参考自基于Tensorflow的android手写数字识别 本人小白,基本上代码都参照以上博客,再大佬的基础上修改了一些错误,并将数字识别的方式改为了用摄像头拍照识别或者从相册中选择功能很简单,大佬勿喷。
一、python代码(主要用来训练pb文件在Android中使用基本和原博客中一样,也可直接用原博客):
#coding=utf-8
# 载入MINIST数据需要的库
from tensorflow.examples.tutorials.mnist import input_data
# 导入其他库
import tensorflow as tf
#获取MINIST数据
mnist = input_data.read_data_sets("/data/",one_hot = True)
# 创建会话
sess = tf.InteractiveSession()
#占位符
x = tf.placeholder(float, shape=[None, 784], name="Mul")
y_ = tf.placeholder(float,shape=[None, 10], name="y_")
#变量
W = tf.Variable(tf.zeros([784,10]),name='x')
b = tf.Variable(tf.zeros([10]),name='y')
#权重
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
#偏差
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
#卷积
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
#最大池化
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME')
#相关变量的创建
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
x_image = tf.reshape(x, [-1,28,28,1])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
#激活函数
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
keep_prob = tf.placeholder("float",name='rob')
h_fc1_drop = tf.nn.dropout(h_fc1, rate = 1 - keep_prob)
#用于训练用的softmax函数
y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2,name='res')
#用于训练作完后,作测试用的softmax函数
y_conv2=tf.nn.softmax(tf.matmul(h_fc1, W_fc2) + b_fc2,name="final_result")
# res = tf.argmax(y_conv2,1,name="result")
#交叉熵的计算,返回包含了损失值的Tensor。
cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))
#优化器,负责最小化交叉熵
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
#计算准确率
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
#初始化所以变量
sess.run(tf.global_variables_initializer())
# 保存输入输出,可以为之后用
tf.add_to_collection('res', y_conv)
tf.add_to_collection('output', y_conv2)
# tf.add_to_collection('result', res)
tf.add_to_collection('x', x)
#训练开始(训练次数可自行修改)
for i in range(15000):
batch = mnist.train.next_batch(50)
if i%100 == 0:
train_accuracy = accuracy.eval(feed_dict={
x:batch[0], y_: batch[1], keep_prob: 1.0})
print ("step %d, training accuracy %g"%(i, train_accuracy))
#run()可以看做输入相关值给到函数中的占位符,然后计算的出结果,这里将batch[0],给xbatch[1]给y_
train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
#将当前图设置为默认图
graph_def = tf.get_default_graph().as_graph_def()
#将上面的变量转化成常量,保存模型为pb模型时需要,注意这里的final_result和前面的y_con2是同名,只有这样才会保存它,否则会报错,
# 如果需要保存其他tensor只需要让tensor的名字和这里保持一直即可
output_graph_def = tf.compat.v1.graph_util.convert_variables_to_constants(sess,
graph_def, ['final_result'])
#保存前面训练后的模型为pb文件
with tf.gfile.GFile("grf.pb", 'wb') as f:
f.write(output_graph_def.SerializeToString())
#保存模型
saver = tf.train.Saver()
saver.save(sess, "/model/")
print("test accracy %g"%accuracy.eval(feed_dict={
x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
运行之后会生成一个grf.pb(文件,这个文件是我们在Android设备运行的关键。
二、接下来就是Android
1.在Android studio新建好一个项目后,在工程根目录下新建一个assets,
再将上面生成grf.pb文件直接复制到assets目录下。
2. 添加依赖库
原帖使用了so库和jar包,我测试时会报错依赖库冲突,还有其他我未能解决的错误,所以这里并没有使用这两个依赖库,而是使用了TensorFlow Mobile依赖库,直接在app:gradle的dependencies节点中加入这一行代码同步即可:
implementation 'org.tensorflow:tensorflow-android:+'
3.接下来新建一个图像识别的工具类TF_MINIST(在原帖基础上略有修改):
package com.example.t_t.tensort1;
import android.content.res.AssetManager;
import android.graphics.Bitmap;
import android.graphics.Color;
import android.graphics.Matrix;
import android.os.Trace;
import android.util.Log;
import org.tensorflow.contrib.android.TensorFlowInferenceInterface;
public class TF_MINIST {
private static final String MODEL_FILE = "file:///android_asset/grf.pb"; //模型存放路径
//数据的维度
private static final int HEIGHT = 28;
private static final int WIDTH = 28;
private static final int MAXL = 10;
//模型中输出变量的名称
private static final String inputName = "Mul";
//用于存储的模型输入数据
private float[] inputs = new float[HEIGHT * WIDTH];
//模型中输出变量的名称
private static final String outputName = "final_result";
//用于存储模型的输出数据,0-9
private float[] outputs = new float[MAXL];
TensorFlowInferenceInterface inferenceInterface;
TF_MINIST(AssetManager assetManager) {
//接口定义
