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"""
The image module supports basic image loading, rescaling and display
operations.
"""
import os
import logging
from pathlib import Path
import warnings
import numpy as np
import PIL.Image
import PIL.PngImagePlugin
import matplotlib as mpl
from matplotlib import _api, cbook
# For clarity, names from _image are given explicitly in this module
from matplotlib import _image
# For user convenience, the names from _image are also imported into
# the image namespace
from matplotlib._image import * # noqa: F401, F403
import matplotlib.artist as martist
import matplotlib.colorizer as mcolorizer
from matplotlib.backend_bases import FigureCanvasBase
import matplotlib.colors as mcolors
from matplotlib.transforms import (
Affine2D, BboxBase, Bbox, BboxTransform, BboxTransformTo,
IdentityTransform, TransformedBbox)
_log = logging.getLogger(__name__)
# map interpolation strings to module constants
_interpd_ = {
'auto': _image.NEAREST, # this will use nearest or Hanning...
'none': _image.NEAREST, # fall back to nearest when not supported
'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,
'antialiased': _image.NEAREST, # this will use nearest or Hanning...
}
interpolations_names = set(_interpd_)
def composite_images(images, renderer, magnification=1.0):
"""
Composite a number of RGBA images into one. The images are
composited in the order in which they appear in the *images* list.
Parameters
----------
images : list of Images
Each must have a `!make_image` method. For each image,
`!can_composite` should return `True`, though this is not
enforced by this function. Each image must have a purely
affine transformation with no shear.
renderer : `.RendererBase`
magnification : float, default: 1
The additional magnification to apply for the renderer in use.
Returns
-------
image : (M, N, 4) `numpy.uint8` array
The composited RGBA image.
offset_x, offset_y : float
The (left, bottom) offset where the composited image should be placed
in the output figure.
"""
if len(images) == 0:
return np.empty((0, 0, 4), dtype=np.uint8), 0, 0
parts = []
bboxes = []
for image in images:
data, x, y, trans = image.make_image(renderer, magnification)
if data is not None:
x *= magnification
y *= magnification
parts.append((data, x, y, image._get_scalar_alpha()))
bboxes.append(
Bbox([[x, y], [x + data.shape[1], y + data.shape[0]]]))
if len(parts) == 0:
return np.empty((0, 0, 4), dtype=np.uint8), 0, 0
bbox = Bbox.union(bboxes)
output = np.zeros(
(int(bbox.height), int(bbox.width), 4), dtype=np.uint8)
for data, x, y, alpha in parts:
trans = Affine2D().translate(x - bbox.x0, y - bbox.y0)
_image.resample(data, output, trans, _image.NEAREST,
resample=False, alpha=alpha)
return output, bbox.x0 / magnification, bbox.y0 / magnification
def _draw_list_compositing_images(
renderer, parent, artists, suppress_composite=None):
"""
Draw a sorted list of artists, compositing images into a single
image where possible.
For internal Matplotlib use only: It is here to reduce duplication
between `Figure.draw` and `Axes.draw`, but otherwise should not be
generally useful.
"""
has_images = any(isinstance(x, _ImageBase) for x in artists)
# override the renderer default if suppressComposite is not None
not_composite = (suppress_composite if suppress_composite is not None
else renderer.option_image_nocomposite())
if not_composite or not has_images:
for a in artists:
a.draw(renderer)
else:
# Composite any adjacent images together
image_group = []
mag = renderer.get_image_magnification()
def flush_images():
if len(image_group) == 1:
image_group[0].draw(renderer)
elif len(image_group) > 1:
data, l, b = composite_images(image_group, renderer, mag)
if data.size != 0:
gc = renderer.new_gc()
gc.set_clip_rectangle(parent.bbox)
gc.set_clip_path(parent.get_clip_path())
renderer.draw_image(gc, round(l), round(b), data)
gc.restore()
del image_group[:]
for a in artists:
if (isinstance(a, _ImageBase) and a.can_composite() and
a.get_clip_on() and not a.get_clip_path()):
image_group.append(a)
else:
flush_images()
a.draw(renderer)
flush_images()
def _resample(
image_obj, data, out_shape, transform, *, resample=None, alpha=1):
"""
Convenience wrapper around `._image.resample` to resample *data* to
*out_shape* (with a third dimension if *data* is RGBA) that takes care of
allocating the output array and fetching the relevant properties from the
Image object *image_obj*.
