前言
1.这是一个是检测人员是否佩戴口罩的的安卓NDK项目，只检测人脸上是否有口罩是，是于检测是否佩戴标准，比如是否盖住嘴巴和鼻子，是否标准的医用的口罩的模型还在优化训练中。项目源码地址：https://download.csdn.net/download/matt45m/85252239
2.开发环境是win10,IDE是Android studio 北极狐，用到的库有NCNN,OpenCV，NCNN库可以用官方编译好的releases库，也可以按官方文档自己编译。OpenCV用的是nihui大佬简化过的opencv-mobile，大小只有10多M,如果不嫌大也可以用OpenCV官方的版本。测试使用的安卓手机是mate 30 pro ,有测试CPU和GPU两个性能。
3.项目的各种依赖版本：
一、模型训练
1.使用的数据集是开源的人脸口罩数据集：https://github.com/X-zhangyang/Real-World-Masked-Face-Dataset。数据集基本都是标佩戴。
2.所用的模型训练框架是 yolov5 lite ,在移动端上在保证精度的同时，如果能用上GPU加速，速度能达到10FPS以上。
3.把训练好的模型先onnx，简化之后再转ncnn的模型，这个可以参考ncnn的官网，如果觉得麻烦，也可以直接转pnnx之后直接转ncnn。
二、项目部署
1.创建一个Native C++项目
2.把下载好的NCNN和OpenCV so库粘贴到cpp目录下，这里只要arm64-v8a和armeadbi-v7a这两个库，这样的话，项目会小很多，但就是不能在安卓的虚拟机上运行，毕竟ncnn在虚拟机也是跑不动的。
3.添加推理代码
3.1 faceMask.h
#ifndef _FACE_FACEMASK_H_
#define _FACE_FACEMASK_H_
#include "net.h"
#include <opencv2/opencv.hpp>
#include "cpu.h" //安卓上有cpu推理优化
struct FaceInfo
{
cv::Rect location_;
float score_;
bool mask_;
float mask_prod;
};
class FaceMask
{
public:
FaceMask(AAssetManager *mgr,bool use_gpu); //如果机子有GPU ,这里可以设置成true，就可以使用GPU进行推理
~FaceMask();
int loadModel(AAssetManager *mgr,bool use_gpu);
int detectFace(const cv::Mat& img_src, std::vector<FaceInfo> *faces);
static FaceMask* face_mask;
private:
ncnn::Net mask_net;
std::vector<std::vector<cv::Rect>> anchors_generated;
bool initialized_;
const int RPNs_[3] = {
32, 16, 8 };
const cv::Size inputSize_ = {
640, 640 };
const float iouThreshold_ = 0.4f;
const float scoreThreshold_ = 0.8f;
const float maskThreshold_ = 0.2f;
};
#endif
3.2 faceMask.cpp
#include "faceMaks.h"
#include <iostream>
FaceMask::FaceMask(AAssetManager *mgr,bool use_gpu)
{
loadModel(mgr,use_gpu);
}
FaceMask::~FaceMask()
{
}
int RatioAnchors(const cv::Rect& anchor,const std::vector<float>& ratios,std::vector<cv::Rect>* anchors)
{
anchors->clear();
cv::Point center = cv::Point(anchor.x + (anchor.width - 1) * 0.5f,
anchor.y + (anchor.height - 1) * 0.5f);
float anchor_size = anchor.width * anchor.height;
#if defined(_OPENMP)
#pragma omp parallel for num_threads(threads_num)
#endif
for (int i = 0; i < static_cast<int>(ratios.size()); ++i) {
float ratio = ratios.at(i);
float anchor_size_ratio = anchor_size / ratio;
float curr_anchor_width = std::sqrt(anchor_size_ratio);
float curr_anchor_height = curr_anchor_width * ratio;
float curr_x = center.x - (curr_anchor_width - 1) * 0.5f;
float curr_y = center.y - (curr_anchor_height - 1) * 0.5f;
cv::Rect curr_anchor = cv::Rect(curr_x, curr_y,
curr_anchor_width - 1, curr_anchor_height - 1);
anchors->push_back(curr_anchor);
}
return 0;
}
int ScaleAnchors(const std::vector<cv::Rect>& ratio_anchors,const std::vector<float>& scales, std::vector<cv::Rect>* anchors)
{
anchors->clear();
#if defined(_OPENMP)
#pragma omp parallel for num_threads(threads_num)
#endif
for (int i = 0; i < static_cast<int>(ratio_anchors.size()); ++i)
{
cv::Rect anchor = ratio_anchors.at(i);
