#!/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()