"""
# AGG can only handle coordinates smaller than 24-bit signed integers,
# so raise errors if the input data is larger than _image.resample can
# handle.
msg = ('Data with more than {n} cannot be accurately displayed. '
'Downsampling to less than {n} before displaying. '
'To remove this warning, manually downsample your data.')
if data.shape[1] > 2**23:
warnings.warn(msg.format(n='2**23 columns'))
step = int(np.ceil(data.shape[1] / 2**23))
data = data[:, ::step]
transform = Affine2D().scale(step, 1) + transform
if data.shape[0] > 2**24:
warnings.warn(msg.format(n='2**24 rows'))
step = int(np.ceil(data.shape[0] / 2**24))
data = data[::step, :]
transform = Affine2D().scale(1, step) + transform
# decide if we need to apply anti-aliasing if the data is upsampled:
# compare the number of displayed pixels to the number of
# the data pixels.
interpolation = image_obj.get_interpolation()
if interpolation in ['antialiased', 'auto']:
# don't antialias if upsampling by an integer number or
# if zooming in more than a factor of 3
pos = np.array([[0, 0], [data.shape[1], data.shape[0]]])
disp = transform.transform(pos)
dispx = np.abs(np.diff(disp[:, 0]))
dispy = np.abs(np.diff(disp[:, 1]))
if ((dispx > 3 * data.shape[1] or
dispx == data.shape[1] or
dispx == 2 * data.shape[1]) and
(dispy > 3 * data.shape[0] or
dispy == data.shape[0] or
dispy == 2 * data.shape[0])):
interpolation = 'nearest'
else:
interpolation = 'hanning'
out = np.zeros(out_shape + data.shape[2:], data.dtype) # 2D->2D, 3D->3D.
if resample is None:
resample = image_obj.get_resample()
# When an output pixel falls exactly on the edge between two input pixels, the Agg
# resampler will use the right input pixel as the nearest neighbor. We want the
# left input pixel to be chosen instead, so we flip the input data and the supplied
# transform. If origin != 'upper', the transform will already include a flip in the
# vertical direction.
if interpolation == 'nearest':
transform = Affine2D().translate(-data.shape[1], 0).scale(-1, 1) + transform
data = np.flip(data, axis=1)
if image_obj.origin == 'upper':
transform = Affine2D().translate(0, -data.shape[0]).scale(1, -1) + transform
data = np.flip(data, axis=0)
_image.resample(data, out, transform,
_interpd_[interpolation],
resample,
alpha,
image_obj.get_filternorm(),
image_obj.get_filterrad())
return out
def _rgb_to_rgba(A):
"""
Convert an RGB image to RGBA, as required by the image resample C++
extension.
"""
rgba = np.zeros((A.shape[0], A.shape[1], 4), dtype=A.dtype)
rgba[:, :, :3] = A
if rgba.dtype == np.uint8:
rgba[:, :, 3] = 255
else:
rgba[:, :, 3] = 1.0
return rgba
class _ImageBase(mcolorizer.ColorizingArtist):
"""
Base class for images.
*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 a ``(left, right, bottom, top)`` tuple in data coordinates, for
making image plots registered with data plots; the default is to label the
pixel centers with the zero-based row and column indices.
Additional kwargs are `.Artist` properties.
