1806 lines
70 KiB
Python
1806 lines
70 KiB
Python
"""
|
|
The image module supports basic image loading, rescaling and display
|
|
operations.
|
|
"""
|
|
|
|
import math
|
|
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, cm
|
|
# 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
|
|
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_ = {
|
|
'antialiased': _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,
|
|
}
|
|
|
|
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 == 'antialiased':
|
|
# 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()
|
|
_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(martist.Artist, cm.ScalarMappable):
|
|
"""
|
|
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 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
|
|
"""
|
|
zorder = 0
|
|
|
|
def __init__(self, ax,
|
|
cmap=None,
|
|
norm=None,
|
|
interpolation=None,
|
|
origin=None,
|
|
filternorm=True,
|
|
filterrad=4.0,
|
|
resample=False,
|
|
*,
|
|
interpolation_stage=None,
|
|
**kwargs
|
|
):
|
|
martist.Artist.__init__(self)
|
|
cm.ScalarMappable.__init__(self, norm, cmap)
|
|
if origin is None:
|
|
origin = mpl.rcParams['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
|
|
cm.ScalarMappable.changed(self)
|
|
|
|
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.
|
|
|
|
*A* may be a greyscale image (M, N) with a dtype of `~numpy.float32`,
|
|
`~numpy.float64`, `~numpy.float128`, `~numpy.uint16` or `~numpy.uint8`,
|
|
or an (M, N, 4) RGBA image with a dtype of `~numpy.float32`,
|
|
`~numpy.float64`, `~numpy.float128`, or `~numpy.uint8`.
|
|
|
|
If *unsampled* is True, the image will not be scaled, but an
|
|
appropriate affine transformation will be returned instead.
|
|
|
|
If *round_to_pixel_border* is 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
|
|
|
|
out_width_base = clipped_bbox.width * magnification
|
|
out_height_base = clipped_bbox.height * magnification
|
|
|
|
if out_width_base == 0 or out_height_base == 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()
|
|
.translate(-clipped_bbox.x0, -clipped_bbox.y0)
|
|
.scale(magnification)))
|
|
|
|
# So that the image is aligned with the edge of the Axes, we want to
|
|
# round up the output width to the next integer. This also means
|
|
# scaling the transform slightly to account for the extra subpixel.
|
|
if ((not unsampled) and t.is_affine and round_to_pixel_border and
|
|
(out_width_base % 1.0 != 0.0 or out_height_base % 1.0 != 0.0)):
|
|
out_width = math.ceil(out_width_base)
|
|
out_height = math.ceil(out_height_base)
|
|
extra_width = (out_width - out_width_base) / out_width_base
|
|
extra_height = (out_height - out_height_base) / out_height_base
|
|
t += Affine2D().scale(1.0 + extra_width, 1.0 + extra_height)
|
|
else:
|
|
out_width = int(out_width_base)
|
|
out_height = int(out_height_base)
|
|
out_shape = (out_height, out_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 A.ndim == 2 and self._interpolation_stage != 'rgba':
|
|
# 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
|
|
|
|
# TODO slice input array first
|
|
a_min = A.min()
|
|
a_max = A.max()
|
|
if a_min is np.ma.masked: # All masked; values don't matter.
|
|
a_min, a_max = np.int32(0), np.int32(1)
|
|
if A.dtype.kind == 'f': # Float dtype: scale to same dtype.
|
|
scaled_dtype = np.dtype(
|
|
np.float64 if A.dtype.itemsize > 4 else np.float32)
|
|
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.
|
|
# Scale to appropriately sized float: use float32 if the
|
|
# dynamic range is small, to limit the memory footprint.
|
|
da = a_max.astype(np.float64) - a_min.astype(np.float64)
|
|
scaled_dtype = np.float64 if da > 1e8 else np.float32
|
|
|
|
# Scale the input data to [.1, .9]. The Agg interpolators clip
|
|
# to [0, 1] internally, and we use a smaller input scale to
|
|
# identify the interpolated points that need to be flagged as
|
|
# over/under. This may introduce numeric instabilities in very
|
|
# broadly scaled data.
|
|
|
|
# Always copy, and don't allow array subtypes.
|
|
A_scaled = np.array(A, dtype=scaled_dtype)
|
|
# Clip scaled data around norm if necessary. This is necessary
|
|
# for big numbers at the edge of float64's ability to represent
|
|
# changes. Applying a norm first would be good, but ruins the
|
|
# interpolation of over numbers.
|
|
self.norm.autoscale_None(A)
|
|
dv = np.float64(self.norm.vmax) - np.float64(self.norm.vmin)
|
|
vmid = np.float64(self.norm.vmin) + dv / 2
|
|
fact = 1e7 if scaled_dtype == np.float64 else 1e4
|
|
newmin = vmid - dv * fact
|
|
if newmin < a_min:
|
|
newmin = None
|
|
else:
|
|
a_min = np.float64(newmin)
|
|
newmax = vmid + dv * fact
|
|
if newmax > a_max:
|
|
newmax = None
|
|
else:
|
|
a_max = np.float64(newmax)
|
|
if newmax is not None or newmin is not None:
|
|
np.clip(A_scaled, newmin, newmax, out=A_scaled)