cv::Point2f center = cv::Point2f(anchor.x + anchor.width * 0.5f,
anchor.y + anchor.height * 0.5f);
for (int j = 0; j < static_cast<int>(scales.size()); ++j)
{
float scale = scales.at(j);
float curr_width = scale * (anchor.width + 1);
float curr_height = scale * (anchor.height + 1);
float curr_x = center.x - curr_width * 0.5f;
float curr_y = center.y - curr_height * 0.5f;
cv::Rect curr_anchor = cv::Rect(curr_x, curr_y,
curr_width, curr_height);
anchors->push_back(curr_anchor);
}
}
return 0;
}
int GenerateAnchors(const int& base_size,const std::vector<float>& ratios,const std::vector<float> scales,std::vector<cv::Rect>* anchors)
{
anchors->clear();
cv::Rect anchor = cv::Rect(0, 0, base_size, base_size);
std::vector<cv::Rect> ratio_anchors;
RatioAnchors(anchor, ratios, &ratio_anchors);
ScaleAnchors(ratio_anchors, scales, anchors);
return 0;
}
int FaceMask::loadModel(AAssetManager *mgr,bool use_gpu)
{
bool has_gpu = false;
#if NCNN_VULKAN
ncnn::create_gpu_instance();
has_gpu = ncnn::get_gpu_count() > 0;
#endif
bool to_use_gpu = has_gpu && use_gpu;
mask_net.opt.use_vulkan_compute = to_use_gpu;
ncnn::set_cpu_powersave(2);
ncnn::set_omp_num_threads(ncnn::get_big_cpu_count());
mask_net.opt.num_threads = ncnn::get_big_cpu_count();
mask_net.opt.use_int8_inference = true;
int rp = mask_net.load_param(mgr, "mask.param");
int rm = mask_net.load_model(mgr, "mask.bin");
// generate anchors
for (int i = 0; i < 3; ++i)
{
std::vector<cv::Rect> anchors;
if (0 == i)
{
GenerateAnchors(16, {
1.0f }, {
32, 16 }, &anchors);
}
else if (1 == i)
{
GenerateAnchors(16, {
1.0f }, {
8, 4 }, &anchors);
}
else
{
GenerateAnchors(16, {
1.0f }, {
2, 1 }, &anchors);
}
anchors_generated.push_back(anchors);
}
initialized_ = true;
return 0;
}
float InterRectArea(const cv::Rect& a, const cv::Rect& b)
{
cv::Point left_top = cv::Point(MAX(a.x, b.x), MAX(a.y, b.y));
cv::Point right_bottom = cv::Point(MIN(a.br().x, b.br().x), MIN(a.br().y, b.br().y));
cv::Point diff = right_bottom - left_top;
return (MAX(diff.x + 1, 0) * MAX(diff.y + 1, 0));
}
int ComputeIOU(const cv::Rect& rect1,const cv::Rect& rect2, float* iou,const std::string& type)
{
float inter_area = InterRectArea(rect1, rect2);
if (type == "UNION")
{
*iou = inter_area / (rect1.area() + rect2.area() - inter_area);
}
else
{
*iou = inter_area / MIN(rect1.area(), rect2.area());
}
return 0;
}
template <typename T>int const NMS(const std::vector<T>& inputs, std::vector<T>* result,const float& threshold, const std::string& type = "UNION")
{
result->clear();
if (inputs.size() == 0)
return -1;
std::vector<T> inputs_tmp;
inputs_tmp.assign(inputs.begin(), inputs.end());
std::sort(inputs_tmp.begin(), inputs_tmp.end(),
[](const T& a, const T& b)
{
return a.score_ > b.score_;
});
std::vector<int> indexes(inputs_tmp.size());
for (int i = 0; i < indexes.size(); i++)
{
indexes[i] = i;
}
while (indexes.size() > 0)