inferenceInterface = new TensorFlowInferenceInterface(assetManager,MODEL_FILE);
}
/**
* 将彩色图转换为灰度图
* @param img 位图
* @return 返回转换好的位图
*/
public Bitmap convertGreyImg(Bitmap img) {
int width = img.getWidth(); //获取位图的宽
int height = img.getHeight(); //获取位图的高
int []pixels = new int[width * height]; //通过位图的大小创建像素点数组
img.getPixels(pixels, 0, width, 0, 0, width, height);
int alpha = 0xFF << 24;
for(int i = 0; i < height; i++) {
for(int j = 0; j < width; j++) {
int grey = pixels[width * i + j];
int red = ((grey & 0x00FF0000 ) >> 16);
int green = ((grey & 0x0000FF00) >> 8);
int blue = (grey & 0x000000FF);
grey = (int)((float) red * 0.3 + (float)green * 0.59 + (float)blue * 0.11);
grey = alpha | (grey << 16) | (grey << 8) | grey;
pixels[width * i + j] = grey;
}
}
Bitmap result = Bitmap.createBitmap(width, height, Bitmap.Config.RGB_565);
result.setPixels(pixels, 0, width, 0, 0, width, height);
return result;
}
//将int数组转化为float数组
public float[] ints2float(int[] src,int w){
float res[]=new float[w];
for(int i=0;i<w;++i) {
res[i]=src[i];
}
return res;
}
//返回数组中最大值的索引
public int argmax(float output[]){
int maxIndex=0;
for(int i=1;i<MAXL;++i){
maxIndex=output[i]>output[maxIndex]? i: maxIndex;
}
return maxIndex;
}
//将图像像素数据转为一维数组,isReverse判断是否需要反化图像
public int[] getGrayPix_R(Bitmap bp,boolean isReverse){
int[]pxs=new int[784];
int acc=0;
for(int m=0;m<28;++m){
for(int n=0;n<28;++n){
if(isReverse)
pxs[acc]=255-Color.red(bp.getPixel(n,m));
else
pxs[acc]=Color.red(bp.getPixel(n,m));
Log.d("12","gray_"+acc+":"+pxs[acc]+"_");
++acc;
}
}
return pxs;
}
//灰化图像
public Bitmap gray(Bitmap bitmap, int schema)
{
Bitmap bm = Bitmap.createBitmap(bitmap.getWidth(),bitmap.getHeight(), bitmap.getConfig());
int width = bitmap.getWidth();
int height = bitmap.getHeight();
for(int row=0; row<height; row++){
for(int col=0; col<width; col++){
int pixel = bitmap.getPixel(col, row);// ARGB
int red = Color.red(pixel); // same as (pixel >> 16) &0xff
int green = Color.green(pixel); // same as (pixel >> 8) &0xff
int blue = Color.blue(pixel); // same as (pixel & 0xff)
int alpha = Color.alpha(pixel); // same as (pixel >>> 24)
int gray = 0;
if(schema == 0)
{
gray = (Math.max(blue, Math.max(red, green)) +
Math.min(blue, Math.min(red, green))) / 2;
}
else if(schema == 1)
{
gray = (red + green + blue) / 3;
}
else if(schema == 2)
{
gray = (int)(0.3 * red + 0.59 * green + 0.11 * blue);
}
Log.d("12","gray:"+gray);
bm.setPixel(col, row, Color.argb(alpha, gray, gray, gray));
}
}
return bm;
}
//获得预测结果
public int getAddResult(Bitmap bitmap) {
int width = bitmap.getWidth();
int height = bitmap.getHeight();
float scaleWidth = ((float)WIDTH) / width;
float scaleHeight = ((float) HEIGHT) / height;
Matrix matrix = new Matrix();
//调整图像大小为28x28
matrix.postScale(scaleWidth, scaleHeight);
Bitmap newbm = Bitmap.createBitmap(bitmap, 0, 0, width, height, matrix, true);
//灰化图片,注意这里虽然是灰化,但只是将R,G,B的值都变成一样的,所以本质上还是RGB的三通道图像
newbm=gray(newbm,2);
//这里的isReverse,true则获得反化的图像数据,否则不是,返回为一维数组
int pxs[]=getGrayPix_R(newbm,true);
//输入图像到模型中
Trace.beginSection("feed");
inferenceInterface.feed(inputName, ints2float(pxs,784),1, 784);
Trace.endSection();
//获得模型输出结果------>这里若报错,可能是因为app:gradle中ndk版本问题,具体自行百度或留言
Trace.beginSection("run");
String[] outputNames = new String[] {outputName};
inferenceInterface.run(outputNames);
Trace.endSection();
//将输出结果存放到outputs中
Trace.beginSection("fetch");
inferenceInterface.fetch(outputName, outputs);
Trace.endSection();
//类似于tf.argmax()的功能,寻找output中最大值的index
return argmax(outputs);
}
}
4.最后是主程序,主要是拍照及读取相册的实现,由于代码太多就不全贴上来了:
这一步调用TF_MINIST工具类,传入图片获取结果
but.setOnClickListener(new View.OnClickListener() {
@Override
public void onClick(View v) {
TF_MINIST m=new TF_MINIST(getAssets());
//直接从ImageView中获取图片
Bitmap bitmap = ((BitmapDrawable)picture.getDrawable()).getBitmap();
tv.append("The digit is "+m.getAddResult(bitmap));
}
});
}
运行结果:
注:
可能是由于测试集的原因,只能识别粗体,自己写的当然也可以,但是一定要描粗了,不然识别不准确,还有就是9一定要将最后一笔写成弯的像这个“9”,不然也是识别不准。