# -*- coding: utf-8 -*-
"""
ImageView.py - Widget for basic image dispay and analysis
Copyright 2010 Luke Campagnola
Distributed under MIT/X11 license. See license.txt for more information.
Widget used for displaying 2D or 3D data. Features:
- float or int (including 16-bit int) image display via ImageItem
- zoom/pan via GraphicsView
- black/white level controls
- time slider for 3D data sets
- ROI plotting
- Image normalization through a variety of methods
"""
import os
from math import log10
import numpy as np
from ..Qt import QtCore, QtGui, QT_LIB
from .. import functions as fn
import importlib
ui_template = importlib.import_module(
f'.ImageViewTemplate_{QT_LIB.lower()}', package=__package__)
from ..graphicsItems.ImageItem import *
from ..graphicsItems.ROI import *
from ..graphicsItems.LinearRegionItem import *
from ..graphicsItems.InfiniteLine import *
from ..graphicsItems.ViewBox import *
from ..graphicsItems.VTickGroup import VTickGroup
from ..graphicsItems.GradientEditorItem import addGradientListToDocstring
from .. import ptime as ptime
from .. import debug as debug
from ..SignalProxy import SignalProxy
from .. import getConfigOption
try:
from bottleneck import nanmin, nanmax
except ImportError:
from numpy import nanmin, nanmax
translate = QtCore.QCoreApplication.translate
class PlotROI(ROI):
def __init__(self, size):
ROI.__init__(self, pos=[0,0], size=size) #, scaleSnap=True, translateSnap=True)
self.addScaleHandle([1, 1], [0, 0])
self.addRotateHandle([0, 0], [0.5, 0.5])
[docs]class ImageView(QtGui.QWidget):
"""
Widget used for display and analysis of image data.
Implements many features:
* Displays 2D and 3D image data. For 3D data, a z-axis
slider is displayed allowing the user to select which frame is displayed.
* Displays histogram of image data with movable region defining the dark/light levels
* Editable gradient provides a color lookup table
* Frame slider may also be moved using left/right arrow keys as well as pgup, pgdn, home, and end.
* Basic analysis features including:
* ROI and embedded plot for measuring image values across frames
* Image normalization / background subtraction
Basic Usage::
imv = pg.ImageView()
imv.show()
imv.setImage(data)
**Keyboard interaction**
* left/right arrows step forward/backward 1 frame when pressed,
seek at 20fps when held.
* up/down arrows seek at 100fps
* pgup/pgdn seek at 1000fps
* home/end seek immediately to the first/last frame
* space begins playing frames. If time values (in seconds) are given
for each frame, then playback is in realtime.
"""
sigTimeChanged = QtCore.Signal(object, object)
sigProcessingChanged = QtCore.Signal(object)
[docs] def __init__(self, parent=None, name="ImageView", view=None, imageItem=None,
levelMode='mono', *args):
"""
By default, this class creates an :class:`ImageItem <pyqtgraph.ImageItem>` to display image data
and a :class:`ViewBox <pyqtgraph.ViewBox>` to contain the ImageItem.
============= =========================================================
**Arguments**
parent (QWidget) Specifies the parent widget to which
this ImageView will belong. If None, then the ImageView
is created with no parent.
name (str) The name used to register both the internal ViewBox
and the PlotItem used to display ROI data. See the *name*
argument to :func:`ViewBox.__init__()
<pyqtgraph.ViewBox.__init__>`.
view (ViewBox or PlotItem) If specified, this will be used
as the display area that contains the displayed image.
