118 lines
3.1 KiB
Python
118 lines
3.1 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Demonstrates common image analysis tools.
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Many of the features demonstrated here are already provided by the ImageView
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widget, but here we present a lower-level approach that provides finer control
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over the user interface.
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"""
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import initExample ## Add path to library (just for examples; you do not need this)
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import pyqtgraph as pg
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from pyqtgraph.Qt import QtCore, QtGui
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import numpy as np
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# Interpret image data as row-major instead of col-major
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pg.setConfigOptions(imageAxisOrder='row-major')
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pg.mkQApp()
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win = pg.GraphicsLayoutWidget()
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win.setWindowTitle('pyqtgraph example: Image Analysis')
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# A plot area (ViewBox + axes) for displaying the image
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p1 = win.addPlot(title="")
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# Item for displaying image data
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img = pg.ImageItem()
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p1.addItem(img)
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# Custom ROI for selecting an image region
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roi = pg.ROI([-8, 14], [6, 5])
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roi.addScaleHandle([0.5, 1], [0.5, 0.5])
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roi.addScaleHandle([0, 0.5], [0.5, 0.5])
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p1.addItem(roi)
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roi.setZValue(10) # make sure ROI is drawn above image
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# Isocurve drawing
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iso = pg.IsocurveItem(level=0.8, pen='g')
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iso.setParentItem(img)
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iso.setZValue(5)
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# Contrast/color control
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hist = pg.HistogramLUTItem()
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hist.setImageItem(img)
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win.addItem(hist)
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# Draggable line for setting isocurve level
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isoLine = pg.InfiniteLine(angle=0, movable=True, pen='g')
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hist.vb.addItem(isoLine)
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hist.vb.setMouseEnabled(y=False) # makes user interaction a little easier
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isoLine.setValue(0.8)
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isoLine.setZValue(1000) # bring iso line above contrast controls
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# Another plot area for displaying ROI data
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win.nextRow()
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p2 = win.addPlot(colspan=2)
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p2.setMaximumHeight(250)
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win.resize(800, 800)
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win.show()
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# Generate image data
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data = np.random.normal(size=(200, 100))
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data[20:80, 20:80] += 2.
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data = pg.gaussianFilter(data, (3, 3))
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data += np.random.normal(size=(200, 100)) * 0.1
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img.setImage(data)
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hist.setLevels(data.min(), data.max())
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# build isocurves from smoothed data
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iso.setData(pg.gaussianFilter(data, (2, 2)))
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# set position and scale of image
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tr = QtGui.QTransform()
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img.setTransform(tr.scale(0.2, 0.2).translate(-50, 0))
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# zoom to fit imageo
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p1.autoRange()
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# Callbacks for handling user interaction
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def updatePlot():
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global img, roi, data, p2
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selected = roi.getArrayRegion(data, img)
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p2.plot(selected.mean(axis=0), clear=True)
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roi.sigRegionChanged.connect(updatePlot)
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updatePlot()
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def updateIsocurve():
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global isoLine, iso
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iso.setLevel(isoLine.value())
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isoLine.sigDragged.connect(updateIsocurve)
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def imageHoverEvent(event):
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"""Show the position, pixel, and value under the mouse cursor.
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"""
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if event.isExit():
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p1.setTitle("")
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return
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pos = event.pos()
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i, j = pos.y(), pos.x()
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i = int(np.clip(i, 0, data.shape[0] - 1))
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j = int(np.clip(j, 0, data.shape[1] - 1))
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val = data[i, j]
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ppos = img.mapToParent(pos)
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x, y = ppos.x(), ppos.y()
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p1.setTitle("pos: (%0.1f, %0.1f) pixel: (%d, %d) value: %.3g" % (x, y, i, j, val))
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# Monkey-patch the image to use our custom hover function.
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# This is generally discouraged (you should subclass ImageItem instead),
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# but it works for a very simple use like this.
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img.hoverEvent = imageHoverEvent
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if __name__ == '__main__':
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pg.exec()
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