL readable if you
have `PIL <http://www.pythonware.com/products/pil/>`_ installed
*thumbfile*
the thumbnail filename
*scale*
the scale factor for the thumbnail
*interpolation*
the interpolation scheme used in the resampling
*preview*
if True, the default backend (presumably a user interface
backend) will be used which will cause a figure to be raised
if :func:`~matplotlib.pyplot.show` is called. If it is False,
a pure image backend will be used depending on the extension,
'png'->FigureCanvasAgg, 'pdf'->FigureCanvasPDF,
'svg'->FigureCanvasSVG
See examples/misc/image_thumbnail.py.
.. htmlonly::
:ref:`misc-image_thumbnail`
Return value is the figure instance containing the thumbnail
"""
basedir, basename = os.path.split(infile)
baseout, extout = os.path.splitext(thumbfile)
im = imread(infile)
rows, cols, depth = im.shape
# this doesn't really matter, it will cancel in the end, but we
# need it for the mpl API
dpi = 100
height = float(rows)/dpi*scale
width = float(cols)/dpi*scale
extension = extout.lower()
if preview:
# let the UI backend do everything
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(width, height), dpi=dpi)
else:
if extension=='.png':
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
elif extension=='.pdf':
from matplotlib.backends.backend_pdf import FigureCanvasPDF as FigureCanvas
elif extension=='.svg':
from matplotlib.backends.backend_svg import FigureCanvasSVG as FigureCanvas
else:
raise ValueError("Can only handle extensions 'png', 'svg' or 'pdf'")
from matplotlib.figure import Figure
fig = Figure(figsize=(width, height), dpi=dpi)
canvas = FigureCanvas(fig)
ax = fig.add_axes([0,0,1,1], aspect='auto', frameon=False, xticks=[], yticks=[])
basename, ext = os.path.splitext(basename)
ax.imshow(im, aspect='auto', resample=True, interpolation='bilinear')
fig.savefig(thumbfile, dpi=dpi)
return fig