1.用于测试3588的程序

.gitignore

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+rknn_yolov5_demo/convert_rknn_demo/yolov5/dataset.txt
+rknn_yolov5_demo/convert_rknn_demo/yolov5/onnx2rknn.py
+rknn_yolov5_demo/convert_rknn_demo/yolov5/README_CN.md
+rknn_yolov5_demo/convert_rknn_demo/yolov5/README.md
+rknn_yolov5_demo/convert_rknn_demo/yolov5/onnx_models/yolov5s_for_apk_demo.onnx
+rknn_yolov5_demo/convert_rknn_demo/yolov5/onnx_models/yolov5s_relu.onnx
+rknn_yolov5_demo/include/drm_func.h
+rknn_yolov5_demo/include/postprocess.h
+rknn_yolov5_demo/include/rga_func.h
+rknn_yolov5_demo/model/bus.jpg
+rknn_yolov5_demo/model/coco_80_labels_list.txt
+rknn_yolov5_demo/model/RK3562/yolov5s-640-640.rknn
+rknn_yolov5_demo/model/RK3566_RK3568/yolov5s-640-640.rknn
+rknn_yolov5_demo/model/RK3588/yolov5s-640-640.rknn
+rknn_yolov5_demo/src/main_video.cc
+rknn_yolov5_demo/src/main.cc
+rknn_yolov5_demo/src/postprocess.cc
+rknn_yolov5_demo/utils/drawing.cpp
+rknn_yolov5_demo/utils/drawing.h
+rknn_yolov5_demo/utils/mpp_decoder.cpp
+rknn_yolov5_demo/utils/mpp_decoder.h
+rknn_yolov5_demo/utils/mpp_encoder.cpp
+rknn_yolov5_demo/utils/mpp_encoder.h
+runtime/Android.bp
+runtime/Android.go
+runtime/init.rknn_server.rc
+runtime/RK356X/Android/librknn_api/arm64-v8a/librknn_api.so
+runtime/RK356X/Android/librknn_api/arm64-v8a/librknnrt.so
+runtime/RK356X/Android/librknn_api/armeabi-v7a/librknn_api.so
+runtime/RK356X/Android/librknn_api/armeabi-v7a/librknnrt.so
+runtime/RK356X/Android/librknn_api/include/rknn_api.h
+runtime/RK356X/Android/librknn_api/include/rknn_matmul_api.h
+runtime/RK356X/Android/rknn_server/arm/rknn_server
+runtime/RK356X/Android/rknn_server/arm64/rknn_server
+runtime/RK356X/Linux/librknn_api/aarch64/librknn_api.so
+runtime/RK356X/Linux/librknn_api/aarch64/librknnrt.so
+runtime/RK356X/Linux/librknn_api/armhf/librknn_api.so
+runtime/RK356X/Linux/librknn_api/armhf/librknnrt.so
+runtime/RK356X/Linux/librknn_api/include/rknn_api.h
+runtime/RK356X/Linux/librknn_api/include/rknn_matmul_api.h
+runtime/RK356X/Linux/rknn_server/aarch64/usr/bin/restart_rknn.sh
+runtime/RK356X/Linux/rknn_server/aarch64/usr/bin/rknn_server
+runtime/RK356X/Linux/rknn_server/aarch64/usr/bin/start_rknn.sh
+runtime/RK356X/Linux/rknn_server/armhf/usr/bin/restart_rknn.sh
+runtime/RK356X/Linux/rknn_server/armhf/usr/bin/rknn_server
+runtime/RK356X/Linux/rknn_server/armhf/usr/bin/start_rknn.sh
+runtime/RK3588/Android/librknn_api/arm64-v8a/librknnrt.so
+runtime/RK3588/Android/librknn_api/armeabi-v7a/librknnrt.so
+runtime/RK3588/Android/librknn_api/include/rknn_api.h
+runtime/RK3588/Android/librknn_api/include/rknn_matmul_api.h
+runtime/RK3588/Android/rknn_server/arm/rknn_server
+runtime/RK3588/Android/rknn_server/arm64/rknn_server
+runtime/RK3588/Linux/librknn_api/aarch64/librknn_api.so
+runtime/RK3588/Linux/librknn_api/aarch64/librknnrt.so
+runtime/RK3588/Linux/librknn_api/armhf/librknn_api.so
+runtime/RK3588/Linux/librknn_api/armhf/librknnrt.so
+runtime/RK3588/Linux/librknn_api/include/rknn_api.h
+runtime/RK3588/Linux/librknn_api/include/rknn_matmul_api.h
+runtime/RK3588/Linux/rknn_server/aarch64/usr/bin/restart_rknn.sh
+runtime/RK3588/Linux/rknn_server/aarch64/usr/bin/rknn_server
+runtime/RK3588/Linux/rknn_server/aarch64/usr/bin/start_rknn.sh
+runtime/RK3588/Linux/rknn_server/armhf/usr/bin/restart_rknn.sh
+runtime/RK3588/Linux/rknn_server/armhf/usr/bin/rknn_server
+runtime/RK3588/Linux/rknn_server/armhf/usr/bin/start_rknn.sh
+runtime/RV1106/Linux/librknn_api/armhf/librknn_api.so
+runtime/RV1106/Linux/librknn_api/armhf/librknnmrt.a
+runtime/RV1106/Linux/librknn_api/armhf/librknnmrt.so
+runtime/RV1106/Linux/librknn_api/include/rknn_api.h
+runtime/RV1106/Linux/rknn_server/arm/usr/bin/restart_rknn.sh
+runtime/RV1106/Linux/rknn_server/arm/usr/bin/rknn_server
+runtime/RV1106/Linux/rknn_server/arm/usr/bin/start_rknn.sh

.vscode/settings.json

@@ -0,0 +1,65 @@
+{
+    "files.associations": {
+        "vector": "cpp",
+        "iostream": "cpp",
+        "string": "cpp",
+        "array": "cpp",
+        "atomic": "cpp",
+        "bit": "cpp",
+        "*.tcc": "cpp",
+        "cctype": "cpp",
+        "chrono": "cpp",
+        "clocale": "cpp",
+        "cmath": "cpp",
+        "compare": "cpp",
+        "complex": "cpp",
+        "concepts": "cpp",
+        "condition_variable": "cpp",
+        "cstdarg": "cpp",
+        "cstddef": "cpp",
+        "cstdint": "cpp",
+        "cstdio": "cpp",
+        "cstdlib": "cpp",
+        "cstring": "cpp",
+        "ctime": "cpp",
+        "cwchar": "cpp",
+        "cwctype": "cpp",
+        "deque": "cpp",
+        "list": "cpp",
+        "map": "cpp",
+        "set": "cpp",
+        "unordered_map": "cpp",
+        "exception": "cpp",
+        "algorithm": "cpp",
+        "functional": "cpp",
+        "iterator": "cpp",
+        "memory": "cpp",
+        "memory_resource": "cpp",
+        "numeric": "cpp",
+        "optional": "cpp",
+        "random": "cpp",
+        "ratio": "cpp",
+        "string_view": "cpp",
+        "system_error": "cpp",
+        "tuple": "cpp",
+        "type_traits": "cpp",
+        "utility": "cpp",
+        "fstream": "cpp",
+        "initializer_list": "cpp",
+        "iomanip": "cpp",
+        "iosfwd": "cpp",
+        "istream": "cpp",
+        "limits": "cpp",
+        "mutex": "cpp",
+        "new": "cpp",
+        "ostream": "cpp",
+        "ranges": "cpp",
+        "sstream": "cpp",
+        "stdexcept": "cpp",
+        "stop_token": "cpp",
+        "streambuf": "cpp",
+        "thread": "cpp",
+        "typeinfo": "cpp",
+        "regex": "cpp"
+    }
+}

