import tkinter as tk
from PIL import Image, ImageTk
class Networking:
    def __init__(self):
        self.Root = tk.Tk()  # 创建一个主窗口
        self.window_width = 900  # 窗口的宽度
        self.window_height = 550  # 窗口的高度
        self.list_image = []  # 图元列表
        self.Item_Record = [[], []]  # 记录图元坐标与图元号
    def window(self):
        self.Root.title("神经网络可视化")
        self.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
        self.Viewcanvas = tk.Canvas(self.Root, width=self.window_width, height=self.window_height, bg="white")
        # 将画布添加到主窗口中
        self.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
        self.Viewcanvas.create_rectangle(5, 5, 895, 545, fill=None,  outline="lightblue", width=2)
    def connecting_lines(self, obj):
        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
        text = obj.ObjLable
        if 'Error' in obj.ObjID:
            x, y = 0, -50
        elif 'Aj' in obj.ObjID:
            x, y = -80, 0
        else:
            x, y = 0, 50
        self.Viewcanvas.create_image(obj_x, obj_y, image=self.list_image[obj.ObjType - 1])  # 创建图元对象
        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

    def conn_lines(self, conn):
        starting = self.Item_Record[1].index(conn[2])
        ending = self.Item_Record[1].index(conn[3])
        smooth = [False, True]
        width = [2, 4]
        start, end = self.Item_Record[0][starting], self.Item_Record[0][ending]
        index = 1 if conn[1] == 1 else 0
        if start[0] == end[0]:
            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])
        elif start[1] == end[1]:
            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])
        else:
            if abs(start[0] - end[0]) > abs(start[1] - end[1]):
                # 创建数据线箭头
                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])
            else:
                # 创建数据线箭头
                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])
    def element(self, 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
        self.Root.img = img  # 保存图片的引用，防止被垃圾回收
        return img
    def read_element(self):
        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"]
        for path in img_path:
            self.list_image.append(self.element(path))
    def visual_output(self, AllModelObj, AllModelConn):
        # 遍历 AllModelObj 列表，在窗口创建图元
        for obj in AllModelObj:
            # 记录图元坐标
            self.Item_Record[0].append((obj.ObjX, obj.ObjY))
            # 记录图元号
            self.Item_Record[1].append(obj.ObjID)
            # 根据图元对象信息在画布上画图元
            self.connecting_lines(obj)
        # 遍历 AllModelConn 列表，在窗口连线图元
        for conn in AllModelConn:
            # 根据连接对象信息在画布上连接图元
            self.conn_lines(conn)
if __name__ == '__main__':
    AllModelObj = [
        ['DataSet1', 1, '数据集1', 'LoadData',
         'SetDataPara', [], 120, 330],
        ['Conv1', 2, '卷积1', 'ConvProc',
         'SetConvPara', [], 250, 330],
        ['Pool1', 3, '最大池化1', 'MaxPoolProc',
         'SetPollPara', [], 380, 330],
        ['FullConn1', 4, '全连接1', 'FullConnProc',
         'SetFullConnPara', [], 510, 330],
        ['Nonline1', 5, '非线性函数1', 'NonlinearProc',
         'SetNonLPara', [], 640, 330],
        ['Classifier1', 6, '分类1', 'ClassifierProc',
         'SetClassifyPara', [], 780, 330],
        ['Error1', 7, '误差计算1', 'ErrorProc',
         'SetErrorPara', [], 710, 124],
        ['AjConv1', 8, '卷积调整1', 'AjConvProc',
         'SetAjConvPara', [], 250, 70],
        ['AjFullconn1', 9, '全连接调整1', 'AjFullconnProc',
         'SetAjFCPara', [], 510, 120]]
    AllModelConn = [
        [1, 1, 'DataSet1', 'Conv1'], [2, 1, 'Conv1', 'Pool1'],
        [3, 1, 'Pool1', 'FullConn1'], [4, 1, 'FullConn1', 'Nonline1'],
        [5, 1, 'Nonline1', 'Classifier1'], [6, 1, 'Classifier1', 'Error1'],
        [7, 2, 'Error1', 'AjFullconn1'], [8, 2, 'Error1', 'AjConv1'],
        [9, 2, 'AjFullconn1', 'FullConn1'], [10, 2, 'AjConv1', 'Conv1']]
    Net = Networking()  # 创建 Networking 实例
    Net.window()  # 构造窗口
    Net.read_element() # 读取图元
    # 在窗口中可视化输出图元和连接
    Net.visual_output(AllModelObj, AllModelConn)
    Net.Root.mainloop()  # 启动主事件循环