MachineLearning/X2.py

import tkinter as tk
from PIL import Image, ImageTk

global Viewcanvas # 定义画布
global Root # 主窗口
global AllModelObj #网络对象

'''
【编程16.5】编制程序：依据AllModelObj和AllModelConn数据结构产生如图16.2的输出界面。
【目的及编程说明】读者通过编程16.5可理解卷积神经网络模型构建的输出界面。数据结构及初始值参见【编程16.1】。
'''
# 定义图元对象类
class ModelObj:
    def __init__(self, ObjID, ObjType, ObjLable, ProcFunc, SetParaFunc, ParaString, ObjX, ObjY):
        self.ObjID = ObjID  # 图元号
        self.ObjType = ObjType  # 图元类别
        self.ObjLable = ObjLable  # 对象标签
        self.ProcFunc = ProcFunc  # 基本操作函数
        self.SetParaFunc = SetParaFunc  # 参数设置函数
        self.ParaString = ParaString  # 参数字符串
        self.ObjX = ObjX  # 对象位置x坐标
        self.ObjY = ObjY  # 对象位置y坐标

    def output(self):  # 输出方法
        # 创建一个空列表
        result = []
        # 将对象的属性添加到列表中
        result.append(self.ObjID)
        result.append(self.ObjType)
        result.append(self.ObjLable)
        result.append(self.ProcFunc)
        result.append(self.SetParaFunc)
        result.append(self.ParaString)
        result.append(self.ObjX)
        result.append(self.ObjY)
        # 返回列表
        return result

# 定义网络连接对象类
class ModelConn:
    def __init__(self, ConnObjID, ConnType, NobjS, NobjE):
        self.ConnObjID = ConnObjID  # 连接线编号
        self.ConnType = ConnType  # 连接线类别
        self.NobjS = NobjS  # 源图元对象
        self.NobjE = NobjE  # 目标图元对象

    def __repr__(self):
        return f"{self.ConnObjID}, {self.ConnType}, {self.NobjS}, {self.NobjE}"

def create_instance():
    # 创建图元对象实例
    DataSet = ModelObj("DataSet", 1, "数据集", "LoadData", "SetDataPara", ".", 120, 330).output()
    Conv = ModelObj("Conv", 2, "卷积", "ConvProc", "SetConvPara", ".", 250, 330).output()
    Pool = ModelObj("Pool", 3, "最大池化", "MaxPoolProc", "SetPollPara", ".", 380, 330).output()
    FullConn = ModelObj("FullConn", 4, "全连接", "FullConnProc", "SetFullConnPara", ".", 510, 330).output()
    Nonline = ModelObj("Nonline", 5, "非线性函数", "NonlinearProc", "SetNonLPara", ".", 640, 330).output()
    Classifier = ModelObj("Classifier", 6, "分类", "ClassifierProc", "SetClassifyPara", ".", 780, 330).output()
    Error = ModelObj("Error", 7, "误差计算", "ErrorProc", "SetErrorPara", ".", 710, 124).output()
    AjConv = ModelObj("AjConv", 8, "卷积调整", "AjConvProc", "SetAjConvPara", ".", 250, 70).output()
    AjFullconn = ModelObj("AjFullconn", 9, "全连接调整", "AjFullconnProc", "SetAjFCPara", ".", 510, 120).output()
    listinstance = [DataSet,Conv,Pool,FullConn,Nonline,Classifier,Error,AjConv,AjFullconn]
    return listinstance # 还回图元对象实例列表