CMakeLists.txt

@@ -0,0 +1,34 @@
+cmake_minimum_required(VERSION 3.10)
+
+project(UsbDevAI)
+set(CMAKE_CXX_STANDARD 14)
+
+
+include_directories(${CMAKE_SOURCE_DIR}/src)
+include_directories(${CMAKE_SOURCE_DIR}/runtime/RK3588/Linux/librknn_api/include)
+include_directories(${CMAKE_SOURCE_DIR}/3rdparty/rga/RK3588/include)
+include_directories(${CMAKE_SOURCE_DIR}/3rdparty/opencv/opencv-linux-aarch64/include)
+include_directories(${CMAKE_SOURCE_DIR}/3rdparty/mpp/include)
+
+set(OpenCV_DIR ${CMAKE_SOURCE_DIR}/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV)
+find_package(OpenCV REQUIRED)
+
+set(TARGET Test)
+
+set(SOURCE src/main.cpp src/beforeProcess.cpp src/postprocess.cpp src/RKNNManager.cpp)
+
+add_executable(${TARGET} ${SOURCE})
+
+target_link_directories(${TARGET} PRIVATE ${CMAKE_SOURCE_DIR}/runtime/RK3588/Linux/librknn_api/aarch64)
+target_link_directories(${TARGET} PRIVATE ${CMAKE_SOURCE_DIR}/3rdparty/rga/RK3588/lib/Linux/aarch64)
+target_link_directories(${TARGET} PRIVATE ${CMAKE_SOURCE_DIR}/3rdparty/opencv/opencv-linux-aarch64/lib)
+target_link_directories(${TARGET} PRIVATE ${CMAKE_SOURCE_DIR}/3rdparty/mpp/Linux/aarch64/)
+
+
+# use lib rknn_api opencv
+target_link_libraries(${TARGET} rknnrt)
+target_link_libraries(${TARGET} rga)
+target_link_libraries(${TARGET} pthread)
+target_link_libraries(${TARGET} opencv_core opencv_imgproc opencv_imgcodecs)
+
+

rknn_yolov5_demo/CMakeLists.txt

@@ -0,0 +1,141 @@
+cmake_minimum_required(VERSION 3.4.1)
+
+project(rknn_yolov5_demo)
+
+set(CMAKE_CXX_STANDARD 11)
+set(CMAKE_CXX_STANDARD_REQUIRED ON)
+
+# skip 3rd-party lib dependencies
+set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} -Wl,--allow-shlib-undefined")
+
+# install target and libraries
+set(CMAKE_INSTALL_PREFIX ${CMAKE_SOURCE_DIR}/install/rknn_yolov5_demo_${CMAKE_SYSTEM_NAME})
+
+set(CMAKE_SKIP_INSTALL_RPATH FALSE)
+set(CMAKE_BUILD_WITH_INSTALL_RPATH TRUE)
+set(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_PREFIX}/lib")
+
+set(LIB_ARCH aarch64)
+
+# if (CMAKE_C_COMPILER MATCHES "aarch64")
+#   set(LIB_ARCH aarch64)
+# else()
+#   set(LIB_ARCH armhf)
+# endif()
+
+include_directories(${CMAKE_SOURCE_DIR})
+
+# rknn api
+if(TARGET_SOC STREQUAL "rk356x")
+  set(RKNN_API_PATH ${CMAKE_SOURCE_DIR}/../../runtime/RK356X/${CMAKE_SYSTEM_NAME}/librknn_api)
+elseif(TARGET_SOC STREQUAL "rk3588")
+  set(RKNN_API_PATH ${CMAKE_SOURCE_DIR}/../runtime/RK3588/${CMAKE_SYSTEM_NAME}/librknn_api)
+else()
+  message(FATAL_ERROR "TARGET_SOC is not set, ref value: rk356x or rk3588 or rv110x")
+endif()
+
+if (CMAKE_SYSTEM_NAME STREQUAL "Android")
+  set(RKNN_RT_LIB ${RKNN_API_PATH}/${CMAKE_ANDROID_ARCH_ABI}/librknnrt.so)
+else()
+  set(RKNN_RT_LIB ${RKNN_API_PATH}/${LIB_ARCH}/librknnrt.so)
+endif()
+include_directories(${RKNN_API_PATH}/include)
+include_directories(${CMAKE_SOURCE_DIR}/../3rdparty)
+
+# opencv
+if (CMAKE_SYSTEM_NAME STREQUAL "Android")
+    set(OpenCV_DIR ${CMAKE_SOURCE_DIR}/../3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/abi-${CMAKE_ANDROID_ARCH_ABI})
+else()
+  if(LIB_ARCH STREQUAL "armhf")
+    set(OpenCV_DIR ${CMAKE_SOURCE_DIR}/../3rdparty/opencv/opencv-linux-armhf/share/OpenCV)
+  else()
+    set(OpenCV_DIR ${CMAKE_SOURCE_DIR}/../3rdparty/opencv/opencv-linux-aarch64/share/OpenCV)
+  endif()
+endif()
+find_package(OpenCV REQUIRED)
+
+#rga
+if(TARGET_SOC STREQUAL "rk356x")
+  set(RGA_PATH ${CMAKE_SOURCE_DIR}/../3rdparty/rga/RK356X)
+elseif(TARGET_SOC STREQUAL "rk3588")
+  set(RGA_PATH ${CMAKE_SOURCE_DIR}/../3rdparty/rga/RK3588)
+else()
+  message(FATAL_ERROR "TARGET_SOC is not set, ref value: rk356x or rk3588")
+endif()
+if (CMAKE_SYSTEM_NAME STREQUAL "Android")
+  set(RGA_LIB ${RGA_PATH}/lib/Android/${CMAKE_ANDROID_ARCH_ABI}/librga.so)
+else()
+  set(RGA_LIB ${RGA_PATH}/lib/Linux//${LIB_ARCH}/librga.so)
+endif()
+include_directories( ${RGA_PATH}/include)
+
+# mpp
+set(MPP_PATH ${CMAKE_CURRENT_SOURCE_DIR}/../3rdparty/mpp)
+if (CMAKE_SYSTEM_NAME STREQUAL "Linux")
+  set(MPP_LIBS ${MPP_PATH}/${CMAKE_SYSTEM_NAME}/${LIB_ARCH}/librockchip_mpp.so)
+elseif (CMAKE_SYSTEM_NAME STREQUAL "Android")
+  set(MPP_LIBS ${MPP_PATH}/${CMAKE_SYSTEM_NAME}/${CMAKE_ANDROID_ARCH_ABI}/libmpp.so)
+endif()
+include_directories(${MPP_PATH}/include)
+
+# zlmediakit
+set(ZLMEDIAKIT_PATH ${CMAKE_SOURCE_DIR}/../3rdparty/zlmediakit)
+if (CMAKE_SYSTEM_NAME STREQUAL "Linux")
+include_directories(${ZLMEDIAKIT_PATH}/include)
+set(ZLMEDIAKIT_LIBS ${ZLMEDIAKIT_PATH}/${LIB_ARCH}/libmk_api.so)
+endif()
+
+if (ZLMEDIAKIT_LIBS)
+    add_definitions(-DBUILD_VIDEO_RTSP)
+endif()
+
+set(CMAKE_INSTALL_RPATH "lib")
+
+# rknn_yolov5_demo
+include_directories( ${CMAKE_SOURCE_DIR}/include)
+
+add_executable(rknn_yolov5_demo
+        src/main.cc
+        src/postprocess.cc
+)
+
+target_link_libraries(rknn_yolov5_demo
+  ${RKNN_RT_LIB}
+  ${RGA_LIB}
+  ${OpenCV_LIBS}
+)
+
+if (MPP_LIBS)
+add_executable(rknn_yolov5_video_demo
+        src/main_video.cc
+        src/postprocess.cc
+        utils/mpp_decoder.cpp
+        utils/mpp_encoder.cpp
+        utils/drawing.cpp
+)
+target_link_libraries(rknn_yolov5_video_demo
+  ${RKNN_RT_LIB}
+  ${RGA_LIB}
+  ${OpenCV_LIBS}
+  ${MPP_LIBS}
+  ${ZLMEDIAKIT_LIBS}
+)
+endif()
+
+
+# install target and libraries
+set(CMAKE_INSTALL_PREFIX ${CMAKE_SOURCE_DIR}/install/rknn_yolov5_demo_${CMAKE_SYSTEM_NAME})
+install(TARGETS rknn_yolov5_demo DESTINATION ./)
+
+install(PROGRAMS ${RKNN_RT_LIB} DESTINATION lib)
+install(PROGRAMS ${RGA_LIB} DESTINATION lib)
+install(DIRECTORY model DESTINATION ./)
+
+if (MPP_LIBS)
+install(TARGETS rknn_yolov5_video_demo DESTINATION ./)
+install(PROGRAMS ${MPP_LIBS} DESTINATION lib)
+endif()
+
+if (ZLMEDIAKIT_LIBS)
+install(PROGRAMS ${ZLMEDIAKIT_LIBS} DESTINATION lib)
+endif()

