850 lines
31 KiB
Python
850 lines
31 KiB
Python
# -*- coding: utf-8 -*-
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"""
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ImageView.py - Widget for basic image dispay and analysis
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Copyright 2010 Luke Campagnola
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Distributed under MIT/X11 license. See license.txt for more information.
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Widget used for displaying 2D or 3D data. Features:
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- float or int (including 16-bit int) image display via ImageItem
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- zoom/pan via GraphicsView
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- black/white level controls
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- time slider for 3D data sets
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- ROI plotting
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- Image normalization through a variety of methods
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"""
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import os, sys
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import numpy as np
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from ..Qt import QtCore, QtGui, QT_LIB
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import importlib
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ui_template = importlib.import_module(
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f'.ImageViewTemplate_{QT_LIB.lower()}', package=__package__)
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from ..graphicsItems.ImageItem import *
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from ..graphicsItems.ROI import *
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from ..graphicsItems.LinearRegionItem import *
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from ..graphicsItems.InfiniteLine import *
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from ..graphicsItems.ViewBox import *
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from ..graphicsItems.VTickGroup import VTickGroup
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from ..graphicsItems.GradientEditorItem import addGradientListToDocstring
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from .. import ptime as ptime
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from .. import debug as debug
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from ..SignalProxy import SignalProxy
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from .. import getConfigOption
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try:
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from bottleneck import nanmin, nanmax
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except ImportError:
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from numpy import nanmin, nanmax
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class PlotROI(ROI):
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def __init__(self, size):
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ROI.__init__(self, pos=[0,0], size=size) #, scaleSnap=True, translateSnap=True)
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self.addScaleHandle([1, 1], [0, 0])
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self.addRotateHandle([0, 0], [0.5, 0.5])
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class ImageView(QtGui.QWidget):
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"""
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Widget used for display and analysis of image data.
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Implements many features:
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* Displays 2D and 3D image data. For 3D data, a z-axis
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slider is displayed allowing the user to select which frame is displayed.
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* Displays histogram of image data with movable region defining the dark/light levels
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* Editable gradient provides a color lookup table
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* Frame slider may also be moved using left/right arrow keys as well as pgup, pgdn, home, and end.
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* Basic analysis features including:
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* ROI and embedded plot for measuring image values across frames
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* Image normalization / background subtraction
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Basic Usage::
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imv = pg.ImageView()
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imv.show()
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imv.setImage(data)
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**Keyboard interaction**
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* left/right arrows step forward/backward 1 frame when pressed,
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seek at 20fps when held.
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* up/down arrows seek at 100fps
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* pgup/pgdn seek at 1000fps
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* home/end seek immediately to the first/last frame
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* space begins playing frames. If time values (in seconds) are given
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for each frame, then playback is in realtime.
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"""
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sigTimeChanged = QtCore.Signal(object, object)
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sigProcessingChanged = QtCore.Signal(object)
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def __init__(self, parent=None, name="ImageView", view=None, imageItem=None,
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levelMode='mono', *args):
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"""
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By default, this class creates an :class:`ImageItem <pyqtgraph.ImageItem>` to display image data
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and a :class:`ViewBox <pyqtgraph.ViewBox>` to contain the ImageItem.
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============= =========================================================
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**Arguments**
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parent (QWidget) Specifies the parent widget to which
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this ImageView will belong. If None, then the ImageView
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is created with no parent.
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name (str) The name used to register both the internal ViewBox
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and the PlotItem used to display ROI data. See the *name*
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argument to :func:`ViewBox.__init__()
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<pyqtgraph.ViewBox.__init__>`.
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view (ViewBox or PlotItem) If specified, this will be used
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as the display area that contains the displayed image.
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Any :class:`ViewBox <pyqtgraph.ViewBox>`,
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:class:`PlotItem <pyqtgraph.PlotItem>`, or other
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compatible object is acceptable.