# if __name__ == '__main__':
#     listinstance = create_instance()
#     for instance in listinstance:
#         print(instance)

def connect_class(listinstance):
    # 创建连接对象实例
    Line1 = ModelConn(1, 1, listinstance[0], listinstance[1])
    Line2 = ModelConn(2, 1, listinstance[1], listinstance[2])
    Line3 = ModelConn(3, 1, listinstance[2], listinstance[3])
    Line4 = ModelConn(4, 1, listinstance[3], listinstance[4])
    Line5 = ModelConn(5, 1, listinstance[4], listinstance[5])
    Line6 = ModelConn(6, 1, listinstance[5], listinstance[6])
    Line7 = ModelConn(7, 2, listinstance[6], listinstance[8])
    Line8 = ModelConn(8, 2, listinstance[6], listinstance[7])
    Line9 = ModelConn(9, 2, listinstance[8], listinstance[3])
    Line10 = ModelConn(10, 2, listinstance[7], listinstance[1])
    listclass = [Line1,Line2, Line3, Line4, Line5, Line6, Line7, Line8, Line9, Line10]
    return listclass # # 还回连接对象实例

# if __name__ == '__main__':
#     listinstance = create_instance()
#     listclass = connect_class(listinstance)
#     for iclass in listclass:
#         print(iclass)

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

# if __name__ == '__main__':
#     Root = tk.Tk() # 创建一个主窗口
#     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"] # 图元路径
#     list_image = [] # 定义一个列表，存储PhotoImage对象
#     for path in img_path:
#         list_image.append(element(path))
#     for image in list_image:
#         print(image) # 打印结果

def window():
    global Root
    global Viewcanvas
    Root = tk.Tk()  # 创建一个主窗口
    # 设置窗口的大小为1200*750
    window_width = 900  # 窗口的宽度
    window_height = 550  # 窗口的高度
    Root.title("神经网络可视化")
    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
    Viewcanvas = tk.Canvas(Root, width=window_width, height=window_height, bg="white")
    # 将画布添加到主窗口中
    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
    Viewcanvas.create_rectangle(5, 5, 895, 545, fill=None, outline="lightblue", width=2)

# if __name__ == '__main__':
#     window()
#     path = "img/data.png" # 图元路径
#     image =element(path)
#     print(image) # 打印结果
#     Root.mainloop()

def connecting_lines(obj_x, obj_y, obj_x1, obj_x2, obj_x3, obj_y1, obj_y2, obj_y3, image, text, smooth, width):
    Viewcanvas.create_image(obj_x, obj_y, image=image) # 创建图元对象
    Viewcanvas.create_text(obj_x1, obj_y1, text=text, font=("黑体", 14)) # 创建图元对象的标签
    Viewcanvas.create_line(obj_x2, obj_y2, obj_x3, obj_y3, arrow=tk.LAST, # 创建数据线箭头
                           arrowshape=(16, 20, 4), fill='lightblue', smooth=smooth, width=width)

def connectings_lines(obj_x, obj_y, obj_x1, obj_x2, obj_x3,obj_x4, obj_y1, obj_y2, obj_y3, obj_y4, image, text, smooth, width):
    # 创建图元对象
    Viewcanvas.create_image(obj_x, obj_y, image=image)
    # 创建图元对象的标签
    Viewcanvas.create_text(obj_x1, obj_y1, text=text, font=("黑体", 14))
    # 创建数据线箭头
    Viewcanvas.create_line(obj_x2, obj_y2, obj_x3, obj_y3, obj_x4, obj_y4, arrow=tk.LAST,
                       arrowshape=(16, 20, 4), fill='lightblue', smooth=smooth, width=width)