Any :class:`ViewBox <pyqtgraph.ViewBox>`,
:class:`PlotItem <pyqtgraph.PlotItem>`, or other
compatible object is acceptable.
imageItem (ImageItem) If specified, this object will be used to
display the image. Must be an instance of ImageItem
or other compatible object.
levelMode See the *levelMode* argument to
:func:`HistogramLUTItem.__init__()
<pyqtgraph.HistogramLUTItem.__init__>`
============= =========================================================
Note: to display axis ticks inside the ImageView, instantiate it
with a PlotItem instance as its view::
pg.ImageView(view=pg.PlotItem())
"""
QtGui.QWidget.__init__(self, parent, *args)
self._imageLevels = None # [(min, max), ...] per channel image metrics
self.levelMin = None # min / max levels across all channels
self.levelMax = None
self.name = name
self.image = None
self.axes = {}
self.imageDisp = None
self.ui = ui_template.Ui_Form()
self.ui.setupUi(self)
self.scene = self.ui.graphicsView.scene()
self.ui.histogram.setLevelMode(levelMode)
self.ignorePlaying = False
if view is None:
self.view = ViewBox()
else:
self.view = view
self.ui.graphicsView.setCentralItem(self.view)
self.view.setAspectLocked(True)
self.view.invertY()
if imageItem is None:
self.imageItem = ImageItem()
else:
self.imageItem = imageItem
self.view.addItem(self.imageItem)
self.currentIndex = 0
self.ui.histogram.setImageItem(self.imageItem)
self.menu = None
self.ui.normGroup.hide()
self.roi = PlotROI(10)
self.roi.setZValue(20)
self.view.addItem(self.roi)
self.roi.hide()
self.normRoi = PlotROI(10)
self.normRoi.setPen('y')
self.normRoi.setZValue(20)
self.view.addItem(self.normRoi)
self.normRoi.hide()
self.roiCurves = []
self.roiCurve = self.ui.roiPlot.plot()
self.timeLine = InfiniteLine(0, movable=True)
if getConfigOption('background')=='w':
self.timeLine.setPen((20, 80,80, 200))
else:
self.timeLine.setPen((255, 255, 0, 200))
self.timeLine.setZValue(1)
self.ui.roiPlot.addItem(self.timeLine)
self.ui.splitter.setSizes([self.height()-35, 35])
# make splitter an unchangeable small grey line:
s = self.ui.splitter
s.handle(1).setEnabled(False)
s.setStyleSheet("QSplitter::handle{background-color: grey}")
s.setHandleWidth(2)
self.ui.roiPlot.hideAxis('left')
self.frameTicks = VTickGroup(yrange=[0.8, 1], pen=0.4)
self.ui.roiPlot.addItem(self.frameTicks, ignoreBounds=True)
self.keysPressed = {}
self.playTimer = QtCore.QTimer()
self.playRate = 0
self.fps = 1 # 1 Hz by default
self.lastPlayTime = 0
self.normRgn = LinearRegionItem()
self.normRgn.setZValue(0)
self.ui.roiPlot.addItem(self.normRgn)
self.normRgn.hide()
## wrap functions from view box
for fn in ['addItem', 'removeItem']:
setattr(self, fn, getattr(self.view, fn))
## wrap functions from histogram
for fn in ['setHistogramRange', 'autoHistogramRange', 'getLookupTable', 'getLevels']:
setattr(self, fn, getattr(self.ui.histogram, fn))
self.timeLine.sigPositionChanged.connect(self.timeLineChanged)
self.ui.roiBtn.clicked.connect(self.roiClicked)
self.roi.sigRegionChanged.connect(self.roiChanged)
#self.ui.normBtn.toggled.connect(self.normToggled)
self.ui.menuBtn.clicked.connect(self.menuClicked)
self.ui.normDivideRadio.clicked.connect(self.normRadioChanged)
self.ui.normSubtractRadio.clicked.connect(self.normRadioChanged)
self.ui.normOffRadio.clicked.connect(self.normRadioChanged)
self.ui.normROICheck.clicked.connect(self.updateNorm)
self.ui.normFrameCheck.clicked.connect(self.updateNorm)
self.ui.normTimeRangeCheck.clicked.connect(self.updateNorm)
self.playTimer.timeout.connect(self.timeout)
self.normProxy = SignalProxy(self.normRgn.sigRegionChanged, slot=self.updateNorm)
self.normRoi.sigRegionChangeFinished.connect(self.updateNorm)
self.ui.roiPlot.registerPlot(self.name + '_ROI')
self.view.register(self.name)
self.noRepeatKeys = [QtCore.Qt.Key.Key_Right, QtCore.Qt.Key.Key_Left, QtCore.Qt.Key.Key_Up, QtCore.Qt.Key.Key_Down, QtCore.Qt.Key.Key_PageUp, QtCore.Qt.Key.Key_PageDown]
self.roiClicked() ## initialize roi plot to correct shape / visibility
[docs] def setImage(self, img, autoRange=True, autoLevels=True, levels=None, axes=None, xvals=None, pos=None, scale=None, transform=None, autoHistogramRange=True, levelMode=None):
"""
Set the image to be displayed in the widget.
