在android studio上使用rknn模块下面的yolov8_pose模子
我的第一想法就是直接把rk的demo当成so库封装来用,我直接在yolov8_pose的c代码下面添加yolov8_pose.cc与yolov8_pose.h用作封装,先上代码yolov8_pose.cc
#include "yolov8_pose.h"
#include <stdint.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include "yolov8-pose.h"
#include "image_utils.h"
#include "file_utils.h"
#include "image_drawing.h"
#include "postprocess.h"
int skeleton ={16, 14, 14, 12, 17, 15, 15, 13, 12, 13, 6, 12, 7, 13, 6, 7, 6, 8,
7, 9, 8, 10, 9, 11, 2, 3, 1, 2, 1, 3, 2, 4, 3, 5, 4, 6, 5, 7};
int ret;
rknn_app_context_t rknn_app_ctx;
image_buffer_t src_image;
int yolov8_init() {
memset(&rknn_app_ctx, 0, sizeof(rknn_app_context_t));
init_post_process();
ret = init_yolov8_pose_model("/data/user/0/com.rockchip.gpadc.yolodemo/cache/yolov8_pose.rknn", &rknn_app_ctx);
if (ret != 0) {
printf("yolov8_init fail! ret=%d\n", ret);
}
return ret;
}
char* run_yolov8_pose(unsigned char* pixeldata,int cound) {
memset(&src_image, 0, sizeof(image_buffer_t));
//ret = read_image("/data/model/test.jpg", &src_image);
// 默认图像为3通道
int w = 1280, h = 720, c = 3;
// printf("load image wxhxc=%dx%dx%d path=%s\n", w, h, c, path);
int msize = w * h * c;
// 设置图像数据
(&src_image)->virt_addr = pixeldata;
(&src_image)->width = w;
(&src_image)->height = h;
(&src_image)->format = IMAGE_FORMAT_RGB888;
//if (ret != 0)
// {
// printf("read image fail! ret=%d image_path=%s\n", ret, "/data/model/test.jpg");
// }
object_detect_result_list od_results;
ret = inference_yolov8_pose_model(&rknn_app_ctx, &src_image, &od_results);
if (ret != 0)
{
printf("inference_yolov8_pose_model fail! ret=%d\n", ret);
}
char buffers; // 用于存储格式化后的字符串
char *finalBuffer = NULL; // 用于存储最终的字符串
// 分配足够的内存来存储所有字符串
finalBuffer = (char *)malloc(4084);
if (finalBuffer == NULL) {
perror("内存分配失败");
}
// 初始化最终缓冲区
finalBuffer = '\0';
// 格式化字符串
snprintf(reinterpret_cast<char *const>(buffers), sizeof(buffers), "%3d\n", "");
// 画框和概率
char text;
for (int i = 0; i < od_results.count; i++)
{
//做一个限制人数的功能
if(od_results.count <= cound){
object_detect_result *det_result = &(od_results.results);
// 将信息格式化到临时缓冲区
//snprintf(buffers, 1024, "%s (left:%d top:%d right:%d bottom:%d) 概率:%.3f length:%d\n",
// coco_cls_to_name(det_result->cls_id),
// det_result->box.left, det_result->box.top,
// det_result->box.right, det_result->box.bottom,
// det_result->prop,od_results.count);
snprintf(buffers, 1024, "%s (left:%d top:%d right:%d bottom:%d) 概率:%.3f length:%d\n",
"person-pose",
det_result->box.left, det_result->box.top,
det_result->box.right, det_result->box.bottom,
det_result->prop,od_results.count);
// 将格式化后的字符串附加到 finalBuffer
strcat(finalBuffer, buffers);
for (int j = 0; j < 38/2; ++j)
{
int x1 = (int)(det_result->keypoints-1]);
int y1 = (int)(det_result->keypoints-1]);
int x2 = (int)(det_result->keypoints-1]);
int y2 = (int)(det_result->keypoints-1]);
// 将线段坐标添加到 finalBuffer
snprintf(buffers, 1024, "skeleton:(%d, %d,%d, %d)\n", x1, y1, x2, y2);
strcat(finalBuffer, buffers);
}
for (int j = 0; j < 17; ++j)
{
int cx = (int)(det_result->keypoints);
int cy = (int)(det_result->keypoints);
// 将关键点坐标添加到 finalBuffer
snprintf(buffers, 1024, "key:(%d, %d)\n",cx, cy);
strcat(finalBuffer, buffers);
}
}
}
// write_image("/data/model/out.jpg", &src_image);
//}
return finalBuffer;
}
void yolov8_cleanup() {
deinit_post_process();
ret = release_yolov8_pose_model(&rknn_app_ctx);
if (ret != 0)
{
printf("release_yolov5_model fail! ret=%d\n", ret);
}
if (src_image.virt_addr != NULL)
{
free(src_image.virt_addr);