|
|
|
|
# Rescale the raw data to [offset, 1-offset] so that the
|
|
# resampling code will run cleanly. Using dyadic numbers here
|
|
# could reduce the error, but would not fully eliminate it and
|
|
# breaks a number of tests (due to the slightly different
|
|
# error bouncing some pixels across a boundary in the (very
|
|
# quantized) colormapping step).
|
|
offset = .1
|
|
frac = .8
|
|
# Run vmin/vmax through the same rescaling as the raw data;
|
|
# otherwise, data values close or equal to the boundaries can
|
|
# end up on the wrong side due to floating point error.
|
|
vmin, vmax = self.norm.vmin, self.norm.vmax
|
|
if vmin is np.ma.masked:
|
|
vmin, vmax = a_min, a_max
|
|
vrange = np.array([vmin, vmax], dtype=scaled_dtype)
|
|
|
|
A_scaled -= a_min
|
|
vrange -= a_min
|
|
# .item() handles a_min/a_max being ndarray subclasses.
|
|
a_min = a_min.astype(scaled_dtype).item()
|
|
a_max = a_max.astype(scaled_dtype).item()
|
|
|
|
if a_min != a_max:
|
|
A_scaled /= ((a_max - a_min) / frac)
|
|
vrange /= ((a_max - a_min) / frac)
|
|
A_scaled += offset
|
|
vrange += offset
|
|
# resample the input data to the correct resolution and shape
|
|
A_resampled = _resample(self, A_scaled, out_shape, t)
|
|
del A_scaled # Make sure we don't use A_scaled anymore!
|
|
# Un-scale the resampled data to approximately the original
|
|
# range. Things that interpolated to outside the original range
|
|
# will still be outside, but possibly clipped in the case of
|
|
# higher order interpolation + drastically changing data.
|
|
A_resampled -= offset
|
|
vrange -= offset
|
|
if a_min != a_max:
|
|
A_resampled *= ((a_max - a_min) / frac)
|
|
vrange *= ((a_max - a_min) / frac)
|
|
A_resampled += a_min
|
|
vrange += a_min
|
|
# if using NoNorm, cast back to the original datatype
|
|
if isinstance(self.norm, mcolors.NoNorm):
|
|
A_resampled = A_resampled.astype(A.dtype)
|
|
|
|
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!
|
|
# Agg updates out_alpha in place. If the pixel has no image
|
|
# data it will not be updated (and still be 0 as we initialized
|
|
# it), if input data that would go into that output pixel than
|
|
# it will be `nan`, if all the input data for a pixel is good
|
|
# it will be 1, and if there is _some_ good data in that output
|
|
# pixel it will be between [0, 1] (such as a rotated image).
|
|
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)
|
|
# we have re-set the vmin/vmax to account for small errors
|
|
# that may have moved input values in/out of range
|
|
s_vmin, s_vmax = vrange
|
|
if isinstance(self.norm, mcolors.LogNorm) and s_vmin <= 0:
|
|
# Don't give 0 or negative values to LogNorm
|
|
s_vmin = np.finfo(scaled_dtype).eps
|
|
# Block the norm from sending an update signal during the
|
|
# temporary vmin/vmax change
|
|
with self.norm.callbacks.blocked(), \
|
|
cbook._setattr_cm(self.norm, vmin=s_vmin, vmax=s_vmax):
|
|
output = self.norm(resampled_masked)
|
|
else:
|
|
if A.ndim == 2: # _interpolation_stage == 'rgba'
|
|
self.norm.autoscale_None(A)
|
|
A = self.to_rgba(A)
|
|
alpha = self._get_scalar_alpha()
|
|
if A.shape[2] == 3:
|
|
# No need to resample alpha or make a full array; NumPy will expand
|
|
# this out and cast to uint8 if necessary when it's assigned to the
|
|
# alpha channel below.
|
|
output_alpha = (255 * alpha) if A.dtype == np.uint8 else alpha
|
|
else:
|
|
output_alpha = _resample( # resample alpha channel
|
|
self, A[..., 3], out_shape, t, alpha=alpha)
|
|
output = _resample( # resample rgb channels
|
|
self, _rgb_to_rgba(A[..., :3]), out_shape, t, alpha=alpha)
|
|
output[..., 3] = output_alpha # recombine rgb and alpha
|
|
|
|
# output is now either a 2D array of normed (int or float) data
|
|
# or an RGBA array of re-sampled input
|
|
output = self.to_rgba(output, 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, clipped_bbox.x0, clipped_bbox.y0, 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 '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 : {'antialiased', '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'.
|
|
"""
|
|
return self._interpolation_stage
|
|
|
|
def set_interpolation_stage(self, s):
|
|
"""
|
|
Set when interpolation happens during the transform to RGBA.
|
|
|
|
Parameters
|
|
----------
|
|
s : {'data', 'rgba'} or None
|
|
Whether to apply up/downsampling interpolation in data or RGBA
|
|
space. If None, use :rc:`image.interpolation_stage`.
|
|
"""
|
|
s = mpl._val_or_rc(s, 'image.interpolation_stage')
|
|
_api.check_in_list(['data', 'rgba'], 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 or None
|
|
If None, use :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 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 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', 'antialiased', '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,
|
|
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,
|
|
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.figure.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 ``ax.dataLim``, and, if autoscaling, sets ``ax.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``.
|
|
"""
|
|
(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:
|
|
return self._extent
|
|
else:
|
|
sz = self.get_size()
|
|
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)
|
|
|
|
def get_cursor_data(self, event):
|
|
"""
|
|
Return the image value at the event position or *None* if the event is
|
|
outside the image.