================== ===========================================================================
**Arguments:**
img (numpy array) the image to be displayed. See :func:`ImageItem.setImage` and
*notes* below.
xvals (numpy array) 1D array of z-axis values corresponding to the first axis
in a 3D image. For video, this array should contain the time of each
frame.
autoRange (bool) whether to scale/pan the view to fit the image.
autoLevels (bool) whether to update the white/black levels to fit the image.
levels (min, max); the white and black level values to use.
axes Dictionary indicating the interpretation for each axis.
This is only needed to override the default guess. Format is::
{'t':0, 'x':1, 'y':2, 'c':3};
pos Change the position of the displayed image
scale Change the scale of the displayed image
transform Set the transform of the displayed image. This option overrides *pos*
and *scale*.
autoHistogramRange If True, the histogram y-range is automatically scaled to fit the
image data.
levelMode If specified, this sets the user interaction mode for setting image
levels. Options are 'mono', which provides a single level control for
all image channels, and 'rgb' or 'rgba', which provide individual
controls for each channel.
================== ===========================================================================
**Notes:**
For backward compatibility, image data is assumed to be in column-major order (column, row).
However, most image data is stored in row-major order (row, column) and will need to be
transposed before calling setImage()::
imageview.setImage(imagedata.T)
This requirement can be changed by the ``imageAxisOrder``
:ref:`global configuration option <apiref_config>`.
"""
profiler = debug.Profiler()
if hasattr(img, 'implements') and img.implements('MetaArray'):
img = img.asarray()
if not isinstance(img, np.ndarray):
required = ['dtype', 'max', 'min', 'ndim', 'shape', 'size']
if not all(hasattr(img, attr) for attr in required):
raise TypeError("Image must be NumPy array or any object "
"that provides compatible attributes/methods:\n"
" %s" % str(required))
self.image = img
self.imageDisp = None
if levelMode is not None:
self.ui.histogram.setLevelMode(levelMode)
profiler()
if axes is None:
x,y = (0, 1) if self.imageItem.axisOrder == 'col-major' else (1, 0)
if img.ndim == 2:
self.axes = {'t': None, 'x': x, 'y': y, 'c': None}
elif img.ndim == 3:
# Ambiguous case; make a guess
if img.shape[2] <= 4:
self.axes = {'t': None, 'x': x, 'y': y, 'c': 2}
else:
self.axes = {'t': 0, 'x': x+1, 'y': y+1, 'c': None}
elif img.ndim == 4:
# Even more ambiguous; just assume the default
self.axes = {'t': 0, 'x': x+1, 'y': y+1, 'c': 3}
else:
raise Exception("Can not interpret image with dimensions %s" % (str(img.shape)))
elif isinstance(axes, dict):
self.axes = axes.copy()
elif isinstance(axes, list) or isinstance(axes, tuple):
self.axes = {}
for i in range(len(axes)):
self.axes[axes[i]] = i
else:
raise Exception("Can not interpret axis specification %s. Must be like {'t': 2, 'x': 0, 'y': 1} or ('t', 'x', 'y', 'c')" % (str(axes)))
for x in ['t', 'x', 'y', 'c']:
self.axes[x] = self.axes.get(x, None)
axes = self.axes
if xvals is not None:
self.tVals = xvals
elif axes['t'] is not None:
if hasattr(img, 'xvals'):
try:
self.tVals = img.xvals(axes['t'])
except:
self.tVals = np.arange(img.shape[axes['t']])
else:
self.tVals = np.arange(img.shape[axes['t']])
profiler()
self.currentIndex = 0
self.updateImage(autoHistogramRange=autoHistogramRange)
if levels is None and autoLevels:
self.autoLevels()
if levels is not None: ## this does nothing since getProcessedImage sets these values again.