{
int good_idx = indexes[0];
result->push_back(inputs_tmp[good_idx]);
std::vector<int> tmp_indexes = indexes;
indexes.clear();
for (int i = 1; i < tmp_indexes.size(); i++)
{
int tmp_i = tmp_indexes[i];
float iou = 0.0f;
ComputeIOU(inputs_tmp[good_idx].location_, inputs_tmp[tmp_i].location_, &iou, type);
if (iou <= threshold)
{
indexes.push_back(tmp_i);
}
}
}
return 0;
}
int FaceMask::detectFace(const cv::Mat & cv_src,std::vector<FaceInfo>* faces)
{
cv::Mat cv_cpy = cv_src.clone();
int img_width = cv_cpy.cols;
int img_height = cv_cpy.rows;
float factor_x = static_cast<float>(img_width) / inputSize_.width;
float factor_y = static_cast<float>(img_height) / inputSize_.height;
ncnn::Extractor ex = mask_net.create_extractor();
ncnn::Mat in = ncnn::Mat::from_pixels_resize(cv_cpy.data,
ncnn::Mat::PIXEL_BGR2RGB, img_width, img_height, inputSize_.width, inputSize_.height);
ex.input("data", in);
std::vector<FaceInfo> faces_tmp;
for (int i = 0; i < 3; ++i)
{
std::string class_layer_name = "face_rpn_cls_prob_reshape_stride" + std::to_string(RPNs_[i]);
std::string bbox_layer_name = "face_rpn_bbox_pred_stride" + std::to_string(RPNs_[i]);
std::string landmark_layer_name = "face_rpn_landmark_pred_stride" + std::to_string(RPNs_[i]);
std::string type_layer_name = "face_rpn_type_prob_reshape_stride" + std::to_string(RPNs_[i]);
ncnn::Mat class_mat, bbox_mat, landmark_mat, type_mat;
ex.extract(class_layer_name.c_str(), class_mat);
ex.extract(bbox_layer_name.c_str(), bbox_mat);
ex.extract(landmark_layer_name.c_str(), landmark_mat);
ex.extract(type_layer_name.c_str(), type_mat);
std::vector<cv::Rect> anchors = anchors_generated.at(i);
int width = class_mat.w;
int height = class_mat.h;
int anchor_num = static_cast<int>(anchors.size());
for (int h = 0; h < height; ++h)
{
for (int w = 0; w < width; ++w)
{
int index = h * width + w;
for (int a = 0; a < anchor_num; ++a)
{
float score = class_mat.channel(anchor_num + a)[index];
if (score < scoreThreshold_)
{
continue;
}
float prob = type_mat.channel(2 * anchor_num + a)[index];
cv::Rect box = cv::Rect(w * RPNs_[i] + anchors[a].x,
h * RPNs_[i] + anchors[a].y,
anchors[a].width,
anchors[a].height);
float delta_x = bbox_mat.channel(a * 4 + 0)[index];
float delta_y = bbox_mat.channel(a * 4 + 1)[index];
float delta_w = bbox_mat.channel(a * 4 + 2)[index];
float delta_h = bbox_mat.channel(a * 4 + 3)[index];
cv::Point2f center = cv::Point2f(box.x + box.width * 0.5f,box.y + box.height * 0.5f);
center.x = center.x + delta_x * box.width;
center.y = center.y + delta_y * box.height;
float curr_width = std::exp(delta_w) * (box.width + 1);
float curr_height = std::exp(delta_h) * (box.height + 1);
cv::Rect curr_box = cv::Rect(center.x - curr_width * 0.5f,
center.y - curr_height * 0.5f, curr_width, curr_height);
curr_box.x = MAX(curr_box.x * factor_x, 0);
curr_box.y = MAX(curr_box.y * factor_y, 0);
