diff --git a/UI/.idea/UI.iml b/UI/.idea/UI.iml
new file mode 100644
index 0000000..d385fe6
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diff --git a/UI/.idea/encodings.xml b/UI/.idea/encodings.xml
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diff --git a/UI/.idea/misc.xml b/UI/.idea/misc.xml
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diff --git a/UI/.idea/modules.xml b/UI/.idea/modules.xml
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diff --git a/UI/.idea/workspace.xml b/UI/.idea/workspace.xml
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diff --git a/UI/UI.py b/UI/UI.py
new file mode 100644
index 0000000..ff1935b
--- /dev/null
+++ b/UI/UI.py
@@ -0,0 +1,217 @@
+#!/usr/bin/python
+# -*- coding: UTF-8 -*-
+
+from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, Activation
+from keras.layers import Flatten, BatchNormalization, PReLU, Dense, Dropout
+from keras.models import Model, Input
+from keras.applications.resnet50 import ResNet50
+import numpy as np
+import tkinter
+import tkinter.filedialog
+import os
+from PIL import Image, ImageTk
+import matplotlib.pyplot as plt
+import time
+
+
+def bn_relu(x):
+
+ x = BatchNormalization()(x)
+ #参数化的ReLU
+ x = PReLU()(x)
+ return x
+
+def Alex_model(out_dims, input_shape=(128, 128, 1)):
+ input_dim = Input(input_shape)
+
+ x = Conv2D(96, (20, 20), strides=(2, 2), padding='valid')(input_dim) # 55 * 55 * 96
+ x = bn_relu(x)
+ x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='valid')(x) # 27 * 27 * 96
+ x = Conv2D(256, (5, 5), strides=(1, 1), padding='same')(x)
+ x = bn_relu(x)
+ x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='valid')(x)
+ x = Conv2D(384, (3, 3), strides=(1, 1), padding='same')(x)
+ x = PReLU()(x)
+ x = Conv2D(384, (3, 3), strides=(1, 1), padding='same')(x)
+ x = PReLU()(x)
+ x = Conv2D(256, (3, 3), strides=(1, 1), padding='same')(x)
+ x = PReLU()(x)
+ x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='valid')(x)
+
+ x = Flatten()(x)
+ fc1 = Dense(4096)(x)
+ dr1 = Dropout(0.2)(fc1)
+ fc2 = Dense(4096)(dr1)
+ dr2 = Dropout(0.25)(fc2)
+ fc3 = Dense(out_dims)(dr2)
+
+ fc3 = Activation('softmax')(fc3)
+
+ model = Model(inputs=input_dim, outputs=fc3)
+ return model
+
+def resner50(out_dims, input_shape=(128, 128, 1)):
+ # input_dim = Input(input_shape)
+ resnet_base_model = ResNet50(include_top=False, weights=None, input_shape=input_shape)
+
+ x = resnet_base_model.output
+ x = Flatten()(x)
+ fc = Dense(512)(x)
+ x = bn_relu(fc)
+ x = Dropout(0.5)(x)
+ x = Dense(out_dims)(x)
+ x = Activation("softmax")(x)
+
+ # buid myself model
+ input_shape = resnet_base_model.input
+ output_shape = x
+
+ resnet50_100_model = Model(inputs=input_shape, outputs=output_shape)
+
+ return resnet50_100_model
+
+
+def my_model(out_dims, input_shape=(128, 128, 1)):
+ input_dim = Input(input_shape) # 生成一个input_shape的张量
+
+ x = Conv2D(32, (3, 3), strides=(2, 2), padding='valid')(input_dim)
+ x = bn_relu(x)
+ x = Conv2D(32, (3, 3), strides=(1, 1), padding='valid')(x)
+ x = bn_relu(x)
+ x = MaxPooling2D(pool_size=(2, 2))(x)
+
+ x = Conv2D(64, (3, 3), strides=(1, 1), padding='valid')(x)
+ x = bn_relu(x)
+ x = Conv2D(64, (3, 3), strides=(1, 1), padding='valid')(x)
+ x = bn_relu(x)
+ x = MaxPooling2D(pool_size=(2, 2))(x)
+
+ x = Conv2D(128, (3, 3), strides=(1, 1), padding='valid')(x)
+ x = bn_relu(x)
+ x = MaxPooling2D(pool_size=(2, 2))(x)
+
+ x = Conv2D(128, (3, 3), strides=(1, 1), padding='valid')(x)
+ x = bn_relu(x)
+ x = AveragePooling2D(pool_size=(2, 2))(x)
+