rknn_yolov5_demo/README.md

@@ -0,0 +1,126 @@
+# Yolo-v5 demo
+
+## Guide for exporting rknn model
+
+Please refer to this link:  https://github.com/airockchip/rknn_model_zoo/tree/main/models/CV/object_detection/yolo
+
+## Precautions
+
+1. Use rknn-toolkit2 version greater than or equal to **1.4.0**.
+2. When using the model trained by yourself, please pay attention to aligning post-processing parameters such as anchor, otherwise it will cause post-processing analysis errors.
+3. The official website and rk pre-training models both detect 80 types of targets. If you train your own model, you need to change the OBJ_CLASS_NUM and NMS_THRESH, BOX_THRESH post-processing parameters in include/postprocess.h.
+4. The demo needs the support of librga.so, please refer to https://github.com/airockchip/librga for compiling and using
+5. Due to hardware limitations, the demo model moves the post-processing part of the yolov5 model to the cpu implementation by default. The models attached to this demo all use relu as the activation function. Compared with the silu activation function, the accuracy is slightly lower, and the performance is greatly improved.
+
+
+## Android Demo
+
+### Compiling && Building
+
+According to the target platform, modifying the path for Android NDK on 'build-android_<TARGET_PLATFORM>.sh'
+
+for example,
+
+```sh
+ANDROID_NDK_PATH=~/opt/tool_chain/android-ndk-r17
+```
+
+then, running this script：
+
+```sh
+./build-android_<TARGET_PLATFORM>.sh
+```
+
+### Push all build output file to the board
+
+Connecting the usb port to PC,  then pushing all demo files to the board,
+
+```sh
+adb root
+adb remount
+adb push install/rknn_yolov5_demo /data/
+```
+
+### Running
+
+```sh
+adb shell
+cd /data/rknn_yolov5_demo/
+
+export LD_LIBRARY_PATH=./lib
+./rknn_yolov5_demo model/<TARGET_PLATFORM>/yolov5s-640-640.rknn model/bus.jpg
+```
+
+## Aarch64 Linux Demo
+
+### Compiling && Building
+
+According to the target platform, modifying the path for 'TOOL_CHAIN' on 'build-android_<TARGET_PLATFORM>.sh'
+
+```sh
+export TOOL_CHAIN=~/opt/tool_chain/gcc-9.3.0-x86_64_aarch64-linux-gnu/host
+```
+
+then run the script：
+
+```sh
+./build-linux_<TARGET_PLATFORM>.sh
+```
+
+### Push all build output file to the board
+
+
+Push install/rknn_yolov5_demo_Linux to the board,
+
+- If using adb via the EVB board：
+
+```
+adb push install/rknn_yolov5_demo_Linux /userdata/
+```
+
+- For other boards,  using the scp or other different approaches to push all files under install/rknn_yolov5_demo_Linux to '/userdata'
+
+### Running
+
+```sh
+adb shell
+cd /userdata/rknn_yolov5_demo_Linux/
+
+export LD_LIBRARY_PATH=./lib
+./rknn_yolov5_demo model/<TARGET_PLATFORM>/yolov5s-640-640.rknn model/bus.jpg
+```
+
+Note: Try searching the location of librga.so and add it to LD_LIBRARY_PATH if the librga.so is not found on the lib folder.
+Using the following commands to add to LD_LIBRARY_PATH.
+
+```sh
+export LD_LIBRARY_PATH=./lib:<LOCATION_LIBRGA.SO>
+```
+
+## Guide for Video Demo：
+- H264
+```
+./rknn_yolov5_video_demo model/<TARGET_PLATFORM>/yolov5s-640-640.rknn xxx.h264 264
+```
+For converting to h264 via the ffmpeg ：
+```
+ffmpeg -i xxx.mp4 -vcodec h264 out.h264
+```
+
+- H265
+```
+./rknn_yolov5_video_demo model/<TARGET_PLATFORM>/yolov5s-640-640.rknn xxx.hevc 265
+```
+For converting to h265 via the ffmpeg ：
+```
+ffmpeg -i xxx.mp4 -vcodec hevc out.hevc
+```
+- RTSP
+```
+./rknn_yolov5_video_demo model/<TARGET_PLATFORM>/yolov5s-640-640.rknn <RTSP_URL> 265
+```
+
+### Remark
+
+- **RK3562 only supports h264 video stream **
+- **rtsp video stream only available on the Linux system **

