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Image Analysis Tool using PyQtGraph
  • Last Updated : 29 Apr, 2021

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.

  1. Import the required libraries like pyqtgraph, pyqt5 and numpy.
  2. Create a main window class using pyqt5.
  3. Create a graphic window to add the widgets required to show the image analysis.
  4. Create two plot area and add a image item to it with the roi object to first plot area.
  5. Create an isocurve object and add it to the image item.
  6. Create a data for image and add it to the image item.
  7. 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.
  8. Create a mouse move event and set the position, pixel value to the title according to the mouse position.
  9. Add this graph window to the main window layout with any additional widgets.

Below is the implementation 



Python3




# importing Qt widgets
from PyQt5.QtWidgets import *
 
# importing system
import sys
 
# importing numpy as np
import numpy as np
 
# importing pyqtgraph as pg
import pyqtgraph as pg
from 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 grpahic 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 positon
            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 widow
        self.setCentralWidget(widget)
 
# create pyqt5 app
App = QApplication(sys.argv)
 
# create the instance of our Window
window = Window()
 
# start the app
sys.exit(App.exec())

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