"""
zorder = 0
def __init__(self, ax,
cmap=None,
norm=None,
colorizer=None,
interpolation=None,
origin=None,
filternorm=True,
filterrad=4.0,
resample=False,
*,
interpolation_stage=None,
**kwargs
):
super().__init__(self._get_colorizer(cmap, norm, colorizer))
origin = mpl._val_or_rc(origin, 'image.origin')
_api.check_in_list(["upper", "lower"], origin=origin)
self.origin = origin
self.set_filternorm(filternorm)
self.set_filterrad(filterrad)
self.set_interpolation(interpolation)
self.set_interpolation_stage(interpolation_stage)
self.set_resample(resample)
self.axes = ax
self._imcache = None
self._internal_update(kwargs)
def __str__(self):
try:
shape = self.get_shape()
return f"{type(self).__name__}(shape={shape!r})"
except RuntimeError:
return type(self).__name__
def __getstate__(self):
# Save some space on the pickle by not saving the cache.
return {**super().__getstate__(), "_imcache": None}
def get_size(self):
"""Return the size of the image as tuple (numrows, numcols)."""
return self.get_shape()[:2]
def get_shape(self):
"""
Return the shape of the image as tuple (numrows, numcols, channels).
"""
if self._A is None:
raise RuntimeError('You must first set the image array')
return self._A.shape
def set_alpha(self, alpha):
"""
Set the alpha value used for blending - not supported on all backends.
Parameters
----------
alpha : float or 2D array-like or None
"""
martist.Artist._set_alpha_for_array(self, alpha)
if np.ndim(alpha) not in (0, 2):
raise TypeError('alpha must be a float, two-dimensional '
'array, or None')
self._imcache = None
def _get_scalar_alpha(self):
"""
Get a scalar alpha value to be applied to the artist as a whole.
If the alpha value is a matrix, the method returns 1.0 because pixels
have individual alpha values (see `~._ImageBase._make_image` for
details). If the alpha value is a scalar, the method returns said value
to be applied to the artist as a whole because pixels do not have
individual alpha values.
"""
return 1.0 if self._alpha is None or np.ndim(self._alpha) > 0 \
else self._alpha
def changed(self):
"""
Call this whenever the mappable is changed so observers can update.
"""
self._imcache = None
super().changed()
def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
unsampled=False, round_to_pixel_border=True):
"""
Normalize, rescale, and colormap the image *A* from the given *in_bbox*
(in data space), to the given *out_bbox* (in pixel space) clipped to
the given *clip_bbox* (also in pixel space), and magnified by the
*magnification* factor.
Parameters
----------
A : ndarray
- a (M, N) array interpreted as scalar (greyscale) image,
with one of the dtypes `~numpy.float32`, `~numpy.float64`,
`~numpy.float128`, `~numpy.uint16` or `~numpy.uint8`.
- (M, N, 4) RGBA image with a dtype of `~numpy.float32`,
`~numpy.float64`, `~numpy.float128`, or `~numpy.uint8`.
in_bbox : `~matplotlib.transforms.Bbox`
out_bbox : `~matplotlib.transforms.Bbox`
clip_bbox : `~matplotlib.transforms.Bbox`
magnification : float, default: 1
unsampled : bool, default: False
If True, the image will not be scaled, but an appropriate
affine transformation will be returned instead.
round_to_pixel_border : bool, default: True
If True, the output image size will be rounded to the nearest pixel
boundary. This makes the images align correctly with the Axes.
It should not be used if exact scaling is needed, such as for
`.FigureImage`.
Returns
-------
image : (M, N, 4) `numpy.uint8` array
The RGBA image, resampled unless *unsampled* is True.
x, y : float
The upper left corner where the image should be drawn, in pixel
space.
trans : `~matplotlib.transforms.Affine2D`
The affine transformation from image to pixel space.
"""
if A is None:
raise RuntimeError('You must first set the image '
'array or the image attribute')
if A.size == 0:
raise RuntimeError("_make_image must get a non-empty image. "
"Your Artist's draw method must filter before "
"this method is called.")
