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"""
Demonstrates common image analysis tools.
Many of the features demonstrated here are already provided by the ImageView
widget, but here we present a lower-level approach that provides finer control
over the user interface.
"""
import numpy as np
import pyqtgraph as pg
from pyqtgraph.Qt import QtGui
# Interpret image data as row-major instead of col-major
pg.setConfigOptions(imageAxisOrder='row-major')
pg.mkQApp()
win = pg.GraphicsLayoutWidget()
win.setWindowTitle('pyqtgraph example: Image Analysis')
# A plot area (ViewBox + axes) for displaying the image
p1 = win.addPlot(title="")
# Item for displaying image data
img = pg.ImageItem()
p1.addItem(img)
# Custom ROI for selecting an image region
roi = pg.ROI([-8, 14], [6, 5])
roi.addScaleHandle([0.5, 1], [0.5, 0.5])
roi.addScaleHandle([0, 0.5], [0.5, 0.5])
p1.addItem(roi)
roi.setZValue(10) # make sure ROI is drawn above image
# Isocurve drawing
iso = pg.IsocurveItem(level=0.8, pen='g')
iso.setParentItem(img)
iso.setZValue(5)
# Contrast/color control
hist = pg.HistogramLUTItem()
hist.setImageItem(img)
win.addItem(hist)
# Draggable line for setting isocurve level
isoLine = pg.InfiniteLine(angle=0, movable=True, pen='g')
hist.vb.addItem(isoLine)
hist.vb.setMouseEnabled(y=False) # makes user interaction a little easier
isoLine.setValue(0.8)
isoLine.setZValue(1000) # bring iso line above contrast controls
# Another plot area for displaying ROI data
win.nextRow()
p2 = win.addPlot(colspan=2)
p2.setMaximumHeight(250)
win.resize(800, 800)
win.show()
# Generate image data
data = np.random.normal(size=(200, 100))
data[20:80, 20:80] += 2.
data = pg.gaussianFilter(data, (3, 3))
data += np.random.normal(size=(200, 100)) * 0.1
img.setImage(data)
hist.setLevels(data.min(), data.max())
# build isocurves from smoothed data
iso.setData(pg.gaussianFilter(data, (2, 2)))
# set position and scale of image
tr = QtGui.QTransform()
img.setTransform(tr.scale(0.2, 0.2).translate(-50, 0))
# zoom to fit imageo
p1.autoRange()
# Callbacks for handling user interaction
def updatePlot():
global img, roi, data, p2
selected = roi.getArrayRegion(data, img)
p2.plot(selected.mean(axis=0), clear=True)
roi.sigRegionChanged.connect(updatePlot)
updatePlot()
def updateIsocurve():
global isoLine, iso
iso.setLevel(isoLine.value())
isoLine.sigDragged.connect(updateIsocurve)
def imageHoverEvent(event):
"""Show the position, pixel, and value under the mouse cursor.
"""
if event.isExit():
p1.setTitle("")
return
pos = event.pos()
i, j = pos.y(), pos.x()
i = int(np.clip(i, 0, data.shape[0] - 1))
j = int(np.clip(j, 0, data.shape[1] - 1))
val = data[i, j]
ppos = img.mapToParent(pos)
x, y = ppos.x(), ppos.y()
p1.setTitle("pos: (%0.1f, %0.1f) pixel: (%d, %d) value: %.3g" % (x, y, i, j, val))
# Monkey-patch the image to use our custom hover function.
# This is generally discouraged (you should subclass ImageItem instead),
# but it works for a very simple use like this.
img.hoverEvent = imageHoverEvent
if __name__ == '__main__':
pg.exec()
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