rknn_yolov5_demo/README_CN.md

@@ -0,0 +1,126 @@
+# Yolo-v5 demo
+
+## 导出rknn模型步骤
+
+请参考 https://github.com/airockchip/rknn_model_zoo/tree/main/models/CV/object_detection/yolo
+
+
+## 注意事项
+
+1. 使用rknn-toolkit2版本大于等于1.4.0。
+2. 切换成自己训练的模型时，请注意对齐anchor等后处理参数，否则会导致后处理解析出错。
+3. 官网和rk预训练模型都是检测80类的目标，如果自己训练的模型,需要更改include/postprocess.h中的OBJ_CLASS_NUM以及NMS_THRESH,BOX_THRESH后处理参数。
+4. demo需要librga.so的支持,编译使用请参考 https://github.com/airockchip/librga
+5. 由于硬件限制，该demo的模型默认把 yolov5 模型的后处理部分，移至cpu实现。本demo附带的模型均使用relu为激活函数，相比silu激活函数精度略微下降，性能大幅上升。
+
+
+
+## Android Demo
+
+### 编译
+
+根据指定平台修改 `build-android_<TARGET_PLATFORM>.sh`中的Android NDK的路径 `ANDROID_NDK_PATH`，<TARGET_PLATFORM>可以是RK3566_RK3568, RK3562或RK3588 例如修改成：
+
+```sh
+ANDROID_NDK_PATH=~/opt/tool_chain/android-ndk-r17
+```
+
+然后执行：
+
+```sh
+./build-android_<TARGET_PLATFORM>.sh
+```
+
+### 推送执行文件到板子
+
+连接板子的usb口到PC,将整个demo目录到 `/data`:
+
+```sh
+adb root
+adb remount
+adb push install/rknn_yolov5_demo /data/
+```
+
+### 运行
+
+```sh
+adb shell
+cd /data/rknn_yolov5_demo/
+
+export LD_LIBRARY_PATH=./lib
+./rknn_yolov5_demo model/<TARGET_PLATFORM>/yolov5s-640-640.rknn model/bus.jpg
+```
+
+## Aarch64 Linux Demo
+
+### 编译
+
+根据指定平台修改 `build-linux_<TARGET_PLATFORM>.sh`中的交叉编译器所在目录的路径 `TOOL_CHAIN`，例如修改成
+
+```sh
+export TOOL_CHAIN=~/opt/tool_chain/gcc-9.3.0-x86_64_aarch64-linux-gnu/host
+```
+
+然后执行：
+
+```sh
+./build-linux_<TARGET_PLATFORM>.sh
+```
+
+### 推送执行文件到板子
+
+将 install/rknn_yolov5_demo_Linux 拷贝到板子的/userdata/目录.
+
+- 如果使用rockchip的EVB板子，可以使用adb将文件推到板子上：
+
+```
+adb push install/rknn_yolov5_demo_Linux /userdata/
+```
+
+- 如果使用其他板子，可以使用scp等方式将install/rknn_yolov5_demo_Linux拷贝到板子的/userdata/目录
+
+### 运行
+
+```sh
+adb shell
+cd /userdata/rknn_yolov5_demo_Linux/
+
+export LD_LIBRARY_PATH=./lib
+./rknn_yolov5_demo model/<TARGET_PLATFORM>/yolov5s-640-640.rknn model/bus.jpg
+```
+
+Note: Try searching the location of librga.so and add it to LD_LIBRARY_PATH if the librga.so is not found on the lib folder.
+Using the following commands to add to LD_LIBRARY_PATH.
+
+```sh
+export LD_LIBRARY_PATH=./lib:<LOCATION_LIBRGA.SO>
+```
+
+## 视频流Demo运行命令参考如下：
+- h264视频
+```
+./rknn_yolov5_video_demo model/<TARGET_PLATFORM>/yolov5s-640-640.rknn xxx.h264 264
+```
+注意需要使用h264码流视频，可以使用如下命令转换得到：
+```
+ffmpeg -i xxx.mp4 -vcodec h264 out.h264
+```
+
+- h265视频
+```
+./rknn_yolov5_video_demo model/<TARGET_PLATFORM>/yolov5s-640-640.rknn xxx.hevc 265
+```
+注意需要使用h265码流视频，可以使用如下命令转换得到：
+```
+ffmpeg -i xxx.mp4 -vcodec hevc out.hevc
+```
+- rtsp视频流
+```
+./rknn_yolov5_video_demo model/<TARGET_PLATFORM>/yolov5s-640-640.rknn <RTSP_URL> 265
+```
+
+### 注意
+
+- 需要根据系统的rga驱动选择正确的librga库，具体依赖请参考： https://github.com/airockchip/librga
+- **rk3562 目前仅支持h264视频流**
+- **rtsp 视频流Demo仅在Linux系统上支持，Android上目前还不支持**