clipped_bbox = Bbox.intersection(out_bbox, clip_bbox)
if clipped_bbox is None:
return None, 0, 0, None
# Define the magnified bbox after clipping
magnified_extents = clipped_bbox.extents * magnification
if ((not unsampled) and round_to_pixel_border):
# Round to the nearest output pixel
# Add a tiny fudge amount to account for numerical precision loss
# on the two sides away from the Agg anchor point (x0, y1)
x0 = np.floor(magnified_extents[0] + 0.5) # round half up
y0 = np.ceil(magnified_extents[1] - 0.5 - 1e-8) # round half down
x1 = np.floor(magnified_extents[2] + 0.5 + 1e-8) # round half up
y1 = np.ceil(magnified_extents[3] - 0.5) # round half down
magnified_bbox = Bbox.from_extents([x0, y0, x1, y1])
else:
magnified_bbox = Bbox.from_extents(magnified_extents)
if magnified_bbox.width == 0 or magnified_bbox.height == 0:
return None, 0, 0, None
if self.origin == 'upper':
# Flip the input image using a transform. This avoids the
# problem with flipping the array, which results in a copy
# when it is converted to contiguous in the C wrapper
t0 = Affine2D().translate(0, -A.shape[0]).scale(1, -1)
else:
t0 = IdentityTransform()
t0 += (
Affine2D()
.scale(
in_bbox.width / A.shape[1],
in_bbox.height / A.shape[0])
.translate(in_bbox.x0, in_bbox.y0)
+ self.get_transform())
t = (t0
+ (Affine2D()
.scale(magnification)
.translate(-magnified_bbox.x0, -magnified_bbox.y0)))
out_shape = (int(magnified_bbox.height), int(magnified_bbox.width))
if not unsampled:
if not (A.ndim == 2 or A.ndim == 3 and A.shape[-1] in (3, 4)):
raise ValueError(f"Invalid shape {A.shape} for image data")
# if antialiased, this needs to change as window sizes
# change:
interpolation_stage = self._interpolation_stage
if interpolation_stage in ['antialiased', 'auto']:
pos = np.array([[0, 0], [A.shape[1], A.shape[0]]])
disp = t.transform(pos)
dispx = np.abs(np.diff(disp[:, 0])) / A.shape[1]
dispy = np.abs(np.diff(disp[:, 1])) / A.shape[0]
if (dispx < 3) or (dispy < 3):
interpolation_stage = 'rgba'
else:
interpolation_stage = 'data'
if A.ndim == 2 and interpolation_stage == 'data':
# if we are a 2D array, then we are running through the
# norm + colormap transformation. However, in general the
# input data is not going to match the size on the screen so we
# have to resample to the correct number of pixels
if A.dtype.kind == 'f': # Float dtype: scale to same dtype.
scaled_dtype = np.dtype("f8" if A.dtype.itemsize > 4 else "f4")
if scaled_dtype.itemsize < A.dtype.itemsize:
_api.warn_external(f"Casting input data from {A.dtype}"
f" to {scaled_dtype} for imshow.")
else: # Int dtype, likely.
# TODO slice input array first
# Scale to appropriately sized float: use float32 if the
# dynamic range is small, to limit the memory footprint.
da = A.max().astype("f8") - A.min().astype("f8")
scaled_dtype = "f8" if da > 1e8 else "f4"
# resample the input data to the correct resolution and shape
A_resampled = _resample(self, A.astype(scaled_dtype), out_shape, t)
# if using NoNorm, cast back to the original datatype
if isinstance(self.norm, mcolors.NoNorm):
A_resampled = A_resampled.astype(A.dtype)
# Compute out_mask (what screen pixels include "bad" data
# pixels) and out_alpha (to what extent screen pixels are
# covered by data pixels: 0 outside the data extent, 1 inside
# (even for bad data), and intermediate values at the edges).
mask = (np.where(A.mask, np.float32(np.nan), np.float32(1))
if A.mask.shape == A.shape # nontrivial mask
else np.ones_like(A, np.float32))
# we always have to interpolate the mask to account for
# non-affine transformations
out_alpha = _resample(self, mask, out_shape, t, resample=True)
del mask # Make sure we don't use mask anymore!
out_mask = np.isnan(out_alpha)
out_alpha[out_mask] = 1
# Apply the pixel-by-pixel alpha values if present
alpha = self.get_alpha()
if alpha is not None and np.ndim(alpha) > 0:
out_alpha *= _resample(self, alpha, out_shape, t, resample=True)
# mask and run through the norm
resampled_masked = np.ma.masked_array(A_resampled, out_mask)
res = self.norm(resampled_masked)
else:
if A.ndim == 2: # interpolation_stage = 'rgba'
self.norm.autoscale_None(A)
A = self.to_rgba(A)
if A.dtype == np.uint8:
# uint8 is too imprecise for premultiplied alpha roundtrips.
A = np.divide(A, 0xff, dtype=np.float32)
alpha = self.get_alpha()
post_apply_alpha = False
if alpha is None: # alpha parameter not specified
if A.shape[2] == 3: # image has no alpha channel
A = np.dstack([A, np.ones(A.shape[:2])])
elif np.ndim(alpha) > 0: # Array alpha
if A.shape[2] == 3: # RGB: use array alpha directly
A = np.dstack([A, alpha])
else: # RGBA: multiply existing alpha by array alpha
A = np.dstack([A[..., :3], A[..., 3] * alpha])
else: # Scalar alpha
if A.shape[2] == 3: # broadcast scalar alpha
A = np.dstack([A, np.full(A.shape[:2], alpha, np.float32)])
else: # or apply scalar alpha to existing alpha channel
post_apply_alpha = True
# Resample in premultiplied alpha space. (TODO: Consider
# implementing premultiplied-space resampling in
# span_image_resample_rgba_affine::generate?)
# Multiplying the whole array and then restoring the alpha channel
# is faster than an in-place multiply of the strided A[..., :3]
# view. If alpha is uniformly 1, premultiplication can be skipped.
alpha_in = A[..., 3]
if (alpha_in != 1).any():
A = A * alpha_in[..., None]
A[..., 3] = alpha_in
res = _resample(self, A, out_shape, t)
# Demultiply. Zeroes in the divisor are replaced by ones,
# which leaves the corresponding (premultiplied) RGB values
# untouched. Dividing the whole contiguous array is several
# times faster than np.divide(..., where=...) into the strided
# res[..., :3] view.
alpha_out = res[..., 3].copy()
res /= np.where(alpha_out != 0, alpha_out, 1)[..., None]
res[..., 3] = alpha_out
if post_apply_alpha:
res[..., 3] *= alpha
# res is now either a 2D array of normed (int or float) data
# or an RGBA array of re-sampled input
output = self.to_rgba(res, bytes=True, norm=False)
# output is now a correctly sized RGBA array of uint8
# Apply alpha *after* if the input was greyscale without a mask
if A.ndim == 2:
alpha = self._get_scalar_alpha()
alpha_channel = output[:, :, 3]
alpha_channel[:] = ( # Assignment will cast to uint8.
alpha_channel.astype(np.float32) * out_alpha * alpha)
else:
if self._imcache is None:
self._imcache = self.to_rgba(A, bytes=True, norm=(A.ndim == 2))
output = self._imcache
# Subset the input image to only the part that will be displayed.
subset = TransformedBbox(clip_bbox, t0.inverted()).frozen()
output = output[
int(max(subset.ymin, 0)):
int(min(subset.ymax + 1, output.shape[0])),
int(max(subset.xmin, 0)):
int(min(subset.xmax + 1, output.shape[1]))]
t = Affine2D().translate(
int(max(subset.xmin, 0)), int(max(subset.ymin, 0))) + t
return (output,
magnified_bbox.x0 / magnification,
magnified_bbox.y0 / magnification,
t)
def make_image(self, renderer, magnification=1.0, unsampled=False):
"""
Normalize, rescale, and colormap this image's data for rendering using
*renderer*, with the given *magnification*.
If *unsampled* is True, the image will not be scaled, but an
appropriate affine transformation will be returned instead.
Returns
-------
image : (M, N, 4) `numpy.uint8` array
The RGBA image, resampled unless *unsampled* is True.
x, y : float
The upper left corner where the image should be drawn, in pixel
space.
trans : `~matplotlib.transforms.Affine2D`
The affine transformation from image to pixel space.
"""
raise NotImplementedError('The make_image method must be overridden')
def _check_unsampled_image(self):
"""
Return whether the image is better to be drawn unsampled.
The derived class needs to override it.
