add Interface

UI/.idea/UI.iml

@@ -0,0 +1,11 @@
+<?xml version="1.0" encoding="UTF-8"?>
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+  <component name="TestRunnerService">
+    <option name="PROJECT_TEST_RUNNER" value="Unittests" />
+  </component>
+</module>

UI/.idea/encodings.xml

@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Encoding" addBOMForNewFiles="with NO BOM" />
+</project>

UI/.idea/misc.xml

@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="JavaScriptSettings">
+    <option name="languageLevel" value="ES6" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.7 (test) (2)" project-jdk-type="Python SDK" />
+</project>

UI/.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/UI.iml" filepath="$PROJECT_DIR$/.idea/UI.iml" />
+    </modules>
+  </component>
+</project>

UI/.idea/workspace.xml

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+    <configuration name="tk" type="PythonConfigurationType" factoryName="Python" temporary="true">
+      <module name="UI" />
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+      <option name="PARENT_ENVS" value="true" />
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+      <option name="INPUT_FILE" value="" />
+      <method v="2" />
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+    <recent_temporary>
+      <list>
+        <item itemvalue="Python.tk" />
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+  <component name="SvnConfiguration">
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+</project>

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()

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()