""" 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 # 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() _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 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, 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)) 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 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 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 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) 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_alpha() if alpha is None: # alpha parameter not specified if A.shape[2] == 3: # image has no alpha channel output_alpha = 255 if A.dtype == np.uint8 else 1.0 else: output_alpha = _resample( # resample alpha channel self, A[..., 3], out_shape, t) output = _resample( # resample rgb channels self, _rgb_to_rgba(A[..., :3]), out_shape, t) elif np.ndim(alpha) > 0: # Array alpha # user-specified array alpha overrides the existing alpha channel output_alpha = _resample(self, alpha, out_shape, t) output = _resample( self, _rgb_to_rgba(A[..., :3]), out_shape, t) else: # Scalar alpha if A.shape[2] == 3: # broadcast scalar alpha output_alpha = (255 * alpha) if A.dtype == np.uint8 else alpha else: # or apply scalar alpha to existing alpha channel output_alpha = _resample(self, A[..., 3], out_shape, t) * alpha output = _resample( self, _rgb_to_rgba(A[..., :3]), out_shape, t) 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 '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'} or None Whether to apply up/downsampling interpolation in data or RGBA space. If None, use :rc:`image.interpolation_stage`. If 'auto' we will check upsampling rate and if less than 3 then use 'rgba', otherwise use 'data'. """ 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 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', '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: 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 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) invertedTransform = self.axes.transData.inverted() x_pix = invertedTransform.transform( [(x, b) for x in np.linspace(l, r, width)])[:, 0] y_pix = invertedTransform.transform( [(l, y) for y in np.linspace(b, t, height)])[:, 1] 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] def set_filternorm(self, filternorm): pass 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, colorizer=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, colorizer=colorizer) 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, colorizer=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, colorizer=colorizer, origin=origin ) self.set_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 fig = self.get_figure(root=True) fac = renderer.dpi/fig.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 = fig.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.""" super().set_data(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, colorizer=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, colorizer=colorizer, 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 ""). 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 = mcolorizer.Colorizer(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, '