update

interface/interface.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()