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imageItem (ImageItem) If specified, this object will be used to
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display the image. Must be an instance of ImageItem
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or other compatible object.
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levelMode See the *levelMode* argument to
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:func:`HistogramLUTItem.__init__()
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<pyqtgraph.HistogramLUTItem.__init__>`
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============= =========================================================
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Note: to display axis ticks inside the ImageView, instantiate it
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with a PlotItem instance as its view::
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pg.ImageView(view=pg.PlotItem())
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"""
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QtGui.QWidget.__init__(self, parent, *args)
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self._imageLevels = None # [(min, max), ...] per channel image metrics
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self.levelMin = None # min / max levels across all channels
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self.levelMax = None
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self.name = name
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self.image = None
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self.axes = {}
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self.imageDisp = None
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self.ui = ui_template.Ui_Form()
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self.ui.setupUi(self)
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self.scene = self.ui.graphicsView.scene()
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self.ui.histogram.setLevelMode(levelMode)
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self.ignorePlaying = False
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if view is None:
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self.view = ViewBox()
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else:
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self.view = view
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self.ui.graphicsView.setCentralItem(self.view)
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self.view.setAspectLocked(True)
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self.view.invertY()
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if imageItem is None:
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self.imageItem = ImageItem()
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else:
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self.imageItem = imageItem
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self.view.addItem(self.imageItem)
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self.currentIndex = 0
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self.ui.histogram.setImageItem(self.imageItem)
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self.menu = None
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self.ui.normGroup.hide()
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self.roi = PlotROI(10)
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self.roi.setZValue(20)
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self.view.addItem(self.roi)
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self.roi.hide()
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self.normRoi = PlotROI(10)
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self.normRoi.setPen('y')
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self.normRoi.setZValue(20)
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self.view.addItem(self.normRoi)
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self.normRoi.hide()
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self.roiCurves = []
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self.roiCurve = self.ui.roiPlot.plot()
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self.timeLine = InfiniteLine(0, movable=True)
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if getConfigOption('background')=='w':
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self.timeLine.setPen((20, 80,80, 200))
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else:
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self.timeLine.setPen((255, 255, 0, 200))
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self.timeLine.setZValue(1)
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self.ui.roiPlot.addItem(self.timeLine)
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self.ui.splitter.setSizes([self.height()-35, 35])
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# make splitter an unchangeable small grey line:
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s = self.ui.splitter
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s.handle(1).setEnabled(False)
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s.setStyleSheet("QSplitter::handle{background-color: grey}")
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s.setHandleWidth(2)
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self.ui.roiPlot.hideAxis('left')
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self.frameTicks = VTickGroup(yrange=[0.8, 1], pen=0.4)
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self.ui.roiPlot.addItem(self.frameTicks, ignoreBounds=True)
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self.keysPressed = {}
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self.playTimer = QtCore.QTimer()
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self.playRate = 0
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self.fps = 1 # 1 Hz by default
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self.lastPlayTime = 0
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self.normRgn = LinearRegionItem()
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self.normRgn.setZValue(0)
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self.ui.roiPlot.addItem(self.normRgn)
