android studio 调用QQ,完美起航-37、使用AndroidStudio调用腾讯官方提供的ncnn包实现Yolo-fastest...-优快云博客

基本思想：想测试一下yolo-fast在Android的运行时间，因为不太喜欢(https://github.com/dog-qiuqiu/Yolo-Fastest)的源码移植，所以使用腾讯提供的ncnn的静态包进行该源码中的ncnn_example中的yolo-fast移植，使用腾讯企鹅的第三方包https://github.com/Tencent/ncnn/releases

ubuntu@ubuntu:$ tree -L 2

├── arm64-v8a

│ └── libncnn.a

├── armeabi-v7a

│ └── libncnn.a

├── include

│ └── ncnn

├── x86

│ └── libncnn.a

└── x86_64

└── libncnn.a

6 directories, 4 files

然后导入Android Studio工程中，本人按照opencv4.4的文件结构导入，未修改文件结构，只做复制操作

ubuntu@ubuntu:~/AndroidStudioProjects/ncnn/app/src/main$ tree -L 3

├── AndroidManifest.xml

├── cpp

│ ├── CMakeLists.txt

│ ├── include

│ │ ├── glslang

│ │ ├── ncnn

│ │ ├── opencv2

│ │ └── SPIRV

│ └── native-lib.cpp

├── java

│ └── com

│ └── yolofastest

├── jniLibs

│ └── libs

│ ├── arm64-v8a

│ ├── armeabi-v7a

│ ├── x86

│ └── x86_64

└── res

├── drawable

│ └── ic_launcher_background.xml

├── drawable-v24

│ └── ic_launcher_foreground.xml

├── layout

│ └── activity_main.xml

├── mipmap-anydpi-v26

│ ├── ic_launcher_round.xml

│ └── ic_launcher.xml

├── mipmap-hdpi

│ ├── ic_launcher.png

│ └── ic_launcher_round.png

├── mipmap-mdpi

│ ├── ic_launcher.png

│ └── ic_launcher_round.png

├── mipmap-xhdpi

│ ├── ic_launcher.png

│ └── ic_launcher_round.png

├── mipmap-xxhdpi

│ ├── ic_launcher.png

│ └── ic_launcher_round.png

├── mipmap-xxxhdpi

│ ├── ic_launcher.png

│ └── ic_launcher_round.png

└── values

├── colors.xml

├── strings.xml

└── styles.xml

26 directories, 21 files

对应的jniLibs文件夹结构为：

ubuntu@ubuntu:~/AndroidStudioProjects/ncnn/app/src/main/jniLibs$ tree -L 3.

└── libs

├── arm64-v8a

│ ├── libglslang.a

│ ├── libncnn.a

│ ├── libOGLCompiler.a

│ ├── libopencv_java4.so

│ ├── libOSDependent.a

│ └── libSPIRV.a

├── armeabi-v7a

│ ├── libglslang.a

│ ├── libncnn.a

│ ├── libOGLCompiler.a

│ ├── libopencv_java4.so

│ ├── libOSDependent.a

│ └── libSPIRV.a

├── x86

│ ├── libglslang.a

│ ├── libncnn.a

│ ├── libOGLCompiler.a

│ ├── libopencv_java4.so

│ ├── libOSDependent.a

│ └── libSPIRV.a

└── x86_64

├── libglslang.a

├── libncnn.a

├── libOGLCompiler.a

├── libopencv_java4.so

├── libOSDependent.a

└── libSPIRV.a

5 directories, 24 files

修改对应build.gradle文件

externalNativeBuild {

cmake {

cppFlags "-std=c++11"

//arguments '-DANDROID=c++_shared'

//abiFilters 'armeabi-v7a','arm64-v8a','x86','x86_64'

}

ndk{

abiFilters 'armeabi-v7a' // x86 armeabi arm64-v8a x86_64

}

sourceSets{

main{

jniLibs.srcDirs=["src/main/jniLibs/libs"]

}

然后配置对应CMakelist.txt文件配置

# For more information about using CMake with Android Studio, read the

# documentation: https://d.android.com/studio/projects/add-native-code.html

# Sets the minimum version of CMake required to build the native library.

cmake_minimum_required(VERSION 3.4.1)

# Creates and names a library, sets it as either STATIC

# or SHARED, and provides the relative paths to its source code.

# You can define multiple libraries, and CMake builds them for you.

# Gradle automatically packages shared libraries with your APK.

# 添加opencv的头文件目录

include_directories(${CMAKE_SOURCE_DIR}/include)

# 导入opencv的so

add_library(libopencv_java4 SHARED IMPORTED)

set_target_properties(libopencv_java4 PROPERTIES IMPORTED_LOCATION

${CMAKE_SOURCE_DIR}/../jniLibs/libs/${ANDROID_ABI}/libopencv_java4.so)

set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fopenmp")

set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fopenmp")

if(DEFINED ANDROID_NDK_MAJOR AND ${ANDROID_NDK_MAJOR} GREATER 20)

set(CMAKE_SHARED_LINKER_FLAGS "${CMAKE_SHARED_LINKER_FLAGS} -static-openmp")

endif()