"""
return False
@martist.allow_rasterization
def draw(self, renderer):
# if not visible, declare victory and return
if not self.get_visible():
self.stale = False
return
# for empty images, there is nothing to draw!
if self.get_array().size == 0:
self.stale = False
return
# actually render the image.
gc = renderer.new_gc()
self._set_gc_clip(gc)
gc.set_alpha(self._get_scalar_alpha())
gc.set_url(self.get_url())
gc.set_gid(self.get_gid())
if (renderer.option_scale_image() # Renderer supports transform kwarg.
and self._check_unsampled_image()
and self.get_transform().is_affine):
im, l, b, trans = self.make_image(renderer, unsampled=True)
if im is not None:
trans = Affine2D().scale(im.shape[1], im.shape[0]) + trans
renderer.draw_image(gc, l, b, im, trans)
else:
im, l, b, trans = self.make_image(
renderer, renderer.get_image_magnification())
if im is not None:
renderer.draw_image(gc, l, b, im)
gc.restore()
self.stale = False
def contains(self, mouseevent):
"""Test whether the mouse event occurred within the image."""
if (self._different_canvas(mouseevent)
# This doesn't work for figimage.
or not self.axes.contains(mouseevent)[0]):
return False, {}
# 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
trans = self.get_transform().inverted()
x, y = trans.transform([mouseevent.x, mouseevent.y])
xmin, xmax, ymin, ymax = self.get_extent()
# This checks xmin <= x <= xmax *or* xmax <= x <= xmin.
inside = (x is not None and (x - xmin) * (x - xmax) <= 0
and y is not None and (y - ymin) * (y - ymax) <= 0)
return inside, {}
def write_png(self, fname):
"""Write the image to png file *fname*."""
im = self.to_rgba(self._A[::-1] if self.origin == 'lower' else self._A,
bytes=True, norm=True)
PIL.Image.fromarray(im).save(fname, format="png")
@staticmethod
def _normalize_image_array(A):
"""
Check validity of image-like input *A* and normalize it to a format suitable for
Image subclasses.
"""
A = cbook.safe_masked_invalid(A, copy=True)
if A.dtype != np.uint8 and not np.can_cast(A.dtype, float, "same_kind"):
raise TypeError(f"Image data of dtype {A.dtype} cannot be "
f"converted to float")
if A.ndim == 3 and A.shape[-1] == 1:
A = A.squeeze(-1) # If just (M, N, 1), assume scalar and apply colormap.
if not (A.ndim == 2 or A.ndim == 3 and A.shape[-1] in [3, 4]):
raise TypeError(f"Invalid shape {A.shape} for image data")
if A.ndim == 3:
# If the input data has values outside the valid range (after
# normalisation), we issue a warning and then clip X to the bounds
# - otherwise casting wraps extreme values, hiding outliers and
# making reliable interpretation impossible.
high = 255 if np.issubdtype(A.dtype, np.integer) else 1
if A.min() < 0 or high < A.max():
_log.warning(
'Clipping input data to the valid range for imshow with '
'RGB data ([0..1] for floats or [0..255] for integers). '
'Got range [%s..%s].',
A.min(), A.max()
)
A = np.clip(A, 0, high)
# Cast unsupported integer types to uint8
if A.dtype != np.uint8 and np.issubdtype(A.dtype, np.integer):
A = A.astype(np.uint8)
return A
def set_data(self, A):
"""
Set the image array.
Note that this function does *not* update the normalization used.
Parameters
----------
A : array-like or `PIL.Image.Image`
"""
if isinstance(A, PIL.Image.Image):
A = pil_to_array(A) # Needed e.g. to apply png palette.
self._A = self._normalize_image_array(A)
self._imcache = None
self.stale = True
def set_array(self, A):
"""
Retained for backwards compatibility - use set_data instead.
Parameters
----------
A : array-like
"""
# 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 'auto', 'antialiased', 'nearest', 'bilinear', 'bicubic',
'spline16', 'spline36', 'hanning', 'hamming', 'hermite', 'kaiser',
'quadric', 'catrom', 'gaussian', 'bessel', 'mitchell', 'sinc', 'lanczos',
or 'none'.
"""
return self._interpolation
def set_interpolation(self, s):
"""
Set the interpolation method the image uses when resizing.