curr_box.width = MIN(img_width - curr_box.x, curr_box.width * factor_x);
curr_box.height = MIN(img_height - curr_box.y, curr_box.height * factor_y);
FaceInfo face_info;
memset(&face_info, 0, sizeof(face_info));
face_info.score_ = score;
face_info.mask_prod = prob;
face_info.mask_ = (prob > maskThreshold_);
face_info.location_ = curr_box;
faces_tmp.push_back(face_info);
}
}
}
}
NMS(faces_tmp, faces, iouThreshold_);
return 0;
}
3.3 在原本的jni.cpp文件上添加代码
#include <android/asset_manager_jni.h>
#include <android/bitmap.h>
#include <android/log.h>
#include <jni.h>
#include <string>
#include <vector>
// ncnn
#include "net.h"
#include "benchmark.h"
#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include "faceMaks.h"
void BitmapToMat2(JNIEnv *env, jobject& bitmap, cv::Mat& mat, jboolean needUnPremultiplyAlpha)
{
AndroidBitmapInfo info;
void *pixels = 0;
cv::Mat &dst = mat;
try {
CV_Assert(AndroidBitmap_getInfo(env, bitmap, &info) >= 0);
CV_Assert(info.format == ANDROID_BITMAP_FORMAT_RGBA_8888 ||
info.format == ANDROID_BITMAP_FORMAT_RGB_565);
CV_Assert(AndroidBitmap_lockPixels(env, bitmap, &pixels) >= 0);
CV_Assert(pixels);
dst.create(info.height, info.width, CV_8UC4);
if (info.format == ANDROID_BITMAP_FORMAT_RGBA_8888) {
cv::Mat tmp(info.height, info.width, CV_8UC4, pixels);
if (needUnPremultiplyAlpha) cvtColor(tmp, dst, cv::COLOR_mRGBA2RGBA);
else tmp.copyTo(dst);
} else {
// info.format == ANDROID_BITMAP_FORMAT_RGB_565
cv::Mat tmp(info.height, info.width, CV_8UC2, pixels);
cvtColor(tmp, dst, cv::COLOR_BGR5652RGBA);
}
AndroidBitmap_unlockPixels(env, bitmap);
return;
} catch (const cv::Exception &e) {
AndroidBitmap_unlockPixels(env, bitmap);
jclass je = env->FindClass("org/opencv/core/CvException");
if (!je) je = env->FindClass("java/lang/Exception");
env->ThrowNew(je, e.what());
return;
} catch (...) {
AndroidBitmap_unlockPixels(env, bitmap);
jclass je = env->FindClass("java/lang/Exception");
env->ThrowNew(je, "Unknown exception in JNI code {nBitmapToMat}");
return;
}
}
void BitmapToMat(JNIEnv *env, jobject& bitmap, cv::Mat& mat) {
BitmapToMat2(env, bitmap, mat, false);
}
void MatToBitmap2(JNIEnv *env, cv::Mat& mat, jobject& bitmap, jboolean needPremultiplyAlpha)
{
AndroidBitmapInfo info;
void *pixels = 0;
cv::Mat &src = mat;
try {
CV_Assert(AndroidBitmap_getInfo(env, bitmap, &info) >= 0);
CV_Assert(info.format == ANDROID_BITMAP_FORMAT_RGBA_8888 ||
info.format == ANDROID_BITMAP_FORMAT_RGB_565);
CV_Assert(src.dims == 2 && info.height == (uint32_t) src.rows &&
info.width == (uint32_t) src.cols);
CV_Assert(src.type() == CV_8UC1 || src.type() == CV_8UC3 || src.type() == CV_8UC4);
CV_Assert(AndroidBitmap_lockPixels(env, bitmap, &pixels) >= 0);
CV_Assert(pixels);
if (info.format == ANDROID_BITMAP_FORMAT_RGBA_8888)
{
cv::Mat tmp(info.height, info.width, CV_8UC4, pixels);
if (src.type() == CV_8UC1)
{
cvtColor(src, tmp, cv::COLOR_GRAY2RGBA);