src/RKNNManager.cpp

@@ -0,0 +1,203 @@
+#include "beforeProcess.h"
+#include "RKNNManager.hpp"
+#include "RgaUtils.h"
+#include "im2d.h"
+#include "postprocess.h"
+#include <dlfcn.h>
+#include <sys/time.h>
+#include <exception>
+#include <iostream>
+
+RKNNManager::RKNNManager() : ctx(0), model_data(nullptr), model_data_size(0), resize_buf(nullptr) {}
+
+RKNNManager::~RKNNManager()
+{
+    release();
+}
+
+bool RKNNManager::initialize(const std::string &model_path)
+{
+    model_data = load_model(model_path.c_str(), &model_data_size);
+    if (!model_data)
+        return false;
+
+    int ret = rknn_init(&ctx, model_data, model_data_size, 0, NULL);
+    if (ret < 0)
+    {
+        std::cerr << "rknn_init error! ret=" << ret << std::endl;
+        return false;
+    }
+
+    ret = rknn_query(ctx, RKNN_QUERY_IN_OUT_NUM, &io_num, sizeof(io_num));
+    if (ret < 0)
+    {
+        std::cerr << "rknn_query error! ret=" << ret << std::endl;
+        return false;
+    }
+
+    memset(input_attrs, 0, sizeof(input_attrs));
+    for (int i = 0; i < io_num.n_input; i++)
+    {
+        input_attrs[i].index = i;
+        ret = rknn_query(ctx, RKNN_QUERY_INPUT_ATTR, &(input_attrs[i]), sizeof(rknn_tensor_attr));
+        if (ret < 0)
+        {
+            std::cerr << "rknn_query error! ret=" << ret << std::endl;
+            return false;
+        }
+    }
+
+    memset(output_attrs, 0, sizeof(output_attrs));
+    for (int i = 0; i < io_num.n_output; i++)
+    {
+        output_attrs[i].index = i;
+        ret = rknn_query(ctx, RKNN_QUERY_OUTPUT_ATTR, &(output_attrs[i]), sizeof(rknn_tensor_attr));
+        if (ret < 0)
+            return false;
+    }
+
+    if (input_attrs[0].fmt == RKNN_TENSOR_NCHW)
+    {
+        channel = input_attrs[0].dims[1];
+        height = input_attrs[0].dims[2];
+        width = input_attrs[0].dims[3];
+    }
+    else
+    {
+        height = input_attrs[0].dims[1];
+        width = input_attrs[0].dims[2];
+        channel = input_attrs[0].dims[3];
+    }
+
+    return true;
+}
+
+/// @brief 用于给输入图像进行推理
+/// @param input_image 输入图像的格式为BGR
+/// @param output_image 直接将结果绘制到输出图像
+/// @return
+bool RKNNManager::infer(int index,const cv::Mat &input_image, cv::Mat &output_image)
+{
+    rknn_input inputs[1];
+    memset(inputs, 0, sizeof(inputs));
+    inputs[0].index = 0;
+    inputs[0].type = RKNN_TENSOR_UINT8;
+    inputs[0].size = width * height * channel;
+    inputs[0].fmt = RKNN_TENSOR_NHWC;
+    inputs[0].pass_through = 0;
+
+    if (input_image.cols != width || input_image.rows != height)
+    {
+        resize_buf = malloc(height * width * channel);
+        memset(resize_buf, 0x00, height * width * channel);
+
+        rga_buffer_t src = wrapbuffer_virtualaddr((void *)input_image.data, input_image.cols, input_image.rows, RK_FORMAT_RGB_888);
+        rga_buffer_t dst = wrapbuffer_virtualaddr((void *)resize_buf, width, height, RK_FORMAT_RGB_888);
+        im_rect src_rect, dst_rect;
+        IM_STATUS status = imresize(src, dst);
+        if (status != IM_STATUS_SUCCESS)
+            return false;
+
+        inputs[0].buf = resize_buf;
+    }
+    else
+    {
+        inputs[0].buf = (void *)input_image.data;
+    }
+
+    int ret = rknn_inputs_set(ctx, io_num.n_input, inputs);
+    if (ret < 0)
+        return false;
+
+    rknn_output outputs[io_num.n_output];
+    memset(outputs, 0, sizeof(outputs));
+    for (int i = 0; i < io_num.n_output; i++)
+    {
+        outputs[i].want_float = 0;
+    }
+
+    ret = rknn_run(ctx, NULL);
+    if (ret < 0)
+        return false;
+
+    ret = rknn_outputs_get(ctx, io_num.n_output, outputs, NULL);
+    if (ret < 0)
+        return false;
+
+    float scale_w = (float)width / input_image.cols;
+    float scale_h = (float)height / input_image.rows;
+
+    detect_result_group_t detect_result_group;
+    std::vector<float> out_scales;
+    std::vector<int32_t> out_zps;
+    for (int i = 0; i < io_num.n_output; ++i)
+    {
+        out_scales.push_back(output_attrs[i].scale);
+        out_zps.push_back(output_attrs[i].zp);
+    }
+
+    post_process((int8_t *)outputs[0].buf, (int8_t *)outputs[1].buf, (int8_t *)outputs[2].buf, height, width,
+                 BOX_THRESH, NMS_THRESH, scale_w, scale_h, out_zps, out_scales, &detect_result_group);
+
+    output_image = input_image.clone();
+    char text[256];
+    for (int i = 0; i < detect_result_group.count; i++)
+    {
+        detect_result_t *det_result = &(detect_result_group.results[i]);
+        //sprintf(text, "%s %.1f%%", det_result->name, det_result->prop * 100);
+        //std::cout << index << " Class: " << det_result->name << ", Confidence: " << det_result->prop * 100 << "%" << std::endl;
+        
+        // int x1 = det_result->box.left;
+        // int y1 = det_result->box.top;
+        // int x2 = det_result->box.right;
+        // int y2 = det_result->box.bottom;
+        // //std::cout << "Box: (" << x1 << ", " << y1 << ") - (" << x2 << ", " << y2 << ")" << std::endl;
+        // rectangle(output_image, cv::Point(x1, y1), cv::Point(x2, y2), cv::Scalar(255, 0, 0, 255), 3);
+        // putText(output_image, text, cv::Point(x1, y1 + 12), cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 0));
+    }
+
+    rknn_outputs_release(ctx, io_num.n_output, outputs);
+    return true;
+}
+
+void RKNNManager::release()
+{
+    if (ctx)
+    {
+        rknn_destroy(ctx);
+        ctx = 0;
+    }
+    if (model_data)
+    {
+        free(model_data);
+        model_data = nullptr;
+    }
+    if (resize_buf)
+    {
+        free(resize_buf);
+        resize_buf = nullptr;
+    }
+}
+
+unsigned char *load_model(const char *filename, int *model_size)
+{
+    FILE *fp;
+    unsigned char *data;
+
+    fp = fopen(filename, "rb");
+    if (NULL == fp)
+    {
+        printf("Open file %s failed.\n", filename);
+        return NULL;
+    }
+
+    fseek(fp, 0, SEEK_END);
+    int size = ftell(fp);
+
+    data = load_data(fp, 0, size);
+
+    fclose(fp);
+
+    *model_size = size;
+    return data;
+}

src/RKNNManager.hpp

@@ -0,0 +1,39 @@
+#ifndef RKNNMANAGER_HPP
+#define RKNNMANAGER_HPP
+
+#include <rknn_api.h>
+#include <opencv2/opencv.hpp>
+#include <string>
+#include <vector>
+
+static unsigned char *load_model(const char *filename, int *model_size);
+
+class RKNNManager
+{
+public:
+    RKNNManager();
+    ~RKNNManager();
+
+    bool initialize(const std::string &model_path);
+    bool infer(int index,const cv::Mat &input_image, cv::Mat &output_image);
+    void release();
+
+private:
+  
+
+    RKNNManager(const RKNNManager &) = delete;
+    RKNNManager &operator=(const RKNNManager &) = delete;
+
+    rknn_context ctx;
+    unsigned char *model_data;
+    int model_data_size;
+    rknn_input_output_num io_num;
+    rknn_tensor_attr input_attrs[1];
+    rknn_tensor_attr output_attrs[3];
+    int width, height, channel;
+    void *resize_buf;
+
+    // output image buffer
+};
+
+#endif // RKNNMANAGER_HPP

