From d4ca53d40fdecbef7dd8d500ac53bac1f040912c Mon Sep 17 00:00:00 2001 From: xu Date: Tue, 3 Dec 2024 20:43:50 +0800 Subject: [PATCH 1/3] yunet case --- contrib/VideoGestureRecognition/README.md | 2 +- contrib/yunet/README.md | 273 +++++-------- contrib/yunet/build.sh | 3 +- contrib/yunet/images/fps.png | Bin 7917 -> 0 bytes contrib/yunet/images/fps1.png | Bin 89893 -> 0 bytes contrib/yunet/images/fps2.png | Bin 5544 -> 0 bytes contrib/yunet/images/problem.png | Bin 38377 -> 0 bytes contrib/yunet/images/process1.png | Bin 12709 -> 0 bytes contrib/yunet/kpmain.py | 80 ---- contrib/yunet/main.py | 3 +- contrib/yunet/pipeline/KPYunet.pipeline | 64 --- contrib/yunet/pipeline/PluginTest.pipeline | 363 ------------------ contrib/yunet/pipeline/Yunet.pipeline | 8 +- contrib/yunet/plugin/CMakeLists.txt | 2 +- contrib/yunet/plugin/YunetPostProcess.cpp | 19 +- contrib/yunet/plugin/YunetPostProcess.h | 16 +- .../yunet/{plugin3 => plugin1}/CMakeLists.txt | 2 +- .../TotalYunetPostProcess.cpp | 37 +- .../TotalYunetPostProcess.h | 18 +- contrib/yunet/{plugin2 => plugin1}/build.sh | 0 contrib/yunet/plugin2/CMakeLists.txt | 31 -- contrib/yunet/plugin2/KPYunetPostProcess.cpp | 235 ------------ contrib/yunet/plugin2/KPYunetPostProcess.h | 75 ---- contrib/yunet/plugin3/build.sh | 36 -- 24 files changed, 155 insertions(+), 1112 deletions(-) delete mode 100644 contrib/yunet/images/fps.png delete mode 100644 contrib/yunet/images/fps1.png delete mode 100644 contrib/yunet/images/fps2.png delete mode 100644 contrib/yunet/images/problem.png delete mode 100644 contrib/yunet/images/process1.png delete mode 100644 contrib/yunet/kpmain.py delete mode 100644 contrib/yunet/pipeline/KPYunet.pipeline delete mode 100644 contrib/yunet/pipeline/PluginTest.pipeline rename contrib/yunet/{plugin3 => plugin1}/CMakeLists.txt (95%) rename contrib/yunet/{plugin3 => plugin1}/TotalYunetPostProcess.cpp (92%) rename contrib/yunet/{plugin3 => plugin1}/TotalYunetPostProcess.h (88%) rename contrib/yunet/{plugin2 => plugin1}/build.sh (100%) delete mode 100644 contrib/yunet/plugin2/CMakeLists.txt delete mode 100644 contrib/yunet/plugin2/KPYunetPostProcess.cpp delete mode 100644 contrib/yunet/plugin2/KPYunetPostProcess.h delete mode 100644 contrib/yunet/plugin3/build.sh diff --git a/contrib/VideoGestureRecognition/README.md b/contrib/VideoGestureRecognition/README.md index 3a1e63396..8dd5ce45e 100644 --- a/contrib/VideoGestureRecognition/README.md +++ b/contrib/VideoGestureRecognition/README.md @@ -91,7 +91,7 @@ export GST_PLUGIN_SCANNER=${MX_SDK_HOME}/opensource/libexec/gstreamer-1.0/gst-pl export GST_PLUGIN_PATH=${MX_SDK_HOME}/opensource/lib/gstreamer-1.0:${MX_SDK_HOME}/lib/plugins ``` -### 3 准备模型 +## 3 准备模型 **步骤1：** 下载Resnet18模型权重和网络以及cfg文件。[下载地址](https://mindx.sdk.obs.cn-north-4.myhuaweicloud.com/mindxsdk-referenceapps%20/contrib/VideoGestureRecognition/model.zip) diff --git a/contrib/yunet/README.md b/contrib/yunet/README.md index 216cfe967..cb80a7709 100644 --- a/contrib/yunet/README.md +++ b/contrib/yunet/README.md @@ -1,22 +1,16 @@ -## yunet实时人脸检测 +# 实时人脸检测 -## 1介绍 +## 1 介绍 -yunet基于MindXSDK开发，在昇腾芯片上进行人脸检测，并实现可视化呈现。输入3路视频，对其进行推理，输出推理结果。 - -### 1.1 支持的产品 - -本产品以昇腾310（推理）卡为硬件平台。 +### 1.1 简介 -### 1.2 支持的版本 - -CANN：7.0.0 +yunet基于MindXSDK开发，在昇腾芯片上进行人脸检测，并实现可视化呈现。输入3路视频，对其进行推理，输出推理结果。 -SDK：mxVision 5.0.0（可通过cat SDK目录下的 version.info 查看） +技术实现流程图如下： -### 1.3 软件方案介绍 +![process](images/process.png) -表1.1 系统方案各子系统功能描述： +表 1.1 系统方案各子系统功能描述： | 序号 | 子系统 | 功能描述 | | ---- | -------------- | ------------------------------------------------------------ | @@ -31,9 +25,26 @@ SDK：mxVision 5.0.0（可通过cat SDK目录下的 version.info 查看） | 9 | OSD可视化插件 | 实现对视频流的每一帧图像进行绘制。 | | 10 | 视频编码插件 | 用于将OSD可视化插件输出的图片进行视频编码，输出视频。 | +### 1.2 支持的产品 +本项目支持昇腾Atlas 300I pro、 Atlas 300V pro -### 1.4 代码目录结构与说明 +### 1.3 支持的版本 + +本样例配套的MxVision版本、CANN版本、Driver/Firmware版本如下所示： + +| MxVision版本 | CANN版本 | Driver/Firmware版本 | +| --------- | ------------------ | -------------- | +| 6.0.RC3 | 8.0.RC3 | 24.1.RC3 | + +### 1.4 三方依赖 + +| 软件名称 | 版本 | 说明 | 使用教程 | +| -------- | ----- | ------------------------------ | ------------------------------------------------------------ | +| live555 | 1.09 | 实现视频转rstp进行推流 | [链接](https://gitee.com/ascend/mindxsdk-referenceapps/blob/master/docs/%E5%8F%82%E8%80%83%E8%B5%84%E6%96%99/Live555%E7%A6%BB%E7%BA%BF%E8%A7%86%E9%A2%91%E8%BD%ACRTSP%E8%AF%B4%E6%98%8E%E6%96%87%E6%A1%A3.md) | +| ffmpeg | 4.2.1 | 实现mp4格式视频转为264格式视频 | [链接](https://gitee.com/ascend/mindxsdk-referenceapps/blob/master/docs/%E5%8F%82%E8%80%83%E8%B5%84%E6%96%99/pc%E7%AB%AFffmpeg%E5%AE%89%E8%A3%85%E6%95%99%E7%A8%8B.md#https://ffmpeg.org/download.html) | + +### 1.5 代码目录结构说明 本项目名为yunet实时人脸检测，项目目录如下所示： @@ -42,242 +53,150 @@ SDK：mxVision 5.0.0（可通过cat SDK目录下的 version.info 查看） ├── config │ ├── face_yunet.cfg # yunet配置文件 │ └── Yunet.aippconfig # 模型转换aipp配置文件 -├── kpmain.py # 关键点信息输出代码 +├── images +│ ├── error1.png +│ ├── error2.png +│ └── process.png ├── main.py # 单路视频输出代码 ├── test.py # 三路后处理性能测试代码 -├── models -│ └── Yunet.onnx +├── models # 用于存放模型，需用户自己创建目录 ├── pipeline │ ├── InferTest.pipeline # 三路后处理性能测试pipeline -│ ├── PluginTest.pipeline # 原方案插件性能测试pipeline -│ ├── KPYunet.pipeline # 关键点信息输出pipeline │ └── Yunet.pipeline # 单路视频输出pipeline ├── plugin │ ├── build.sh │ ├── CMakeLists.txt │ ├── YunetPostProcess.cpp # 人脸检测框后处理代码 │ └── YunetPostProcess.h -├── plugin2 -│ ├── build.sh -│ ├── CMakeLists.txt -│ ├── KPYunetPostProcess.cpp # 人脸关键点后处理代码 -│ ├── KPYunetPostProcess.h -├── plugin3 +├── plugin1 │ ├── build.sh │ ├── CMakeLists.txt │ ├── TotalYunetPostProcess.cpp # 人脸检测框与关键点后处理代码（以供可视化） │ └── TotalYunetPostProcess.h +├── test # 需用户自己创建目录 ├── README.md └── run.sh ```` - - -### 1.5 技术实现流程图 - -![process](images/process.png) - -### 1.6 特性及适用场景 +### 1.6 相关约束 本项目适用于单人及多人正脸视频。对于人脸侧面视频，可以将人脸位置正确标出，但关键点信息标注准确率较低。本项目可以适用于仰卧人脸，但不适用于侧卧人脸。 -特别地，在无人脸的情况下，我们在视频左上角设置了红色提示点。当左上角像素出现红色时，说明此场景没有检测出人脸。（下面给出该特殊点检测框的数据信息） - -```` -"MxpiObject":[{"classVec":[{"classId":3,"className":"","confidence":0,"headerVec":[]}],"x0":0,"x1":0,"y0":0,"y1":0}] -```` -另外，本项目要求输入视频为1920*1080 25fps视频，不支持25帧率以上视频。 - - - - -## 2 环境依赖 - -推荐系统为ubuntu 18.04,环境软件和版本如下： - -| 软件名称 | 版本 | 说明 | 获取方式 | -| ------------------- |-------| ----------------------------- | :-------------------------------------------------------- | -| MindX SDK | 5.0.0 | mxVision软件包 | [链接](https://www.hiascend.com/software/Mindx-sdk) | -| ubuntu | 18.04 | 操作系统 | 请上ubuntu官网获取 | -| Ascend-CANN-toolkit | 7.0.0 | Ascend-cann-toolkit开发套件包 | [链接](https://www.hiascend.com/software/cann/commercial) | - - - -在编译运行项目前，需要设置环境变量： - -MindXSDK 环境变量： - -``` -. ${SDK-path}/set_env.sh -``` - -CANN 环境变量： - -``` -. ${ascend-toolkit-path}/set_env.sh -``` +另外，本项目要求输入视频为 1920*1080 25fps 视频，不支持25帧率以上视频。 -- 环境变量介绍 +## 2 设置环境变量 +```bash +. /usr/local/Ascend/ascend-toolkit/set_env.sh # toolkit默认安装路径，根据实际安装路径修改 +. ${SDK_INSTALL_PATH}/mxVision/set_env.sh # sdk安装路径，根据实际安装路径修改 ``` -SDK-path: mxVision SDK 安装路径 -ascend-toolkit-path: CANN 安装路径。 -``` - - - - -## 3 软件依赖说明 +## 3 准备模型 +**步骤1：** yunet模型下载。本项目中使用的模型是yunet模型，onnx模型可以直接下载，[下载链接](https://mindx.sdk.obs.cn-north-4.myhuaweicloud.com/mindxsdk-referenceapps%20/contrib/yunet/yunet.onnx) -| 软件名称 | 版本 | 说明 | 使用教程 | -| -------- | ----- | ------------------------------ | ------------------------------------------------------------ | -| live555 | 1.09 | 实现视频转rstp进行推流 | [链接](https://gitee.com/ascend/mindxsdk-referenceapps/blob/master/docs/%E5%8F%82%E8%80%83%E8%B5%84%E6%96%99/Live555%E7%A6%BB%E7%BA%BF%E8%A7%86%E9%A2%91%E8%BD%ACRTSP%E8%AF%B4%E6%98%8E%E6%96%87%E6%A1%A3.md) | -| ffmpeg | 4.2.1 | 实现mp4格式视频转为264格式视频 | [链接](https://gitee.com/ascend/mindxsdk-referenceapps/blob/master/docs/%E5%8F%82%E8%80%83%E8%B5%84%E6%96%99/pc%E7%AB%AFffmpeg%E5%AE%89%E8%A3%85%E6%95%99%E7%A8%8B.md#https://ffmpeg.org/download.html) | - +**步骤2：** 将获取到的文件存放至样例项目所在目录`/models/`下 -设置视频源，此处用rtsp拉流，将视频源转化为.264格式。 +**步骤3：** 模型转换。`cd`到`models`文件夹，使用模型转换工具ATC将onnx模型转换为om模型，命令如下： ```` -ffmpeg -i xxx.mp4 -vcodec h264 -bf 0 -g 25 -r 25 -s 1920*1080 -an -f h264 xxx.264 +atc --framework=5 --model=yunet.onnx --output=yunet --input_format=NCHW --input_shape="input:1,3,120,160" --log=debug --soc_version=Ascend310P3 --insert_op_conf=../config/Yunet.aippconfig ```` -将转化后的.264视频用live555产生rtsp拉流。 +执行该命令后会在`/models/`下生成项目指定模型文件`yunet.om`。若模型转换成功则输出： -由于本项目是支持端对端3路推理，故设置3个视频源，请使用者自行将pipeline中的对应位置修改为自己所使用的的服务器和文件名。 +``` +ATC start working now, please wait for a moment. +ATC run success, welcome to the next use. +``` +## 4 编译与运行 +**步骤1：** 准备测试视频。视频流格式为264，放入 `test/` 文件夹下。 -## 4 模型转化 +**步骤2：** 在 `test/` 文件夹下拉起Live555服务。[Live555拉流教程](../../docs/参考资料/Live555离线视频转RTSP说明文档.md) -本项目中使用的模型是yunet模型，onnx模型可以直接[下载](https://mindx.sdk.obs.cn-north-4.myhuaweicloud.com/mindxsdk-referenceapps%20/contrib/yunet/yunet.onnx)。下载后使用模型转换工具ATC将onnx模型转换为om模型，模型转换工具相关介绍参考[链接](https://gitee.com/ascend/docs-openmind/blob/master/guide/mindx/sdk/tutorials/%E5%8F%82%E8%80%83%E8%B5%84%E6%96%99.md) +**步骤3：** 修改`pipeline/Yunet.pipeline`文件： -模型转换步骤如下： +①：将文件中第8、196、282行的 “rtspUrl” 字段值替换为可用的 rtsp 流源地址（目前只支持264格式的rtsp流，例："rtsp://xxx.xxx.xxx.xxx:xxx/xxx.264", 其中xxx.xxx.xxx.xxx:xxx为ip和端口号，端口号需同Live555服务的起流端口号一致，xxx.264为待测视频流文件名）； -按照2环境依赖设置环境变量 +②：将文件中第4行的 “deviceId” 字段值替换为实际使用的device的id值，可用的 device id 值可以使用命令：`npu-smi info` 查看 -`cd`到`models`文件夹，运行 +注意：由于本项目是支持端对端3路推理，故需设置3个视频源，请使用者自行将pipeline中的所有对应位置修改为自己所使用的源流地址和文件名。 -```` -atc --framework=5 --model=yunet.onnx --output=yunet --input_format=NCHW --input_shape="input:1,3,120,160" --log=debug --soc_version=Ascend310 --insert_op_conf=../config/Yunet.aippconfig -```` +**步骤4：** 编译。在项目根目录下，先执行命令`bash ${MX_SDK_HOME}/operators/opencvosd/generate_osd_om.sh`编译opencv_osd算子，然后再执行命令`bash build.sh` -执行该命令后会在指定输出.om模型路径生成项目指定模型文件`yunet.om`。若模型转换成功则输出： +**步骤5：** 拷贝so文件至MindXSDK安装路径的`lib/modelpostprocessors/`目录下。在根目录下执行命令： -``` -ATC start working now, please wait for a moment. -ATC run success, welcome to the next use. +```bash +chmod 640 plugin/build/libyunetpostprocess.so +cp plugin/build/libyunetpostprocess.so ${MX_SDK_HOME}/lib/modelpostprocessors/ ``` -aipp文件配置如下： +**步骤6：** 运行。在项目根目录下，执行命令`bash run.sh`，即可在根目录下得到输出结果`result.264`。 -``` -aipp_op { - related_input_rank : 0 - src_image_size_w : 160 - src_image_size_h : 120 - crop : false - aipp_mode: static - input_format : YUV420SP_U8 - csc_switch : true - rbuv_swap_switch : false - matrix_r0c0 : 256 - matrix_r0c1 : 454 - matrix_r0c2 : 0 - matrix_r1c0 : 256 - matrix_r1c1 : -88 - matrix_r1c2 : -183 - matrix_r2c0 : 256 - matrix_r2c1 : 0 - matrix_r2c2 : 359 - input_bias_0 : 0 - input_bias_1 : 128 - input_bias_2 : 128 - mean_chn_0 : 0 - mean_chn_1 : 0 - mean_chn_2 : 0 - min_chn_0 : 0.0 - min_chn_1 : 0.0 - min_chn_2 : 0.0 - var_reci_chn_0 : 1.0 - var_reci_chn_1 : 1.0 - var_reci_chn_2 : 1.0 -} -``` +使用`ctrl + c`终止程序运行。 +## 5 性能验证 +在第 4 节的基础上，继续进行如下步骤 -## 5 编译运行 +**步骤1：** 修改`pipeline/InferTest.pipeline`文件。操作方式参考[第4节步骤3](#4-编译与运行) -`main.py`：用来生成端对端单路推理的可视化视频，以提供单路推理结果可视化的应用样例 +**步骤2：** 拷贝so文件至MindXSDK安装路径的`lib/modelpostprocessors/`目录下。在根目录下执行命令： -`kpmain.py`：用来生成单路关键点后处理的数据结果（用来确保关键点类型后处理的实现成功，关键点效果看main.py的可视化结果） +```bash +chmod 640 plugin1/build/libtotalyunetpostprocess.so +cp plugin1/build/libtotalyunetpostprocess.so ${MX_SDK_HOME}/lib/modelpostprocessors/ +``` -（`kpmain.py`在此项目中不是必须的，当前没有keypoint类型osd支持下，仅给出单路pipeline输出数据信息供参考） +**步骤3：** 性能测试。修改根目录下`run.sh`文件中第32行，由`python3 main.py`改为`python3 test.py`，再次在根目录下运行`bash run.sh`，即可获得`libtotalyunetpostprocess.so`的性能结果。 -`test.py`：用来输出端对端三路推理的后处理结果，以检测三路推理性能是否达标 +因为性能检测结果实时输出，使用者在`test.py`运行过程中可以实时查看检测结果，并且在需要时可以在输出帧率后立刻按`ctrl + c`终止程序运行，以查看帧率。 -需要注意的是，本项目后处理插件支持三路视频推理的后处理，但由于mxVision-2.0.4暂不支持三路后处理输出，所以此处采取单路视频可视化和三路推理性能检测两部分，分别提供可视化应用与性能检测的功能。 +## 6 常见问题 -1.编译后处理插件 +### 6.1 视频解码器负荷过高，报内存相关错误 -`cd`到`plugin`目录，`mkdir`新建文件夹`build` +**问题描述：** -`cd`到`build`，运行 +若视频解码器负荷过高则会出现以下问题： -```` -cmake .. -make -j -make install -```` +![error1](images/error1.png) +![error2](images/error2.png) -将生成的模型后处理so拷贝至MindXSDK安装路径的`lib/modelpostprocessors`目录。如果权限问题，`cd`到MindXSDK安装路径的`lib/modelpostprocessors`目录，将`libyunetpostprocess.so`的权限更改为`640`。 +**解决方案：** -对于`plugin2`、`plugin3`目录也同样处理。 +导致此问题的可能原因为：视频帧率过高、视频尺寸过大或解码器正在同时解码过多其他视频 -2.`config/face_yunet.cfg` 确认权限`640`。 +解决方案：确保三路视频都为 1920*1080 25fps 并且减少其它任务的运行 -3.