+ x_flat = Flatten()(x)
+
+ fc1 = Dense(512)(x_flat)
+ fc1 = bn_relu(fc1)
+ dp_1 = Dropout(0.3)(fc1)
+
+ fc2 = Dense(out_dims)(dp_1)
+ fc2 = Activation('softmax')(fc2)
+
+ model = Model(inputs=input_dim, outputs=fc2)
+ return model
+
+
+def load_image(image):
+ img = Image.open(image).convert('L')
+ img = img.resize((128,128))
+ img = np.array(img)
+ img = img / 255
+ img = img.reshape((1,) + img.shape + (1,))
+ return img
+
+def get_label(image, model, top_k):
+ prediction = model.predict(image)
+ predict_list = list(prediction[0])
+ min_label = min(predict_list)
+ label_k = []
+ for i in range(top_k):
+ label = np.argmax(predict_list)
+ predict_list.remove(predict_list[label])
+ predict_list.insert(label, min_label)
+ label_k.append(label)
+ return label_k
+
+
+def label_of_directory(directory):
+ classes = []
+ for subdir in sorted(os.listdir(directory)):
+ if os.path.isdir(os.path.join(directory, subdir)):
+ classes.append(subdir)
+
+ class_indices = dict(zip(classes, range(len(classes))))
+ return class_indices
+
+
+def get_key_from_classes(dict, index):
+ for key, value in dict.items():
+ if value == index:
+ return key
+
+
+def choose_file():
+ selectFileName = tkinter.filedialog.askopenfilename(title='选择文件') # 选择文件
+ e.set(selectFileName)
+
+
+
+def upload_file(f):
+ img = Image.open(f)
+ plt.imshow(img)
+ path = "/home/shallow/PycharmProjects/MyDesign/image_test/"
+ name = time.time()
+ img.save(path + str(name) + ".jpg")
+ plt.show()
+
+
+def start():
+ img_name = os.listdir(image_path)
+ img_list = []
+ for name in img_name:
+ img_list.append(os.path.join(image_path, name))
+
+ img_list.sort()
+ ans_key = []
+ for img in img_list:
+ image = load_image(img)
+ temp_label = get_label(image, model, 5)
+ key = get_key_from_classes(class_indices, temp_label[0])
+ ans_key.append(key)
+ tkinter.messagebox.showinfo(title="识别结果", message="您提交图片的识别汉字为：" + str(ans_key))
+
+
+if __name__ == "__main__":
+ """
+ 初始化权重
+ """
+ weight_path = 'best_weights_Alex.h5'
+ image_path = '/home/shallow/PycharmProjects/MyDesign/image_test/'
+ train_path = "/home/shallow/TMD_data/myTrain/train_data/"
+ model = Alex_model(100)
+ model.load_weights(weight_path)
+ class_indices = label_of_directory(train_path) # 获取训练集中标签对应汉字序列
+
+ """
+ 窗口初始化
+ """
+ top = tkinter.Tk()
+ top.title('基于深度学习的汉字书法字体识别')
+ # top.geometry('1280x800')
+
+ e = tkinter.StringVar() # 可变字符型
+ e_entry = tkinter.Entry(top, font = (18), width=68, textvariable=e) # 文本框
+ e_entry.pack() # 布局
+
+
+ submit_button = tkinter.Button(top, text ="选择文件", width = 10, font = (15), command = choose_file)
+ submit_button.pack()
+ submit_button = tkinter.Button(top, text ="上传", width = 10, font= (15), command = lambda:upload_file(e_entry.get()))
+ submit_button.pack()
+ submit_button = tkinter.Button(top, text="开始识别", width = 10, font=(15), command = start)
+ submit_button.pack()
+
+
+ img = Image.open("/home/shallow/PycharmProjects/MyDesign/UI/back.jpg")
+ (x, y) = img.size
+ x //= 4
+ y //= 4
+ img = img.resize((x, y))
+ img = ImageTk.PhotoImage(img)
+ backLabel = tkinter.Label(top, image=img)
+ backLabel.pack()
+ top.mainloop()
diff --git a/UI/back.jpg b/UI/back.jpg
new file mode 100644
index 0000000..50e6c6d
Binary files /dev/null and b/UI/back.jpg differ
diff --git a/UI/tk.py b/UI/tk.py
new file mode 100644
index 0000000..b670846
--- /dev/null
+++ b/UI/tk.py
@@ -0,0 +1,289 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Time : 2019/5/23 14:25
+# @Author : shadow
+# @Site :
+# @File : tk.py
+# @Software: PyCharm
+
