PyQtGraph - 图像视图的自动范围

在本文中，我们将看到如何在 PyQTGaph 中设置图像视图的自动范围。 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。用于显示和分析图像数据的小部件。实现许多功能，例如显示 2D 和 3D 图像数据。对于 3D 数据，会显示一个 z 轴滑块，允许用户选择要显示的帧。显示带有定义暗/亮级别的可移动区域的图像数据直方图，可编辑渐变提供颜色查找表，也可以使用向左/向右箭头键以及 pgup、pgdn、home 和 end 移动帧滑块。围绕图像自动缩放和平移视图，以便图像填充视图。
我们可以在下面给出的命令的帮助下创建一个图像视图

# creating a pyqtgraph image view object
imv = pg.ImageView()

为了做到这一点，我们对图像视图对象使用autoRange()方法

Syntax : imv.autoRange()
Argument : It takes no argument
Return : It returns None 

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 *
  
from collections import namedtuple
  
class Window(QMainWindow):
  
    def __init__(self):
        super().__init__()
  
        # setting title
        self.setWindowTitle("PyQtGraph")
  
        # setting geometry
        self.setGeometry(100, 100, 600, 500)
  
        # 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()
  
        # creating a label
        label = QLabel("Geeksforgeeks Image View")
  
        # setting minimum width
        label.setMinimumWidth(130)
  
        # making label do word wrap
        label.setWordWrap(True)
  
        # setting configuration options
        pg.setConfigOptions(antialias = True)
  
        # creating image view view object
        imv = pg.ImageView()
  
        # Create random 3D data set with noisy signals
        img = pg.gaussianFilter(np.random.normal(size = (200, 200)), 
                                (5, 5)) * 20 + 100
  
        # setting new axis to image
        img = img[np.newaxis, :, :]
  
        # decay data
        decay = np.exp(-np.linspace(0, 0.3, 100))[:, np.newaxis, np.newaxis]
  
        # random data
        data = np.random.normal(size = (100, 200, 200))
        data += img * decay
        data += 2
  
        # adding time-varying signal
        sig = np.zeros(data.shape[0])
        sig[30:] += np.exp(-np.linspace(1, 10, 70))
        sig[40:] += np.exp(-np.linspace(1, 10, 60))
        sig[70:] += np.exp(-np.linspace(1, 10, 30))
  
        sig = sig[:, np.newaxis, np.newaxis] * 3
        data[:, 50:60, 30:40] += sig
  
        # Displaying the data and assign each frame a time value from 1.0 to 3.0
        imv.setImage(data, xvals=np.linspace(1., 3., data.shape[0]))
  
        # Set a custom color map
        colors = [
            (0, 0, 0),
            (45, 5, 61),
            (84, 42, 55),
            (150, 87, 60),
            (208, 171, 141),
            (255, 255, 255)
        ]
  
        # color map
        cmap = pg.ColorMap(pos=np.linspace(0.0, 1.0, 6), color=colors)
  
        # setting color map to the image view
        imv.setColorMap(cmap)
  
        # Creating a grid layout
        layout = QGridLayout()
  
        # minimum width value of the label
        label.setFixedWidth(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(imv, 0, 1, 3, 1)
  
        # setting this widget as central widget of the main widow
        self.setCentralWidget(widget)
  
        # setting automatic range of image view
        imv.autoRange()
  
# create pyqt5 app
App = QApplication(sys.argv)
  
# create the instance of our Window
window = Window()
  
# start the app
sys.exit(App.exec())

输出 ：