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self.normRgn.hide()
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## wrap functions from view box
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for fn in ['addItem', 'removeItem']:
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setattr(self, fn, getattr(self.view, fn))
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## wrap functions from histogram
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for fn in ['setHistogramRange', 'autoHistogramRange', 'getLookupTable', 'getLevels']:
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setattr(self, fn, getattr(self.ui.histogram, fn))
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self.timeLine.sigPositionChanged.connect(self.timeLineChanged)
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self.ui.roiBtn.clicked.connect(self.roiClicked)
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self.roi.sigRegionChanged.connect(self.roiChanged)
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#self.ui.normBtn.toggled.connect(self.normToggled)
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self.ui.menuBtn.clicked.connect(self.menuClicked)
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self.ui.normDivideRadio.clicked.connect(self.normRadioChanged)
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self.ui.normSubtractRadio.clicked.connect(self.normRadioChanged)
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self.ui.normOffRadio.clicked.connect(self.normRadioChanged)
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self.ui.normROICheck.clicked.connect(self.updateNorm)
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self.ui.normFrameCheck.clicked.connect(self.updateNorm)
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self.ui.normTimeRangeCheck.clicked.connect(self.updateNorm)
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self.playTimer.timeout.connect(self.timeout)
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self.normProxy = SignalProxy(self.normRgn.sigRegionChanged, slot=self.updateNorm)
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self.normRoi.sigRegionChangeFinished.connect(self.updateNorm)
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self.ui.roiPlot.registerPlot(self.name + '_ROI')
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self.view.register(self.name)
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self.noRepeatKeys = [QtCore.Qt.Key_Right, QtCore.Qt.Key_Left, QtCore.Qt.Key_Up, QtCore.Qt.Key_Down, QtCore.Qt.Key_PageUp, QtCore.Qt.Key_PageDown]
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self.roiClicked() ## initialize roi plot to correct shape / visibility
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def setImage(self, img, autoRange=True, autoLevels=True, levels=None, axes=None, xvals=None, pos=None, scale=None, transform=None, autoHistogramRange=True, levelMode=None):
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"""
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Set the image to be displayed in the widget.
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================== ===========================================================================
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**Arguments:**
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img (numpy array) the image to be displayed. See :func:`ImageItem.setImage` and
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*notes* below.
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xvals (numpy array) 1D array of z-axis values corresponding to the first axis
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in a 3D image. For video, this array should contain the time of each
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frame.
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autoRange (bool) whether to scale/pan the view to fit the image.
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autoLevels (bool) whether to update the white/black levels to fit the image.
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levels (min, max); the white and black level values to use.
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axes Dictionary indicating the interpretation for each axis.
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This is only needed to override the default guess. Format is::
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{'t':0, 'x':1, 'y':2, 'c':3};
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pos Change the position of the displayed image
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scale Change the scale of the displayed image
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transform Set the transform of the displayed image. This option overrides *pos*
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and *scale*.
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autoHistogramRange If True, the histogram y-range is automatically scaled to fit the
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image data.
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levelMode If specified, this sets the user interaction mode for setting image
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levels. Options are 'mono', which provides a single level control for
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all image channels, and 'rgb' or 'rgba', which provide individual
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controls for each channel.
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================== ===========================================================================
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**Notes:**
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For backward compatibility, image data is assumed to be in column-major order (column, row).
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However, most image data is stored in row-major order (row, column) and will need to be
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transposed before calling setImage()::
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imageview.setImage(imagedata.T)
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This requirement can be changed by the ``imageAxisOrder``
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:ref:`global configuration option <apiref_config>`.
