基本思想：想測驗一下yolo-fast在Android的運行時間，因為不太喜歡（https://github.com/dog-qiuqiu/Yolo-Fastest)的原始碼移植 ，所以使用騰訊提供的ncnn的靜態包進行該原始碼中的ncnn_example中的yolo-fast移植，使用騰訊企鵝的第三方包https://github.com/Tencent/ncnn/releases
第一步：創建Android Studio工程，匯入opencv4.4模塊（java端和c++端,具體參考:https://blog.csdn.net/sxj731533730/article/details/108380540）
然后去官網https://github.com/Tencent/ncnn/releases 下載ncnn-android-lib.zip，解壓；
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
然后匯入Android Studio工程中，本人按照opencv4.4的檔案結構匯入，未修改檔案結構，只做復制操作
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
對應的jniLibs檔案夾結構為：
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修改對應build.gradle檔案
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"]
}
}然后配置對應CMakelist.txt檔案配置
# 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的頭檔案目錄
include_directories(${CMAKE_SOURCE_DIR}/include)
# 匯入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()
#匯入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 # 鏈接opencv的so
libncnn #鏈接ncnn靜態的.a
# Links the target library to the log library
# included in the NDK.
${log-lib}
${android-lib}
)然后開始在native-lib.cpp中開發使用小企鵝的ncnn 呼叫fast-yolo模型了；
#include <jni.h>
#include <string>
#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/opencv.hpp>
#include <ncnn/benchmark.h>
#include <ncnn/cpu.h>
#include <ncnn/datareader.h>
#include <ncnn/net.h>
#include <ncnn/gpu.h>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <cstdio>
#include <vector>
#include <algorithm>
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);
//資料預處理
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];
//處理坐標越界問題
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手機上運行Android Studio看看YOLO-FAST識別我們的看門狗（此看門狗非彼看門狗） 哈哈哈
本工程的源代碼
鏈接：https://pan.baidu.com/s/1Ot_JjBQ75zks_d40SJ-YEQ
提取碼：msev
復制這段內容后打開百度網盤手機App，操作更方便哦
以及組態檔
鏈接：https://pan.baidu.com/s/1VyVUfByxotiTAwQ6BrCiYA
提取碼：0oca
轉載請註明出處，本文鏈接：https://www.uj5u.com/qita/68871.html
標籤：其他