}
}
yolov8_pose.h
#ifndef YOLOV8_POSE_H
#define YOLOV8_POSE_H
#ifdef __cplusplus
extern "C" {
#endif
// 初始化模型
int yolov8_init();
// 执行推理
char*run_yolov8_pose(unsigned char* pixeldata,int cound);
// 清理工作
void yolov8_cleanup();
#ifdef __cplusplus
}
#endif
#endif // YOLOV8_POSE_H
1.代码解析
[*] 我细致研究了一下代码,我发现,rk的demo是读取一张640*640的图片输入模子,然后再输出一张640*640带上骨架等信息的图片
[*] 我的功能逻辑是实时输入摄像头帧数据,然后输出骨架信息,人相框信息等
[*] 我这里默认使用1280*720的像素, 我这里写死了rknn模子的地点,你们可以自行更改
[*] 既然我把功能代码封装好了,我们就需要把这些功能代码打包成库,所以我们需要更改一下配置文件
[*] 这里的CMakeLists.txt文件我没有去做编削,我只是在原根本上增加打包库文件,并且使用全部的接口,如下源码
cmake_minimum_required(VERSION 3.10)
project(rknn_yolov8_pose_demo)
set(rknpu_yolov8-pose_file rknpu2/yolov8-pose.cc)
# 添加第三方库和工具库
add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/../../../3rdparty/ 3rdparty.out)
add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/../../../utils/ utils.out)
set(CMAKE_INSTALL_RPATH "$ORIGIN/lib")
file(GLOB SRCS ${CMAKE_CURRENT_SOURCE_DIR}/*.cc)
# 定义共享库 yolov8_pose
add_library(yolov8_pose SHARED
yolov8_pose.cc
postprocess.cc
${rknpu_yolov8-pose_file}
)
# 将包含目录添加到 yolov8_pose 库
target_include_directories(yolov8_pose PRIVATE
${CMAKE_CURRENT_SOURCE_DIR}
${LIBRKNNRT_INCLUDES}
${CMAKE_CURRENT_SOURCE_DIR}/../../../3rdparty
${CMAKE_CURRENT_SOURCE_DIR}/../../../utils/
)
# 定义一个变量，包含公共库
set(COMMON_LIBS
imageutils
fileutils
imagedrawing
${LIBRKNNRT}
dl
)
# 将共享库 yolov8_pose 链接到相关库
target_link_libraries(yolov8_pose ${COMMON_LIBS})
# 添加可执行文件
add_executable(${PROJECT_NAME}
main.cc
postprocess.cc
${rknpu_yolov8-pose_file}
)
# 将可执行文件链接到共享库和公共库
target_link_libraries(${PROJECT_NAME}
yolov8_pose
${COMMON_LIBS}# 也链接公共库
)
if (CMAKE_SYSTEM_NAME STREQUAL "Android")
target_link_libraries(${PROJECT_NAME}
log
)
endif()
if (CMAKE_SYSTEM_NAME STREQUAL "Linux")
set(THREADS_PREFER_PTHREAD_FLAG ON)
find_package(Threads REQUIRED)
target_link_libraries(${PROJECT_NAME} Threads::Threads)
endif()
target_include_directories(${PROJECT_NAME} PRIVATE
${CMAKE_CURRENT_SOURCE_DIR}
${LIBRKNNRT_INCLUDES}
)
# 安装目标
install(TARGETS ${PROJECT_NAME} DESTINATION .)
install(FILES ${CMAKE_CURRENT_SOURCE_DIR}/../model/bus.jpg DESTINATION ./model)
install(FILES ${CMAKE_CURRENT_SOURCE_DIR}/../model/yolov8_pose_labels_list.txt DESTINATION ./model)
file(GLOB RKNN_FILES "${CMAKE_CURRENT_SOURCE_DIR}/../model/*.rknn")
install(FILES ${RKNN_FILES} DESTINATION model)
# 安装共享库
install(TARGETS yolov8_pose DESTINATION lib)
编译:这里就能在install下面找到我们编译成功的库
https://i-blog.csdnimg.cn/direct/e1f3cceb505f47378ed939709207d379.png
2.android studio使用,直接使用ndk开辟
https://i-blog.csdnimg.cn/direct/257af99ced5a4f0490732aba7b4c191f.png
先上代码:
native-lib.cc
#include <jni.h>
#include <unistd.h>
#include <errno.h>
#include <string.h>
#include <pthread.h>
#include <sys/syscall.h>
#include <sched.h>
#include <stdint.h>
#include <stdio.h>
#include <stdlib.h>
#include "yolov8_pose.h"
/******************************************************yolov8***************************************************/
// 其他必要的引入
#include <android/log.h>
//#define LOGI(...) ((void)__android_log_print(ANDROID_LOG_INFO, "YOLOv8PoseJNI", __VA_ARGS__))
#define LOGI(...) __android_log_print(ANDROID_LOG_INFO, "YOLOv8PoseJNI", ##__VA_ARGS__);
extern "C" JNIEXPORT void JNICALL
Java_com_rockchip_gpadc_demo_yolo_InferenceWrapper_native_1inityolov8(JNIEnv *env, jobject obj) {
int ret = yolov8_init();
LOGI("Java_com_rockchip_gpadc_demo_yolo_InferenceWrapper_native_1inityolov8yolov8_init: %d",
ret);
}
// 推理
extern "C" JNIEXPORT void JNICALL