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"""
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profiler = debug.Profiler()
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if hasattr(img, 'implements') and img.implements('MetaArray'):
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img = img.asarray()
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if not isinstance(img, np.ndarray):
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required = ['dtype', 'max', 'min', 'ndim', 'shape', 'size']
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if not all([hasattr(img, attr) for attr in required]):
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raise TypeError("Image must be NumPy array or any object "
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"that provides compatible attributes/methods:\n"
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" %s" % str(required))
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self.image = img
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self.imageDisp = None
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if levelMode is not None:
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self.ui.histogram.setLevelMode(levelMode)
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profiler()
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if axes is None:
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x,y = (0, 1) if self.imageItem.axisOrder == 'col-major' else (1, 0)
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if img.ndim == 2:
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self.axes = {'t': None, 'x': x, 'y': y, 'c': None}
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elif img.ndim == 3:
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# Ambiguous case; make a guess
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if img.shape[2] <= 4:
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self.axes = {'t': None, 'x': x, 'y': y, 'c': 2}
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else:
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self.axes = {'t': 0, 'x': x+1, 'y': y+1, 'c': None}
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elif img.ndim == 4:
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# Even more ambiguous; just assume the default
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self.axes = {'t': 0, 'x': x+1, 'y': y+1, 'c': 3}
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else:
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raise Exception("Can not interpret image with dimensions %s" % (str(img.shape)))
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elif isinstance(axes, dict):
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self.axes = axes.copy()
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elif isinstance(axes, list) or isinstance(axes, tuple):
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self.axes = {}
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for i in range(len(axes)):
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self.axes[axes[i]] = i
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else:
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raise Exception("Can not interpret axis specification %s. Must be like {'t': 2, 'x': 0, 'y': 1} or ('t', 'x', 'y', 'c')" % (str(axes)))
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for x in ['t', 'x', 'y', 'c']:
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self.axes[x] = self.axes.get(x, None)
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axes = self.axes
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if xvals is not None:
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self.tVals = xvals
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elif axes['t'] is not None:
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if hasattr(img, 'xvals'):
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try:
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self.tVals = img.xvals(axes['t'])
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except:
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self.tVals = np.arange(img.shape[axes['t']])
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else:
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self.tVals = np.arange(img.shape[axes['t']])
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profiler()
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self.currentIndex = 0
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self.updateImage(autoHistogramRange=autoHistogramRange)
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if levels is None and autoLevels:
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self.autoLevels()
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if levels is not None: ## this does nothing since getProcessedImage sets these values again.
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self.setLevels(*levels)
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if self.ui.roiBtn.isChecked():
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self.roiChanged()
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profiler()
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if self.axes['t'] is not None:
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self.ui.roiPlot.setXRange(self.tVals.min(), self.tVals.max())
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self.frameTicks.setXVals(self.tVals)
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self.timeLine.setValue(0)
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if len(self.tVals) > 1:
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start = self.tVals.min()
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stop = self.tVals.max() + abs(self.tVals[-1] - self.tVals[0]) * 0.02
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elif len(self.tVals) == 1:
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start = self.tVals[0] - 0.5
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stop = self.tVals[0] + 0.5
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else:
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start = 0
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stop = 1
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for s in [self.timeLine, self.normRgn]:
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s.setBounds([start, stop])
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profiler()
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self.imageItem.resetTransform()
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if scale is not None:
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self.imageItem.scale(*scale)
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if pos is not None:
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self.imageItem.setPos(*pos)
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if transform is not None:
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self.imageItem.setTransform(transform)
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profiler()
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if autoRange:
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self.autoRange()
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self.roiClicked()
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profiler()
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def clear(self):
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self.image = None
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self.imageItem.clear()
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def play(self, rate=None):
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"""Begin automatically stepping frames forward at the given rate (in fps).
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This can also be accessed by pressing the spacebar."""
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#print "play:", rate
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if rate is None:
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rate = self.fps
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self.playRate = rate
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if rate == 0:
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self.playTimer.stop()
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return
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self.lastPlayTime = ptime.time()
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if not self.playTimer.isActive():
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self.playTimer.start(16)
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def autoLevels(self):
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"""Set the min/max intensity levels automatically to match the image data."""
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self.setLevels(rgba=self._imageLevels)
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def setLevels(self, *args, **kwds):
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"""Set the min/max (bright and dark) levels.
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See :func:`HistogramLUTItem.setLevels <pyqtgraph.HistogramLUTItem.setLevels>`.
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"""
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self.ui.histogram.setLevels(*args, **kwds)
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def autoRange(self):
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"""Auto scale and pan the view around the image such that the image fills the view."""
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image = self.getProcessedImage()
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self.view.autoRange()
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def getProcessedImage(self):
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"""Returns the image data after it has been processed by any normalization options in use.
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"""
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if self.imageDisp is None:
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image = self.normalize(self.image)
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self.imageDisp = image
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self._imageLevels = self.quickMinMax(self.imageDisp)
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self.levelMin = min([level[0] for level in self._imageLevels])
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self.levelMax = max([level[1] for level in self._imageLevels])
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return self.imageDisp
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def close(self):
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"""Closes the widget nicely, making sure to clear the graphics scene and release memory."""