self.setLevels(*levels)
if self.ui.roiBtn.isChecked():
self.roiChanged()
profiler()
if self.axes['t'] is not None:
self.ui.roiPlot.setXRange(self.tVals.min(), self.tVals.max())
self.frameTicks.setXVals(self.tVals)
self.timeLine.setValue(0)
if len(self.tVals) > 1:
start = self.tVals.min()
stop = self.tVals.max() + abs(self.tVals[-1] - self.tVals[0]) * 0.02
elif len(self.tVals) == 1:
start = self.tVals[0] - 0.5
stop = self.tVals[0] + 0.5
else:
start = 0
stop = 1
for s in [self.timeLine, self.normRgn]:
s.setBounds([start, stop])
profiler()
self.imageItem.resetTransform()
if scale is not None:
self.imageItem.scale(*scale)
if pos is not None:
self.imageItem.setPos(*pos)
if transform is not None:
self.imageItem.setTransform(transform)
profiler()
if autoRange:
self.autoRange()
self.roiClicked()
profiler()
def clear(self):
self.image = None
self.imageItem.clear()
[docs] def play(self, rate=None):
"""Begin automatically stepping frames forward at the given rate (in fps).
This can also be accessed by pressing the spacebar."""
#print "play:", rate
if rate is None:
rate = self.fps
self.playRate = rate
if rate == 0:
self.playTimer.stop()
return
self.lastPlayTime = ptime.time()
if not self.playTimer.isActive():
self.playTimer.start(16)
[docs] def autoLevels(self):
"""Set the min/max intensity levels automatically to match the image data."""
self.setLevels(rgba=self._imageLevels)
[docs] def setLevels(self, *args, **kwds):
"""Set the min/max (bright and dark) levels.
See :func:`HistogramLUTItem.setLevels <pyqtgraph.HistogramLUTItem.setLevels>`.
"""
self.ui.histogram.setLevels(*args, **kwds)
[docs] def autoRange(self):
"""Auto scale and pan the view around the image such that the image fills the view."""
image = self.getProcessedImage()
self.view.autoRange()
[docs] def getProcessedImage(self):
"""Returns the image data after it has been processed by any normalization options in use.
"""
if self.imageDisp is None:
image = self.normalize(self.image)
self.imageDisp = image
self._imageLevels = self.quickMinMax(self.imageDisp)
self.levelMin = min([level[0] for level in self._imageLevels])
self.levelMax = max([level[1] for level in self._imageLevels])
return self.imageDisp
[docs] def close(self):
"""Closes the widget nicely, making sure to clear the graphics scene and release memory."""