+from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, Activation
+from keras.layers import Flatten, BatchNormalization, PReLU, Dense, Dropout
+from keras.models import Model, Input
+from keras.applications.resnet50 import ResNet50
+import numpy as np
+import tkinter as tk
+import tkinter.filedialog
+from PIL import Image, ImageTk
+import matplotlib.pyplot as plt
+import time
+import os
+import tkinter.font as tkFont
+
+def bn_relu(x):
+
+ x = BatchNormalization()(x)
+ #参数化的ReLU
+ x = PReLU()(x)
+ return x
+
+def Alex_model(out_dims, input_shape=(128, 128, 1)):
+ input_dim = Input(input_shape)
+
+ x = Conv2D(96, (20, 20), strides=(2, 2), padding='valid')(input_dim) # 55 * 55 * 96
+ x = bn_relu(x)
+ x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='valid')(x) # 27 * 27 * 96
+ x = Conv2D(256, (5, 5), strides=(1, 1), padding='same')(x)
+ x = bn_relu(x)
+ x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='valid')(x)
+ x = Conv2D(384, (3, 3), strides=(1, 1), padding='same')(x)
+ x = PReLU()(x)
+ x = Conv2D(384, (3, 3), strides=(1, 1), padding='same')(x)
+ x = PReLU()(x)
+ x = Conv2D(256, (3, 3), strides=(1, 1), padding='same')(x)
+ x = PReLU()(x)
+ x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='valid')(x)
+
+ x = Flatten()(x)
+ fc1 = Dense(4096)(x)
+ dr1 = Dropout(0.2)(fc1)
+ fc2 = Dense(4096)(dr1)
+ dr2 = Dropout(0.25)(fc2)
+ fc3 = Dense(out_dims)(dr2)
+
+ fc3 = Activation('softmax')(fc3)
+
+ model = Model(inputs=input_dim, outputs=fc3)
+ return model
+
+def resner50(out_dims, input_shape=(128, 128, 1)):
+ # input_dim = Input(input_shape)
+ resnet_base_model = ResNet50(include_top=False, weights=None, input_shape=input_shape)
+
+ x = resnet_base_model.output
+ x = Flatten()(x)
+ fc = Dense(512)(x)
+ x = bn_relu(fc)
+ x = Dropout(0.5)(x)
+ x = Dense(out_dims)(x)
+ x = Activation("softmax")(x)
+
+ # buid myself model
+ input_shape = resnet_base_model.input
+ output_shape = x
+
+ resnet50_100_model = Model(inputs=input_shape, outputs=output_shape)
+
+ return resnet50_100_model
+
+
+def my_model(out_dims, input_shape=(128, 128, 1)):
+ input_dim = Input(input_shape) # 生成一个input_shape的张量
+
+ x = Conv2D(32, (3, 3), strides=(2, 2), padding='valid')(input_dim)
+ x = bn_relu(x)
+ x = Conv2D(32, (3, 3), strides=(1, 1), padding='valid')(x)
+ x = bn_relu(x)
+ x = MaxPooling2D(pool_size=(2, 2))(x)
+
+ x = Conv2D(64, (3, 3), strides=(1, 1), padding='valid')(x)
+ x = bn_relu(x)
+ x = Conv2D(64, (3, 3), strides=(1, 1), padding='valid')(x)
+ x = bn_relu(x)
+ x = MaxPooling2D(pool_size=(2, 2))(x)
+
+ x = Conv2D(128, (3, 3), strides=(1, 1), padding='valid')(x)
+ x = bn_relu(x)
+ x = MaxPooling2D(pool_size=(2, 2))(x)
+
+ x = Conv2D(128, (3, 3), strides=(1, 1), padding='valid')(x)
+ x = bn_relu(x)
+ x = AveragePooling2D(pool_size=(2, 2))(x)
+
+ x_flat = Flatten()(x)
+
+ fc1 = Dense(512)(x_flat)
+ fc1 = bn_relu(fc1)
+ dp_1 = Dropout(0.3)(fc1)
+
+ fc2 = Dense(out_dims)(dp_1)
+ fc2 = Activation('softmax')(fc2)
+
+ model = Model(inputs=input_dim, outputs=fc2)
+ return model
+
+
+def load_image(image):
+ img = Image.open(image).convert('L')
+ img = img.resize((128,128))
+ img = np.array(img)
+ img = img / 255
+ img = img.reshape((1,) + img.shape + (1,))
+ return img
+
+def get_label(image, model, top_k):
+ prediction = model.predict(image)
+ predict_list = list(prediction[0])
+ min_label = min(predict_list)
+ label_k = []
+ for i in range(top_k):
+ label = np.argmax(predict_list)
+ predict_list.remove(predict_list[label])
+ predict_list.insert(label, min_label)