}
else if (src.type() == CV_8UC3)
{
cvtColor(src, tmp, cv::COLOR_RGB2RGBA);
}
else if (src.type() == CV_8UC4)
{
if (needPremultiplyAlpha)
{
cvtColor(src, tmp, cv::COLOR_RGBA2mRGBA);
}
else{
src.copyTo(tmp);
}
}
} else {
// info.format == ANDROID_BITMAP_FORMAT_RGB_565
cv::Mat tmp(info.height, info.width, CV_8UC2, pixels);
if (src.type() == CV_8UC1)
{
cvtColor(src, tmp, cv::COLOR_GRAY2BGR565);
}
else if (src.type() == CV_8UC3)
{
cvtColor(src, tmp, cv::COLOR_RGB2BGR565);
}
else if (src.type() == CV_8UC4)
{
cvtColor(src, tmp, cv::COLOR_RGBA2BGR565);
}
}
AndroidBitmap_unlockPixels(env, bitmap);
return;
}catch (const cv::Exception &e) {
AndroidBitmap_unlockPixels(env, bitmap);
jclass je = env->FindClass("org/opencv/core/CvException");
if (!je) je = env->FindClass("java/lang/Exception");
env->ThrowNew(je, e.what());
return;
} catch (...) {
AndroidBitmap_unlockPixels(env, bitmap);
jclass je = env->FindClass("java/lang/Exception");
env->ThrowNew(je, "Unknown exception in JNI code {nMatToBitmap}");
return;
}
}
void MatToBitmap(JNIEnv *env, cv::Mat& mat, jobject& bitmap)
{
MatToBitmap2(env, mat, bitmap, false);
}
FaceMask *FaceMask::face_mask = nullptr;
extern "C" {
JNIEXPORT jint JNI_OnLoad(JavaVM* vm, void* reserved)
{
__android_log_print(ANDROID_LOG_DEBUG, "photo", "JNI_OnLoad");
ncnn::create_gpu_instance();
return JNI_VERSION_1_4;
}
JNIEXPORT void JNI_OnUnload(JavaVM* vm, void* reserved)
{
__android_log_print(ANDROID_LOG_DEBUG, "photo", "JNI_OnUnload");
ncnn::destroy_gpu_instance();
}
//模型加载
JNIEXPORT jboolean JNICALL Java_com_dashu_photo_Mask_Init(JNIEnv* env, jobject thiz, jobject assetManager)
{
AAssetManager *mgr = AAssetManager_fromJava(env, assetManager);
bool use_gpu = false;//不使用GPU推理
FaceMask::face_mask = new FaceMask(mgr,use_gpu);
return JNI_TRUE;
}
}
//模型推理
extern "C" JNIEXPORT jobject JNICALL Java_com_dashu_photo_Mask_maskDetect(JNIEnv *env, jobject, jobject image)
{
cv::Mat cv_src,cv_dst,cv_doc;
//bitmap转化成mat
BitmapToMat(env,image,cv_src);
cv::cvtColor(cv_src,cv_doc,cv::COLOR_BGRA2BGR);
std::vector<FaceInfo> faces;
double start = static_cast<double>(cv::getTickCount());
FaceMask::face_mask->detectFace(cv_doc,&faces);
double end = static_cast<double>(cv::getTickCount());
double time_cost = (end - start) / cv::getTickFrequency() * 1000;
std::string time = std::to_string(time_cost);
cv::putText(cv_doc,time, cv::Point(10,20), cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0,255,0), 2);
int num_face = static_cast<int>(faces.size());
for (int i = 0; i < num_face; ++i)
{
if (faces[i].mask_)
{
std::string mask = "mask = " + std::to_string(faces[i].mask_prod);
cv::putText(cv_doc,mask , faces[i].location_.tl(), cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0,0,255), 1);
cv::rectangle(cv_doc, faces[i].location_, cv::Scalar(0, 255, 0), 2);
}
else {
std::string mask = "No Mask = " + std::to_string(faces[i].mask_prod);