self.clear()
self.imageDisp = None
self.imageItem.setParent(None)
super(ImageView, self).close()
self.setParent(None)
def keyPressEvent(self, ev):
if not self.hasTimeAxis():
super().keyPressEvent(ev)
return
if ev.key() == QtCore.Qt.Key.Key_Space:
if self.playRate == 0:
self.play()
else:
self.play(0)
ev.accept()
elif ev.key() == QtCore.Qt.Key.Key_Home:
self.setCurrentIndex(0)
self.play(0)
ev.accept()
elif ev.key() == QtCore.Qt.Key.Key_End:
self.setCurrentIndex(self.getProcessedImage().shape[0]-1)
self.play(0)
ev.accept()
elif ev.key() in self.noRepeatKeys:
ev.accept()
if ev.isAutoRepeat():
return
self.keysPressed[ev.key()] = 1
self.evalKeyState()
else:
super().keyPressEvent(ev)
def keyReleaseEvent(self, ev):
if not self.hasTimeAxis():
super().keyReleaseEvent(ev)
return
if ev.key() in [QtCore.Qt.Key.Key_Space, QtCore.Qt.Key.Key_Home, QtCore.Qt.Key.Key_End]:
ev.accept()
elif ev.key() in self.noRepeatKeys:
ev.accept()
if ev.isAutoRepeat():
return
try:
del self.keysPressed[ev.key()]
except:
self.keysPressed = {}
self.evalKeyState()
else:
super().keyReleaseEvent(ev)
def evalKeyState(self):
if len(self.keysPressed) == 1:
key = list(self.keysPressed.keys())[0]
if key == QtCore.Qt.Key.Key_Right:
self.play(20)
self.jumpFrames(1)
self.lastPlayTime = ptime.time() + 0.2 ## 2ms wait before start
## This happens *after* jumpFrames, since it might take longer than 2ms
elif key == QtCore.Qt.Key.Key_Left:
self.play(-20)
self.jumpFrames(-1)
self.lastPlayTime = ptime.time() + 0.2
elif key == QtCore.Qt.Key.Key_Up:
self.play(-100)
elif key == QtCore.Qt.Key.Key_Down:
self.play(100)
elif key == QtCore.Qt.Key.Key_PageUp:
self.play(-1000)
elif key == QtCore.Qt.Key.Key_PageDown:
self.play(1000)
else:
self.play(0)
def timeout(self):
now = ptime.time()
dt = now - self.lastPlayTime
if dt < 0:
return
n = int(self.playRate * dt)
if n != 0:
self.lastPlayTime += (float(n)/self.playRate)
if self.currentIndex+n > self.image.shape[self.axes['t']]:
self.play(0)
self.jumpFrames(n)
[docs] def setCurrentIndex(self, ind):
"""Set the currently displayed frame index."""
index = fn.clip_scalar(ind, 0, self.getProcessedImage().shape[self.axes['t']]-1)
self.ignorePlaying = True
# Implicitly call timeLineChanged
self.timeLine.setValue(self.tVals[index])
self.ignorePlaying = False
[docs] def jumpFrames(self, n):
"""Move video frame ahead n frames (may be negative)"""
if self.axes['t'] is not None:
self.setCurrentIndex(self.currentIndex + n)
def normRadioChanged(self):
self.imageDisp = None
self.updateImage()
self.autoLevels()
self.roiChanged()
self.sigProcessingChanged.emit(self)
def updateNorm(self):
if self.ui.normTimeRangeCheck.isChecked():
self.normRgn.show()
else:
self.normRgn.hide()
if self.ui.normROICheck.isChecked():
self.normRoi.show()
else:
self.normRoi.hide()
if not self.ui.normOffRadio.isChecked():
self.imageDisp = None
self.updateImage()
self.autoLevels()
self.roiChanged()
self.sigProcessingChanged.emit(self)
def normToggled(self, b):
self.ui.normGroup.setVisible(b)
self.normRoi.setVisible(b and self.ui.normROICheck.isChecked())
self.normRgn.setVisible(b and self.ui.normTimeRangeCheck.isChecked())
def hasTimeAxis(self):
return 't' in self.axes and self.axes['t'] is not None
def roiClicked(self):