#导入ncnn

add_library(libncnn STATIC IMPORTED)

set_target_properties(libncnn PROPERTIES IMPORTED_LOCATION

${CMAKE_SOURCE_DIR}/../jniLibs/libs/${ANDROID_ABI}/libncnn.a)

find_library(android-lib android)

add_library( # Sets the name of the library.

native-lib

# Sets the library as a shared library.

SHARED

# Provides a relative path to your source file(s).

native-lib.cpp )

# Searches for a specified prebuilt library and stores the path as a

# variable. Because CMake includes system libraries in the search path by

# default, you only need to specify the name of the public NDK library

# you want to add. CMake verifies that the library exists before

# completing its build.

find_library( # Sets the name of the path variable.

log-lib

# Specifies the name of the NDK library that

# you want CMake to locate.

log )

# Specifies libraries CMake should link to your target library. You

# can link multiple libraries, such as libraries you define in this

# build script, prebuilt third-party libraries, or system libraries.

target_link_libraries( # Specifies the target library.

native-lib

jnigraphics

libopencv_java4 # 链接opencv的so

libncnn #链接ncnn静态的.a

# Links the target library to the log library

# included in the NDK.

${log-lib}

${android-lib}

)

然后开始在native-lib.cpp中开发使用小企鹅的ncnn 调用fast-yolo模型了；

#include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include using namespace cv;

using namespace std;

extern "C" JNIEXPORT jstring JNICALL

Java_com_yolofastest_ncnn_MainActivity_stringFromJNI(

JNIEnv* env,

jobject /* this */) {

std::string hello = "Hello from C++";

static const char* class_names[] = {"background",

"aeroplane", "bicycle", "bird", "boat",

"bottle", "bus", "car", "cat", "chair",

"cow", "diningtable", "dog", "horse",

"motorbike", "person", "pottedplant",

"sheep", "sofa", "train", "tvmonitor"

};

ncnn::Net detector;

detector.load_param("/sdcard/1/yolo-fastest.param");

detector.load_model("/sdcard/1/yolo-fastest.bin");

int detector_size_width = 320;

int detector_size_height = 320;

cv::Mat image=imread("/sdcard/1/dog.jpg");

cv::Mat bgr = image.clone();

int img_w = bgr.cols;

int img_h = bgr.rows;

ncnn::Mat in = ncnn::Mat::from_pixels_resize(bgr.data, ncnn::Mat::PIXEL_BGR2RGB,\

bgr.cols, bgr.rows, detector_size_width, detector_size_height);

//数据预处理

const float mean_vals[3] = {0.f, 0.f, 0.f};

const float norm_vals[3] = {1/255.f, 1/255.f, 1/255.f};

in.substract_mean_normalize(mean_vals, norm_vals);

ncnn::Extractor ex = detector.create_extractor();

ex.set_num_threads(8);

ex.input("data", in);

ncnn::Mat out;

ex.extract("output", out);

for (int i = 0; i < out.h; i++)

{

int label;

float x1, y1, x2, y2, score;

float pw,ph,cx,cy;

const float* values = out.row(i);

x1 = values[2] * img_w;

y1 = values[3] * img_h;

x2 = values[4] * img_w;

y2 = values[5] * img_h;

score = values[1];

label = values[0];

//处理坐标越界问题

if(x1<0) x1=0;

if(y1<0) y1=0;

if(x2<0) x2=0;

if(y2<0) y2=0;

if(x1>img_w) x1=img_w;

if(y1>img_h) y1=img_h;

if(x2>img_w) x2=img_w;

if(y2>img_h) y2=img_h;

cv::rectangle (image, cv::Point(x1, y1), cv::Point(x2, y2), cv::Scalar(255, 255, 0), 1, 1, 0);

char text[256];

sprintf(text, "%s %.1f%%", class_names[label], score * 100);

int baseLine = 0;

cv::Size label_size = cv::getTextSize(text, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);

cv::putText(image, text, cv::Point(x1, y1 + label_size.height),

cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 0));

}

cv::imwrite("/sdcard/1/demo.jpg", image);

return env->NewStringUTF(hello.c_str());

}

一切修改完成，然后在我的OPPO R9手机上运行Android Studio看看YOLO-FAST识别我们的看门狗(此看门狗非彼看门狗) 哈哈哈

本工程的源代码

链接：https://pan.baidu.com/s/1Ot_JjBQ75zks_d40SJ-YEQ

提取码：msev

复制这段内容后打开百度网盘手机App，操作更方便哦

以及配置文件

链接：https://pan.baidu.com/s/1VyVUfByxotiTAwQ6BrCiYA

提取码：0oca