基于Android搭建tensorflow lite,实现官网的Demo以及运行自定义tensorflow模型(二)
16lz
2021-01-25
基于上一篇在android studio 中已经布置好的环境进行开发。
这篇文章是基于手写识别的例子,在tensorflow中搭建一个简单的BP神经网络,在实现手写数字的识别,然后把这个网络生成文件,在android的tensorflow lite中运行。
一 在tensorflow 中生成tflite文件
我的python是3.6,tensorflow配置的是1.8.0,然后直接上代码。
import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("mnist",one_hot=True)# 定义批次大小batch_size = 100n_batch = mnist.train.num_examples# 定义placeholderx = tf.placeholder(tf.float32,[1,784],name='input_x')y = tf.placeholder(tf.float32,[1,10],name='output_y')# 定义 测试x_test = tf.placeholder(tf.float32,[None,784],name='input_test_x')y_test = tf.placeholder(tf.float32,[None,10],name='input_test_y')# 创建一个简单的神经网络W = tf.Variable(tf.zeros([784,10]),name="W")b = tf.Variable(tf.zeros([1,10]),name="b")prediction = tf.nn.softmax(tf.matmul(x,W)+b)# 创建损失函数train = tf.train.GradientDescentOptimizer(0.02).minimize(tf.reduce_mean(tf.square(y-prediction)))# 名称转换def canonical_name(x): return x.name.split(":")[0]# 计算准确率test_prediction = tf.nn.softmax(tf.matmul(x_test,W)+b)accuarcy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(y_test,1),tf.argmax(test_prediction,1)),tf.float32))init = tf.global_variables_initializer()out = tf.identity(prediction, name="output")with tf.Session() as sess: sess.run(init) for epoch in range(10): for batch in range(n_batch): batch_xs,batch_ys = mnist.train.next_batch(batch_size) for index in range(len(batch_xs)): xs = batch_xs[index].reshape(1,784) ys = batch_ys[index].reshape(1,10) sess.run(train, feed_dict={x: xs, y: ys}) acc = sess.run(accuarcy,feed_dict={x_test:mnist.test.images,y_test:mnist.test.labels}) print("over"+str(acc)) frozen_tensors = [out] out_tensors = [out] frozen_graphdef = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def, list(map(canonical_name, frozen_tensors))) tflite_model = tf.contrib.lite.toco_convert(frozen_graphdef, [x], out_tensors) open("writer_model.tflite", "wb").write(tflite_model)
运行之后就可以生文件,writer_model.tflite.
二 创建自己的分类器
在上一篇搭建好平台之后,最重要的是模型的输入和输出,模型的输入函数。
private ByteBuffer convertBitmapToByteBuffer(Bitmap bitmap) { // 获取图片的宽度 int width = bitmap.getWidth(); // 获取图片的高度 int height = bitmap.getHeight(); // 传入模型数据必须是ByteBuffer格式的,所以说必须把数据转入到 ByteBuffer tempData = ByteBuffer.allocateDirect(width * height * 4); // 数组排列用nativeOrder tempData.order(ByteOrder.nativeOrder()); // 获取图片的像素值 int[] pixels = getPicturePixel(bitmap); for (int i = 0; i < pixels.length; i++) { byte[] bytes = float2byte((float)(pixels[i])); for (int k = 0; k < bytes.length; k++) { tempData.put(bytes[k]); } } return tempData; }
直接上完整的分类器代码
package com.fangt.classifer;import android.content.Context;import android.content.res.AssetFileDescriptor;import android.graphics.Bitmap;import org.tensorflow.lite.Interpreter;import java.io.FileInputStream;import java.io.IOException;import java.nio.ByteBuffer;import java.nio.ByteOrder;import java.nio.MappedByteBuffer;import java.nio.channels.FileChannel;public class WriterIdentify { // 运行生成的文件,形成分类器 private Interpreter tflite; // 输出的结构 private float[][] labelProbArray = null; public static WriterIdentify newInstance(Context context) { WriterIdentify writerIdentify = new WriterIdentify(context); return writerIdentify; } private WriterIdentify(Context context) { try { tflite = new Interpreter(loadModelFile(context)); } catch (Exception e) { } labelProbArray = new float[1][10]; } public void run(Bitmap bitmap) { tflite.run(convertBitmapToByteBuffer(bitmap), labelProbArray); //convertBitmapToByteBuffer(bitmap,width,height); } // 返回输出的结果 public int getResult() { int[] resultDict = new int[]{0, 1, 2, 3, 4, 5, 6, 7, 8, 9}; for (int i = 0; i < labelProbArray[0].length; i++) { if (labelProbArray[0][i] == 1.0f) { return resultDict[i]; } } return -1; } private ByteBuffer convertBitmapToByteBuffer(Bitmap bitmap) { int width = bitmap.getWidth(); int height = bitmap.getHeight(); ByteBuffer tempData = ByteBuffer.allocateDirect(width * height * 4); // 数组排列用nativeOrder tempData.order(ByteOrder.nativeOrder()); int[] pixels = getPicturePixel(bitmap); for (int i = 0; i < pixels.length; i++) { byte[] bytes = float2byte((float)(pixels[i])); for (int k = 0; k < bytes.length; k++) { tempData.put(bytes[k]); } } return tempData; } // 读取图片像素 private int[] getPicturePixel(Bitmap bitmap) { int width = bitmap.getWidth(); int height = bitmap.getHeight(); // 保存所有的像素的数组,图片宽×高 int[] pixels = new int[width * height]; bitmap.getPixels(pixels, 0, width, 0, 0, width, height); String str = ""; for (int i = 0; i < pixels.length; i++) { pixels[i] = pixels[i] & 0x000000ff; } return pixels; } // 把float转bytes字节 private byte[] float2byte(float f) { // 把float转换为byte[] int fbit = Float.floatToIntBits(f); byte[] b = new byte[4]; for (int i = 0; i < 4; i++) { b[i] = (byte) (fbit >> (24 - i * 8)); } // 翻转数组 int len = b.length; // 建立一个与源数组元素类型相同的数组 byte[] dest = new byte[len]; // 为了防止修改源数组,将源数组拷贝一份副本 System.arraycopy(b, 0, dest, 0, len); byte temp; // 将顺位第i个与倒数第i个交换 for (int i = 0; i < len / 2; ++i) { temp = dest[i]; dest[i] = dest[len - i - 1]; dest[len - i - 1] = temp; } return dest; } // 获取文件 private MappedByteBuffer loadModelFile(Context context) throws IOException { AssetFileDescriptor fileDescriptor = context.getAssets().openFd(getModelPath()); FileInputStream inputStream = new FileInputStream(fileDescriptor.getFileDescriptor()); FileChannel fileChannel = inputStream.getChannel(); long startOffset = fileDescriptor.getStartOffset(); long declaredLength = fileDescriptor.getDeclaredLength(); return fileChannel.map(FileChannel.MapMode.READ_ONLY, startOffset, declaredLength); } private String getModelPath() { return "writer_model.tflite"; }}
三 读取MNIST数据集中的数据
由于我们测试数据,就需要把图片从MNIST中提取出来,这里写了一个小工具,先从MNIST官网下载文件。
http://yann.lecun.com/exdb/mnist/
下载之后解压,运行下下面的小工具就可以了。
import numpy as npimport structfrom PIL import Imageimport osdata_file = 'MNIST_data/train-images.idx3-ubyte' # 需要修改的路径# It's 47040016B, but we should set to 47040000Bdata_file_size = 47040016data_file_size = str(data_file_size - 16) + 'B'data_buf = open(data_file, 'rb').read()magic, numImages, numRows, numColumns = struct.unpack_from( '>IIII', data_buf, 0)datas = struct.unpack_from( '>' + data_file_size, data_buf, struct.calcsize('>IIII'))datas = np.array(datas).astype(np.uint8).reshape( numImages, 1, numRows, numColumns)datas_root = 'images/' # 需要修改的路径for ii in range(100): print(ii) img = Image.fromarray(datas[ii, 0, 0:28, 0:28]) file_name = datas_root + 'mnist_' + str(ii) + '.png' img.save(file_name)
运行之后的图片展示:
四 在android中运行自定的分类器
先需要把图片导入到文件中
先创建XML文件,页面布局
之后是后台文件,也就是调用分类器。
package com.fangt.fragment;import android.content.Context;import android.graphics.Bitmap;import android.graphics.BitmapFactory;import android.net.Uri;import android.os.Bundle;import android.app.Fragment;import android.view.LayoutInflater;import android.view.View;import android.view.ViewGroup;import android.widget.Button;import android.widget.ImageView;import android.widget.TextView;import android.widget.Toast;import com.example.android.tflitecamerademo.R;import com.fangt.classifer.WriterIdentify;public class WriterFragment extends Fragment implements View.OnClickListener { private Button btnStart; private Button btnChange; private TextView tvContent; private ImageView ivNumber; private Context context; // 图片数据 private int[] imageIds; private static int currentImageIds; public WriterFragment() { } // TODO: Rename and change types and number of parameters public static WriterFragment newInstance(String param1, String param2) { WriterFragment fragment = new WriterFragment(); return fragment; } @Override public void onCreate(Bundle savedInstanceState) { super.onCreate(savedInstanceState); } @Override public View onCreateView(LayoutInflater inflater, ViewGroup container, Bundle savedInstanceState) { View view = inflater.inflate(R.layout.fragment_writer, container, false); context = view.getContext(); init(view); return view; } private void init(View view) { btnStart = (Button) view.findViewById(R.id.btnStart); tvContent = (TextView) view.findViewById(R.id.tvContent); ivNumber = (ImageView) view.findViewById(R.id.ivNumber); btnChange = (Button) view.findViewById(R.id.btnChange); btnStart.setOnClickListener(this); btnChange.setOnClickListener(this); imageIds = new int[]{R.drawable.mnist_0,R.drawable.mnist_1,R.drawable.mnist_2, R.drawable.mnist_3,R.drawable.mnist_4,R.drawable.mnist_5, R.drawable.mnist_6,R.drawable.mnist_7,R.drawable.mnist_8, R.drawable.mnist_9,R.drawable.mnist_10,R.drawable.mnist_11, R.drawable.mnist_12}; currentImageIds = 0; ivNumber.setImageResource(imageIds[currentImageIds]); } @Override public void onClick(View v) { switch (v.getId()){ case R.id.btnStart: WriterIdentify writerIdentify = WriterIdentify.newInstance(context); BitmapFactory.Options bfoOptions = new BitmapFactory.Options(); bfoOptions.inScaled = false; Bitmap bitmap = BitmapFactory.decodeResource(getResources(), imageIds[currentImageIds],bfoOptions); writerIdentify.run(bitmap); tvContent.setText("Result:" + writerIdentify.getResult()); break; case R.id.btnChange: currentImageIds = (++currentImageIds) % imageIds.length; ivNumber.setImageResource(imageIds[currentImageIds]); break; } }}
到这里基本内容就完成了。
下面展示几张效果图:
对5进行分类
到这就结束了,喜欢的可以关注一下,有什么问题可以给我私信。谢谢。
我把APP上传到CSDN下载,地址
https://download.csdn.net/download/qq_22765745/10443505
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