|
|
|
|
See Also
|
|
--------
|
|
matplotlib.artist.Artist.get_cursor_data
|
|
"""
|
|
xmin, xmax, ymin, ymax = self.get_extent()
|
|
if self.origin == 'upper':
|
|
ymin, ymax = ymax, ymin
|
|
arr = self.get_array()
|
|
data_extent = Bbox([[xmin, ymin], [xmax, ymax]])
|
|
array_extent = Bbox([[0, 0], [arr.shape[1], arr.shape[0]]])
|
|
trans = self.get_transform().inverted()
|
|
trans += BboxTransform(boxin=data_extent, boxout=array_extent)
|
|
point = trans.transform([event.x, event.y])
|
|
if any(np.isnan(point)):
|
|
return None
|
|
j, i = point.astype(int)
|
|
# Clip the coordinates at array bounds
|
|
if not (0 <= i < arr.shape[0]) or not (0 <= j < arr.shape[1]):
|
|
return None
|
|
else:
|
|
return arr[i, j]
|
|
|
|
|
|
class NonUniformImage(AxesImage):
|
|
|
|
def __init__(self, ax, *, interpolation='nearest', **kwargs):
|
|
"""
|
|
Parameters
|
|
----------
|
|
ax : `~matplotlib.axes.Axes`
|
|
The Axes the image will belong to.
|
|
interpolation : {'nearest', 'bilinear'}, default: 'nearest'
|
|
The interpolation scheme used in the resampling.
|
|
**kwargs
|
|
All other keyword arguments are identical to those of `.AxesImage`.
|
|
"""
|
|
super().__init__(ax, **kwargs)
|
|
self.set_interpolation(interpolation)
|
|
|
|
def _check_unsampled_image(self):
|
|
"""Return False. Do not use unsampled image."""
|
|
return False
|
|
|
|
def make_image(self, renderer, magnification=1.0, unsampled=False):
|
|
# docstring inherited
|
|
if self._A is None:
|
|
raise RuntimeError('You must first set the image array')
|
|
if unsampled:
|
|
raise ValueError('unsampled not supported on NonUniformImage')
|
|
A = self._A
|
|
if A.ndim == 2:
|
|
if A.dtype != np.uint8:
|
|
A = self.to_rgba(A, bytes=True)
|
|
else:
|
|
A = np.repeat(A[:, :, np.newaxis], 4, 2)
|
|
A[:, :, 3] = 255
|
|
else:
|
|
if A.dtype != np.uint8:
|
|
A = (255*A).astype(np.uint8)
|
|
if A.shape[2] == 3:
|
|
B = np.zeros(tuple([*A.shape[0:2], 4]), np.uint8)
|
|
B[:, :, 0:3] = A
|
|
B[:, :, 3] = 255
|
|
A = B
|
|
vl = self.axes.viewLim
|
|
l, b, r, t = self.axes.bbox.extents
|
|
width = int(((round(r) + 0.5) - (round(l) - 0.5)) * magnification)
|
|
height = int(((round(t) + 0.5) - (round(b) - 0.5)) * magnification)
|
|
x_pix = np.linspace(vl.x0, vl.x1, width)
|
|
y_pix = np.linspace(vl.y0, vl.y1, height)
|
|
if self._interpolation == "nearest":
|
|
x_mid = (self._Ax[:-1] + self._Ax[1:]) / 2
|
|
y_mid = (self._Ay[:-1] + self._Ay[1:]) / 2
|
|
x_int = x_mid.searchsorted(x_pix)
|
|
y_int = y_mid.searchsorted(y_pix)
|
|
# The following is equal to `A[y_int[:, None], x_int[None, :]]`,
|
|
# but many times faster. Both casting to uint32 (to have an
|
|
# effectively 1D array) and manual index flattening matter.
|
|
im = (
|
|
np.ascontiguousarray(A).view(np.uint32).ravel()[
|
|
np.add.outer(y_int * A.shape[1], x_int)]
|
|
.view(np.uint8).reshape((height, width, 4)))
|
|
else: # self._interpolation == "bilinear"
|
|
# Use np.interp to compute x_int/x_float has similar speed.
|
|
x_int = np.clip(
|
|
self._Ax.searchsorted(x_pix) - 1, 0, len(self._Ax) - 2)
|
|
y_int = np.clip(
|
|
self._Ay.searchsorted(y_pix) - 1, 0, len(self._Ay) - 2)
|
|
idx_int = np.add.outer(y_int * A.shape[1], x_int)
|
|
x_frac = np.clip(
|
|
np.divide(x_pix - self._Ax[x_int], np.diff(self._Ax)[x_int],
|
|
dtype=np.float32), # Downcasting helps with speed.
|
|
0, 1)
|
|
y_frac = np.clip(
|
|
np.divide(y_pix - self._Ay[y_int], np.diff(self._Ay)[y_int],
|
|
dtype=np.float32),
|
|
0, 1)
|
|
f00 = np.outer(1 - y_frac, 1 - x_frac)
|
|
f10 = np.outer(y_frac, 1 - x_frac)
|
|
f01 = np.outer(1 - y_frac, x_frac)
|
|
f11 = np.outer(y_frac, x_frac)
|
|
im = np.empty((height, width, 4), np.uint8)
|
|
for chan in range(4):
|
|
ac = A[:, :, chan].reshape(-1) # reshape(-1) avoids a copy.
