加载已径训练好的模型（使用.pb格式）,实现手写数字识别界面的设计,多一些功能的界面比如可以识别单张图片单个数字和单张图片多个数字的识别,显示出完整的代码任何图片社手写数字都要满足shape (None, 10)进行识别

好的，这个任务需要使用 TensorFlow 框架来实现。首先，我们需要加载已经训练好的模型文件，然后设计一个手写数字识别界面，支持单张图片单个数字和单张图片多个数字的识别，并且可以显示出完整的代码。下面是实现这个任务的代码示例。

首先，我们需要加载训练好的模型文件，使用 TensorFlow 的 `tf.saved_model.load()` 函数来加载模型文件。假设模型文件的路径为 `model.pb`，则可以使用以下代码进行加载：

import tensorflow as tf

model_dir = 'path/to/model/directory'
model = tf.saved_model.load(model_dir)

接下来，我们需要设计一个手写数字识别界面。可以使用 Python 的 Tkinter 模块来实现一个简单的 GUI 界面。下面是一个例子：

from tkinter import *
from PIL import Image, ImageTk

class App:
    def __init__(self, master):
        self.master = master
        master.title("Handwritten Digit Recognition")

        self.canvas = Canvas(master, width=300, height=300, bg='white')
        self.canvas.grid(row=0, column=0, padx=10, pady=10)

        self.label = Label(master, text="Draw a digit", font=("Helvetica", 18))
        self.label.grid(row=1, column=0, padx=10, pady=10)

        self.button_clear = Button(master, text="Clear", command=self.clear_canvas)
        self.button_clear.grid(row=2, column=0, padx=10, pady=10)

        self.button_recognize = Button(master, text="Recognize", command=self.recognize_digit)
        self.button_recognize.grid(row=3, column=0, padx=10, pady=10)

        self.result = Label(master, text="", font=("Helvetica", 18))
        self.result.grid(row=4, column=0, padx=10, pady=10)

        self.canvas.bind("<B1-Motion>", self.draw)

    def draw(self, event):
        x, y = event.x, event.y
        r = 8
        self.canvas.create_oval(x-r, y-r, x+r, y+r, fill='black')

    def clear_canvas(self):
        self.canvas.delete("all")
        self.result.configure(text="")

    def recognize_digit(self):
        digit_image = self.get_digit_image()
        digit = self.recognize_digit_image(digit_image)
        self.result.configure(text=f"Recognized digit: {digit}")

    def get_digit_image(self):
        x = self.master.winfo_rootx() + self.canvas.winfo_x()
        y = self.master.winfo_rooty() + self.canvas.winfo_y()
        x1 = x + self.canvas.winfo_width()
        y1 = y + self.canvas.winfo_height()
        digit_image = ImageGrab.grab((x, y, x1, y1))
        digit_image = digit_image.convert('L').resize((28, 28))
        digit_image = 255 - np.array(digit_image)
        digit_image = digit_image.astype(np.float32) / 255.0
        digit_image = digit_image.reshape((1, 28, 28, 1))
        return digit_image

    def recognize_digit_image(self, digit_image):
        output = model(digit_image)
        digit = np.argmax(output[0])
        return digit

root = Tk()
app = App(root)
root.mainloop()

这个界面包含了一个画板，可以在上面手写数字，然后点击 “Recognize” 按钮进行识别。识别结果会显示在界面上方的标签中。

接下来，我们需要修改 `recognize_digit_image()` 函数，使其支持识别单张图片单个数字和单张图片多个数字。这可以通过将输入图片分割成多个小图像，然后对每个小图像进行单独的识别来实现。下面是修改后的函数：

def recognize_digit_image(self, digit_image):
    digits = []
    for i in range(4):
        for j in range(4):
            x1, y1 = i * 7, j * 7
            x2, y2 = x1 + 21, y1 + 21
            digit = digit_image[:, x1:x2, y1:y2, :]
            output = model(digit)
            digit = np.argmax(output[0])
            digits.append(digit)
    return digits

这个函数将输入图片分割成 $4 \times 4$ 个小图像，每个小图像的大小为 $21 \times 21$ 像素。然后对每个小图像进行单独的识别，并将识别结果保存在一个列表中。

最后，我们需要将完整的代码整合起来。下面是一个完整的代码示例：

import tensorflow as tf
from tkinter import *
from PIL import Image, ImageGrab
import numpy as np

model_dir = 'path/to/model/directory'
model = tf.saved_model.load(model_dir)

class App:
    def __init__(self, master):
        self.master = master
        master.title("Handwritten Digit Recognition")

        self.canvas = Canvas(master, width=300, height=300, bg='white')
        self.canvas.grid(row=0, column=0, padx=10, pady=10)

        self.label = Label(master, text="Draw a digit", font=("Helvetica", 18))
        self.label.grid(row=1, column=0, padx=10, pady=10)

        self.button_clear = Button(master, text="Clear", command=self.clear_canvas)
        self.button_clear.grid(row=2, column=0, padx=10, pady=10)

        self.button_recognize = Button(master, text="Recognize", command=self.recognize_digit)
        self.button_recognize.grid(row=3, column=0, padx=10, pady=10)

        self.result = Label(master, text="", font=("Helvetica", 18))
        self.result.grid(row=4, column=0, padx=10, pady=10)

        self.canvas.bind("<B1-Motion>", self.draw)

    def draw(self, event):
        x, y = event.x, event.y
        r = 8
        self.canvas.create_oval(x-r, y-r, x+r, y+r, fill='black')

    def clear_canvas(self):
        self.canvas.delete("all")
        self.result.configure(text="")

    def recognize_digit(self):
        digit_image = self.get_digit_image()
        digits = self.recognize_digit_image(digit_image)
        self.result.configure(text=f"Recognized digits: {digits}")

    def get_digit_image(self):
        x = self.master.winfo_rootx() + self.canvas.winfo_x()
        y = self.master.winfo_rooty() + self.canvas.winfo_y()
        x1 = x + self.canvas.winfo_width()
        y1 = y + self.canvas.winfo_height()
        digit_image = ImageGrab.grab((x, y, x1, y1))
        digit_image = digit_image.convert('L').resize((28 * 4, 28 * 4))
        digit_image = 255 - np.array(digit_image)
        digit_image = digit_image.astype(np.float32) / 255.0
        digit_image = digit_image.reshape((1, 28 * 4, 28 * 4, 1))
        return digit_image

    def recognize_digit_image(self, digit_image):
        digits = []
        for i in range(4):
            for j in range(4):
                x1, y1 = i * 7, j * 7
                x2, y2 = x1 + 21, y1 + 21
                digit = digit_image[:, x1:x2, y1:y2, :]
                output = model(digit)
                digit = np.argmax(output[0])
                digits.append(digit)
        return digits

root = Tk()
app = App(root)
root.mainloop()

这个程序会弹出一个 GUI 窗口，可以在上面手写数字。点击 “Recognize” 按钮可以识别单张图片单个数字和单张图片多个数字。识别结果会显示在界面上方的标签中。