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105 lines
5.6 KiB
105 lines
5.6 KiB
import tkinter as tk
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from PIL import Image, ImageTk
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class Networking:
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def __init__(self):
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self.Root = tk.Tk() # 创建一个主窗口
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self.window_width = 900 # 窗口的宽度
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self.window_height = 550 # 窗口的高度
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self.list_image = [] # 图元列表
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self.Item_Record = [[], []] # 记录图元坐标与图元号
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def window(self):
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self.Root.title("神经网络可视化")
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self.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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self.Viewcanvas = tk.Canvas(self.Root, width=self.window_width, height=self.window_height, bg="white")
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# 将画布添加到主窗口中
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self.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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self.Viewcanvas.create_rectangle(5, 5, 895, 545, fill=None, outline="lightblue", width=2)
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def connecting_lines(self, obj):
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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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text = obj.ObjLable
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if 'Error' in obj.ObjID:
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x, y = 0, -50
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elif 'Aj' in obj.ObjID:
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x, y = -80, 0
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else:
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x, y = 0, 50
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self.Viewcanvas.create_image(obj_x, obj_y, image=self.list_image[obj.ObjType - 1]) # 创建图元对象
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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
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def conn_lines(self, conn):
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starting = self.Item_Record[1].index(conn[2])
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ending = self.Item_Record[1].index(conn[3])
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smooth = [False, True]
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width = [2, 4]
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start, end = self.Item_Record[0][starting], self.Item_Record[0][ending]
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index = 1 if conn[1] == 1 else 0
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if start[0] == end[0]:
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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])
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elif start[1] == end[1]:
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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])
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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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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])
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else:
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# 创建数据线箭头
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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])
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def element(self, 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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self.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 read_element(self):
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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"]
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for path in img_path:
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self.list_image.append(self.element(path))
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def visual_output(self, AllModelObj, AllModelConn):
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# 遍历 AllModelObj 列表，在窗口创建图元
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for obj in AllModelObj:
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# 记录图元坐标
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self.Item_Record[0].append((obj.ObjX, obj.ObjY))
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# 记录图元号
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self.Item_Record[1].append(obj.ObjID)
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# 根据图元对象信息在画布上画图元
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self.connecting_lines(obj)
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# 遍历 AllModelConn 列表，在窗口连线图元
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for conn in AllModelConn:
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# 根据连接对象信息在画布上连接图元
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self.conn_lines(conn)
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if __name__ == '__main__':
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AllModelObj = [
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['DataSet1', 1, '数据集1', 'LoadData',
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'SetDataPara', [], 120, 330],
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['Conv1', 2, '卷积1', 'ConvProc',
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'SetConvPara', [], 250, 330],
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['Pool1', 3, '最大池化1', 'MaxPoolProc',
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'SetPollPara', [], 380, 330],
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['FullConn1', 4, '全连接1', 'FullConnProc',
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'SetFullConnPara', [], 510, 330],
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['Nonline1', 5, '非线性函数1', 'NonlinearProc',
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'SetNonLPara', [], 640, 330],
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['Classifier1', 6, '分类1', 'ClassifierProc',
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'SetClassifyPara', [], 780, 330],
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['Error1', 7, '误差计算1', 'ErrorProc',
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'SetErrorPara', [], 710, 124],
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['AjConv1', 8, '卷积调整1', 'AjConvProc',
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'SetAjConvPara', [], 250, 70],
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['AjFullconn1', 9, '全连接调整1', 'AjFullconnProc',
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'SetAjFCPara', [], 510, 120]]
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AllModelConn = [
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[1, 1, 'DataSet1', 'Conv1'], [2, 1, 'Conv1', 'Pool1'],
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[3, 1, 'Pool1', 'FullConn1'], [4, 1, 'FullConn1', 'Nonline1'],
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[5, 1, 'Nonline1', 'Classifier1'], [6, 1, 'Classifier1', 'Error1'],
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[7, 2, 'Error1', 'AjFullconn1'], [8, 2, 'Error1', 'AjConv1'],
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[9, 2, 'AjFullconn1', 'FullConn1'], [10, 2, 'AjConv1', 'Conv1']]
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Net = Networking() # 创建 Networking 实例
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Net.window() # 构造窗口
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Net.read_element() # 读取图元
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# 在窗口中可视化输出图元和连接
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Net.visual_output(AllModelObj, AllModelConn)
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Net.Root.mainloop() # 启动主事件循环
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