|
|
# Shifting the buffer start (`ac[offset:]`) avoids an array
|
|
# addition (`ac[idx_int + offset]`).
|
|
buf = f00 * ac[idx_int]
|
|
buf += f10 * ac[A.shape[1]:][idx_int]
|
|
buf += f01 * ac[1:][idx_int]
|
|
buf += f11 * ac[A.shape[1] + 1:][idx_int]
|
|
im[:, :, chan] = buf # Implicitly casts to uint8.
|
|
return im, l, b, IdentityTransform()
|
|
|
|
def set_data(self, x, y, A):
|
|
"""
|
|
Set the grid for the pixel centers, and the pixel values.
|
|
|
|
Parameters
|
|
----------
|
|
x, y : 1D array-like
|
|
Monotonic arrays of shapes (N,) and (M,), respectively, specifying
|
|
pixel centers.
|
|
A : array-like
|
|
(M, N) `~numpy.ndarray` or masked array of values to be
|
|
colormapped, or (M, N, 3) RGB array, or (M, N, 4) RGBA array.
|
|
"""
|
|
A = self._normalize_image_array(A)
|
|
x = np.array(x, np.float32)
|
|
y = np.array(y, np.float32)
|
|
if not (x.ndim == y.ndim == 1 and A.shape[:2] == y.shape + x.shape):
|
|
raise TypeError("Axes don't match array shape")
|
|
self._A = A
|
|
self._Ax = x
|
|
self._Ay = y
|
|
self._imcache = None
|
|
self.stale = True
|
|
|
|
def set_array(self, *args):
|
|
raise NotImplementedError('Method not supported')
|
|
|
|
def set_interpolation(self, s):
|
|
"""
|
|
Parameters
|
|
----------
|
|
s : {'nearest', 'bilinear'} or None
|
|
If None, use :rc:`image.interpolation`.
|
|
"""
|
|
if s is not None and s not in ('nearest', 'bilinear'):
|
|
raise NotImplementedError('Only nearest neighbor and '
|
|
'bilinear interpolations are supported')
|
|
super().set_interpolation(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]
|
|
|
|
@_api.rename_parameter("3.8", "s", "filternorm")
|
|
def set_filternorm(self, filternorm):
|
|
pass
|
|
|
|
@_api.rename_parameter("3.8", "s", "filterrad")
|
|
def set_filterrad(self, filterrad):
|
|
pass
|
|
|
|
def set_norm(self, norm):
|
|
if self._A is not None:
|
|
raise RuntimeError('Cannot change colors after loading data')
|
|
super().set_norm(norm)
|
|
|
|
def set_cmap(self, cmap):
|
|
if self._A is not None:
|
|
raise RuntimeError('Cannot change colors after loading data')
|
|
super().set_cmap(cmap)
|
|
|
|
def get_cursor_data(self, event):
|
|
# docstring inherited
|
|
x, y = event.xdata, event.ydata
|
|
if (x < self._Ax[0] or x > self._Ax[-1] or
|
|
y < self._Ay[0] or y > self._Ay[-1]):
|
|
return None
|
|
j = np.searchsorted(self._Ax, x) - 1
|
|
i = np.searchsorted(self._Ay, y) - 1
|
|
return self._A[i, j]
|
|
|
|
|
|
class PcolorImage(AxesImage):
|
|
"""
|
|
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
|
|
):
|
|
"""
|
|
Parameters
|
|
----------
|
|
ax : `~matplotlib.axes.Axes`
|
|
The Axes the image will belong to.
|
|
x, y : 1D array-like, optional
|
|
Monotonic arrays of length N+1 and M+1, respectively, specifying
|
|
rectangle boundaries. If not given, will default to
|
|
``range(N + 1)`` and ``range(M + 1)``, respectively.
|
|
A : array-like
|
|
The data to be color-coded. The interpretation depends on the
|
|
shape:
|
|
|
|
- (M, N) `~numpy.ndarray` or masked array: values to be colormapped
|
|
- (M, N, 3): RGB array
|
|
- (M, N, 4): RGBA array
|
|
|
|
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.
|
|
**kwargs : `~matplotlib.artist.Artist` properties
|
|
"""
|
|
super().__init__(ax, norm=norm, cmap=cmap)
|
|
self._internal_update(kwargs)
|
|
if A is not None:
|
|
self.set_data(x, y, A)
|
|
|
|
def make_image(self, renderer, magnification=1.0, unsampled=False):
|
|
# docstring inherited
|
|
if self._A is None:
|
|
raise RuntimeError('You must first set the image array')
|
|
if unsampled:
|
|
raise ValueError('unsampled not supported on PColorImage')
|
|
|
|
if self._imcache is None:
|
|
A = self.to_rgba(self._A, bytes=True)
|
|
self._imcache = np.pad(A, [(1, 1), (1, 1), (0, 0)], "constant")
|
|
padded_A = self._imcache
|
|
bg = mcolors.to_rgba(self.axes.patch.get_facecolor(), 0)
|
|
bg = (np.array(bg) * 255).astype(np.uint8)
|
|
if (padded_A[0, 0] != bg).all():
|
|
padded_A[[0, -1], :] = padded_A[:, [0, -1]] = bg
|
|
|
|
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 = round(width * magnification)
|
|
height = round(height * magnification)
|
|
vl = self.axes.viewLim
|
|
|
|
x_pix = np.linspace(vl.x0, vl.x1, width)
|
|
y_pix = np.linspace(vl.y0, vl.y1, height)
|
|
x_int = self._Ax.searchsorted(x_pix)
|
|
y_int = self._Ay.searchsorted(y_pix)
|
|
im = ( # See comment in NonUniformImage.make_image re: performance.