cv::putText(cv_doc, mask, faces[i].location_.tl(), cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 255), 1);
cv::rectangle(cv_doc, faces[i].location_, cv::Scalar(255, 0, 0), 2);
}
}
MatToBitmap(env,cv_doc,image);
return image;
}
以上C++部分的代码已添加完成。
4.编写makefile文件
project(photo)
cmake_minimum_required(VERSION 3.4.1)
#导入ncnn库
set(ncnn_DIR ${CMAKE_SOURCE_DIR}/ncnn/${ANDROID_ABI}/lib/cmake/ncnn)
find_package(ncnn REQUIRED)
#导入opencv库
set(OpenCV_DIR ${CMAKE_SOURCE_DIR}/opencv/sdk/native/jni)
find_package(OpenCV REQUIRED core imgproc)
#导入cpp文件
add_library(${PROJECT_NAME} SHARED faceMask.cpp mask_jni.cpp)
target_link_libraries(${PROJECT_NAME} ncnn ${OpenCV_LIBS})
5.添加java接口文件
package com.dashu.photo;
import android.content.res.AssetManager;
import android.graphics.Bitmap;
public class Mask
{
public native boolean Init(AssetManager mgr);
public native Bitmap maskDetect(Bitmap bitmap);
static {
System.loadLibrary("photo");
}
}
6.jave实现代码
package com.dashu.photo;
import android.app.Activity;
import android.content.Intent;
import android.graphics.Bitmap;
import android.graphics.BitmapFactory;
import android.net.Uri;
import android.os.Bundle;
import android.util.Log;
import android.view.View;
import android.widget.ImageButton;
import android.widget.ImageView;
import android.media.ExifInterface;
import android.graphics.Matrix;
import java.io.IOException;
import java.io.FileNotFoundException;
public class MainActivity extends Activity
{
private static final int SELECT_IMAGE = 1;
private ImageView imageView;
private Bitmap bitmap = null;
private Bitmap temp = null;
private Bitmap showImage = null;
private Bitmap bitmapCopy = null;
private Bitmap dst = null;
boolean useGPU = true;
private Mask mask = new Mask();
@Override
public void onCreate(Bundle savedInstanceState)
{
super.onCreate(savedInstanceState);
setContentView(R.layout.main);
imageView = (ImageView) findViewById(R.id.imageView);
boolean ret_init = mask.Init(getAssets());
if (!ret_init)
{
Log.e("MainActivity", "Mask Init failed");
}
//打开图像
ImageButton openFile = (ImageButton) findViewById(R.id.btn_open_images);
openFile.setOnClickListener(new View.OnClickListener()
{
@Override
public void onClick(View arg0)
{
Intent i = new Intent(Intent.ACTION_PICK);
i.setType("image/*");
startActivityForResult(i, SELECT_IMAGE);
}
});
//检测图像
ImageButton image_gray = (ImageButton) findViewById(R.id.btn_detection);
image_gray.setOnClickListener(new View.OnClickListener()
{
@Override
public void onClick(View arg0)
{
if (showImage == null)
{
return;
}
Bitmap bitmap = mask.maskDetect(showImage);
imageView.setImageBitmap(bitmap);
}
});
}
@Override
protected void onActivityResult(int requestCode, int resultCode, Intent data)
{
super.onActivityResult(requestCode, resultCode, data);
if (resultCode == RESULT_OK && null != data) {
Uri selectedImage = data.getData();