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self.clear()
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self.imageDisp = None
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self.imageItem.setParent(None)
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super(ImageView, self).close()
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self.setParent(None)
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def keyPressEvent(self, ev):
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#print ev.key()
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if ev.key() == QtCore.Qt.Key_Space:
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if self.playRate == 0:
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self.play()
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else:
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self.play(0)
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ev.accept()
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elif ev.key() == QtCore.Qt.Key_Home:
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self.setCurrentIndex(0)
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self.play(0)
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ev.accept()
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elif ev.key() == QtCore.Qt.Key_End:
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self.setCurrentIndex(self.getProcessedImage().shape[0]-1)
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self.play(0)
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ev.accept()
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elif ev.key() in self.noRepeatKeys:
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ev.accept()
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if ev.isAutoRepeat():
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return
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self.keysPressed[ev.key()] = 1
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self.evalKeyState()
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else:
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super().keyPressEvent(ev)
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def keyReleaseEvent(self, ev):
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if ev.key() in [QtCore.Qt.Key_Space, QtCore.Qt.Key_Home, QtCore.Qt.Key_End]:
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ev.accept()
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elif ev.key() in self.noRepeatKeys:
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ev.accept()
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if ev.isAutoRepeat():
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return
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try:
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del self.keysPressed[ev.key()]
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except:
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self.keysPressed = {}
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self.evalKeyState()
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else:
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super().keyReleaseEvent(ev)
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def evalKeyState(self):
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if len(self.keysPressed) == 1:
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key = list(self.keysPressed.keys())[0]
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if key == QtCore.Qt.Key_Right:
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self.play(20)
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self.jumpFrames(1)
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self.lastPlayTime = ptime.time() + 0.2 ## 2ms wait before start
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## This happens *after* jumpFrames, since it might take longer than 2ms
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elif key == QtCore.Qt.Key_Left:
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self.play(-20)
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self.jumpFrames(-1)
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self.lastPlayTime = ptime.time() + 0.2
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elif key == QtCore.Qt.Key_Up:
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self.play(-100)
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elif key == QtCore.Qt.Key_Down:
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self.play(100)
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elif key == QtCore.Qt.Key_PageUp:
|
|
self.play(-1000)
|
|
elif key == QtCore.Qt.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)
|
|
|
|
def setCurrentIndex(self, ind):
|
|
"""Set the currently displayed frame index."""
|
|
index = np.clip(ind, 0, self.getProcessedImage().shape[self.axes['t']]-1)
|
|
self.ignorePlaying = True
|
|
# Implicitly call timeLineChanged
|
|
self.timeLine.setValue(self.tVals[index])
|
|
self.ignorePlaying = False
|
|
|
|
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([self.height()*0.6, 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)
|
|
|
|
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])]
|
|
|
|
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 self.image is None:
|
|
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
|
|
|
|
def getView(self):
|
|
"""Return the ViewBox (or other compatible object) which displays the ImageItem"""
|
|
return self.view
|
|
|
|
def getImageItem(self):
|
|
"""Return the ImageItem for this ImageView."""
|
|
return self.imageItem
|
|
|
|
def getRoiPlot(self):
|
|
"""Return the ROI PlotWidget for this ImageView"""
|
|
return self.ui.roiPlot
|
|
|
|
def getHistogramWidget(self):
|
|
"""Return the HistogramLUTWidget for this ImageView"""
|
|
return self.ui.histogram
|
|
|
|
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(np.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("Normalization", self.menu)
|
|
self.normAction.setCheckable(True)
|
|
self.normAction.toggled.connect(self.normToggled)
|
|
self.menu.addAction(self.normAction)
|
|
self.exportAction = QtGui.QAction("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())
|
|
|
|
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)
|
|
|
|
@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)
|