|
|
padded_A.view(np.uint32).ravel()[
|
|
np.add.outer(y_int * padded_A.shape[1], x_int)]
|
|
.view(np.uint8).reshape((height, width, 4)))
|
|
return im, l, b, IdentityTransform()
|
|
|
|
def _check_unsampled_image(self):
|
|
return False
|
|
|
|
def set_data(self, x, y, A):
|
|
"""
|
|
Set the grid for the rectangle boundaries, and the data values.
|
|
|
|
Parameters
|
|
----------
|
|
x, y : 1D array-like, optional
|
|
Monotonic arrays of length N+1 and M+1, respectively, specifying
|
|
rectangle boundaries. If not given, will default to
|
|
``range(N + 1)`` and ``range(M + 1)``, respectively.
|
|
A : array-like
|
|
The data to be color-coded. The interpretation depends on the
|
|
shape:
|
|
|
|
- (M, N) `~numpy.ndarray` or masked array: values to be colormapped
|
|
- (M, N, 3): RGB array
|
|
- (M, N, 4): RGBA array
|
|
"""
|
|
A = self._normalize_image_array(A)
|
|
x = np.arange(0., A.shape[1] + 1) if x is None else np.array(x, float).ravel()
|
|
y = np.arange(0., A.shape[0] + 1) if y is None else np.array(y, float).ravel()
|
|
if A.shape[:2] != (y.size - 1, x.size - 1):
|
|
raise ValueError(
|
|
"Axes don't match array shape. Got %s, expected %s." %
|
|
(A.shape[:2], (y.size - 1, x.size - 1)))
|
|
# For efficient cursor readout, ensure x and y are increasing.
|
|
if x[-1] < x[0]:
|
|
x = x[::-1]
|
|
A = A[:, ::-1]
|
|
if y[-1] < y[0]:
|
|
y = y[::-1]
|
|
A = A[::-1]
|
|
self._A = A
|
|
self._Ax = x
|
|
self._Ay = y
|
|
self._imcache = None
|
|
self.stale = True
|
|
|
|
def set_array(self, *args):
|
|
raise NotImplementedError('Method not supported')
|
|
|
|
def get_cursor_data(self, event):
|
|
# docstring inherited
|
|
x, y = event.xdata, event.ydata
|
|
if (x < self._Ax[0] or x > self._Ax[-1] or
|
|
y < self._Ay[0] or y > self._Ay[-1]):
|
|
return None
|
|
j = np.searchsorted(self._Ax, x) - 1
|
|
i = np.searchsorted(self._Ay, y) - 1
|
|
return self._A[i, j]
|
|
|
|
|
|
class FigureImage(_ImageBase):
|
|
"""An image attached to a figure."""
|
|
|
|
zorder = 0
|
|
|
|
_interpolation = 'nearest'
|
|
|
|
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
|
|
"""
|
|
super().__init__(
|
|
None,
|
|
norm=norm,
|
|
cmap=cmap,
|
|
origin=origin
|
|
)
|
|
self.figure = fig
|
|
self.ox = offsetx
|
|
self.oy = offsety
|
|
self._internal_update(kwargs)
|
|
self.magnification = 1.0
|
|
|
|
def get_extent(self):
|
|
"""Return the image extent as tuple (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 make_image(self, renderer, magnification=1.0, unsampled=False):
|
|
# docstring inherited
|
|
fac = renderer.dpi/self.figure.dpi
|
|
# fac here is to account for pdf, eps, svg backends where
|
|
# figure.dpi is set to 72. This means we need to scale the
|
|
# image (using magnification) and offset it appropriately.
|
|
bbox = Bbox([[self.ox/fac, self.oy/fac],
|
|
[(self.ox/fac + self._A.shape[1]),
|
|
(self.oy/fac + self._A.shape[0])]])
|
|
width, height = self.figure.get_size_inches()
|
|
width *= renderer.dpi
|
|
height *= renderer.dpi
|
|
clip = Bbox([[0, 0], [width, height]])
|
|
return self._make_image(
|
|
self._A, bbox, bbox, clip, magnification=magnification / fac,
|
|
unsampled=unsampled, round_to_pixel_border=False)
|
|
|
|
def set_data(self, A):
|
|
"""Set the image array."""
|
|
cm.ScalarMappable.set_array(self, A)
|
|
self.stale = True
|
|
|
|
|
|
class BboxImage(_ImageBase):
|
|
"""The Image class whose size is determined by the given bbox."""
|
|
|
|
def __init__(self, bbox,
|
|
*,
|
|
cmap=None,
|
|
norm=None,
|
|
interpolation=None,
|
|
origin=None,
|
|
filternorm=True,
|
|
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
|
|
"""
|
|
super().__init__(
|
|
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()._get_renderer()
|
|
|
|
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 occurred within the image."""
