基于深度学习和opencv的甲骨文识别系统

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基于深度学习和opencv的甲骨文识别系统

2024-07-10 17:38| 来源: 网络整理| 查看: 265

最近做了一个甲骨文识别项目，在此分享一下。

文末附有源码。视频展示：

基于opencv和深度学习的甲骨文识别

运行界面：

识别结果：在这里插入图片描述本文一共可识别1135个甲骨文，数据集已经放到下载链接中：下面是代码部分：读取数据：

import cv2 as cv import os import numpy as np f=open('lab.txt',encoding='utf-8') lines=f.readlines() f.close() split=0.2 np.random.seed(10) np.random.shuffle(lines) bb=int(len(lines)*(1-0.2)) vv=len(lines)-bb train_lines=lines[:bb] valid_lines=lines[bb:] def generate(train=True,valid=False,batch_size=32): while True: ee=np.eye(1185) if train: linnns=train_lines elif valid: linnns=valid_lines else:break x=[] y=[] for lin in linnns: lu_jing=lin.split()[0] lab=lin.split()[1] src=cv.imdecode(np.fromfile(lu_jing, dtype=np.uint8), 1) h,w,d=src.shape[0],src.shape[1],src.shape[2] if h==96 and w==96 and d==3: src=(src/255).astype('float32') ll=ee[int(lab)] x.append(src) y.append(ll) if len(x)==batch_size: x_train=np.array(x) y_train=np.array(y) x=[] y=[] yield x_train,y_train if __name__ == '__main__': generate()

训练网络模型：

from tensorflow.keras.layers import Conv2D,MaxPooling2D,Input,Flatten,Dense,Dropout from tensorflow.keras import Model from tensorflow.keras.callbacks import (EarlyStopping, ReduceLROnPlateau, TensorBoard,ModelCheckpoint) from get_data import generate,bb,vv def mode(shape=(96,96,3)): inputs=Input(shape=shape) x=Conv2D(filters=6,kernel_size=5,strides=(1,1),padding='same',activation='relu')(inputs) x=MaxPooling2D(pool_size=(2,2),strides=(2,2),padding='same')(x) x=Conv2D(filters=16,kernel_size=3,strides=(1,1),padding='same',activation='relu')(x) x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same')(x) x=Flatten()(x) x=Dense(1000,activation='relu')(x) x=Dropout(rate=0.5)(x) x=Dense(500,activation='relu')(x) y = Dense(1185, activation='softmax')(x) model=Model(inputs,y) return model modell=mode() modell.summary() if __name__ == '__main__': modell.compile( loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] ) log_dir = 'logs/' logging = TensorBoard(log_dir=log_dir) checkpoint = ModelCheckpoint(log_dir + 'ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5', monitor='val_loss', save_weights_only=True, save_best_only=False, period=1) reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, verbose=1) early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=1) TensorBoard = TensorBoard(log_dir='./model', histogram_freq=1) batch_size=50 hist = modell.fit_generator(generate(batch_size=batch_size), steps_per_epoch=bb//batch_size, validation_data=generate(valid=True,batch_size=batch_size), validation_steps=vv//batch_size, epochs=50, callbacks=[logging, checkpoint, reduce_lr, early_stopping])

预测：

from model import modell import cv2 as cv import numpy as np with open('answer.txt','r',encoding='utf-8') as f: lines=f.readlines() modell.load_weights('logs/ep030-loss0.037-val_loss0.024.h5') def ppre(src): #src=cv.imdecode(np.fromfile('ancient_3_exp/jia/丘/0/O000255_exp_1.png', dtype=np.uint8), 1) src=np.reshape(src,(1,src.shape[0],src.shape[1],src.shape[2])) src=(src/255).astype('float32') pre=modell.predict(src)[0] pre=np.argmax(pre) return lines[pre] if __name__ == '__main__': src = cv.imdecode(np.fromfile('ancient_3_exp/jia/丘/0/O000255_exp_1.png', dtype=np.uint8), 1) zti=ppre(src) print(zti) 界面已经使用pyqt5做好，下载以后只需运行main.py程序即可，下图是程序目录：

在这里插入图片描述如有疑问可私信作者。下载地址：https://gitee.com/mqwdasddqw/project-download-address

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基于深度学习和opencv的甲骨文识别系统

基于深度学习和opencv的甲骨文识别系统

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