import numpy as np from ..qt import QtWidgets, has_qt, FigureManagerQT, FigureCanvasQTAgg from ..._shared.utils import warn import matplotlib as mpl from matplotlib.figure import Figure from matplotlib import _pylab_helpers from matplotlib.colors import LinearSegmentedColormap if has_qt and 'agg' not in mpl.get_backend().lower(): warn("Recommended matplotlib backend is `Agg` for full " "skimage.viewer functionality.") __all__ = ['init_qtapp', 'start_qtapp', 'RequiredAttr', 'figimage', 'LinearColormap', 'ClearColormap', 'FigureCanvas', 'new_plot', 'update_axes_image'] QApp = None def init_qtapp(): """Initialize QAppliction. The QApplication needs to be initialized before creating any QWidgets """ global QApp QApp = QtWidgets.QApplication.instance() if QApp is None: QApp = QtWidgets.QApplication([]) return QApp def is_event_loop_running(app=None): """Return True if event loop is running.""" if app is None: app = init_qtapp() if hasattr(app, '_in_event_loop'): return app._in_event_loop else: return False def start_qtapp(app=None): """Start Qt mainloop""" if app is None: app = init_qtapp() if not is_event_loop_running(app): app._in_event_loop = True app.exec_() app._in_event_loop = False else: app._in_event_loop = True class RequiredAttr(object): """A class attribute that must be set before use.""" instances = dict() def __init__(self, init_val=None): self.instances[self, None] = init_val def __get__(self, obj, objtype): value = self.instances[self, obj] if value is None: raise AttributeError('Required attribute not set') return value def __set__(self, obj, value): self.instances[self, obj] = value class LinearColormap(LinearSegmentedColormap): """LinearSegmentedColormap in which color varies smoothly. This class is a simplification of LinearSegmentedColormap, which doesn't support jumps in color intensities. Parameters ---------- name : str Name of colormap. segmented_data : dict Dictionary of 'red', 'green', 'blue', and (optionally) 'alpha' values. Each color key contains a list of `x`, `y` tuples. `x` must increase monotonically from 0 to 1 and corresponds to input values for a mappable object (e.g. an image). `y` corresponds to the color intensity. """ def __init__(self, name, segmented_data, **kwargs): segmented_data = dict((key, [(x, y, y) for x, y in value]) for key, value in segmented_data.items()) LinearSegmentedColormap.__init__(self, name, segmented_data, **kwargs) class ClearColormap(LinearColormap): """Color map that varies linearly from alpha = 0 to 1 """ def __init__(self, rgb, max_alpha=1, name='clear_color'): r, g, b = rgb cg_speq = {'blue': [(0.0, b), (1.0, b)], 'green': [(0.0, g), (1.0, g)], 'red': [(0.0, r), (1.0, r)], 'alpha': [(0.0, 0.0), (1.0, max_alpha)]} LinearColormap.__init__(self, name, cg_speq) class FigureCanvas(FigureCanvasQTAgg): """Canvas for displaying images.""" def __init__(self, figure, **kwargs): self.fig = figure FigureCanvasQTAgg.__init__(self, self.fig) FigureCanvasQTAgg.setSizePolicy(self, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding) FigureCanvasQTAgg.updateGeometry(self) def resizeEvent(self, event): FigureCanvasQTAgg.resizeEvent(self, event) # Call to `resize_event` missing in FigureManagerQT. # See https://github.com/matplotlib/matplotlib/pull/1585 self.resize_event() def new_canvas(*args, **kwargs): """Return a new figure canvas.""" allnums = _pylab_helpers.Gcf.figs.keys() num = max(allnums) + 1 if allnums else 1 FigureClass = kwargs.pop('FigureClass', Figure) figure = FigureClass(*args, **kwargs) canvas = FigureCanvas(figure) fig_manager = FigureManagerQT(canvas, num) return fig_manager.canvas def new_plot(parent=None, subplot_kw=None, **fig_kw): """Return new figure and axes. Parameters ---------- parent : QtWidget Qt widget that displays the plot objects. If None, you must manually call ``canvas.setParent`` and pass the parent widget. subplot_kw : dict Keyword arguments passed ``matplotlib.figure.Figure.add_subplot``. fig_kw : dict Keyword arguments passed ``matplotlib.figure.Figure``. """ if subplot_kw is None: subplot_kw = {} canvas = new_canvas(**fig_kw) canvas.setParent(parent) fig = canvas.figure ax = fig.add_subplot(1, 1, 1, **subplot_kw) return fig, ax def figimage(image, scale=1, dpi=None, **kwargs): """Return figure and axes with figure tightly surrounding image. Unlike pyplot.figimage, this actually plots onto an axes object, which fills the figure. Plotting the image onto an axes allows for subsequent overlays of axes artists. Parameters ---------- image : array image to plot scale : float If scale is 1, the figure and axes have the same dimension as the image. Smaller values of `scale` will shrink the figure. dpi : int Dots per inch for figure. If None, use the default rcParam. """ dpi = dpi if dpi is not None else mpl.rcParams['figure.dpi'] kwargs.setdefault('interpolation', 'nearest') kwargs.setdefault('cmap', 'gray') h, w, d = np.atleast_3d(image).shape figsize = np.array((w, h), dtype=float) / dpi * scale fig, ax = new_plot(figsize=figsize, dpi=dpi) fig.subplots_adjust(left=0, bottom=0, right=1, top=1) ax.set_axis_off() ax.imshow(image, **kwargs) ax.figure.canvas.draw() return fig, ax def update_axes_image(image_axes, image): """Update the image displayed by an image plot. This sets the image plot's array and updates its shape appropriately Parameters ---------- image_axes : `matplotlib.image.AxesImage` Image axes to update. image : array Image array. """ image_axes.set_array(image) # Adjust size if new image shape doesn't match the original h, w = image.shape[:2] image_axes.set_extent((0, w, h, 0))