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| English\n   \n简介\n\n\n中医药是中华民族的传统文化瑰宝，目前，中药种类繁多，辨认多依靠于经验，这增加了我们辨识出中药的难度；同时，我们需要记忆大量的中药特征和药效，查询效率慢。\n\n\n我们目标是开发一款中药识别的APP，主要利用细粒度图像识别技术，用户只需利用手机对中药进行拍照便可识别，提高学习效率，高效的识别与学习各类中药的特征和药效价值，促进人工智能与医疗的结合。\n\n\n\n预览\n\n\n\n\n\n\n\n视频\n\n视频演示\n\n下载\n\nAPP下载地址\n\n版本说明 2.x\n对版本1.x进行重构升级，重新组织了代码结构，移除了特别不稳定且重量级的组件Deeplearn4j。\n开发计划\n使用Flutter对移动端重写，提高性能与跨平台性，同时，进一步调高检测模型的精度。\n交流、反馈与参与贡献\n\n如需关注项目最新动态，请Watch、Star项目，同时也是对项目最好的支持\n欢迎参与技术讨论、二次开发咨询、问题反馈和建议！\nQQ：993021993\n微信：\n\n感谢！^_^\n\n\n\n"},"docs/function/":{"url":"docs/function/","title":"产品功能","keywords":"","body":"主要功能展示\n首页及中药信息页\n\n\n拍照识别\n点击导航栏拍照按钮，拍摄中药的图片上传后进行识别。\n\n\n\n\n分类检索\n点击右上方筛选按钮，对中药进行分类检索。\n\n\n全文检索\n输入关键词对中药库进行检索，对关键词进行粉刺搜索并高亮显示。\n\n\n问题社区\n设立中医药问题讨论区，可以提出问题或者回答。\n\n\n"},"docs/technology/":{"url":"docs/technology/","title":"开发技术","keywords":"","body":"后端\n主要包含六个模块：\n\nmedicine-server：服务器端\nmedicine-collection：爬虫工程\nmedicine-model：卷积神经网络\nmedicine-util：公用工具类\nmedicine-dataset：数据集\nmedicine-runtime-data：运行时数据\n\nApp端\n主要使用React Native + Ant Design 设计实现。\n主要流程\n\n"},"docs/technology/DATASET.html":{"url":"docs/technology/DATASET.html","title":"数据集","keywords":"","body":"中药字段\n\n\n\n编号\n字段名\n\n\n\n\n1\n中药名字\n\n\n2\n中药形态\n\n\n3\n中药图片\n\n\n4\n别名\n\n\n5\n化学成分\n\n\n6\n英文名\n\n\n7\n中药名字\n\n\n8\n配伍配方\n\n\n9\n功效作用\n\n\n10\n临床应用\n\n\n11\n产地分布\n\n\n12\n药用部位\n\n\n13\n性味归经\n\n\n14\n药理研究\n\n\n15\n使用禁忌\n\n\n16\n采集加工\n\n\n17\n药材形状\n\n\n18\n药材炮制\n\n\n19\n现代研究\n\n\n\n样例数据\n\n中药一级分类信息\n  \n\n中药详细信息\n  \n\n\n数据集\n\n"},"docs/technology/ALGORITHM.html":{"url":"docs/technology/ALGORITHM.html","title":"检测算法","keywords":"","body":"深度可分离卷积检测算法\nLanguage: Python\n使用TensorFlow 深度学习框架，使用Keras会大幅缩减代码量\n训练机器：华为Atlas 200 AI开发板（或本地计算机）\n数据集\n常用的卷积网络模型及在ImageNet上的准确率\n\n\n\n模型\n大小\nTop-1准确率\nTop-5准确率\n参数数量\n深度\n\n\n\n\nXception\n88 MB\n0.790\n0.945\n22,910,480\n126\n\n\nVGG16\n528 MB\n0.713\n0.901\n138,357,544\n23\n\n\nVGG19\n549 MB\n0.713\n0.900\n143,667,240\n26\n\n\nResNet50\n98 MB\n0.749\n0.921\n25,636,712\n168\n\n\nResNet101\n171 MB\n0.764\n0.928\n44,707,176\n-\n\n\nResNet152\n232 MB\n0.766\n0.931\n60,419,944\n-\n\n\nResNet50V2\n98 MB\n0.760\n0.930\n25,613,800\n-\n\n\nResNet101V2\n171 MB\n0.772\n0.938\n44,675,560\n-\n\n\nResNet152V2\n232 MB\n0.780\n0.942\n60,380,648\n-\n\n\nResNeXt50\n96 MB\n0.777\n0.938\n25,097,128\n-\n\n\nResNeXt101\n170 MB\n0.787\n0.943\n44,315,560\n-\n\n\nInceptionV3\n92 MB\n0.779\n0.937\n23,851,784\n159\n\n\nInceptionResNetV2\n215 MB\n0.803\n0.953\n55,873,736\n572\n\n\nMobileNet\n16 MB\n0.704\n0.895\n4,253,864\n88\n\n\nMobileNetV2\n14 MB\n0.713\n0.901\n3,538,984\n88\n\n\nDenseNet121\n33 MB\n0.750\n0.923\n8,062,504\n121\n\n\nDenseNet169\n57 MB\n0.762\n0.932\n14,307,880\n169\n\n\nDenseNet201\n80 MB\n0.773\n0.936\n20,242,984\n201\n\n\nNASNetMobile\n23 MB\n0.744\n0.919\n5,326,716\n-\n\n\nNASNetLarge\n343 MB\n0.825\n0.960\n88,949,818\n-\n\n\n\n由于硬件条件限制，综合考虑模型的准确率、大小以及复杂度等因素，采用了Xception模型，\n该模型是134层（包含激活层，批标准化层等）拓扑深度的卷积网络模型。\n检测算法\ndef Xception(include_top=True,\n    weights='imagenet',\n    input_tensor=None,\n    input_shape=None,\n    pooling=None,\n    classes=1000,\n    **kwargs)\n\n# 参数\n# include_top：是否保留顶层的全连接网络\n# weights：None代表随机初始化，即不加载预训练权重。'