showRoiPlot = False
if self.ui.roiBtn.isChecked():
showRoiPlot = True
self.roi.show()
#self.ui.roiPlot.show()
self.ui.roiPlot.setMouseEnabled(True, True)
self.ui.splitter.setSizes([int(self.height()*0.6), int(self.height()*0.4)])
self.ui.splitter.handle(1).setEnabled(True)
self.roiCurve.show()
self.roiChanged()
self.ui.roiPlot.showAxis('left')
else:
self.roi.hide()
self.ui.roiPlot.setMouseEnabled(False, False)
for c in self.roiCurves:
c.hide()
self.ui.roiPlot.hideAxis('left')
if self.hasTimeAxis():
showRoiPlot = True
mn = self.tVals.min()
mx = self.tVals.max()
self.ui.roiPlot.setXRange(mn, mx, padding=0.01)
self.timeLine.show()
self.timeLine.setBounds([mn, mx])
self.ui.roiPlot.show()
if not self.ui.roiBtn.isChecked():
self.ui.splitter.setSizes([self.height()-35, 35])
self.ui.splitter.handle(1).setEnabled(False)
else:
self.timeLine.hide()
#self.ui.roiPlot.hide()
self.ui.roiPlot.setVisible(showRoiPlot)
def roiChanged(self):
# Extract image data from ROI
if self.image is None:
return
image = self.getProcessedImage()
# getArrayRegion axes should be (x, y) of data array for col-major,
# (y, x) for row-major
# can't just transpose input because ROI is axisOrder aware
colmaj = self.imageItem.axisOrder == 'col-major'
if colmaj:
axes = (self.axes['x'], self.axes['y'])
else:
axes = (self.axes['y'], self.axes['x'])
data, coords = self.roi.getArrayRegion(
image.view(np.ndarray), img=self.imageItem, axes=axes,
returnMappedCoords=True)
if data is None:
return
# Convert extracted data into 1D plot data
if self.axes['t'] is None:
# Average across y-axis of ROI
data = data.mean(axis=self.axes['y'])
# get coordinates along x axis of ROI mapped to range (0, roiwidth)
if colmaj:
coords = coords[:, :, 0] - coords[:, 0:1, 0]
else:
coords = coords[:, 0, :] - coords[:, 0, 0:1]
xvals = (coords**2).sum(axis=0) ** 0.5
else:
# Average data within entire ROI for each frame
data = data.mean(axis=axes)
xvals = self.tVals
# Handle multi-channel data
if data.ndim == 1:
plots = [(xvals, data, 'w')]
if data.ndim == 2:
if data.shape[1] == 1:
colors = 'w'
else:
colors = 'rgbw'
plots = []
for i in range(data.shape[1]):
d = data[:,i]
plots.append((xvals, d, colors[i]))
# Update plot line(s)
while len(plots) < len(self.roiCurves):
c = self.roiCurves.pop()
c.scene().removeItem(c)
while len(plots) > len(self.roiCurves):
self.roiCurves.append(self.ui.roiPlot.plot())
for i in range(len(plots)):
x, y, p = plots[i]
self.roiCurves[i].setData(x, y, pen=p)
[docs] def quickMinMax(self, data):
"""
Estimate the min/max values of *data* by subsampling.
Returns [(min, max), ...] with one item per channel
"""
while data.size > 1e6:
ax = np.argmax(data.shape)
sl = [slice(None)] * data.ndim
sl[ax] = slice(None, None, 2)
data = data[tuple(sl)]
cax = self.axes['c']
if cax is None:
if data.size == 0:
return [(0, 0)]
return [(float(nanmin(data)), float(nanmax(data)))]
else:
if data.size == 0:
return [(0, 0)] * data.shape[-1]
return [(float(nanmin(data.take(i, axis=cax))),
float(nanmax(data.take(i, axis=cax)))) for i in range(data.shape[-1])]
[docs] def normalize(self, image):
"""
Process *image* using the normalization options configured in the
control panel.
This can be repurposed to process any data through the same filter.
