218 lines
6.4 KiB
Python
218 lines
6.4 KiB
Python
#!/usr/bin/python
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# -*- coding: UTF-8 -*-
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from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, Activation
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from keras.layers import Flatten, BatchNormalization, PReLU, Dense, Dropout
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from keras.models import Model, Input
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from keras.applications.resnet50 import ResNet50
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import numpy as np
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import tkinter
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import tkinter.filedialog
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import os
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from PIL import Image, ImageTk
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import matplotlib.pyplot as plt
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import time
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def bn_relu(x):
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x = BatchNormalization()(x)
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#参数化的ReLU
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x = PReLU()(x)
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return x
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def Alex_model(out_dims, input_shape=(128, 128, 1)):
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input_dim = Input(input_shape)
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x = Conv2D(96, (20, 20), strides=(2, 2), padding='valid')(input_dim) # 55 * 55 * 96
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x = bn_relu(x)
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x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='valid')(x) # 27 * 27 * 96
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x = Conv2D(256, (5, 5), strides=(1, 1), padding='same')(x)
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x = bn_relu(x)
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x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='valid')(x)
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x = Conv2D(384, (3, 3), strides=(1, 1), padding='same')(x)
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x = PReLU()(x)
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x = Conv2D(384, (3, 3), strides=(1, 1), padding='same')(x)
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x = PReLU()(x)
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x = Conv2D(256, (3, 3), strides=(1, 1), padding='same')(x)
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x = PReLU()(x)
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x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='valid')(x)
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x = Flatten()(x)
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fc1 = Dense(4096)(x)
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dr1 = Dropout(0.2)(fc1)
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fc2 = Dense(4096)(dr1)
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dr2 = Dropout(0.25)(fc2)
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fc3 = Dense(out_dims)(dr2)
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fc3 = Activation('softmax')(fc3)
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model = Model(inputs=input_dim, outputs=fc3)
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return model
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def resner50(out_dims, input_shape=(128, 128, 1)):
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# input_dim = Input(input_shape)
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resnet_base_model = ResNet50(include_top=False, weights=None, input_shape=input_shape)
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x = resnet_base_model.output
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x = Flatten()(x)
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fc = Dense(512)(x)
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x = bn_relu(fc)
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x = Dropout(0.5)(x)
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x = Dense(out_dims)(x)
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x = Activation("softmax")(x)
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# buid myself model
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input_shape = resnet_base_model.input
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output_shape = x
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resnet50_100_model = Model(inputs=input_shape, outputs=output_shape)
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return resnet50_100_model
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def my_model(out_dims, input_shape=(128, 128, 1)):
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input_dim = Input(input_shape) # 生成一个input_shape的张量
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x = Conv2D(32, (3, 3), strides=(2, 2), padding='valid')(input_dim)
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x = bn_relu(x)
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x = Conv2D(32, (3, 3), strides=(1, 1), padding='valid')(x)
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x = bn_relu(x)
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x = MaxPooling2D(pool_size=(2, 2))(x)
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x = Conv2D(64, (3, 3), strides=(1, 1), padding='valid')(x)
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x = bn_relu(x)
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x = Conv2D(64, (3, 3), strides=(1, 1), padding='valid')(x)
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x = bn_relu(x)
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x = MaxPooling2D(pool_size=(2, 2))(x)
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x = Conv2D(128, (3, 3), strides=(1, 1), padding='valid')(x)
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x = bn_relu(x)
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x = MaxPooling2D(pool_size=(2, 2))(x)
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x = Conv2D(128, (3, 3), strides=(1, 1), padding='valid')(x)
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x = bn_relu(x)
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x = AveragePooling2D(pool_size=(2, 2))(x)
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x_flat = Flatten()(x)
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fc1 = Dense(512)(x_flat)
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fc1 = bn_relu(fc1)
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dp_1 = Dropout(0.3)(fc1)
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fc2 = Dense(out_dims)(dp_1)
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fc2 = Activation('softmax')(fc2)
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model = Model(inputs=input_dim, outputs=fc2)
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return model
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def load_image(image):
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img = Image.open(image).convert('L')
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img = img.resize((128,128))
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img = np.array(img)
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img = img / 255
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img = img.reshape((1,) + img.shape + (1,))
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return img
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def get_label(image, model, top_k):
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prediction = model.predict(image)
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predict_list = list(prediction[0])
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min_label = min(predict_list)
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label_k = []
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for i in range(top_k):
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label = np.argmax(predict_list)
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predict_list.remove(predict_list[label])
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predict_list.insert(label, min_label)
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label_k.append(label)
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return label_k
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def label_of_directory(directory):
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classes = []
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for subdir in sorted(os.listdir(directory)):
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if os.path.isdir(os.path.join(directory, subdir)):
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classes.append(subdir)
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class_indices = dict(zip(classes, range(len(classes))))
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return class_indices
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def get_key_from_classes(dict, index):
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for key, value in dict.items():
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if value == index:
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return key
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def choose_file():
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selectFileName = tkinter.filedialog.askopenfilename(title='选择文件') # 选择文件
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e.set(selectFileName)
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def upload_file(f):
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img = Image.open(f)
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plt.imshow(img)
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path = "/home/shallow/PycharmProjects/MyDesign/image_test/"
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name = time.time()
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img.save(path + str(name) + ".jpg")
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plt.show()
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def start():
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img_name = os.listdir(image_path)
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img_list = []
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for name in img_name:
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img_list.append(os.path.join(image_path, name))
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img_list.sort()
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ans_key = []
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for img in img_list:
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image = load_image(img)
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temp_label = get_label(image, model, 5)
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key = get_key_from_classes(class_indices, temp_label[0])
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ans_key.append(key)
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tkinter.messagebox.showinfo(title="识别结果", message="您提交图片的识别汉字为：" + str(ans_key))
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if __name__ == "__main__":
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"""
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初始化权重
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"""
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weight_path = 'best_weights_Alex.h5'
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image_path = '/home/shallow/PycharmProjects/MyDesign/image_test/'
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train_path = "/home/shallow/TMD_data/myTrain/train_data/"
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model = Alex_model(100)
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model.load_weights(weight_path)
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class_indices = label_of_directory(train_path) # 获取训练集中标签对应汉字序列
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"""
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窗口初始化
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"""
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top = tkinter.Tk()
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top.title('基于深度学习的汉字书法字体识别')
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# top.geometry('1280x800')
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e = tkinter.StringVar() # 可变字符型
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e_entry = tkinter.Entry(top, font = (18), width=68, textvariable=e) # 文本框
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e_entry.pack() # 布局
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submit_button = tkinter.Button(top, text ="选择文件", width = 10, font = (15), command = choose_file)
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submit_button.pack()
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submit_button = tkinter.Button(top, text ="上传", width = 10, font= (15), command = lambda:upload_file(e_entry.get()))
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submit_button.pack()
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submit_button = tkinter.Button(top, text="开始识别", width = 10, font=(15), command = start)
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submit_button.pack()
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img = Image.open("/home/shallow/PycharmProjects/MyDesign/UI/back.jpg")
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(x, y) = img.size
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x //= 4
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y //= 4
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img = img.resize((x, y))
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img = ImageTk.PhotoImage(img)
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backLabel = tkinter.Label(top, image=img)
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backLabel.pack()
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top.mainloop()
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