MachineLearning/X2_new2.py

import tkinter as tk
from PIL import Image, ImageTk
from X1 import *
def create_instance():
    global AllModelObj
    global DataSet, Conv, Pool, FullConn, Nonline, Classifier, Error, AjConv, AjFullconn
    DataSet = Data_Class("DataSet1", 1, "数据集1", ".", 120, 330)
    Conv = Conv_Class("Conv1", 2, "卷积1", ".", 250, 330)
    Pool = Pool_Class("Pool1", 3, "最大池化1", ".", 380, 330)
    FullConn = FullConn_Class("FullConn1", 4, "全连接1", ".", 510, 330)
    Nonline = Nonline_Class("Nonline1", 5, "非线性函数1", ".", 640, 330)
    Classifier = Classifier_Class("Classifier1", 6, "分类1", ".", 780, 330)
    Error = Error_Class("Error1", 7, "误差计算1", ".", 710, 124)
    AjConv = AjConv_Class("AjConv1", 8, "卷积调整1", ".", 250, 70)
    AjFullconn = AjFullconn_Class("AjFullconn1", 9, "全连接调整1", ".", 510, 120)
    AllModelObj = [DataSet, Conv, Pool, FullConn, Nonline, Classifier, Error, AjConv, AjFullconn]

def connect_class():
    global AllModelConn
    # 创建连接对象实例
    Line1 = ModelConn(1, 1, DataSet.ObjID, Conv.ObjID).output()
    Line2 = ModelConn(2, 1, Conv.ObjID, Pool.ObjID).output()
    Line3 = ModelConn(3, 1, Pool.ObjID, FullConn.ObjID).output()
    Line4 = ModelConn(4, 1, FullConn.ObjID, Nonline.ObjID).output()
    Line5 = ModelConn(5, 1, Nonline.ObjID, Classifier.ObjID).output()
    Line6 = ModelConn(6, 1, Classifier.ObjID, Error.ObjID).output()
    Line7 = ModelConn(7, 2, Error.ObjID, AjFullconn.ObjID).output()
    Line8 = ModelConn(8, 2, Error.ObjID, AjConv.ObjID).output()
    Line9 = ModelConn(9, 2, AjFullconn.ObjID, FullConn.ObjID).output()
    Line10 = ModelConn(10, 2, AjConv.ObjID, Conv.ObjID).output()
    # 网络连接对象总表
    AllModelConn = [Line1, Line2, Line3, Line4,
                    Line5, Line6, Line7, Line8,
                    Line9, Line10]
class Networking:
    def __init__(self):
        self.Root = tk.Tk()  # 创建一个主窗口
        # 设置窗口的大小为1200*750
        self.window_width = 900  # 窗口的宽度
        self.window_height = 550  # 窗口的高度
        self.list_image = []
        self.Item_Record = [[], []]  # 记录图元坐标与图元号
    def window(self):
        self.Root.title("神经网络可视化")
        self.Root.geometry("900x550")  # 设置窗口的大小和位置
        # 创建一个画布，用于绘制矩形框，设置画布的大小和背景色
        self.Viewcanvas = tk.Canvas(self.Root, width=self.window_width, height=self.window_height, bg="white")
        # 将画布添加到主窗口中
        self.Viewcanvas.pack()
        # 绘制矩形框，使用不同的颜色和线宽，指定矩形框的左上角和右下角坐标，填充色，边框色和边框宽度
        self.Viewcanvas.create_rectangle(5, 5, 895, 545, fill=None, outline="lightblue", width=2)
    def connecting_lines(self, obj):
        obj_x = obj.ObjX  # 根据对象的id计算x坐标
        obj_y = obj.ObjY  # 根据对象的id计算y坐标
        text = obj.ObjLable
        if 'Error' in obj.ObjID:
            x, y = 0, -50
        elif 'Aj' in obj.ObjID:
            x, y = -80, 0
        else:
            x, y = 0, 50
        self.Viewcanvas.create_image(obj_x, obj_y, image=self.list_image[obj.ObjType - 1])  # 创建图元对象
        self.Viewcanvas.create_text(obj_x + x, obj_y + y, text=text, font=("黑体", 14))  # 创建图元对象的标签

    def conn_lines(self, conn):
        starting = self.Item_Record[1].index(conn[2])
        ending = self.Item_Record[1].index(conn[3])
        smooth = [False, True]
        width = [2, 4]
        start, end = self.Item_Record[0][starting], self.Item_Record[0][ending]
        index = 1 if conn[1] == 1 else 0
        if start[0] == end[0]:
            self.Viewcanvas.create_line(start[0], start[1] + 30, end[0], end[1] - 30, arrow=tk.LAST,
                                   arrowshape=(16, 20, 4), fill='lightblue', smooth=smooth[index], width=width[index])
        elif start[1] == end[1]:
            self.Viewcanvas.create_line(start[0] + 30, start[1], end[0] - 30, end[1], arrow=tk.LAST,
                                   arrowshape=(16, 20, 4), fill='lightblue', smooth=smooth[index], width=width[index])
        else:
            if abs(start[0] - end[0]) > abs(start[1] - end[1]):
                # 创建数据线箭头
                self.Viewcanvas.create_line(start[0] - 15, start[1], int((start[0] + end[0]) / 2), end[1], end[0] + 30,
                                       end[1], arrow=tk.LAST,
                                       arrowshape=(16, 20, 4), fill='lightblue', smooth=smooth[index],
                                       width=width[index])
            else:
                # 创建数据线箭头
                self.Viewcanvas.create_line(start[0], start[1] - 20, start[0], end[1], end[0] + 30, end[1], arrow=tk.LAST,
                                       arrowshape=(16, 20, 4), fill='lightblue', smooth=smooth[index],
                                       width=width[index])
    def creating_elements(self):
        text_record = [(0, -50), (0, 50), (-80, 0)]
        # 遍历AllModelObj列表，在窗口左侧创建图元菜单
        for obj in self.AllModelObj:
            # 并且要根据需求调整每个对象的位置
            obj_x = obj.ObjX  # 根据对象的id计算x坐标
            obj_y = obj.ObjY  # 根据对象的id计算y坐标
            self.Item_Record.append((obj_x, obj_y))
            self.connecting_lines(obj,)

    def element(self, path):
        img = Image.open(path)  # 加载图元对应的图片文件
        img = img.resize((60, 50))  # 使用resize方法调整图片
        img = ImageTk.PhotoImage(img)  # 把Image对象转换成PhotoImage对象
        self.Root.img = img  # 保存图片的引用，防止被垃圾回收
        return img

    def read_element(self):
        img_path = ["img/data.png", "img/conv.png", "img/pool.png", "img/full_connect.png", "img/nonlinear.png",
                    "img/classifier.png", "img/error.png", "img/adjust.png", "img/adjust.png"]
        for path in img_path:
            self.list_image.append(self.element(path))

    def visual_output(self, AllModelObj, AllModelConn):
        for obj in AllModelObj:  # 遍历AllModelObj列表，在窗口创建图元
            self.Item_Record[0].append((obj.ObjX, obj.ObjY))  # 记录图元坐标
            self.Item_Record[1].append(obj.ObjID)  # 记录图元号
            self.connecting_lines(obj)  # 根据图元对象信息在画布上画图元
        for conn in AllModelConn:  # 遍历AllModelConn列表，在窗口连线图元
            self.conn_lines(conn)

if __name__ == '__main__':
    create_instance()
    connect_class()
    Net = Networking()
    Net.window()
    Net.read_element()
    Net.visual_output(AllModelObj, AllModelConn)
    Net.Root.mainloop()