"""
if self.ui.normOffRadio.isChecked():
return image
div = self.ui.normDivideRadio.isChecked()
norm = image.view(np.ndarray).copy()
#if div:
#norm = ones(image.shape)
#else:
#norm = zeros(image.shape)
if div:
norm = norm.astype(np.float32)
if self.ui.normTimeRangeCheck.isChecked() and image.ndim == 3:
(sind, start) = self.timeIndex(self.normRgn.lines[0])
(eind, end) = self.timeIndex(self.normRgn.lines[1])
#print start, end, sind, eind
n = image[sind:eind+1].mean(axis=0)
n.shape = (1,) + n.shape
if div:
norm /= n
else:
norm -= n
if self.ui.normFrameCheck.isChecked() and image.ndim == 3:
n = image.mean(axis=1).mean(axis=1)
n.shape = n.shape + (1, 1)
if div:
norm /= n
else:
norm -= n
if self.ui.normROICheck.isChecked() and image.ndim == 3:
n = self.normRoi.getArrayRegion(norm, self.imageItem, (1, 2)).mean(axis=1).mean(axis=1)
n = n[:,np.newaxis,np.newaxis]
#print start, end, sind, eind
if div:
norm /= n
else:
norm -= n
return norm
def timeLineChanged(self):
if not self.ignorePlaying:
self.play(0)
(ind, time) = self.timeIndex(self.timeLine)
if ind != self.currentIndex:
self.currentIndex = ind
self.updateImage()
self.sigTimeChanged.emit(ind, time)
def updateImage(self, autoHistogramRange=True):
## Redraw image on screen
if self.image is None:
return
image = self.getProcessedImage()
if autoHistogramRange:
self.ui.histogram.setHistogramRange(self.levelMin, self.levelMax)
# Transpose image into order expected by ImageItem
if self.imageItem.axisOrder == 'col-major':
axorder = ['t', 'x', 'y', 'c']
else:
axorder = ['t', 'y', 'x', 'c']
axorder = [self.axes[ax] for ax in axorder if self.axes[ax] is not None]
image = image.transpose(axorder)
# Select time index
if self.axes['t'] is not None:
self.ui.roiPlot.show()
image = image[self.currentIndex]
self.imageItem.updateImage(image)
def timeIndex(self, slider):
## Return the time and frame index indicated by a slider
if not self.hasTimeAxis():
return (0,0)
t = slider.value()
xv = self.tVals
if xv is None:
ind = int(t)
else:
if len(xv) < 2:
return (0,0)
totTime = xv[-1] + (xv[-1]-xv[-2])
inds = np.argwhere(xv <= t)
if len(inds) < 1:
return (0,t)
ind = inds[-1,0]
return ind, t
[docs] def getView(self):
"""Return the ViewBox (or other compatible object) which displays the ImageItem"""
return self.view
[docs] def getImageItem(self):
"""Return the ImageItem for this ImageView."""
return self.imageItem
[docs] def getRoiPlot(self):
"""Return the ROI PlotWidget for this ImageView"""
return self.ui.roiPlot
[docs] def export(self, fileName):
"""
Export data from the ImageView to a file, or to a stack of files if
the data is 3D. Saving an image stack will result in index numbers
being added to the file name. Images are saved as they would appear
onscreen, with levels and lookup table applied.
"""
img = self.getProcessedImage()
if self.hasTimeAxis():
base, ext = os.path.splitext(fileName)
fmt = "%%s%%0%dd%%s" % int(log10(img.shape[0])+1)
for i in range(img.shape[0]):
self.imageItem.setImage(img[i], autoLevels=False)
self.imageItem.save(fmt % (base, i, ext))
self.updateImage()
else:
self.imageItem.save(fileName)
def exportClicked(self):
fileName = QtGui.QFileDialog.getSaveFileName()
if isinstance(fileName, tuple):
fileName = fileName[0] # Qt4/5 API difference
if fileName == '':
return
self.export(str(fileName))
def buildMenu(self):
self.menu = QtGui.QMenu()
self.normAction = QtGui.QAction(translate("ImageView", "Normalization"), self.menu)
self.normAction.setCheckable(True)
self.normAction.toggled.connect(self.normToggled)
self.menu.addAction(self.normAction)
self.exportAction = QtGui.QAction(translate("ImageView", "Export"), self.menu)
self.exportAction.triggered.connect(self.exportClicked)
self.menu.addAction(self.exportAction)
def menuClicked(self):
if self.menu is None:
self.buildMenu()
self.menu.popup(QtGui.QCursor.pos())
[docs] def setColorMap(self, colormap):
"""Set the color map.
============= =========================================================
**Arguments**
colormap (A ColorMap() instance) The ColorMap to use for coloring
images.
============= =========================================================
"""
self.ui.histogram.gradient.setColorMap(colormap)
[docs] @addGradientListToDocstring()
def setPredefinedGradient(self, name):
"""Set one of the gradients defined in :class:`GradientEditorItem <pyqtgraph.graphicsItems.GradientEditorItem>`.
Currently available gradients are:
"""
self.ui.histogram.gradient.loadPreset(name)