# if __name__ == '__main__':
#     window()
#     listinstance = create_instance()
#     # 创建网络对象总表和网络连接对象总表
#     AllModelObj = [listinstance[0], listinstance[1], listinstance[2], listinstance[3], listinstance[4], listinstance[5],
#                        listinstance[6], listinstance[7], listinstance[8]]
#     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"]
#     list_image = []
#     for path in img_path:
#         list_image.append(element(path))
#     obj_x = AllModelObj[0][6]   # 根据对象的id计算x坐标
#     obj_y = AllModelObj[0][7]   # 根据对象的id计算y坐标
#     obj_x2 = AllModelObj[5][6]  # 根据对象的id计算x坐标
#     obj_y2 = AllModelObj[5][7]  # 根据对象的id计算y坐标
#     connecting_lines(obj_x, obj_y, 0, 32, 100, 50, 0, 0, list_image[0], " 加载" + "\n" + "数据集", True, 3)
#     connectings_lines(obj_x2, obj_y2, 0, 0, 0, -50, 50, -30, -120, -180, list_image[5], "类别", False, 3)
#     Root.mainloop()
def switch(obj_type, obj_x, obj_y,listimage):
    if obj_type == 1:  # 加载数据集
        connecting_lines(obj_x, obj_y, obj_x+0, obj_x+32, obj_x+100, obj_y+50, obj_y+0, obj_y+0, listimage[0], " 加载" + "\n" + "数据集", True, 3)
    elif obj_type == 2:  # 卷积
        connecting_lines(obj_x, obj_y, obj_x+0, obj_x+30, obj_x+100, obj_y+50, obj_y+0, obj_y+0, listimage[1], "卷积", True, 3)
    elif obj_type == 3:  # 池化
        connecting_lines(obj_x, obj_y, obj_x+0, obj_x+30, obj_x+100, obj_y+50, obj_y+0, obj_y+0, listimage[2], "池化", True, 3)
    elif obj_type == 4:  # 全连接
        connecting_lines(obj_x, obj_y, obj_x+0, obj_x+30, obj_x+100, obj_y+50, obj_y+0, obj_y+0, listimage[3], "全连接" + "\n" + " 函数", True, 3)
    elif obj_type == 5:  # 非线性
        connecting_lines(obj_x, obj_y, obj_x+0, obj_x+30, obj_x+110, obj_y+50, obj_y+0, obj_y+0, listimage[4], "非线性" + "\n" + " 函数", True, 3)
    elif obj_type == 6:  # 分类
        connectings_lines(obj_x, obj_y, obj_x+0, obj_x+0, obj_x+0, obj_x-50, obj_y+50, obj_y-30, obj_y-120, obj_y-180, listimage[5], "类别", False, 3)
    elif obj_type == 7:  # 误差计算
        connectings_lines(obj_x, obj_y, obj_x+0, obj_x-20, obj_x-50, obj_x-420, obj_y-40,obj_y -20,obj_y -60,obj_y -60, listimage[6], "误差", False, 2)
        connecting_lines(obj_x, obj_y, obj_x+0, obj_x-40, obj_x-170, obj_y-40, obj_y+0, obj_y+0, listimage[6], "误差", False, 2)
    elif obj_type == 8:  # 调整
        connecting_lines(obj_x, obj_y, obj_x-80, obj_x+0, obj_x+0, obj_y+0, obj_y+30, obj_y+235, listimage[7], "调整1", False, 2)
    elif obj_type == 9:  # 调整
        connecting_lines(obj_x, obj_y, obj_x-80, obj_x+0, obj_x+0, obj_y+0,obj_y+ 30,obj_y+ 183, listimage[7], "调整2", False, 2)

def creating_elements(AllModelObj,listimage):
    # 遍历AllModelObj列表，在窗口左侧创建图元菜单
    for obj in AllModelObj:
        # 获取图元对象的类型、标签等信息
        obj_type = obj[1]
        # 并且要根据需求调整每个对象的位置
        obj_x = obj[6]      # 根据对象的id计算x坐标
        obj_y = obj[7]   # 根据对象的id计算y坐标
        # 根据对象的类型，绘制相应的图形
        switch(obj_type, obj_x, obj_y,listimage)


def main():
    global AllModelObj
    window()
    listinstance = create_instance()
    listclass = connect_class(listinstance)
    # 创建网络对象总表和网络连接对象总表
    AllModelObj = [listinstance[0],listinstance[1], listinstance[2], listinstance[3], listinstance[4], listinstance[5],
                   listinstance[6], listinstance[7], listinstance[8]]
    AllModelConn = [listclass[0], listclass[1], listclass[2], listclass[3], listclass[4], listclass[5], listclass[6],
                    listclass[7], listclass[8], listclass[9]]

    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"]
    list_image = []
    for path in img_path:
        list_image.append(element(path))
    creating_elements(AllModelObj, list_image)
    print(1)
    Root.mainloop()
if __name__ == '__main__':
    main()