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()