src/beforeProcess.cpp

@@ -0,0 +1,61 @@
+#include "beforeProcess.h"
+#include <string>
+
+void dump_tensor_attr(rknn_tensor_attr *attr)
+{
+    std::string shape_str = attr->n_dims < 1 ? "" : std::to_string(attr->dims[0]);
+    for (int i = 1; i < attr->n_dims; ++i)
+    {
+        shape_str += ", " + std::to_string(attr->dims[i]);
+    }
+
+    printf("  index=%d, name=%s, n_dims=%d, dims=[%s], n_elems=%d, size=%d, w_stride = %d, size_with_stride=%d, fmt=%s, "
+           "type=%s, qnt_type=%s, "
+           "zp=%d, scale=%f\n",
+           attr->index, attr->name, attr->n_dims, shape_str.c_str(), attr->n_elems, attr->size, attr->w_stride,
+           attr->size_with_stride, get_format_string(attr->fmt), get_type_string(attr->type),
+           get_qnt_type_string(attr->qnt_type), attr->zp, attr->scale);
+}
+
+double __get_us(struct timeval t) { return (t.tv_sec * 1000000 + t.tv_usec); }
+
+unsigned char *load_data(FILE *fp, size_t ofst, size_t sz)
+{
+    unsigned char *data;
+    int ret;
+
+    data = NULL;
+
+    if (NULL == fp)
+    {
+        return NULL;
+    }
+
+    ret = fseek(fp, ofst, SEEK_SET);
+    if (ret != 0)
+    {
+        printf("blob seek failure.\n");
+        return NULL;
+    }
+
+    data = (unsigned char *)malloc(sz);
+    if (data == NULL)
+    {
+        printf("buffer malloc failure.\n");
+        return NULL;
+    }
+    ret = fread(data, 1, sz, fp);
+    return data;
+}
+
+int saveFloat(const char *file_name, float *output, int element_size)
+{
+    FILE *fp;
+    fp = fopen(file_name, "w");
+    for (int i = 0; i < element_size; i++)
+    {
+        fprintf(fp, "%.6f\n", output[i]);
+    }
+    fclose(fp);
+    return 0;
+}

src/beforeProcess.h

@@ -0,0 +1,15 @@
+#include <dlfcn.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+#include <sys/time.h>
+
+#include <rknn_api.h>
+
+void dump_tensor_attr(rknn_tensor_attr *attr);
+
+double __get_us(struct timeval t);
+
+unsigned char *load_data(FILE *fp, size_t ofst, size_t sz);
+
+int saveFloat(const char *file_name, float *output, int element_size);

src/main.cpp

@@ -0,0 +1,69 @@
+#include "RKNNManager.hpp"
+#include <opencv2/opencv.hpp>
+#include <sys/time.h>
+#include <thread>
+#include <iostream>
+
+void thread_infer(int index,RKNNManager* manager,cv::Mat *input, cv::Mat *output, int number)
+{
+    struct timeval start_time, stop_time;
+    gettimeofday(&start_time, NULL);
+
+    for (int i = number; i > 0; i--)
+    {
+       manager->infer(index,*input, *output);
+    }
+
+    gettimeofday(&stop_time, NULL);
+    printf("infer time: %.3f ms\n",
+           (stop_time.tv_sec - start_time.tv_sec) * 1000.0 +
+               (stop_time.tv_usec - start_time.tv_usec) / 1000.0 / 10);
+}
+
+int main()
+{
+
+    bool res = true;
+    struct timeval start_time, stop_time;
+    RKNNManager manager1,manager2,manager3,manager4,manager5,manager6;
+
+    res = manager1.initialize("../rknn_yolov5_demo/model/RK3588/yolov5s-640-640.rknn");
+    res = manager2.initialize("../rknn_yolov5_demo/model/RK3588/yolov5s-640-640.rknn");
+    res = manager3.initialize("../rknn_yolov5_demo/model/RK3588/yolov5s-640-640.rknn");
+    // res = manager4.initialize("../rknn_yolov5_demo/model/RK3588/yolov5s-640-640.rknn");
+    // res = manager5.initialize("../rknn_yolov5_demo/model/RK3588/yolov5s-640-640.rknn");
+    // res = manager6.initialize("../rknn_yolov5_demo/model/RK3588/yolov5s-640-640.rknn");
+
+    if (!res)
+    {
+        printf("initialize failed\n");
+        return -1;
+    }
+
+    cv::Mat InputImage, OutputImage;
+    cv::Mat OutputImage2, OutputImage3, OutputImage4, OutputImage5, OutputImage6;
+    InputImage = cv::imread("../20170.jpg");
+
+    cv::Mat img;
+    cv::cvtColor(InputImage, img, cv::COLOR_BGR2RGB);
+
+    std::thread(thread_infer,0,&manager1, &img, &OutputImage, 1000).detach();
+    std::thread(thread_infer,1,&manager2, &img, &OutputImage2, 1000).detach();
+    std::thread(thread_infer,2,&manager3, &img, &OutputImage3, 1000).detach();
+    // std::thread(thread_infer,3,&manager4, &img, &OutputImage4, 17).detach();
+    // std::thread(thread_infer,4,&manager5, &img, &OutputImage5, 17).detach();
+    // std::thread(thread_infer,5,&manager6, &img, &OutputImage6, 17).detach();
+    //std::thread(thread_infer, &img, &OutputImage3, 30).detach();
+    // std::thread(thread_infer, &InputImage, &OutputImage4).detach();
+    // // std::thread(thread_infer, &InputImage, &OutputImage5).detach();
+    // // std::thread(thread_infer, &InputImage, &OutputImage6).detach();
+
+    char a;
+
+    std::cin >> a;
+
+    cv::cvtColor(OutputImage, OutputImage, cv::COLOR_RGB2BGR);
+    cv::imwrite("output.jpg", OutputImage);
+
+    return 0;
+}

