使用 PyQtGraph 的图像分析工具
在本文中，我们将看到如何使用Python中的 PyQtGraph 模块执行常见的图像分析。 PyQtGraph 是一个用于Python的图形和用户界面库，它提供了设计和科学应用程序中通常需要的功能。它的主要目标是提供用于显示数据（绘图、视频等）的快速交互式图形，其次是提供有助于快速应用程序开发的工具（例如，Qt Designer 中使用的属性树）。
为了安装 PyQtGraph，我们使用下面给出的命令。
pip install pyqtgraph图像分析是从图像中提取有意义的信息；主要是通过数字图像处理技术从数字图像中提取。图像分析任务可以像读取条形码标签一样简单，也可以像从面部识别一个人一样复杂。
为此，我们必须执行以下操作。
- 导入所需的库，如 pyqtgraph、pyqt5 和 numpy。
- 使用 pyqt5 创建一个主窗口类。
- 创建一个图形窗口以添加显示图像分析所需的小部件。
- 创建两个绘图区域并将图像项添加到其中，并将 roi 对象添加到第一个绘图区域。
- 创建一个 isocurve 对象并将其添加到图像项中。
- 为图像创建数据并将其添加到图像项中。
- 当区域发生变化时，将更新方法连接到 roi 对象，在更新方法中获取区域并将其设置为第二个绘图区域。
- 创建一个鼠标移动事件，并根据鼠标位置为标题设置位置、像素值。
- 将此图形窗口添加到带有任何其他小部件的主窗口布局。
下面是实现
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 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 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())输出：