|
|
if self._different_canvas(mouseevent) or not self.get_visible():
|
|
return False, {}
|
|
x, y = mouseevent.x, mouseevent.y
|
|
inside = self.get_window_extent().contains(x, y)
|
|
return inside, {}
|
|
|
|
def make_image(self, renderer, magnification=1.0, unsampled=False):
|
|
# docstring inherited
|
|
width, height = renderer.get_canvas_width_height()
|
|
bbox_in = self.get_window_extent(renderer).frozen()
|
|
bbox_in._points /= [width, height]
|
|
bbox_out = self.get_window_extent(renderer)
|
|
clip = Bbox([[0, 0], [width, height]])
|
|
self._transform = BboxTransformTo(clip)
|
|
return self._make_image(
|
|
self._A,
|
|
bbox_in, bbox_out, clip, magnification, unsampled=unsampled)
|
|
|
|
|
|
def imread(fname, format=None):
|
|
"""
|
|
Read an image from a file into an array.
|
|
|
|
.. note::
|
|
|
|
This function exists for historical reasons. It is recommended to
|
|
use `PIL.Image.open` instead for loading images.
|
|
|
|
Parameters
|
|
----------
|
|
fname : str or file-like
|
|
The image file to read: a filename, a URL or a file-like object opened
|
|
in read-binary mode.
|
|
|
|
Passing a URL is deprecated. Please open the URL
|
|
for reading and pass the result to Pillow, e.g. with
|
|
``np.array(PIL.Image.open(urllib.request.urlopen(url)))``.
|
|
format : str, optional
|
|
The image file format assumed for reading the data. The image is
|
|
loaded as a PNG file if *format* is set to "png", if *fname* is a path
|
|
or opened file with a ".png" extension, or if it is a URL. In all
|
|
other cases, *format* is ignored and the format is auto-detected by
|
|
`PIL.Image.open`.
|
|
|
|
Returns
|
|
-------
|
|
`numpy.array`
|
|
The image data. The returned array has shape
|
|
|
|
- (M, N) for grayscale images.
|
|
- (M, N, 3) for RGB images.
|
|
- (M, N, 4) for RGBA images.
|
|
|
|
PNG images are returned as float arrays (0-1). All other formats are
|
|
returned as int arrays, with a bit depth determined by the file's
|
|
contents.
|
|
"""
|
|
# hide imports to speed initial import on systems with slow linkers
|
|
from urllib import parse
|
|
|
|
if format is None:
|
|
if isinstance(fname, str):
|
|
parsed = parse.urlparse(fname)
|
|
# If the string is a URL (Windows paths appear as if they have a
|
|
# length-1 scheme), assume png.
|
|
if len(parsed.scheme) > 1:
|
|
ext = 'png'
|
|
else:
|
|
ext = Path(fname).suffix.lower()[1:]
|
|
elif hasattr(fname, 'geturl'): # Returned by urlopen().
|
|
# We could try to parse the url's path and use the extension, but
|
|
# returning png is consistent with the block above. Note that this
|
|
# if clause has to come before checking for fname.name as
|
|
# urlopen("file:///...") also has a name attribute (with the fixed
|
|
# value "<urllib response>").
|
|
ext = 'png'
|
|
elif hasattr(fname, 'name'):
|
|
ext = Path(fname.name).suffix.lower()[1:]
|
|
else:
|
|
ext = 'png'
|
|
else:
|
|
ext = format
|
|
img_open = (
|
|
PIL.PngImagePlugin.PngImageFile if ext == 'png' else PIL.Image.open)
|
|
if isinstance(fname, str) and len(parse.urlparse(fname).scheme) > 1:
|
|
# Pillow doesn't handle URLs directly.
|
|
raise ValueError(
|
|
"Please open the URL for reading and pass the "
|
|
"result to Pillow, e.g. with "
|
|
"``np.array(PIL.Image.open(urllib.request.urlopen(url)))``."
|
|
)
|
|
with img_open(fname) as image:
|
|
return (_pil_png_to_float_array(image)
|
|
if isinstance(image, PIL.PngImagePlugin.PngImageFile) else
|
|
pil_to_array(image))
|
|
|
|
|
|
def imsave(fname, arr, vmin=None, vmax=None, cmap=None, format=None,
|
|
origin=None, dpi=100, *, metadata=None, pil_kwargs=None):
|
|
"""
|
|
Colormap and save an array as an image file.
|
|
|
|
RGB(A) images are passed through. Single channel images will be
|
|
colormapped according to *cmap* and *norm*.
|
|
|
|
.. note::
|
|
|
|
If you want to save a single channel image as gray scale please use an
|
|
image I/O library (such as pillow, tifffile, or imageio) directly.
|
|
|
|
Parameters
|
|
----------
|
|
fname : str or path-like or file-like
|
|
A path or a file-like object to store the image in.
|
|
If *format* is not set, then the output format is inferred from the
|
|
extension of *fname*, if any, and from :rc:`savefig.format` otherwise.
|
|
If *format* is set, it determines the output format.
|
|
arr : array-like
|
|
The image data. The shape can be one of
|
|
MxN (luminance), MxNx3 (RGB) or MxNx4 (RGBA).
|
|
vmin, vmax : float, optional
|
|
*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 : str or `~matplotlib.colors.Colormap`, default: :rc:`image.cmap`
|
|
A Colormap instance or registered colormap name. The colormap
|
|
maps scalar data to colors. It is ignored for RGB(A) data.
|
|
format : str, optional
|
|
The file format, e.g. 'png', 'pdf', 'svg', ... The behavior when this
|
|
is unset is documented under *fname*.
|
|
origin : {'upper', 'lower'}, default: :rc:`image.origin`
|
|
Indicates whether the ``(0, 0)`` index of the array is in the upper
|
|
left or lower left corner of the Axes.
