In this article we will see how we can perform common image analysis using PyQtGraph module in Python. PyQtGraph is a graphics and user interface library for Python that provides functionality commonly required in designing and science applications. Its primary goals are to provide fast, interactive graphics for displaying data (plots, video, etc.) and second is to provide tools to aid in rapid application development (for example, property trees such as used in Qt Designer).
In order to install the PyQtGraph we use the command given below.
pip install pyqtgraph
Image analysis is the extraction of meaningful information from images; mainly from digital images by means of digital image processing techniques. Image analysis tasks can be as simple as reading bar coded tags or as sophisticated as identifying a person from their face.
In order to do this we have to do the following.
- Import the required libraries like pyqtgraph, pyqt5 and numpy.
- Create a main window class using pyqt5.
- Create a graphic window to add the widgets required to show the image analysis.
- Create two plot area and add a image item to it with the roi object to first plot area.
- Create an isocurve object and add it to the image item.
- Create a data for image and add it to the image item.
- Connect a update method to the roi object when the region is changed, inside the update method get the region and set it to the second plot area.
- Create a mouse move event and set the position, pixel value to the title according to the mouse position.
- Add this graph window to the main window layout with any additional widgets.
Below is the implementation
Python3
# importing Qt widgetsfrom PyQt5.QtWidgets import *# importing systemimport sys# importing numpy as npimport numpy as np# importing pyqtgraph as pgimport pyqtgraph as pgfrom PyQt5.QtGui import *from PyQt5.QtCore import *class Window(QMainWindow): def __init__(self): super().__init__() # setting title self.setWindowTitle("PyQtGraph") # setting geometry self.setGeometry(100, 100, 900, 550) # icon icon = QIcon("skin.png") # setting icon to the window self.setWindowIcon(icon) # calling method self.UiComponents() # showing all the widgets self.show() # method for components def UiComponents(self): # creating a widget object widget = QWidget() # text text = "Image Analysis" # creating a label label = QLabel(text) # setting minimum width label.setMinimumWidth(130) # making label do word wrap label.setWordWrap(True) # creating a graphic layout widget win = pg.GraphicsLayoutWidget() # plot area (ViewBox + axes) for displaying the image p1 = win.addPlot(title="") # item for displaying image data img = pg.ImageItem() # adding image to the plot area p1.addItem(img) # Custom ROI for selecting an image region roi = pg.ROI([-10, 14], [5, 5]) roi.addScaleHandle([0.5, 1], [0.5, 0.5]) roi.addScaleHandle([0, 0.5], [0.5, 0.5]) # adding roi to the plot area p1.addItem(roi) # setting z value to roi # making sure ROI is drawn above image roi.setZValue(10) # creating a Isocurve drawing on the image iso = pg.IsocurveItem(level=1.2, pen='r') # setting parent as image iso.setParentItem(img) # setting z axis value of isocurve iso.setZValue(5) # Contrast/color control hist = pg.HistogramLUTItem() # setting image to the control hist.setImageItem(img) # adding control widget to the plot window win.addItem(hist) # creating draggable line for setting isocurve level isoLine = pg.InfiniteLine(angle=0, movable=True, pen='r') hist.vb.addItem(isoLine) # making user interaction a little easier hist.vb.setMouseEnabled(y=False) isoLine.setValue(0.8) # bring iso line above contrast controls isoLine.setZValue(1000) # going to next row of graphic window win.nextRow() # another plot area for displaying ROI data p2 = win.addPlot(colspan=2) # setting maximum height of plot area p2.setMaximumHeight(250) # generating image data data = np.random.normal(size=(200, 100)) data[20:80, 20:80] += 2. # setting gaussian filter to the data data = pg.gaussianFilter(data, (3, 3)) data += np.random.normal(size=(200, 100)) * 0.1 # setting data to the image img.setImage(data) # setting level hist.setLevels(data.min(), data.max()) # build isocurves from smoothed data iso.setData(pg.gaussianFilter(data, (2, 2))) # set position and scale of image img.scale(0.2, 0.2) img.translate(-50, 0) # zoom to fit image p1.autoRange() # method for updating the plot def updatePlot(): # getting the selected region by the roi selected = roi.getArrayRegion(data, img) # plot the selected region p2.plot(selected.mean(axis=0), clear=True) # connecting the update plot method # it get called when the region is changed roi.sigRegionChanged.connect(updatePlot) # call the update plot method updatePlot() # method for updating the isocurve def updateIsocurve(): # setting iso level iso.setLevel(isoLine.value()) isoLine.sigDragged.connect(updateIsocurve) # method for image hover event def imageHoverEvent(event): # showing the position, pixel, and value under the mouse cursor # if cursor is not on the plot area if event.isExit(): # set title as blank p1.setTitle("") return # getting cursor position pos = event.pos() i, j = pos.y(), pos.x() # pixel values i = int(np.clip(i, 0, data.shape[0] - 1)) j = int(np.clip(j, 0, data.shape[1] - 1)) # value of point val = data[i, j] ppos = img.mapToParent(pos) x, y = ppos.x(), ppos.y() # setting plot title data p1.setTitle( "pos: (%0.1f, %0.1f) pixel: (%d, %d) value: %g" % (x, y, i, j, val)) # Monkey-patch the image to use our custom hover function. img.hoverEvent = imageHoverEvent # Creating a grid layout layout = QGridLayout() # minimum width value of the label label.setMinimumWidth(130) # setting this layout to the widget widget.setLayout(layout) # adding label in the layout layout.addWidget(label, 1, 0) # plot window goes on right side, spanning 3 rows layout.addWidget(win, 0, 1, 3, 1) # setting this widget as central widget of the main window self.setCentralWidget(widget)# create pyqt5 appApp = QApplication(sys.argv)# create the instance of our Windowwindow = Window()# start the appsys.exit(App.exec()) |
Output:
