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import tkinter as tk
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from PIL import Image, ImageTk
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from X1 import *
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global Viewcanvas # 定义画布
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global Root # 主窗口
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global AllModelObj #网络对象
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'''
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【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。
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【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】。
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'''
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def create_instance():
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global AllModelObj
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global DataSet, Conv, Pool, FullConn, Nonline, Classifier, Error, AjConv, AjFullconn
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DataSet = Data_Class("DataSet1", 1, "数据集1", ".", 120, 330)
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Conv = Conv_Class("Conv1", 2, "卷积1", ".", 250, 330)
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Pool = Pool_Class("Pool1", 3, "最大池化1", ".", 380, 330)
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FullConn = FullConn_Class("FullConn1", 4, "全连接1", ".", 510, 330)
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Nonline = Nonline_Class("Nonline1", 5, "非线性函数1", ".", 640, 330)
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Classifier = Classifier_Class("Classifier1", 6, "分类1", ".", 780, 330)
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Error = Error_Class("Error1", 7, "误差计算1", ".", 710, 124)
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AjConv = AjConv_Class("AjConv1", 8, "卷积调整1", ".", 250, 70)
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AjFullconn = AjFullconn_Class("AjFullconn1", 9, "全连接调整1", ".", 510, 120)
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AllModelObj = [DataSet, Conv, Pool, FullConn, Nonline, Classifier, Error, AjConv, AjFullconn]
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def connect_class():
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global AllModelConn
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# 创建连接对象实例
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Line1 = ModelConn(1, 1, DataSet.ObjID, Conv.ObjID).output()
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Line2 = ModelConn(2, 1, Conv.ObjID, Pool.ObjID).output()
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Line3 = ModelConn(3, 1, Pool.ObjID, FullConn.ObjID).output()
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Line4 = ModelConn(4, 1, FullConn.ObjID, Nonline.ObjID).output()
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Line5 = ModelConn(5, 1, Nonline.ObjID, Classifier.ObjID).output()
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Line6 = ModelConn(6, 1, Classifier.ObjID, Error.ObjID).output()
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Line7 = ModelConn(7, 2, Error.ObjID, AjFullconn.ObjID).output()
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Line8 = ModelConn(8, 2, Error.ObjID, AjConv.ObjID).output()
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Line9 = ModelConn(9, 2, AjFullconn.ObjID, FullConn.ObjID).output()
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Line10 = ModelConn(10, 2, AjConv.ObjID, Conv.ObjID).output()
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# 网络连接对象总表
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AllModelConn = [Line1, Line2, Line3, Line4,
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Line5, Line6, Line7, Line8,
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Line9, Line10]
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def element(path):
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img = Image.open(path) # 加载图元对应的图片文件
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img = img.resize((60, 50)) # 使用resize方法调整图片
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img = ImageTk.PhotoImage(img) # 把Image对象转换成PhotoImage对象
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Root.img = img # 保存图片的引用，防止被垃圾回收
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return img
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def window():
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global Root
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global Viewcanvas
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Root = tk.Tk() # 创建一个主窗口
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# 设置窗口的大小为1200*750
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window_width = 900 # 窗口的宽度
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window_height = 550 # 窗口的高度
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Root.title("神经网络可视化")
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Root.geometry("900x550") # 设置窗口的大小和位置
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# 创建一个画布，用于绘制矩形框，设置画布的大小和背景色
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Viewcanvas = tk.Canvas(Root, width=window_width, height=window_height, bg="white")
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# 将画布添加到主窗口中
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Viewcanvas.pack()
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# 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
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Viewcanvas.create_rectangle(5, 5, 895, 545, fill=None, outline="lightblue", width=2)
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def connecting_lines(obj_x, obj_y, text, text_record,image):
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Viewcanvas.create_image(obj_x, obj_y, image=image) # 创建图元对象
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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
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def conn_lines(start, end, index):
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smooth = [False, True]
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width = [2, 4]
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if start[0] == end[0]:
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Viewcanvas.create_line(start[0], start[1] + 30, end[0] , end[1] - 30, arrow=tk.LAST,
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arrowshape=(16, 20, 4), fill='lightblue', smooth=smooth[index], width=width[index])
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elif start[1] == end[1]:
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Viewcanvas.create_line(start[0] + 30, start[1], end[0] - 30, end[1], arrow=tk.LAST,
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arrowshape=(16, 20, 4), fill='lightblue', smooth=smooth[index], width=width[index])
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else:
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if abs(start[0]-end[0]) > abs(start[1]-end[1]):
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# 创建数据线箭头
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Viewcanvas.create_line(start[0]-15, start[1], int((start[0] + end[0])/2), end[1], end[0] + 30, end[1], arrow=tk.LAST,
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arrowshape=(16, 20, 4), fill='lightblue', smooth=smooth[index], width=width[index])
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else:
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# 创建数据线箭头
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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])
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def creating_elements():
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text_record = [(0, -50), (0, 50), (-80, 0)]
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# 遍历AllModelObj列表，在窗口左侧创建图元菜单
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for obj in AllModelObj:
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# 并且要根据需求调整每个对象的位置
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obj_x = obj.ObjX # 根据对象的id计算x坐标
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obj_y = obj.ObjY # 根据对象的id计算y坐标
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Item_Record.append((obj_x, obj_y))
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Item_Name.append(obj.ObjID)
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# 根据对象的类型，绘制相应的图形
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if 'Error' in obj.ObjID:
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connecting_lines(obj_x, obj_y, obj.ObjLable, text_record[0], list_image[obj.ObjType - 1])
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elif 'Aj' in obj.ObjID:
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connecting_lines(obj_x, obj_y, obj.ObjLable, text_record[2], list_image[-1])
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else:
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connecting_lines(obj_x, obj_y, obj.ObjLable, text_record[1], list_image[obj.ObjType - 1])
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def ligature(): # 连接线
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# print(Item_Record)
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for conn in AllModelConn:
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starting = Item_Name.index(conn[2])
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# print(starting)
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ending = Item_Name.index(conn[3])
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if conn[1] == 1:
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# print(Item_Record[starting])
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conn_lines(Item_Record[starting], Item_Record[ending], 1)
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else:
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conn_lines(Item_Record[starting], Item_Record[ending], 0)
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if __name__ == '__main__':
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global AllModelObj
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Item_Record = []
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Item_Name = []
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window()
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create_instance()
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connect_class()
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img_path = ["img/data.png", "img/conv.png", "img/pool.png", "img/full_connect.png", "img/nonlinear.png",
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"img/classifier.png", "img/error.png", "img/adjust.png"]
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list_image = []
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for path in img_path:
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list_image.append(element(path))
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creating_elements()
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ligature()
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Root.mainloop()
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# print(Item_Record) |
