128 lines
3.5 KiB
Markdown
128 lines
3.5 KiB
Markdown
# Yolo-v5 demo
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## 导出rknn模型步骤
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请参考 https://github.com/airockchip/rknn_model_zoo/tree/main/models/CV/object_detection/yolo
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## 注意事项
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1. 使用rknn-toolkit2版本大于等于1.4.0。
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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。
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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。
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4. demo需要librga.so的支持,编译使用请参考 https://github.com/airockchip/librga
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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。
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## Android Demo
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### 编译
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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：
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```sh
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ANDROID_NDK_PATH=~/opt/tool_chain/android-ndk-r17
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```
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然后执行：
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```sh
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./build-android_<TARGET_PLATFORM>.sh
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```
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### 推送执行文件到板子
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连接板子的usb口到PC,将整个demo目录到 `/data`:
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```sh
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adb root
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adb remount
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adb push install/rknn_yolov5_demo /data/
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```
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### 运行
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```sh
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adb shell
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cd /data/rknn_yolov5_demo/
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export LD_LIBRARY_PATH=./lib
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./rknn_yolov5_demo model/<TARGET_PLATFORM>/yolov5s-640-640.rknn model/bus.jpg
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```
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## Aarch64 Linux Demo
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### 编译
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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
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```sh
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export TOOL_CHAIN=~/opt/tool_chain/gcc-9.3.0-x86_64_aarch64-linux-gnu/host
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```
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然后执行：
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```sh
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./build-linux_<TARGET_PLATFORM>.sh
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```
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### 推送执行文件到板子
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将 install/rknn_yolov5_demo_Linux 拷贝到板子的/userdata/目录.
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- 如果使用rockchip的EVB板子，可以使用adb将文件推到板子上：
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```
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adb push install/rknn_yolov5_demo_Linux /userdata/
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```
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- 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
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### 运行
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```sh
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adb shell
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cd /userdata/rknn_yolov5_demo_Linux/
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export LD_LIBRARY_PATH=./lib
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./rknn_yolov5_demo model/<TARGET_PLATFORM>/yolov5s-640-640.rknn model/bus.jpg
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```
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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.
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Using the following commands to add to LD_LIBRARY_PATH.
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```sh
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export LD_LIBRARY_PATH=./lib:<LOCATION_LIBRGA.SO>
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```
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## 视频流Demo运行命令参考如下：
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- h264视频
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```
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./rknn_yolov5_video_demo model/<TARGET_PLATFORM>/yolov5s-640-640.rknn xxx.h264 264
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```
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注意需要使用h264码流视频，可以使用如下命令转换得到：
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```
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ffmpeg -i xxx.mp4 -vcodec h264 xxx.h264
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```
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- h265视频
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```
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./rknn_yolov5_video_demo model/<TARGET_PLATFORM>/yolov5s-640-640.rknn xxx.hevc 265
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```
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注意需要使用h265码流视频，可以使用如下命令转换得到：
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```
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ffmpeg -i xxx.mp4 -vcodec hevc xxx.hevc
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```
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- rtsp视频流
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```
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./rknn_yolov5_video_demo model/<TARGET_PLATFORM>/yolov5s-640-640.rknn <RTSP_URL> 265
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```
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### 注意
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- 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
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- **rk3562 目前仅支持h264视频流**
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- **rtsp 视频流Demo仅在Linux系统上支持，Android上目前还不支持**
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- 视频流输入的h264名称不能为"out.h264"，会被覆盖 |