imagenet’代表加载预训练权重\n# input_tensor：可填入Keras tensor作为模型的图像输入tensor\n# input_shape：可选，仅当include_top=False有效，应为长为3的tuple，指明输入图片的shape，图片的宽高必须大于71，如(150,150,3)\n# pooling：当include_top=False时，该参数指定了池化方式。None代表不池化，最后一个卷积层的输出为4D张量。‘avg’代表全局平均池化，‘max’代表全局最大值池化。\n# classes：可选，图片分类的类别数，仅当include_top=True并且不加载预训练权重时可用\n\n基于Xception的模型微调,详细请参考代码\n\n设置Xception参数\n 迁移学习参数权重加载：xception_weights\n # 设置输入图像的宽高以及通道数\n img_size = (299, 299, 3)\n\n base_model = keras.applications.xception.Xception(include_top=False,\n                                                 weights='..\\\\resources\\\\keras-model\\\\xception_weights_tf_dim_ordering_tf_kernels_notop.h5',\n                                                 input_shape=img_size,\n                                                 pooling='avg')\n\n # 全连接层，使用softmax激活函数计算概率值，分类大小是628\n model = keras.layers.Dense(628, activation='softmax', name='predictions')(base_model.output)\n model = keras.Model(base_model.input, model)\n\n # 锁定卷积层\n for layer in base_model.layers:\n   layer.trainable = False\n\n\n全连接层训练(v1.0)\n from base_model import model\n\n # 设置训练集图片大小以及目录参数\n img_size = (299, 299)\n dataset_dir = '..\\\\dataset\\\\dataset'\n img_save_to_dir = 'resources\\\\image-traing\\\\'\n log_dir = 'resources\\\\train-log'\n\n model_dir = 'resources\\\\keras-model\\\\'\n\n # 使用数据增强\n train_datagen = keras.preprocessing.image.ImageDataGenerator(\n     rescale=1. / 255,\n     shear_range=0.2,\n     width_shift_range=0.4,\n     height_shift_range=0.4,\n     rotation_range=90,\n     zoom_range=0.7,\n     horizontal_flip=True,\n     vertical_flip=True,\n     preprocessing_function=keras.applications.xception.preprocess_input)\n\n test_datagen = keras.preprocessing.image.ImageDataGenerator(\n     preprocessing_function=keras.applications.xception.preprocess_input)\n\n train_generator = train_datagen.flow_from_directory(\n     dataset_dir,\n     save_to_dir=img_save_to_dir,\n     target_size=img_size,\n     class_mode='categorical')\n\n validation_generator = test_datagen.flow_from_directory(\n     dataset_dir,\n     save_to_dir=img_save_to_dir,\n     target_size=img_size,\n     class_mode='categorical')\n\n # 早停法以及动态学习率设置\n early_stop = EarlyStopping(monitor='val_loss', patience=13)\n reduce_lr = ReduceLROnPlateau(monitor='val_loss', patience=7, mode='auto', factor=0.2)\n tensorboard = keras.callbacks.tensorboard_v2.TensorBoard(log_dir=log_dir)\n\n for layer in model.layers:\n     layer.trainable = False\n\n # 模型编译\n model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])\n\n history = model.fit_generator(train_generator,\n                               steps_per_epoch=train_generator.samples // train_generator.batch_size,\n                               epochs=100,\n                               validation_data=validation_generator,\n                               validation_steps=validation_generator.samples // validation_generator.batch_size,\n                               callbacks=[early_stop, reduce_lr, tensorboard])\n # 模型导出\n model.save(model_dir + 'chinese_medicine_model_v1.0.h5')\n\n\n对于顶部的6层卷积层，我们使用数据集对权重参数进行微调\n # 加载模型\n