If None, use :rc:`image.interpolation`. If 'none', the image is
shown as is without interpolating. 'none' is only supported in
agg, ps and pdf backends and will fall back to 'nearest' mode
for other backends.
Parameters
----------
s : {'auto', 'nearest', 'bilinear', 'bicubic', 'spline16', \
'spline36', 'hanning', 'hamming', 'hermite', 'kaiser', 'quadric', 'catrom', \
'gaussian', 'bessel', 'mitchell', 'sinc', 'lanczos', 'none'} or None
"""
s = mpl._val_or_rc(s, 'image.interpolation').lower()
_api.check_in_list(interpolations_names, interpolation=s)
self._interpolation = s
self.stale = True
def get_interpolation_stage(self):
"""
Return when interpolation happens during the transform to RGBA.
One of 'data', 'rgba', 'auto'.
"""
return self._interpolation_stage
def set_interpolation_stage(self, s):
"""
Set when interpolation happens during the transform to RGBA.
Parameters
----------
s : {'data', 'rgba', 'auto'}, default: :rc:`image.interpolation_stage`
Whether to apply resampling interpolation in data or RGBA space.
If 'auto', 'rgba' is used if the upsampling rate is less than 3,
otherwise 'data' is used.
"""
s = mpl._val_or_rc(s, 'image.interpolation_stage')
_api.check_in_list(['data', 'rgba', 'auto'], s=s)
self._interpolation_stage = s
self.stale = True
def can_composite(self):
"""Return whether the image can be composited with its neighbors."""
trans = self.get_transform()
return (
self._interpolation != 'none' and
trans.is_affine and
trans.is_separable)
def set_resample(self, v):
"""
Set whether image resampling is used.
Parameters
----------
v : bool, default: :rc:`image.resample`
"""
v = mpl._val_or_rc(v, 'image.resample')
self._resample = v
self.stale = True
def get_resample(self):
"""Return whether image resampling is used."""
return self._resample
def set_filternorm(self, filternorm):
"""
Set whether the resize filter normalizes the weights.
See help for `~.Axes.imshow`.
Parameters
----------
filternorm : bool
"""
self._filternorm = bool(filternorm)
self.stale = True
def get_filternorm(self):
"""Return whether the resize filter normalizes the weights."""
return self._filternorm
def set_filterrad(self, filterrad):
"""
Set the resize filter radius (only applicable to some
interpolation schemes).
See help for `~.Axes.imshow`.
Parameters
----------
filterrad : positive float
"""
r = float(filterrad)
if r <= 0:
raise ValueError("The filter radius must be a positive number")
self._filterrad = r
self.stale = True
def get_filterrad(self):
"""Return the filterrad setting."""
return self._filterrad
class AxesImage(_ImageBase):
"""
An image with pixels on a regular grid, attached to an Axes.
Parameters
----------
ax : `~matplotlib.axes.Axes`
The Axes the image will belong to.
cmap : str or `~matplotlib.colors.Colormap`, default: :rc:`image.cmap`
The Colormap instance or registered colormap name used to map scalar
data to colors.
norm : str or `~matplotlib.colors.Normalize`
Maps luminance to 0-1.
interpolation : str, default: :rc:`image.interpolation`
Supported values are 'none', 'auto', 'nearest', 'bilinear',
'bicubic', 'spline16', 'spline36', 'hanning', 'hamming', 'hermite',
'kaiser', 'quadric', 'catrom', 'gaussian', 'bessel', 'mitchell',
'sinc', 'lanczos', 'blackman'.
interpolation_stage : {'data', 'rgba'}, default: 'data'
If 'data', interpolation
is carried out on the data provided by the user. If 'rgba', the
interpolation is carried out after the colormapping has been
applied (visual interpolation).
origin : {'upper', 'lower'}, default: :rc:`image.origin`
Place the [0, 0] index of the array in the upper left or lower left
corner of the Axes. The convention 'upper' is typically used for
matrices and images.
extent : tuple, optional
The 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.
filternorm : bool, default: True
A parameter for the antigrain image resize filter
(see the antigrain documentation).