Java_com_rockchip_gpadc_demo_yolo_InferenceWrapper_native_1inference(JNIEnv *env, jobject obj,
jobject call_back,
jbyteArray imageData,
jint width, jint height) {
// 创建一个 JNI 全局引用
jobject global_callback = (*env).NewGlobalRef(call_back);
// 获取回调方法的方法ID
jclass cls = (*env).GetObjectClass(call_back);
jmethodID gCallBackMid = (*env).GetMethodID(cls, "onCall", "(Ljava/lang/String;)V");
// 获取字节数组的指针
jbyte *byteArray = (*env).GetByteArrayElements(imageData, NULL);
if (byteArray == nullptr) {
return; // 获取数组元素失败
}
//我这里限制五个人 前五个 后面的不管 数据量太大的话可能刷新不过来
char *result = run_yolov8_pose(reinterpret_cast<unsigned char *>(byteArray),5);
// 将 char* 转换为 Java 字符串
jstring resultString = env->NewStringUTF(result);
// 调用回调方法，将结果字符串传递回 Java 侧
env->CallVoidMethod(global_callback, gCallBackMid, resultString);
// 释放 JNI 创建的字符串
env->DeleteLocalRef(resultString); // 删除局部引用的字符串
// 释放字节数组的元素
env->ReleaseByteArrayElements(imageData, byteArray, 0);
// 释放全局引用
env->DeleteGlobalRef(global_callback);
}
// 清理资源
extern "C" JNIEXPORT void JNICALL
Java_com_rockchip_gpadc_demo_yolo_InferenceWrapper_native_1releaseModel(JNIEnv *env, jobject obj) {
yolov8_cleanup();
} CMakeLists.txt
# 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.
# include rga.
#include_directories(src/main/cpp/rga)
add_library( # Sets the name of the library.
rknn4j
# Sets the library as a shared library.
SHARED
# Provides a relative path to your source file(s).
src/main/cpp/native-lib.cc
src/main/cpp/yolov8_pose.h
)
# 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.
rknn4j
# Links the target library to the log library
# included in the NDK.
${CMAKE_SOURCE_DIR}/src/main/jniLibs/${ANDROID_ABI}/librknnrt.so
${CMAKE_SOURCE_DIR}/src/main/jniLibs/${ANDROID_ABI}/librga.so
${CMAKE_SOURCE_DIR}/src/main/jniLibs/${ANDROID_ABI}/libyolov8_pose.so
${log-lib} ) 这里我在native-lib.cc使用了实时回调,把模子推理数据实时的回调给app,app调用
package com.rockchip.gpadc.demo.yolo;
import com.rockchip.gpadc.demo.callstr;
/**
* Created by randall on 18-4-18.
*/
public class InferenceWrapper {
private final String TAG = "rkyolo.InferenceWrapper";
static {
System.loadLibrary("rknn4j");
}
public InferenceWrapper() {
}
public void setmessage(byte[] bytes, final callstr mcallstr){
// Log.e("TAG","setmessage bytes==="+bytes.length);
native_inference(new INativeListener() {
@Override
public void onCall(String str) {
//Log.e("TAG","模型回调数据======"+str);
mcallstr.onCall(str);
}
},bytes,1280,720);
}
public int initModel(){
native_inityolov8();
return 0;
}
public void deinit() {
native_releaseModel();
}
// java回调接口 INativeListener.java
interface INativeListener {
void onCall(String str);
}
/**************************************************yolov8******************************************/
private native void native_inityolov8();
private native void native_inference(INativeListener mINativeListener,byte[] imageData, int width, int height);
private native void native_releaseModel();
} 这里我做了个回调接口,在对应的地方调用即可
这里还有个说明,因为rknn的推理数据是rgb的数据,但是很多摄像头的数据是yuv-nv12或者nv21,这里输入数据的时候需要转化一下,这个网上随便搜,很多.
[*]详细的打开摄像头,解析数据等代码这里我不能贴出来
[*]其实步骤很简单,在oncreat里面先初始化调用 public int initModel(){
native_inityolov8();
return 0;
}
[*]然后转换一下数据格式传入jni,就能实时得到数据了 native_inference(new INativeListener() {
@Override
public void onCall(String str) {
Log.e("TAG","模型回调数据======"+str);
//解析
}
},bytes,1280,720);
[*]详细怎么解析你们可以自定义上面的格式,这里我是一个数据一行,你们可以更改上面的yolov8_pose.cc里面的finalBuffer数据,这个数据就是全部数据回调
[*]详细就这些了,有什么不懂的可以找我咨询.
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