matplotlib实现同一页面显示两张图片且单独缩放和拖动各自

需求如下：

• 1、在一个页面中显示两张图片
• 2、进入页面可以使用鼠标拖动各自的图片，相互不受影响
• 3、进入页面后可以使用鼠标滚轮放大或缩小图片，相互不受影响，即鼠标移动到图片A上，可对图片A进行放大或缩小，图片B不受影响，反之亦然
• 4、拖动需求同3

实现代码：

import matplotlib.pyplot as plt
from PIL import Image
import numpy as np

class Scale:
    def __init__(self,fig,base_scale=1.5):
        self.fig=fig
        self.base_scale=base_scale
        self.x0 = None
        self.y0 = None
        self.x1 = None
        self.y1 = None
        self.xpress = None
        self.ypress = None
        self.cur_xlim = None
        self.cur_ylim = None
        self.press = None
        self.fig.canvas.mpl_connect('scroll_event', self.enter_axes)
        self.fig.canvas.mpl_connect("button_press_event", self.enter_axes)
        self.fig.canvas.mpl_connect('button_press_event',self.onPress)
        self.fig.canvas.mpl_connect('button_release_event',self.onRelease)
        self.fig.canvas.mpl_connect('motion_notify_event',self.onMotion)

    def enter_axes(self,event):
        # print(f"enter axes {event.inaxes}")
        axtemp = event.inaxes
        self.cur_xlim=axtemp.get_xlim()
        self.cur_ylim=axtemp.get_ylim()
        # print(f"x {self.cur_xlim} y {self.cur_ylim}")
        xdata=event.xdata
        ydata=event.ydata

        if event.button == "up":
           scale_factor=1/self.base_scale
        elif event.button == "down":
           scale_factor=self.base_scale
        else:
            scale_factor=1

        new_width = (self.cur_xlim[1] - self.cur_xlim[0]) * scale_factor
        new_height = (self.cur_ylim[1] - self.cur_ylim[0]) * scale_factor

        relx = (self.cur_xlim[1] - xdata) / (self.cur_xlim[1] - self.cur_xlim[0])
        rely = (self.cur_ylim[1] - ydata) / (self.cur_ylim[1] - self.cur_ylim[0])

        axtemp.set_xlim([xdata - new_width * (1 - relx), xdata + new_width * (relx)])
        axtemp.set_ylim([ydata - new_height * (1 - rely), ydata + new_height * (rely)])

        self.fig.canvas.draw_idle()

    def onPress(self,event):
        axtemp = event.inaxes
        if event.inaxes != axtemp:
            return
        self.cur_xlim = axtemp.get_xlim()
        self.cur_ylim = axtemp.get_ylim()
        self.press = self.x0, self.y0, event.xdata, event.ydata
        self.x0, self.y0, self.xpress, self.ypress = self.press

    def onMotion(self,event):
        if self.press is None:
            return
        if event.inaxes != event.inaxes:
            return
        dx = event.xdata - self.xpress
        dy = event.ydata - self.ypress
        self.cur_xlim -= dx
        self.cur_ylim -= dy
        event.inaxes.set_xlim(self.cur_xlim)
        event.inaxes.set_ylim(self.cur_ylim)

    def onRelease(self,event):
        self.press = None
        event.inaxes.figure.canvas.draw_idle()

class ViewImg:

    def __init__(self,imgPath):
        self.imgPath=imgPath

    def viewImage(self):
        try:
            img1=Image.open(self.imgPath[0])
            img2=Image.open(self.imgPath[1])
            imgList=[]

            for item in [img1,img2]:
                if item == img1:
                    scale=2000/item.size[0]
                    print(f"img1:{item},{scale}")
                elif item == img2:
                    scale=3000/item.size[0]
                    print(f"img2:{item},{scale}")
                width=int(item.size[0]*scale)
                height=int(item.size[1]*scale)
                print(f"(width,height):{(width,height)}")
                item=item.resize((width,height),Image.ANTIALIAS)
                item=np.array(item)
                imgList.append(item)

            if len(imgList)==2:
                img1,img2=imgList
            else:
                print(f"读取图片数量出错，{len(imgList)}")
                return
        except Exception as ex:
            print(f"view image error\n{ex}")
            return
        else:
            # 设置窗口最大化前参数
            plt.switch_backend("QT5Agg")
            fig=plt.figure("染色体图和细胞图")
            # 添加1行2列格子并添加第一个子格
            ax1=fig.add_subplot(1,2,1)
            # 仅显示坐标轴，但不显示刻度线
            plt.xticks([])
            plt.yticks([])
            # 显示图片
            plt.imshow(img1, aspect="auto")
            plt.axis('on')
            # 添加1行2列格子并添加第二个子格
            ax2=fig.add_subplot(1, 2, 2)
            # 添加自动布局
            plt.tight_layout()
            plt.xticks([])
            plt.yticks([])
            plt.imshow(img2, aspect="auto")
            plt.axis('on')
            # 调整四周和子图之间的间距
            plt.subplots_adjust(left=0.005,right=0.995,bottom=0.005,top=0.995,wspace=0.010,hspace=0.1)
            # 设置最大化
            plt.get_current_fig_manager().window.showMaximized()
            ax1.set_title("dna")
            ax2.set_title("cell")
            scale=Scale(fig)
            plt.show()

if __name__ == '__main__':
    imgPath=[r"C:\Users\Surpass\Documents\PycharmProjects\a.jpg",r"C:\Users\Surpass\Documents\PycharmProjects\b.jpg" ]
    viewImg=ViewImg(imgPath)
    viewImg.viewImage()

参考网址：

• 1.https://matplotlib.org/users/event_handling.html
• 2.https://blog.csdn.net/dcyywin8/article/details/84101319