|
|
dpi : float
|
|
The DPI to store in the metadata of the file. This does not affect the
|
|
resolution of the output image. Depending on file format, this may be
|
|
rounded to the nearest integer.
|
|
metadata : dict, optional
|
|
Metadata in the image file. The supported keys depend on the output
|
|
format, see the documentation of the respective backends for more
|
|
information.
|
|
Currently only supported for "png", "pdf", "ps", "eps", and "svg".
|
|
pil_kwargs : dict, optional
|
|
Keyword arguments passed to `PIL.Image.Image.save`. If the 'pnginfo'
|
|
key is present, it completely overrides *metadata*, including the
|
|
default 'Software' key.
|
|
"""
|
|
from matplotlib.figure import Figure
|
|
if isinstance(fname, os.PathLike):
|
|
fname = os.fspath(fname)
|
|
if format is None:
|
|
format = (Path(fname).suffix[1:] if isinstance(fname, str)
|
|
else mpl.rcParams["savefig.format"]).lower()
|
|
if format in ["pdf", "ps", "eps", "svg"]:
|
|
# Vector formats that are not handled by PIL.
|
|
if pil_kwargs is not None:
|
|
raise ValueError(
|
|
f"Cannot use 'pil_kwargs' when saving to {format}")
|
|
fig = Figure(dpi=dpi, frameon=False)
|
|
fig.figimage(arr, cmap=cmap, vmin=vmin, vmax=vmax, origin=origin,
|
|
resize=True)
|
|
fig.savefig(fname, dpi=dpi, format=format, transparent=True,
|
|
metadata=metadata)
|
|
else:
|
|
# Don't bother creating an image; this avoids rounding errors on the
|
|
# size when dividing and then multiplying by dpi.
|
|
if origin is None:
|
|
origin = mpl.rcParams["image.origin"]
|
|
else:
|
|
_api.check_in_list(('upper', 'lower'), origin=origin)
|
|
if origin == "lower":
|
|
arr = arr[::-1]
|
|
if (isinstance(arr, memoryview) and arr.format == "B"
|
|
and arr.ndim == 3 and arr.shape[-1] == 4):
|
|
# Such an ``arr`` would also be handled fine by sm.to_rgba below
|
|
# (after casting with asarray), but it is useful to special-case it
|
|
# because that's what backend_agg passes, and can be in fact used
|
|
# as is, saving a few operations.
|
|
rgba = arr
|
|
else:
|
|
sm = cm.ScalarMappable(cmap=cmap)
|
|
sm.set_clim(vmin, vmax)
|
|
rgba = sm.to_rgba(arr, bytes=True)
|
|
if pil_kwargs is None:
|
|
pil_kwargs = {}
|
|
else:
|
|
# we modify this below, so make a copy (don't modify caller's dict)
|
|
pil_kwargs = pil_kwargs.copy()
|
|
pil_shape = (rgba.shape[1], rgba.shape[0])
|
|
rgba = np.require(rgba, requirements='C')
|
|
image = PIL.Image.frombuffer(
|
|
"RGBA", pil_shape, rgba, "raw", "RGBA", 0, 1)
|
|
if format == "png":
|
|
# Only use the metadata kwarg if pnginfo is not set, because the
|
|
# semantics of duplicate keys in pnginfo is unclear.
|
|
if "pnginfo" in pil_kwargs:
|
|
if metadata:
|
|
_api.warn_external("'metadata' is overridden by the "
|
|
"'pnginfo' entry in 'pil_kwargs'.")
|
|
else:
|
|
metadata = {
|
|
"Software": (f"Matplotlib version{mpl.__version__}, "
|
|
f"https://matplotlib.org/"),
|
|
**(metadata if metadata is not None else {}),
|
|
}
|
|
pil_kwargs["pnginfo"] = pnginfo = PIL.PngImagePlugin.PngInfo()
|
|
for k, v in metadata.items():
|
|
if v is not None:
|
|
pnginfo.add_text(k, v)
|
|
elif metadata is not None:
|
|
raise ValueError(f"metadata not supported for format {format!r}")
|
|
if format in ["jpg", "jpeg"]:
|
|
format = "jpeg" # Pillow doesn't recognize "jpg".
|
|
facecolor = mpl.rcParams["savefig.facecolor"]
|
|
if cbook._str_equal(facecolor, "auto"):
|
|
facecolor = mpl.rcParams["figure.facecolor"]
|
|
color = tuple(int(x * 255) for x in mcolors.to_rgb(facecolor))
|
|
background = PIL.Image.new("RGB", pil_shape, color)
|
|
background.paste(image, image)
|
|
image = background
|
|
pil_kwargs.setdefault("format", format)
|
|
pil_kwargs.setdefault("dpi", (dpi, dpi))
|
|
image.save(fname, **pil_kwargs)
|
|
|
|
|
|
def pil_to_array(pilImage):
|
|
"""
|
|
Load a `PIL image`_ and return it as a numpy int array.
|
|
|
|
.. _PIL image: https://pillow.readthedocs.io/en/latest/reference/Image.html
|
|
|
|
Returns
|
|
-------
|
|
numpy.array
|
|
|
|
The array shape depends on the image type:
|
|
|
|
- (M, N) for grayscale images.
|
|
- (M, N, 3) for RGB images.
|
|
- (M, N, 4) for RGBA images.