src/postprocess.cpp

@@ -0,0 +1,374 @@
+// Copyright (c) 2021 by Rockchip Electronics Co., Ltd. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "postprocess.h"
+
+#include <math.h>
+#include <stdint.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+#include <sys/time.h>
+
+#include <set>
+#include <vector>
+#define LABEL_NALE_TXT_PATH "./coco_80_labels_list.txt"
+
+static char *labels[OBJ_CLASS_NUM];
+
+const int anchor0[6] = {10, 13, 16, 30, 33, 23};
+const int anchor1[6] = {30, 61, 62, 45, 59, 119};
+const int anchor2[6] = {116, 90, 156, 198, 373, 326};
+
+inline static int clamp(float val, int min, int max) { return val > min ? (val < max ? val : max) : min; }
+
+char *readLine(FILE *fp, char *buffer, int *len)
+{
+    int ch;
+    int i = 0;
+    size_t buff_len = 0;
+
+    buffer = (char *)malloc(buff_len + 1);
+    if (!buffer)
+        return NULL; // Out of memory
+
+    while ((ch = fgetc(fp)) != '\n' && ch != EOF)
+    {
+        buff_len++;
+        void *tmp = realloc(buffer, buff_len + 1);
+        if (tmp == NULL)
+        {
+            free(buffer);
+            return NULL; // Out of memory
+        }
+        buffer = (char *)tmp;
+
+        buffer[i] = (char)ch;
+        i++;
+    }
+    buffer[i] = '\0';
+
+    *len = buff_len;
+
+    // Detect end
+    if (ch == EOF && (i == 0 || ferror(fp)))
+    {
+        free(buffer);
+        return NULL;
+    }
+    return buffer;
+}
+
+int readLines(const char *fileName, char *lines[], int max_line)
+{
+    FILE *file = fopen(fileName, "r");
+    char *s;
+    int i = 0;
+    int n = 0;
+
+    if (file == NULL)
+    {
+        printf("Open %s fail!\n", fileName);
+        return -1;
+    }
+
+    while ((s = readLine(file, s, &n)) != NULL)
+    {
+        lines[i++] = s;
+        if (i >= max_line)
+            break;
+    }
+    fclose(file);
+    return i;
+}
+
+int loadLabelName(const char *locationFilename, char *label[])
+{
+    printf("loadLabelName %s\n", locationFilename);
+    readLines(locationFilename, label, OBJ_CLASS_NUM);
+    return 0;
+}
+
+static float CalculateOverlap(float xmin0, float ymin0, float xmax0, float ymax0, float xmin1, float ymin1, float xmax1,
+                              float ymax1)
+{
+    float w = fmax(0.f, fmin(xmax0, xmax1) - fmax(xmin0, xmin1) + 1.0);
+    float h = fmax(0.f, fmin(ymax0, ymax1) - fmax(ymin0, ymin1) + 1.0);
+    float i = w * h;
+    float u = (xmax0 - xmin0 + 1.0) * (ymax0 - ymin0 + 1.0) + (xmax1 - xmin1 + 1.0) * (ymax1 - ymin1 + 1.0) - i;
+    return u <= 0.f ? 0.f : (i / u);
+}
+
+static int nms(int validCount, std::vector<float> &outputLocations, std::vector<int> classIds, std::vector<int> &order,
+               int filterId, float threshold)
+{
+    for (int i = 0; i < validCount; ++i)
+    {
+        if (order[i] == -1 || classIds[i] != filterId)
+        {
+            continue;
+        }
+        int n = order[i];
+        for (int j = i + 1; j < validCount; ++j)
+        {
+            int m = order[j];
+            if (m == -1 || classIds[i] != filterId)
+            {
+                continue;
+            }
+            float xmin0 = outputLocations[n * 4 + 0];
+            float ymin0 = outputLocations[n * 4 + 1];
+            float xmax0 = outputLocations[n * 4 + 0] + outputLocations[n * 4 + 2];
+            float ymax0 = outputLocations[n * 4 + 1] + outputLocations[n * 4 + 3];
+
+            float xmin1 = outputLocations[m * 4 + 0];
+            float ymin1 = outputLocations[m * 4 + 1];
+            float xmax1 = outputLocations[m * 4 + 0] + outputLocations[m * 4 + 2];
+            float ymax1 = outputLocations[m * 4 + 1] + outputLocations[m * 4 + 3];
+
+            float iou = CalculateOverlap(xmin0, ymin0, xmax0, ymax0, xmin1, ymin1, xmax1, ymax1);
+
+            if (iou > threshold)
+            {
+                order[j] = -1;
+            }
+        }
+    }
+    return 0;
+}
+
+static int quick_sort_indice_inverse(std::vector<float> &input, int left, int right, std::vector<int> &indices)
+{
+    float key;
+    int key_index;
+    int low = left;
+    int high = right;
+    if (left < right)
+    {
+        key_index = indices[left];
+        key = input[left];
+        while (low < high)
+        {
+            while (low < high && input[high] <= key)
+            {
+                high--;
+            }
+            input[low] = input[high];
+            indices[low] = indices[high];
+            while (low < high && input[low] >= key)
+            {
+                low++;
+            }
+            input[high] = input[low];
+            indices[high] = indices[low];
+        }
+        input[low] = key;
+        indices[low] = key_index;
+        quick_sort_indice_inverse(input, left, low - 1, indices);
+        quick_sort_indice_inverse(input, low + 1, right, indices);
+    }
+    return low;
+}
+
+static float sigmoid(float x) { return 1.0 / (1.0 + expf(-x)); }
+
+static float unsigmoid(float y) { return -1.0 * logf((1.0 / y) - 1.0); }
+
+inline static int32_t __clip(float val, float min, float max)
+{
+    float f = val <= min ? min : (val >= max ? max : val);
+    return f;
+}
+
+static int8_t qnt_f32_to_affine(float f32, int32_t zp, float scale)
+{
+    float dst_val = (f32 / scale) + zp;
+    int8_t res = (int8_t)__clip(dst_val, -128, 127);
+    return res;
+}
+
+static float deqnt_affine_to_f32(int8_t qnt, int32_t zp, float scale) { return ((float)qnt - (float)zp) * scale; }
+
+static int process(int8_t *input, int *anchor, int grid_h, int grid_w, int height, int width, int stride,
+                   std::vector<float> &boxes, std::vector<float> &objProbs, std::vector<int> &classId, float threshold,
+                   int32_t zp, float scale)
+{
+    int validCount = 0;
+    int grid_len = grid_h * grid_w;
+    int8_t thres_i8 = qnt_f32_to_affine(threshold, zp, scale);
+    for (int a = 0; a < 3; a++)
+    {
+        for (int i = 0; i < grid_h; i++)
+        {
+            for (int j = 0; j < grid_w; j++)
+            {
+                int8_t box_confidence = input[(PROP_BOX_SIZE * a + 4) * grid_len + i * grid_w + j];
+                if (box_confidence >= thres_i8)
+                {
+                    int offset = (PROP_BOX_SIZE * a) * grid_len + i * grid_w + j;
+                    int8_t *in_ptr = input + offset;
+                    float box_x = (deqnt_affine_to_f32(*in_ptr, zp, scale)) * 2.0 - 0.5;
+                    float box_y = (deqnt_affine_to_f32(in_ptr[grid_len], zp, scale)) * 2.0 - 0.5;
+                    float box_w = (deqnt_affine_to_f32(in_ptr[2 * grid_len], zp, scale)) * 2.0;
+                    float box_h = (deqnt_affine_to_f32(in_ptr[3 * grid_len], zp, scale)) * 2.0;
+                    box_x = (box_x + j) * (float)stride;
+                    box_y = (box_y + i) * (float)stride;
+                    box_w = box_w * box_w * (float)anchor[a * 2];
+                    box_h = box_h * box_h * (float)anchor[a * 2 + 1];
+                    box_x -= (box_w / 2.0);
+                    box_y -= (box_h / 2.0);
+
+                    int8_t maxClassProbs = in_ptr[5 * grid_len];
+                    int maxClassId = 0;
+                    for (int k = 1; k < OBJ_CLASS_NUM; ++k)
+                    {
+                        int8_t prob = in_ptr[(5 + k) * grid_len];
+                        if (prob > maxClassProbs)
+                        {
+                            maxClassId = k;
+                            maxClassProbs = prob;
+                        }
+                    }
+                    if (maxClassProbs > thres_i8)
+                    {
+                        objProbs.push_back((deqnt_affine_to_f32(maxClassProbs, zp, scale)) * (deqnt_affine_to_f32(box_confidence, zp, scale)));
+                        classId.push_back(maxClassId);
+                        validCount++;
+                        boxes.push_back(box_x);
+                        boxes.push_back(box_y);
+                        boxes.push_back(box_w);
+                        boxes.push_back(box_h);
+                    }
+                }
+            }
+        }
+    }
+    return validCount;
+}
+
+int post_process(int8_t *input0, int8_t *input1, int8_t *input2, int model_in_h, int model_in_w, float conf_threshold,
+                 float nms_threshold, float scale_w, float scale_h, std::vector<int32_t> &qnt_zps,
+                 std::vector<float> &qnt_scales, detect_result_group_t *group)
+{
+    static int init = -1;
+    if (init == -1)
+    {
+        int ret = 0;
+        ret = loadLabelName(LABEL_NALE_TXT_PATH, labels);
+        if (ret < 0)
+        {
+            return -1;
+        }
+
+        init = 0;
+    }
+    memset(group, 0, sizeof(detect_result_group_t));
+
+    std::vector<float> filterBoxes;
+    std::vector<float> objProbs;
+    std::vector<int> classId;
+
+    // stride 8
+    int stride0 = 8;
+    int grid_h0 = model_in_h / stride0;
+    int grid_w0 = model_in_w / stride0;
+    int validCount0 = 0;
+    validCount0 = process(input0, (int *)anchor0, grid_h0, grid_w0, model_in_h, model_in_w, stride0, filterBoxes, objProbs,
+                          classId, conf_threshold, qnt_zps[0], qnt_scales[0]);
+
+    // stride 16
+    int stride1 = 16;
+    int grid_h1 = model_in_h / stride1;
+    int grid_w1 = model_in_w / stride1;
+    int validCount1 = 0;
+    validCount1 = process(input1, (int *)anchor1, grid_h1, grid_w1, model_in_h, model_in_w, stride1, filterBoxes, objProbs,
+                          classId, conf_threshold, qnt_zps[1], qnt_scales[1]);
+
+    // stride 32
+    int stride2 = 32;
+    int grid_h2 = model_in_h / stride2;
+    int grid_w2 = model_in_w / stride2;
+    int validCount2 = 0;
+    validCount2 = process(input2, (int *)anchor2, grid_h2, grid_w2, model_in_h, model_in_w, stride2, filterBoxes, objProbs,
+                          classId, conf_threshold, qnt_zps[2], qnt_scales[2]);
+
+    int validCount = validCount0 + validCount1 + validCount2;
+    // no object detect
+    if (validCount <= 0)
+    {
+        return 0;
+    }
+
+    std::vector<int> indexArray;
+    for (int i = 0; i < validCount; ++i)
+    {
+        indexArray.push_back(i);
+    }
+
+    quick_sort_indice_inverse(objProbs, 0, validCount - 1, indexArray);
+
+    std::set<int> class_set(std::begin(classId), std::end(classId));
+
+    for (auto c : class_set)
+    {
+        nms(validCount, filterBoxes, classId, indexArray, c, nms_threshold);
+    }
+
+    int last_count = 0;
+    group->count = 0;
+    /* box valid detect target */
+    for (int i = 0; i < validCount; ++i)
+    {
+        if (indexArray[i] == -1 || last_count >= OBJ_NUMB_MAX_SIZE)
+        {
+            continue;
+        }
+        int n = indexArray[i];
+
+        float x1 = filterBoxes[n * 4 + 0];
+        float y1 = filterBoxes[n * 4 + 1];
+        float x2 = x1 + filterBoxes[n * 4 + 2];
+        float y2 = y1 + filterBoxes[n * 4 + 3];
+        int id = classId[n];
+        float obj_conf = objProbs[i];
+
+        group->results[last_count].box.left = (int)(clamp(x1, 0, model_in_w) / scale_w);
+        group->results[last_count].box.top = (int)(clamp(y1, 0, model_in_h) / scale_h);
+        group->results[last_count].box.right = (int)(clamp(x2, 0, model_in_w) / scale_w);
+        group->results[last_count].box.bottom = (int)(clamp(y2, 0, model_in_h) / scale_h);
+        group->results[last_count].prop = obj_conf;
+        char *label = labels[id];
+        strncpy(group->results[last_count].name, label, OBJ_NAME_MAX_SIZE);
+
+        // printf("result %2d: (%4d, %4d, %4d, %4d), %s\n", i, group->results[last_count].box.left,
+        // group->results[last_count].box.top,
+        //        group->results[last_count].box.right, group->results[last_count].box.bottom, label);
+        last_count++;
+    }
+    group->count = last_count;
+
+    return 0;
+}
+
+void deinitPostProcess()
+{
+    for (int i = 0; i < OBJ_CLASS_NUM; i++)
+    {
+        if (labels[i] != nullptr)
+        {
+            free(labels[i]);
+            labels[i] = nullptr;
+        }
+    }
+}