try
{
if (requestCode == SELECT_IMAGE)
{
bitmap = decodeUri(selectedImage);
showImage = bitmap.copy(Bitmap.Config.ARGB_8888, true);
bitmapCopy = bitmap.copy(Bitmap.Config.ARGB_8888, true);
imageView.setImageBitmap(bitmap);
}
}
catch (FileNotFoundException e)
{
Log.e("MainActivity", "FileNotFoundException");
return;
}
}
}
private Bitmap decodeUri(Uri selectedImage) throws FileNotFoundException
{
// Decode image size
BitmapFactory.Options o = new BitmapFactory.Options();
o.inJustDecodeBounds = true;
BitmapFactory.decodeStream(getContentResolver().openInputStream(selectedImage), null, o);
// The new size we want to scale to
final int REQUIRED_SIZE = 640;
// Find the correct scale value. It should be the power of 2.
int width_tmp = o.outWidth, height_tmp = o.outHeight;
int scale = 1;
while (true) {
if (width_tmp / 2 < REQUIRED_SIZE
|| height_tmp / 2 < REQUIRED_SIZE) {
break;
}
width_tmp /= 2;
height_tmp /= 2;
scale *= 2;
}
// Decode with inSampleSize
BitmapFactory.Options o2 = new BitmapFactory.Options();
o2.inSampleSize = scale;
Bitmap bitmap = BitmapFactory.decodeStream(getContentResolver().openInputStream(selectedImage), null, o2);
// Rotate according to EXIF
int rotate = 0;
try
{
ExifInterface exif = new ExifInterface(getContentResolver().openInputStream(selectedImage));
int orientation = exif.getAttributeInt(ExifInterface.TAG_ORIENTATION, ExifInterface.ORIENTATION_NORMAL);
switch (orientation) {
case ExifInterface.ORIENTATION_ROTATE_270:
rotate = 270;
break;
case ExifInterface.ORIENTATION_ROTATE_180:
rotate = 180;
break;
case ExifInterface.ORIENTATION_ROTATE_90:
rotate = 90;
break;
}
}
catch (IOException e)
{
Log.e("MainActivity", "ExifInterface IOException");
}
Matrix matrix = new Matrix();
matrix.postRotate(rotate);
return Bitmap.createBitmap(bitmap, 0, 0, bitmap.getWidth(), bitmap.getHeight(), matrix, true);
}
}
7.布局文件
<?xml version="1.0" encoding="utf-8"?>
<LinearLayout
xmlns:android="http://schemas.android.com/apk/res/android"
xmlns:tools="http://schemas.android.com/tools"
xmlns:app="http://schemas.android.com/apk/res-auto"
android:layout_width="match_parent"
android:layout_height="match_parent"
android:orientation="vertical"
tools:context=".MainActivity">
<ImageView
android:id="@+id/imageView"
android:layout_width="match_parent"
android:layout_height="0dp"
android:layout_weight="1">
</ImageView>
<LinearLayout
android:layout_width="match_parent"
android:layout_height="wrap_content"
android:orientation="horizontal">
<ImageButton
android:id="@+id/btn_open_images"
android:layout_width="0dp"
android:layout_height="wrap_content"
android:layout_weight="1"
android:src="@mipmap/open_images" />
<ImageButton
android:id="@+id/btn_detection"
android:layout_width="0dp"
android:layout_height="wrap_content"
android:layout_weight="1"
android:src="@mipmap/detection" />
</LinearLayout>
</LinearLayout>
三、项目效果
1.佩戴口罩
2.不佩戴口罩