|
|
"""
|
|
if pilImage.mode in ['RGBA', 'RGBX', 'RGB', 'L']:
|
|
# return MxNx4 RGBA, MxNx3 RBA, or MxN luminance array
|
|
return np.asarray(pilImage)
|
|
elif pilImage.mode.startswith('I;16'):
|
|
# return MxN luminance array of uint16
|
|
raw = pilImage.tobytes('raw', pilImage.mode)
|
|
if pilImage.mode.endswith('B'):
|
|
x = np.frombuffer(raw, '>u2')
|
|
else:
|
|
x = np.frombuffer(raw, '<u2')
|
|
return x.reshape(pilImage.size[::-1]).astype('=u2')
|
|
else: # try to convert to an rgba image
|
|
try:
|
|
pilImage = pilImage.convert('RGBA')
|
|
except ValueError as err:
|
|
raise RuntimeError('Unknown image mode') from err
|
|
return np.asarray(pilImage) # return MxNx4 RGBA array
|
|
|
|
|
|
def _pil_png_to_float_array(pil_png):
|
|
"""Convert a PIL `PNGImageFile` to a 0-1 float array."""
|
|
# Unlike pil_to_array this converts to 0-1 float32s for backcompat with the
|
|
# old libpng-based loader.
|
|
# The supported rawmodes are from PIL.PngImagePlugin._MODES. When
|
|
# mode == "RGB(A)", the 16-bit raw data has already been coarsened to 8-bit
|
|
# by Pillow.
|
|
mode = pil_png.mode
|
|
rawmode = pil_png.png.im_rawmode
|
|
if rawmode == "1": # Grayscale.
|
|
return np.asarray(pil_png, np.float32)
|
|
if rawmode == "L;2": # Grayscale.
|
|
return np.divide(pil_png, 2**2 - 1, dtype=np.float32)
|
|
if rawmode == "L;4": # Grayscale.
|
|
return np.divide(pil_png, 2**4 - 1, dtype=np.float32)
|
|
if rawmode == "L": # Grayscale.
|
|
return np.divide(pil_png, 2**8 - 1, dtype=np.float32)
|
|
if rawmode == "I;16B": # Grayscale.
|
|
return np.divide(pil_png, 2**16 - 1, dtype=np.float32)
|
|
if mode == "RGB": # RGB.
|
|
return np.divide(pil_png, 2**8 - 1, dtype=np.float32)
|
|
if mode == "P": # Palette.
|
|
return np.divide(pil_png.convert("RGBA"), 2**8 - 1, dtype=np.float32)
|
|
if mode == "LA": # Grayscale + alpha.
|
|
return np.divide(pil_png.convert("RGBA"), 2**8 - 1, dtype=np.float32)
|
|
if mode == "RGBA": # RGBA.
|
|
return np.divide(pil_png, 2**8 - 1, dtype=np.float32)
|
|
raise ValueError(f"Unknown PIL rawmode: {rawmode}")
|
|
|
|
|
|
def thumbnail(infile, thumbfile, scale=0.1, interpolation='bilinear',
|
|
preview=False):
|
|
"""
|
|
Make a thumbnail of image in *infile* with output filename *thumbfile*.
|
|
|
|
See :doc:`/gallery/misc/image_thumbnail_sgskip`.
|
|
|
|
Parameters
|
|
----------
|
|
infile : str or file-like
|
|
The image file. Matplotlib relies on Pillow_ for image reading, and
|
|
thus supports a wide range of file formats, including PNG, JPG, TIFF
|
|
and others.
|
|
|
|
.. _Pillow: https://python-pillow.org/
|
|
|
|
thumbfile : str or file-like
|
|
The thumbnail filename.
|
|
|
|
scale : float, default: 0.1
|
|
The scale factor for the thumbnail.
|
|
|
|
interpolation : str, default: 'bilinear'
|
|
The interpolation scheme used in the resampling. See the
|
|
*interpolation* parameter of `~.Axes.imshow` for possible values.
|
|
|
|
preview : bool, default: False
|
|
If True, the default backend (presumably a user interface
|
|
backend) will be used which will cause a figure to be raised if
|
|
`~matplotlib.pyplot.show` is called. If it is False, the figure is
|
|
created using `.FigureCanvasBase` and the drawing backend is selected
|
|
as `.Figure.savefig` would normally do.
|
|
|
|
Returns
|
|
-------
|
|
`.Figure`
|
|
The figure instance containing the thumbnail.
|
|
"""
|
|
|
|
im = imread(infile)
|
|
rows, cols, depth = im.shape
|
|
|
|
# This doesn't really matter (it cancels in the end) but the API needs it.
|
|
dpi = 100
|
|
|
|
height = rows / dpi * scale
|
|
width = cols / dpi * scale
|
|
|
|
if preview:
|
|
# Let the UI backend do everything.
|
|
import matplotlib.pyplot as plt
|
|
fig = plt.figure(figsize=(width, height), dpi=dpi)
|
|
else:
|
|
from matplotlib.figure import Figure
|
|
fig = Figure(figsize=(width, height), dpi=dpi)
|
|
FigureCanvasBase(fig)
|
|
|
|
ax = fig.add_axes([0, 0, 1, 1], aspect='auto',
|
|
frameon=False, xticks=[], yticks=[])
|
|
ax.imshow(im, aspect='auto', resample=True, interpolation=interpolation)
|
|
fig.savefig(thumbfile, dpi=dpi)
|
|
return fig
|