MachineLearning/X2_new.py

import tkinter as tk
from PIL import Image, ImageTk
from X1 import *
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】。
'''


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]

def element(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
    Root.img = img                # 保存图片的引用，防止被垃圾回收
    return img

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)

def connecting_lines(obj_x, obj_y, text, text_record,image):
    Viewcanvas.create_image(obj_x, obj_y, image=image) # 创建图元对象
    Viewcanvas.create_text(obj_x + text_record[0], obj_y + text_record[1], text=text, font=("黑体", 14)) # 创建图元对象的标签

def conn_lines(start, end, index):
    smooth = [False, True]
    width = [2, 4]
    if start[0] == end[0]:
        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]:
        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
            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
            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():
    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 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
        Item_Record.append((obj_x, obj_y))
        Item_Name.append(obj.ObjID)
        # 根据对象的类型，绘制相应的图形
        if 'Error' in obj.ObjID:
            connecting_lines(obj_x, obj_y, obj.ObjLable, text_record[0], list_image[obj.ObjType - 1])
        elif 'Aj' in obj.ObjID:
            connecting_lines(obj_x, obj_y, obj.ObjLable, text_record[2], list_image[-1])
        else:
            connecting_lines(obj_x, obj_y, obj.ObjLable, text_record[1], list_image[obj.ObjType - 1])

def ligature(): # 连接线
    # print(Item_Record)
    for conn in AllModelConn:
        starting = Item_Name.index(conn[2])
        # print(starting)
        ending = Item_Name.index(conn[3])
        if conn[1] == 1:
            # print(Item_Record[starting])
            conn_lines(Item_Record[starting], Item_Record[ending], 1)
        else:
            conn_lines(Item_Record[starting], Item_Record[ending], 0)


if __name__ == '__main__':
    global AllModelObj
    Item_Record = []
    Item_Name = []
    window()
    create_instance()
    connect_class()
    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()
    ligature()
    Root.mainloop()
    # print(Item_Record)