model=keras.models.load_model('resources\\\\keras-model\\\\chinese_medicine_model_v2.0.h5')\n\n for layer in model.layers:\n    layer.trainable = False\n for layer in model.layers[126:132]:\n    layer.trainable = True\n\n history = model.fit_generator(train_generator,\n                               steps_per_epoch=train_generator.samples // train_generator.batch_size,\n                               epochs=100,\n                               validation_data=validation_generator,\n                               validation_steps=validation_generator.samples // validation_generator.batch_size,\n                               callbacks=[early_stop, reduce_lr, tensorboard])\n model.save(model_dir + 'chinese_medicine_model_v2.0.h5')\n\n\n在后端项目中，我们使用Deeplearn4j调用训练好的模型\n```\n  public class CnnModelUtil {\n private static ComputationGraph CNN_MODEL = null;\n\n /**\n  * 中药名字的编码\n  */\n private static final Map MEDICINE_NAME_MAP = new HashMap<>();\n\n /**\n  * 定义cnn model的文件夹路径\n  */\n private static final String DATA_DIR = System.getProperty(\"os.name\")\n         .toLowerCase().contains(\"windows\") ? \"D:\\\\data\\\\model\\\\\"\n         : \"./data/model/\";\n\n /**\n  * 定义中药编码表的文件名\n  */\n private static final String MEDICINE_LABLE_FILE_NAME = \"medicine_name-lable.txt\";\n\n /**\n  * 定义模型的文件名\n  */\n private static final String CNN_MODEL_FILE_NAME = \"chinese_medicine_model.h5\";\n\n\n    /**\n     * 图片的加载器\n     */\n    private static final NativeImageLoader IMAGE_LOADER = new NativeImageLoader(299, 299, 3);\n\n\n    /**\n     * 初始化\n     */\n    static {\n        try {\n            CNN_MODEL = KerasModelImport.importKerasModelAndWeights(DATA_DIR + CNN_MODEL_FILE_NAME);\n\n            Files.readAllLines(Paths.get(DATA_DIR, MEDICINE_LABLE_FILE_NAME)).forEach(v -> {\n                String[] split = v.split(\",\");\n                MEDICINE_NAME_MAP.put(Integer.valueOf(split[1]), split[0]);\n            });\n        } catch (IOException | InvalidKerasConfigurationException | UnsupportedKerasConfigurationException e) {\n            e.printStackTrace();\n        }\n    }\n\n    /**\n     * 对图像进行预测\n     * 对预测的概率值进行排序处理\n     * 返回值是概率值前10的中药的名字\n     * @param file\n     * @return\n     * @throws \n     */\n    public static Map medicineNamePredict(File file) throws IOException {\n        INDArray image = IMAGE_LOADER.asMatrix(file).divi(127.5).subi(1);\n        INDArray output = CNN_MODEL.outputSingle(image);\n        Map resultMap = new HashMap<>();\n        float[] floats = output.toFloatVector();\n        for (int i = 0; i > resultList = new LinkedList<>(resultMap.entrySet());\n        resultList.sort(Map.Entry.comparingByValue(Comparator.reverseOrder()));\n        Map medicinePredict = new LinkedHashMap<>();\n        resultList.stream().limit(10).forEach(v -> {\n            medicinePredict.put(MEDICINE_NAME_MAP.get(v.getKey()), v.getValue());\n        });\n        return medicinePredict;\n    }\n}\n```\n模型概览\n模型详细结构\n训练过程正确率以及损失函数可视化展示\n\n\n"},"docs/technology/APP.html":{"url":"docs/technology/APP.html","title":"App 端构建","keywords":"","body":"待补充\n"},"docs/technology/SERVER.html":{"url":"docs/technology/SERVER.html","title":"后端构建","keywords":"","body":"待补充\n"}}}