If filternorm is set, the filter normalizes integer values and corrects
the rounding errors. It doesn't do anything with the source floating
point values, it corrects only integers according to the rule of 1.0
which means that any sum of pixel weights must be equal to 1.0. So,
the filter function must produce a graph of the proper shape.
filterrad : float > 0, default: 4
The filter radius for filters that have a radius parameter, i.e. when
interpolation is one of: 'sinc', 'lanczos' or 'blackman'.
resample : bool, default: False
When True, use a full resampling method. When False, only resample when
the output image is larger than the input image.
**kwargs : `~matplotlib.artist.Artist` properties
"""
def __init__(self, ax,
*,
cmap=None,
norm=None,
colorizer=None,
interpolation=None,
origin=None,
extent=None,
filternorm=True,
filterrad=4.0,
resample=False,
interpolation_stage=None,
**kwargs
):
self._extent = extent
super().__init__(
ax,
cmap=cmap,
norm=norm,
colorizer=colorizer,
interpolation=interpolation,
origin=origin,
filternorm=filternorm,
filterrad=filterrad,
resample=resample,
interpolation_stage=interpolation_stage,
**kwargs
)
def get_window_extent(self, renderer=None):
x0, x1, y0, y1 = self._extent
bbox = Bbox.from_extents([x0, y0, x1, y1])
return bbox.transformed(self.get_transform())
def make_image(self, renderer, magnification=1.0, unsampled=False):
# docstring inherited
trans = self.get_transform()
# image is created in the canvas coordinate.
x1, x2, y1, y2 = self.get_extent()
bbox = Bbox(np.array([[x1, y1], [x2, y2]]))
transformed_bbox = TransformedBbox(bbox, trans)
clip = ((self.get_clip_box() or self.axes.bbox) if self.get_clip_on()
else self.get_figure(root=True).bbox)
return self._make_image(self._A, bbox, transformed_bbox, clip,
magnification, unsampled=unsampled)
def _check_unsampled_image(self):
"""Return whether the image would be better drawn unsampled."""
return self.get_interpolation() == "none"
def set_extent(self, extent, **kwargs):
"""
Set the image extent.
Parameters
----------
extent : 4-tuple of float
The position and size of the image as tuple
``(left, right, bottom, top)`` in data coordinates.
**kwargs
Other parameters from which unit info (i.e., the *xunits*,
*yunits*, *zunits* (for 3D Axes), *runits* and *thetaunits* (for
polar Axes) entries are applied, if present.
Notes
-----
This updates `.Axes.dataLim`, and, if autoscaling, sets `.Axes.viewLim`
to tightly fit the image, regardless of `~.Axes.dataLim`. Autoscaling
state is not changed, so a subsequent call to `.Axes.autoscale_view`
will redo the autoscaling in accord with `~.Axes.dataLim`.
"""
(xmin, xmax), (ymin, ymax) = self.axes._process_unit_info(
[("x", [extent[0], extent[1]]),
("y", [extent[2], extent[3]])],
kwargs)
if kwargs:
raise _api.kwarg_error("set_extent", kwargs)
xmin = self.axes._validate_converted_limits(
xmin, self.convert_xunits)
xmax = self.axes._validate_converted_limits(
xmax, self.convert_xunits)
ymin = self.axes._validate_converted_limits(
ymin, self.convert_yunits)
ymax = self.axes._validate_converted_limits(
ymax, self.convert_yunits)
extent = [xmin, xmax, ymin, ymax]
self._extent = extent
corners = (xmin, ymin), (xmax, ymax)
self.axes.update_datalim(corners)
self.sticky_edges.x[:] = [xmin, xmax]
self.sticky_edges.y[:] = [ymin, ymax]
if self.axes.get_autoscalex_on():
self.axes.set_xlim(xmin, xmax, auto=None)
if self.axes.get_autoscaley_on():
self.axes.set_ylim(ymin, ymax, auto=None)
self.stale = True
def get_extent(self):
"""Return the image extent as tuple (left, right, bottom, top)."""
if self._extent is not None:
You can’t perform that action at this time.