src/postprocess.h

@@ -0,0 +1,42 @@
+#ifndef _RKNN_ZERO_COPY_DEMO_POSTPROCESS_H_
+#define _RKNN_ZERO_COPY_DEMO_POSTPROCESS_H_
+
+#include <stdint.h>
+#include <vector>
+
+#define OBJ_NAME_MAX_SIZE 16
+#define OBJ_NUMB_MAX_SIZE 64
+#define OBJ_CLASS_NUM 80
+#define NMS_THRESH 0.45
+#define BOX_THRESH 0.25
+#define PROP_BOX_SIZE (5 + OBJ_CLASS_NUM)
+
+typedef struct _BOX_RECT
+{
+    int left;
+    int right;
+    int top;
+    int bottom;
+} BOX_RECT;
+
+typedef struct __detect_result_t
+{
+    char name[OBJ_NAME_MAX_SIZE];
+    BOX_RECT box;
+    float prop;
+} detect_result_t;
+
+typedef struct _detect_result_group_t
+{
+    int id;
+    int count;
+    detect_result_t results[OBJ_NUMB_MAX_SIZE];
+} detect_result_group_t;
+
+int post_process(int8_t *input0, int8_t *input1, int8_t *input2, int model_in_h, int model_in_w,
+                 float conf_threshold, float nms_threshold, float scale_w, float scale_h,
+                 std::vector<int32_t> &qnt_zps, std::vector<float> &qnt_scales,
+                 detect_result_group_t *group);
+
+void deinitPostProcess();
+#endif //_RKNN_ZERO_COPY_DEMO_POSTPROCESS_H_