+ label_k.append(label)
+ return label_k
+
+
+def label_of_directory(directory):
+ classes = []
+ for subdir in sorted(os.listdir(directory)):
+ if os.path.isdir(os.path.join(directory, subdir)):
+ classes.append(subdir)
+
+ class_indices = dict(zip(classes, range(len(classes))))
+ return class_indices
+
+
+def get_key_from_classes(dict, index):
+ for key, value in dict.items():
+ if value == index:
+ return key
+
+
+# 弹窗
+class PopupDialog(tk.Toplevel):
+ def __init__(self, parent):
+ super().__init__()
+ self.title('输出结果')
+
+ self.parent = parent # 显式地保留父窗口
+
+ sw = self.parent.winfo_screenwidth()
+ sh = self.parent.winfo_screenheight()
+ ww = 500
+ wh = 200
+ x = (sw-ww) // 2
+ y = (sh-wh) // 2
+ self.geometry("%dx%d+%d+%d"%(ww, wh, x, y))
+
+ ft = tkFont.Font(family='宋体', size=23)
+ row1 = tk.Frame(self)
+ row1.pack(anchor='s', ipady=25)
+ tk.Label(row1, text=str(self.parent.ans_key), font = ft).pack()
+
+
+ row2 = tk.Frame(self)
+ row2.pack(fill="x")
+ tk.Button(row2, text="确认", font = ft, command=self.cancel).pack()
+
+
+ def cancel(self):
+ self.destroy()
+
+
+# 主窗
+class MyApp(tk.Tk):
+
+ def __init__(self):
+ super().__init__()
+ self.title('基于深度学习的书法字体识别')
+
+ # 程序参数
+ self.string = tk.StringVar()
+ self.ans_key = []
+ # 程序界面
+ self.setupUI()
+
+ def setupUI(self):
+ # 初始化字体
+ ft1 = tkFont.Font(family='宋体', size=17) # 地址栏字体
+ ft2 = tkFont.Font(family='宋体', size=20) # 按钮字体
+
+ row1 = tk.Frame(self)
+ row1.pack(fill="x") # x方向填充
+ tk.Entry(row1, font=ft1, width=68, textvariable=self.string).pack() # 选择文件地址栏
+
+ # 三个按钮
+ row2 = tk.Frame(self)
+ row2.pack(fill="x")
+ tk.Button(row2, text="选择文件", width=10, font=ft2, command = self.choose_file).pack()
+
+ row3 = tk.Frame(self)
+ row3.pack(fill="x")
+ tk.Button(row3, text="上传", width=10, font=ft2, command = self.upload_file).pack()
+
+ row4 = tk.Frame(self)
+ row4.pack(fill="x")
+ tk.Button(row4, text="开始识别", width=10, font=ft2, command = self.start).pack()
+
+ # 背景图片
+ row5 = tk.Frame(self)
+ row5.pack(fill="x")
+ global img
+ img = Image.open("D:/Deep_learning_design/汉字/毕设重要版本/UI/back.jpg")
+ (x, y) = img.size
+ x //= 2
+ y //= 2
+ img = img.resize((x, y))
+ img = ImageTk.PhotoImage(img)
+ lab = tk.Label(row5, image=img)
+ lab.pack()
+
+
+ # 选择文件
+ def choose_file(self):
+ selectFileName = tk.filedialog.askopenfilename(title = "选择文件")
+ self.string.set(selectFileName)
+
+
+ # 上传文件
+ def upload_file(self):
+ img = Image.open(self.string.get())
+ plt.imshow(img)
+ path = "D:/Deep_learning_design/汉字/毕设重要版本/image_test"
+ name = time.time()
+ img.save(path + str(name) + ".jpg")
+ plt.show()
+
+
+ # 开始识别
+ def start(self):
+ img_name = os.listdir(image_path)
+ img_list = []
+ for name in img_name:
+ img_list.append(os.path.join(image_path, name))
+
+ img_list.sort()
+ ans_key = []
+ for img in img_list:
+ image = load_image(img)
+ temp_label = get_label(image, model, 5)
+ key = get_key_from_classes(class_indices, temp_label[0])
+ ans_key.append(key)
+ self.ans_key = ans_key
+ self.output_ans()
+
+
+ # 输出结果
+ def output_ans(self):
+ pw = PopupDialog(self)
+ self.wait_window(pw)
+
+ return
+
+
+if __name__ == '__main__':
+ """
+ 初始化权重
+ """
+ weight_path = 'best_weights_Alex.h5'
+ image_path = 'D:/Deep_learning_design/汉字/毕设重要版本/image_test'
+ train_path = "D:/Deep_learning_design/TMD_data/myTrain/train_data"
+ model = Alex_model(100)
+ model.load_weights(weight_path)
+ class_indices = label_of_directory(train_path) # 获取训练集中标签对应汉字序列
+
+ """
+ 界面加载
+ """
+ app = MyApp()
+ app.mainloop()
\ No newline at end of file