基于TensorFlow2.3.0的垃圾分类Android APP设计

一、开发环境

• Windows 10
• Python 3.7.3
• TensorFlow 2.3.0
• Anaconda 4.12.0
• CUDA 10.1
• cuDNN 7.6.5

二、步骤

        2.1 创建一个python 3.7.3的虚拟环境

conda create -n trash_gpu python==3.7.3

        2.2 激活虚拟环境

conda activate trash_gpu

        2.3 安装tensorflow-gpu，提前安装好CUDA 10.1和cuDNN 7.6.5

pip install tensorflow-gpu==2.3.0

        2.4 准备垃圾分类数据集

        2.5 编写训练模型代码，为了使模型文件更加轻量化，使用MobileNetV2来训练模型。

        代码如下：

# 模型加载
def model_load(IMG_SHAPE=(224, 224, 3), class_num=214):base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE, include_top=False, weights='imagenet')base_model.trainable = Falsemodel = tf.keras.models.Sequential([tf.keras.layers.experimental.preprocessing.Rescaling(1. / 127.5, offset=-1, input_shape=IMG_SHAPE),base_model,tf.keras.layers.GlobalAveragePooling2D(),tf.keras.layers.Dense(class_num, activation='softmax')])# 输出模型信息model.summary() model.compile(optimizer='adam', loss='categorical_crossentropy',metrics=['accuracy'])return model# 训练模型
def train(epochs):# 1. 加载数据集train_dataset, validate_dataset, class_names = data_load("E:/trash_image_set/data", 224, 224, 16) # print(class_names)print('类别的个数-->')print(len(class_names))# 2. 加载模型model = model_load(class_num=len(class_names))# 3. 训练history = model.fit(train_dataset, validation_data=validate_dataset, epochs=epochs)# 4. 保存模型model.save("models/trash_model.h5")  # 5. 转换为tflite模型h5_model = tf.keras.models.load_model("models/trash_model.h5")converter = tf.lite.TFLiteConverter.from_keras_model(h5_model)tflite_model = converter.convert()open("models/model.tflite", "wb").write(tflite_model)if __name__ == '__main__':train(epochs=30)

        2.6 经过漫长的训练过程后，在models文件夹中得到名称为model.tflite的模型文件，接下来将这个模型文件导入Android Studio工程中。

三、编写Android APP

        3.1 将model.tflite模型文件拷贝到Android工程的assets文件中，如图：

         3.2 同时要在app下build.gradle文件添加如下内容

    aaptOptions {noCompress "tflite"}

        3.3 编写activity_main.xml布局文件

  3.4 编写MainActivity.java代码

@Overrideprotected void onCreate(Bundle savedInstanceState) {super.onCreate(savedInstanceState);if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.LOLLIPOP) {Window window = this.getWindow();window.clearFlags(WindowManager.LayoutParams.FLAG_TRANSLUCENT_STATUS);window.getDecorView().setSystemUiVisibility(View.SYSTEM_UI_FLAG_LAYOUT_FULLSCREEN| View.SYSTEM_UI_FLAG_LAYOUT_STABLE);window.addFlags(WindowManager.LayoutParams.FLAG_DRAWS_SYSTEM_BAR_BACKGROUNDS);window.setStatusBarColor(Color.GRAY);}setContentView(R.layout.activity_main);/** 在选择图片的时候，在android 7.0及以上通过FileProvider获取Uri，不需要文件权限*/if (Build.VERSION.SDK_INT < Build.VERSION_CODES.N) {List permissionList = new ArrayList<>(Arrays.asList(neededPermissions));permissionList.add(Manifest.permission.READ_EXTERNAL_STORAGE);neededPermissions = permissionList.toArray(new String[0]);}initView();TFLiteLoader loader = TFLiteLoader.newInstance(this);interpreter = loader.get();showToast("模型加载成功！");mBitmap = BitmapFactory.decodeResource(getResources(), R.drawable.cup);}private void initView() {tv_trash_detail = findViewById(R.id.tv_trash_detail);iv_trash = findViewById(R.id.iv_trash);tv_waste_name = findViewById(R.id.tv_waste_name);}private void showToast(String text) {Toast.makeText(this, text, Toast.LENGTH_LONG).show();}// 更换图片public void choose_image(View view) {Intent intent = new Intent(Intent.ACTION_PICK);intent.setDataAndType(MediaStore.Images.Media.EXTERNAL_CONTENT_URI, "image/*");startActivityForResult(intent, 0);}private int maxIndex = 0;@Overrideprotected void onActivityResult(int requestCode, int resultCode, Intent data) {super.onActivityResult(requestCode, resultCode, data);if (data == null || data.getData() == null) {showToast("获取图片失败");return;}try {mBitmap = MediaStore.Images.Media.getBitmap(getContentResolver(), data.getData());} catch (IOException e) {e.printStackTrace();}// 识别图片detect_image();// 更新显示的图片iv_trash.setImageBitmap(mBitmap);// 更新垃圾分类的名称tv_waste_name.setText(class_names[maxIndex]);// 更新垃圾分类的介绍String text = class_names[maxIndex];if (text.contains("厨余垃圾")){tv_trash_detail.setText(waste_detail[0]);} else if (text.contains("有害垃圾")) {tv_trash_detail.setText(waste_detail[1]);} else if (text.contains("可回收物")) {tv_trash_detail.setText(waste_detail[2]);} else if (text.contains("其他垃圾")) {tv_trash_detail.setText(waste_detail[3]);}}// 识别图片public void detect_image() {// bitmap convert to arrayfloat[][][][] pixels = getScaledMatrix(mBitmap, input);interpreter.run(pixels, output);for (int j = 0; j < output[0].length; j++) {BigDecimal b = new BigDecimal(output[0][j]);float f1 = b.setScale(3, BigDecimal.ROUND_HALF_UP).floatValue();Log.i("Test", f1 + "--> "+ j);}float max = output[0][0];for(int i = 1; i < output[0].length;i++){if(max < output[0][i]){max = output[0][i];maxIndex = i;}}String text = class_names[maxIndex];// 显示ToastshowToast(text);}

        3.5 Android实际效果图       

基于TensorFlow2.3.0的垃圾分类

四、资料下载

        APK下载：https://wwi.lanzoup.com/itLZV0a53qni 密码:1a9y

        源码下载(包含数据集、模型文件、APP源码) https://
item.taobao.com/item.htm?ft=t&id=681383960366

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