运行`main.py`程序 +### 6.2 无法打开视频流 -`cd`到根目录，运行 +**问题描述：** -```` -bash run.sh -```` +在运行的过程中，无法打开视频流 -最后会得到`result.264`即为输出结果 +**解决方案：** +pipeline中需要配置多路rtsp视频地址，完善pipeline中的配置项 +### 6.3 未编译opencv_osd算子 -## 6 性能检测 -修改`run.sh`中 -```` -python3 main.py -```` -改为 -```` -python3 test.py -```` -运行 -```` -bash run.sh -```` -测试插件`libtotalyunetpostprocess.so`的性能： +**问题描述：** -![fps](images/fps2.png) +未编译opencv_osd算子，运行过程中报错`Realpath parsing failed` -因为性能检测结果实时输出，使用者在`test.py`运行过程中可以实时查看检测结果，并且在需要时，可以在输出帧率后立刻按CTRL+C停止，以查看帧率。 -性能检测结果如上。本项目三路推理的3秒平均帧率在74.0-75.0之间，满足端对端三路25fps的需求。 +**解决方案：** +在项目根目录下，执行命令`bash ${MX_SDK_HOME}/operators/opencvosd/generate_osd_om.sh`编译opencv_osd算子 +### 6.4 推流失败 +**问题描述：** -## 7 常见问题 +运行过程中报错`streamInstance GetResult return nullptr.` -若视频解码器负荷过高则会出现以下问题： -![error1](images/error1.png) -![error2](images/error2.png) +**解决方案：** -导致此问题的可能原因为：视频帧率过高、视频尺寸过大或解码器正在同时解码过多其他视频 -解决方案：确保三路视频都为1920*1080 25fps并且减少其它任务的运行 \ No newline at end of file +检查pipeline中的配置项中rtsp流地址是否正确，如确认正确则重新起流(可能由于某些原因导致已启动的live555服务不可用) \ No newline at end of file diff --git a/contrib/yunet/build.sh b/contrib/yunet/build.sh index cfc0863e3..aaf1d35df 100644 --- a/contrib/yunet/build.sh +++ b/contrib/yunet/build.sh @@ -19,8 +19,7 @@ current_folder="$( cd "$(dirname "$0")" ;pwd -P )" SAMPLE_FOLDER=( /plugin/ -/plugin2/ 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Huawei Technologies 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. -""" - -import sys -import re -import json -import os -import stat -import random -import signal -import datetime -import threading -import cv2 -import numpy as np -import MxpiDataType_pb2 as MxpiDataType -from PIL import Image -from StreamManagerApi import StreamManagerApi, MxDataInput, StringVector - - -def sigint_handler(signum, frame): - signum = signum - frame = frame - global ISSIGINTUP - ISSIGINTUP = True - print("catched interrupt signal") - -signal.signal(signal.SIGINT, sigint_handler) -signal.signal(signal.SIGHUP, sigint_handler) -signal.signal(signal.SIGTERM, sigint_handler) -ISSIGINTUP = False - -if __name__ == '__main__': - - # init stream manager - STREAM_MANAGER_API = StreamManagerApi() - ret = STREAM_MANAGER_API.InitManager() - if ret != 0: - print("Failed to init Stream manager, ret=%s" % str(ret)) - exit() - - # create streams by pipeline config file - with open("./pipeline/KPYunet.pipeline", 'rb') as f: - pipelineStr = f.read() - ret = STREAM_MANAGER_API.CreateMultipleStreams(pipelineStr) - if ret != 0: - print("Failed to create Stream, ret=%s" % str(ret)) - exit() - - # get result from stream and write - STREAM_NAME = b'KPYunet' - IN_PLUGIN_ID = 0 - FRAME_COUNT = 0 - - while FRAME_COUNT <= 100: - FRAME_COUNT += 1 - inferResult = STREAM_MANAGER_API.GetResult(STREAM_NAME, IN_PLUGIN_ID) - print(inferResult.data) - if ISSIGINTUP: - print("Exit") - break - - - # destroy streams - STREAM_MANAGER_API.DestroyAllStreams() \ No newline at end of file diff --git a/contrib/yunet/main.py b/contrib/yunet/main.py index 451a269f2..6513d8471 100644 --- a/contrib/yunet/main.py +++ b/contrib/yunet/main.py @@ -74,10 +74,11 @@ if __name__ == '__main__': FLAGS = os.O_WRONLY | os.O_CREAT MODES = stat.S_IWUSR | stat.S_IRUSR with os.fdopen(os.open('./result.264', FLAGS, MODES), 'wb') as fp: - while FRAME_COUNT <= 100: + while True: FRAME_COUNT += 1 inferResult = STREAM_MANAGER_API.GetResult(STREAM_NAME, IN_PLUGIN_ID) fp.write(inferResult.data) + print("%d frame has been processed and saved" % FRAME_COUNT) if ISSIGINTUP: print("Exit") break diff --git a/contrib/yunet/pipeline/KPYunet.pipeline b/contrib/yunet/pipeline/KPYunet.pipeline deleted file mode 100644 index 9b30eadb0..000000000 --- a/contrib/yunet/pipeline/KPYunet.pipeline +++ /dev/null @@ -1,64 +0,0 @@ -{ - "KPYunet": { - "stream_config": { - "deviceId": "1" - }, - "mxpi_rtspsrc0": { - "props": { - "rtspUrl": "rtsp://xxx.xxx.xxx.xxx:xxxx/xxx.264", - "channelId": "0" - }, - "factory": "mxpi_rtspsrc", - "next": "mxpi_videodecoder0" - }, - "mxpi_videodecoder0": { - "props": { - "inputVideoFormat": "H264", - "outputImageFormat": "YUV420SP_NV12", - "vdecChannelId": "0" - }, - "factory": "mxpi_videodecoder", - "next": "mxpi_imageresize0" - }, - - "mxpi_imageresize0": { - "props": { - "dataSource": "mxpi_videodecoder0", - "resizeHeight": "120", - "resizeWidth": "160" - }, - "factory": "mxpi_imageresize", - "next": "mxpi_tensorinfer0" - }, - "mxpi_tensorinfer0": { - "props": { - "modelPath": "./models/yunet.om", - "singleBatchInfer": "1", - "outputHasBatchDim": "0" - }, - "factory": "mxpi_tensorinfer", - "next": "mxpi_keypointpostprocessor0" - }, - "mxpi_keypointpostprocessor0": { - "props": { - "dataSource": "mxpi_tensorinfer0", - "postProcessConfigPath": "./config/face_yunet.cfg", - "postProcessLibPath": "libkpyunetpostprocess.so" - }, - "factory": "mxpi_keypointpostprocessor", - "next": "mxpi_dataserialize0" - }, - "mxpi_dataserialize0": { - "props": { - "outputDataKeys": "mxpi_keypointpostprocessor0" - }, - "factory": "mxpi_dataserialize", - "next": "appsink0" - }, - - "appsink0": { - "factory": "appsink" - } - - } -} diff --git a/contrib/yunet/pipeline/PluginTest.pipeline b/contrib/yunet/pipeline/PluginTest.pipeline deleted file mode 100644 index 8b206a158..000000000 --- a/contrib/yunet/pipeline/PluginTest.pipeline +++ /dev/null @@ -1,363 +0,0 @@ -{ - "Yunet": { - "stream_config": { - "deviceId": "0" - }, - "mxpi_rtspsrc0": { - "props": { - "rtspUrl": "rtsp://xxx.xxx.xx.xxx:xxxx/xxx.264", - "channelId": "0" - }, - "factory": "mxpi_rtspsrc", - "next": "mxpi_videodecoder0" - }, - "mxpi_videodecoder0": { - "props": { - "inputVideoFormat": "H264", - "outputImageFormat": "YUV420SP_NV12", - "vdecChannelId": "0" - }, - "factory": "mxpi_videodecoder", - "next": "mxpi_imageresize0" - }, - - "mxpi_imageresize0": { - "props": { - "dataSource": "mxpi_videodecoder0", - "resizeHeight": "120", - "resizeWidth": "160" - }, - "factory": "mxpi_imageresize", - "next": "tee0" - }, - "tee0":{ - "factory":"tee", - "next":[ - "queue1", - "queue2" - ] - }, - "queue1":{ - "props":{ - "max-size-buffers":"20000" - }, - "factory":"queue", - "next":"mxpi_tensorinfer0" - }, - "queue2":{ - "props":{ - "max-size-buffers":"20000" - }, - "factory":"queue", - "next":"mxpi_imageresize11" - }, - "mxpi_tensorinfer0": { - "props": { - "modelPath": "./models/yunet.om", - "singleBatchInfer": "1", - "outputHasBatchDim": "0" - }, - "factory": "mxpi_tensorinfer", - "next": "mxpi_objectpostprocessor0" - }, - "mxpi_objectpostprocessor0": { - "props": { - "dataSource": "mxpi_tensorinfer0", - "postProcessConfigPath": "./config/face_yunet.cfg", - "postProcessLibPath": "libtotalyunetpostprocess.so" - }, - "factory": "mxpi_objectpostprocessor", - "next": "mxpi_object2osdinstances0" - }, - - "mxpi_object2osdinstances0":{ - "props":{ - "colorMap":"100,100,100|200,200,200|0,128,255|255,0,0", - "fontFace":"16", - "fontScale":"0.5", - "fontThickness":"2", - "fontLineType":"16", - "rectThickness":"2", - "rectLineType":"16", - "createText":"0" - }, - "factory":"mxpi_object2osdinstances", - "next":"mxpi_channelosdcoordsconverter0:1" - }, - "mxpi_imageresize11": { - "props": { - "dataSource": "mxpi_videodecoder0", - "resizeHeight": "1080", - "resizeWidth": "1920" - }, - "factory": "mxpi_imageresize", - "next": "queue11" - }, - "queue11":{ - "props":{ - "max-size-buffers":"500" - }, - "factory":"queue", - "next":"mxpi_channelimagesstitcher0:0" - }, - "mxpi_imageresize12": { - "props": { - "dataSource": "mxpi_videodecoder1", - "resizeHeight": "1080", - "resizeWidth": "1920" - }, - "factory": "mxpi_imageresize", - "next": "queue12" - }, - "queue12":{ - "props":{ - "max-size-buffers":"500" - }, - "factory":"queue", - "next":"mxpi_channelimagesstitcher0:1" - }, - "mxpi_imageresize13": { - "props": { - "dataSource": "mxpi_videodecoder2", - "resizeHeight": "1080", - "resizeWidth": "1920" - }, - "factory": "mxpi_imageresize", - "next": "queue13" - }, - "queue13":{ - "props":{ - "max-size-buffers":"500" - }, - "factory":"queue", - "next":"mxpi_channelimagesstitcher0:2" - }, - "mxpi_channelimagesstitcher0":{ - "factory":"mxpi_channelimagesstitcher", - "props":{ - "channelIds":"0,1,2", - "outputWidth":"1920", - "outputHeight":"1080" - }, - "next":["mxpi_opencvosd0:0","mxpi_channelosdcoordsconverter0:0"] - }, - "mxpi_channelosdcoordsconverter0":{ - "props":{ - "dataSourceStitchInfo":"mxpi_channelimagesstitcher0_1", - "channelIds":"0,1,2", - "enableFixedArea":"1" - }, - "factory":"mxpi_channelosdcoordsconverter", - "next":"mxpi_opencvosd0:1" - }, - "mxpi_opencvosd0":{ - "props":{ - "dataSourceImage":"mxpi_channelimagesstitcher0_0", - "dataSourceOsd":"mxpi_channelosdcoordsconverter0" - }, - "factory":"mxpi_opencvosd", - "next":"mxpi_imageresize10" - }, - "mxpi_imageresize10": { - "props": { - "dataSource": "mxpi_opencvosd0", - "resizeHeight": "1080", - "resizeWidth": "1920" - }, - "factory": "mxpi_imageresize", - "next": "mxpi_videoencoder0" - }, - "mxpi_videoencoder0":{ - "props":{ - "dataSource":"mxpi_imageresize10", - "inputFormat":"YUV420SP_NV12", - "fps":"1", - "iFrameInterval":"25", - "vencChannelId":"0" - }, - "factory":"mxpi_videoencoder", - "next":"queue0" - }, - "queue0":{ - "factory":"queue", - "next":"appsink0" - }, - "appsink0":{ - "factory":"appsink" - }, - - - - - - - "mxpi_rtspsrc1": { - "props": { - "rtspUrl": "rtsp://xxx.xxx.xx.xxx:xxxx/xxx.264", - "channelId": "1" - }, - "factory": "mxpi_rtspsrc", - "next": "mxpi_videodecoder1" - }, - "mxpi_videodecoder1": { - "props": { - "inputVideoFormat": "H264", - "outputImageFormat": "YUV420SP_NV12", - "vdecChannelId": "1" - }, - "factory": "mxpi_videodecoder", - "next": "mxpi_imageresize2" - }, - "mxpi_imageresize2": { - "props": { - "dataSource": "mxpi_videodecoder1", - "resizeHeight": "120", - "resizeWidth": "160" - }, - "factory": "mxpi_imageresize", - "next": "tee1" - }, - "tee1":{ - "factory":"tee", - "next":[ - "queue3", - "queue4" - ] - }, - "queue3":{ - "props":{ - "max-size-buffers":"20000" - }, - "factory":"queue", - "next":"mxpi_tensorinfer1" - }, - "queue4":{ - "props":{ - "max-size-buffers":"20000" - }, - "factory":"queue", - "next":"mxpi_imageresize12" - }, - "mxpi_tensorinfer1": { - "props": { - "modelPath": "./models/yunet.om", - "singleBatchInfer": "1", - "outputHasBatchDim": "0" - }, - "factory": "mxpi_tensorinfer", - "next": "mxpi_objectpostprocessor1" - }, - "mxpi_objectpostprocessor1": { - "props": { - "dataSource": "mxpi_tensorinfer1", - "postProcessConfigPath": "./config/face_yunet.cfg", - "postProcessLibPath": "libtotalyunetpostprocess.so" - }, - "factory": "mxpi_objectpostprocessor", - "next": "mxpi_object2osdinstances1" - }, - - "mxpi_object2osdinstances1":{ - "props":{ - "colorMap":"100,100,100|200,200,200|0,128,255|255,0,0", - "fontFace":"16", - "fontScale":"0.5", - "fontThickness":"2", - "fontLineType":"16", - "rectThickness":"2", - "rectLineType":"16", - "createText":"0" - }, - "factory":"mxpi_object2osdinstances", - "next":"mxpi_channelosdcoordsconverter0:2" - }, - - - - - - - "mxpi_rtspsrc2": { - "props": { - "rtspUrl": "rtsp://xxx.xxx.xx.xxx:xxxx/xxx.264", - "channelId": "2" - }, - "factory": "mxpi_rtspsrc", - "next": "mxpi_videodecoder2" - }, - "mxpi_videodecoder2": { - "props": { - "inputVideoFormat": "H264", - "outputImageFormat": "YUV420SP_NV12", - "vdecChannelId": "2" - }, - "factory": "mxpi_videodecoder", - "next": "mxpi_imageresize4" - }, - "mxpi_imageresize4": { - "props": { - "dataSource": "mxpi_videodecoder2", - "resizeHeight": "120", - "resizeWidth": "160" - }, - "factory": "mxpi_imageresize", - "next": "tee2" - }, - "tee2":{ - "factory":"tee", - "next":[ - "queue6", - "queue7" - ] - }, - "queue6":{ - "props":{ - "max-size-buffers":"20000" - }, - "factory":"queue", - "next":"mxpi_tensorinfer2" - }, - "queue7":{ - "props":{ - "max-size-buffers":"20000" - }, - "factory":"queue", - "next":"mxpi_imageresize13" - }, - "mxpi_tensorinfer2": { - "props": { - "modelPath": "./models/yunet.om", - "singleBatchInfer": "1", - "outputHasBatchDim": "0" - }, - "factory": "mxpi_tensorinfer", - "next": "mxpi_objectpostprocessor2" - }, - "mxpi_objectpostprocessor2": { - "props": { - "dataSource": "mxpi_tensorinfer2", - "postProcessConfigPath": "./config/face_yunet.cfg", - "postProcessLibPath": "libtotalyunetpostprocess.so" - }, - "factory": "mxpi_objectpostprocessor", - "next": "mxpi_object2osdinstances2" - }, - - "mxpi_object2osdinstances2":{ - "props":{ - "colorMap":"100,100,100|200,200,200|0,128,255|255,0,0", - "fontFace":"16", - "fontScale":"0.5", - "fontThickness":"2", - "fontLineType":"16", - "rectThickness":"2", - "rectLineType":"16", - "createText":"0" - }, - "factory":"mxpi_object2osdinstances", - "next":"mxpi_channelosdcoordsconverter0:3" - } - - - } -} diff --git a/contrib/yunet/pipeline/Yunet.pipeline b/contrib/yunet/pipeline/Yunet.pipeline index f811d978b..349cbf57f 100644 --- a/contrib/yunet/pipeline/Yunet.pipeline +++ b/contrib/yunet/pipeline/Yunet.pipeline @@ -1,7 +1,7 @@ { "Yunet": { "stream_config": { - "deviceId": "1" + "deviceId": "0" }, "mxpi_rtspsrc0": { "props": { @@ -64,7 +64,7 @@ "props": { "dataSource": "mxpi_tensorinfer0", "postProcessConfigPath": "./config/face_yunet.cfg", - "postProcessLibPath": "libtotalyunetpostprocess.so" + "postProcessLibPath": "libyunetpostprocess.so" }, "factory": "mxpi_objectpostprocessor", "next": "mxpi_object2osdinstances0" @@ -251,7 +251,7 @@ "props": { "dataSource": "mxpi_tensorinfer1", "postProcessConfigPath": "./config/face_yunet.cfg", - "postProcessLibPath": "libtotalyunetpostprocess.so" + "postProcessLibPath": "libyunetpostprocess.so" }, "factory": "mxpi_objectpostprocessor", "next": "mxpi_object2osdinstances1" @@ -337,7 +337,7 @@ "props": { "dataSource": "mxpi_tensorinfer2", "postProcessConfigPath": "./config/face_yunet.cfg", - "postProcessLibPath": "libtotalyunetpostprocess.so" + "postProcessLibPath": "libyunetpostprocess.so" }, "factory": "mxpi_objectpostprocessor", "next": "mxpi_object2osdinstances2" diff --git a/contrib/yunet/plugin/CMakeLists.txt b/contrib/yunet/plugin/CMakeLists.txt index 24a8f2ba6..057acccc7 100644 --- a/contrib/yunet/plugin/CMakeLists.txt +++ b/contrib/yunet/plugin/CMakeLists.txt @@ -18,7 +18,7 @@ include_directories(${MX_SDK_HOME}/opensource/include/opencv4) link_directories(${MX_SDK_HOME}/opensource/lib/) link_directories(${MX_SDK_HOME}/lib) -add_compile_options(-std=c++11 -fPIC -fstack-protector-all -pie -Wno-deprecated-declarations) +add_compile_options(-std=c++14 -fPIC -fstack-protector-all -pie -Wno-deprecated-declarations) add_compile_options("-DPLUGIN_NAME=${PLUGIN_NAME}") add_definitions(-DENABLE_DVPP_INTERFACE) diff --git a/contrib/yunet/plugin/YunetPostProcess.cpp b/contrib/yunet/plugin/YunetPostProcess.cpp index 236c30585..c91943b3e 100644 --- a/contrib/yunet/plugin/YunetPostProcess.cpp +++ b/contrib/yunet/plugin/YunetPostProcess.cpp @@ -68,25 +68,25 @@ namespace MxBase { cv::Mat& res) { auto shape = tensors[0].GetShape(); - float width_resize_scale = (float)resizedImageInfos[0].widthResize / resizedImageInfos[0].widthOriginal; - float height_resize_scale = (float)resizedImageInfos[0].heightResize / resizedImageInfos[0].heightOriginal; + float widthResizeScale = (float)resizedImageInfos[0].widthResize / resizedImageInfos[0].widthOriginal; + float heightResizeScale = (float)resizedImageInfos[0].heightResize / resizedImageInfos[0].heightOriginal; uint32_t batchSize = shape[0]; - uint32_t VectorNum = shape[1]; + uint32_t vectorNum = shape[1]; for (uint32_t i = 0; i < batchSize; i++) { std::vector objectInfo, objectInfoSorted; auto dataPtr_Conf = (float *) tensors[1].GetBuffer() + i * tensors[1].GetByteSize() / batchSize; - for (uint32_t j = 0; j < VectorNum; j++) { + for (uint32_t j = 0; j < vectorNum; j++) { float* begin_Conf = dataPtr_Conf + j * 2; float conf = *(begin_Conf + 1); if (conf> confThresh_) { ObjectInfo objInfo; objInfo.confidence = j; - objInfo.x0 = res.at(j, LEFTTOPX) * IMAGE_WIDTH / width_resize_scale; - objInfo.y0 = res.at(j, LEFTTOPY) * IMAGE_HEIGHT / height_resize_scale; - objInfo.x1 = res.at(j, RIGHTTOPX) * IMAGE_WIDTH / width_resize_scale; - objInfo.y1 = res.at(j, RIGHTTOPY) * IMAGE_HEIGHT / height_resize_scale; + objInfo.x0 = res.at(j, LEFTTOPX) * IMAGE_WIDTH / widthResizeScale; + objInfo.y0 = res.at(j, LEFTTOPY) * IMAGE_HEIGHT / heightResizeScale; + objInfo.x1 = res.at(j, RIGHTTOPX) * IMAGE_WIDTH / widthResizeScale; + objInfo.y1 = res.at(j, RIGHTTOPY) * IMAGE_HEIGHT / heightResizeScale; objInfo.classId = RECTANGLE_COLOR; objectInfo.push_back(objInfo); @@ -198,7 +198,8 @@ namespace MxBase { * @param resize_scale_factor: The factor of min(WidthOriginal/WidthResize, HeightOriginal/HeightResize) * @param boxes: The matrix which contains detection box coordinates(x0,y0,x1,y1), shape[21824,4] */ - cv::Mat YunetPostProcess::decode_for_loc(cv::Mat &loc, cv::Mat &prior) { + cv::Mat YunetPostProcess::decode_for_loc(cv::Mat &loc, cv::Mat &prior) + { cv::Mat loc_first = loc.colRange(0, 2); cv::Mat loc_last = loc.colRange(2, 4); cv::Mat prior_first = prior.colRange(0, 2); diff --git a/contrib/yunet/plugin/YunetPostProcess.h b/contrib/yunet/plugin/YunetPostProcess.h index 99e23ef66..dd197e953 100644 --- a/contrib/yunet/plugin/YunetPostProcess.h +++ b/contrib/yunet/plugin/YunetPostProcess.h @@ -14,18 +14,20 @@ * limitations under the License. */ -#ifndef Yunet_POST_PROCESS_H -#define Yunet_POST_PROCESS_H +#ifndef YUNET_POST_PROCESS_H +#define YUNET_POST_PROCESS_H #include "MxBase/PostProcessBases/ObjectPostProcessBase.h" #include "MxBase/CV/ObjectDetection/Nms/Nms.h" #include "opencv2/opencv.hpp" -#define DEFAULT_OBJECT_CONF_TENSOR 1 -#define DEFAULT_OBJECT_INFO_TENSOR 0 -#define DEFAULT_IOU_THRESH 0.3 -#define DEFAULT_CONFIDENCE_THRESH 0.9 + +constexpr uint32_t DEFAULT_OBJECT_CONF_TENSOR = 1; +constexpr uint32_t DEFAULT_OBJECT_INFO_TENSOR = 0; +constexpr float DEFAULT_IOU_THRESH = 0.3; +constexpr float DEFAULT_CONFIDENCE_THRESH = 0.9; namespace MxBase { - bool operator<(const ObjectInfo &a, const ObjectInfo &b) { + bool operator<(const ObjectInfo &a, const ObjectInfo &b) + { return a.confidence < b.confidence; } diff --git a/contrib/yunet/plugin3/CMakeLists.txt b/contrib/yunet/plugin1/CMakeLists.txt similarity index 95% rename from contrib/yunet/plugin3/CMakeLists.txt rename to contrib/yunet/plugin1/CMakeLists.txt index 46341a0c1..6fd40a222 100644 --- a/contrib/yunet/plugin3/CMakeLists.txt +++ b/contrib/yunet/plugin1/CMakeLists.txt @@ -18,7 +18,7 @@ include_directories(${MX_SDK_HOME}/opensource/include/opencv4) link_directories(${MX_SDK_HOME}/opensource/lib/) link_directories(${MX_SDK_HOME}/lib) -add_compile_options(-std=c++11 -fPIC -fstack-protector-all -pie -Wno-deprecated-declarations) +add_compile_options(-std=c++14 -fPIC -fstack-protector-all -pie -Wno-deprecated-declarations) add_compile_options("-DPLUGIN_NAME=${PLUGIN_NAME}") add_definitions(-DENABLE_DVPP_INTERFACE) diff --git a/contrib/yunet/plugin3/TotalYunetPostProcess.cpp b/contrib/yunet/plugin1/TotalYunetPostProcess.cpp similarity index 92% rename from contrib/yunet/plugin3/TotalYunetPostProcess.cpp rename to contrib/yunet/plugin1/TotalYunetPostProcess.cpp index 1c5440c0d..7189c2277 100644 --- a/contrib/yunet/plugin3/TotalYunetPostProcess.cpp +++ b/contrib/yunet/plugin1/TotalYunetPostProcess.cpp @@ -70,25 +70,25 @@ namespace MxBase { cv::Mat& res) { auto shape = tensors[0].GetShape(); - float width_resize_scale = (float)resizedImageInfos[0].widthResize / resizedImageInfos[0].widthOriginal; - float height_resize_scale = (float)resizedImageInfos[0].heightResize / resizedImageInfos[0].heightOriginal; + float widthResizeScale = (float)resizedImageInfos[0].widthResize / resizedImageInfos[0].widthOriginal; + float heightResizeScale = (float)resizedImageInfos[0].heightResize / resizedImageInfos[0].heightOriginal; uint32_t batchSize = shape[0]; - uint32_t VectorNum = shape[1]; + uint32_t vectorNum = shape[1]; for (uint32_t i = 0; i < batchSize; i++) { std::vector objectInfo, objectInfoSorted; auto dataPtr_Conf = (float *) tensors[1].GetBuffer() + i * tensors[1].GetByteSize() / batchSize; - for (uint32_t j = 0; j < VectorNum; j++) { + for (uint32_t j = 0; j < vectorNum; j++) { float* begin_Conf = dataPtr_Conf + j * 2; float conf = *(begin_Conf + 1); if (conf> confThresh_) { ObjectInfo objInfo; objInfo.confidence = j; - objInfo.x0 = res.at(j, LEFTTOPX) * IMAGE_WIDTH / width_resize_scale; - objInfo.y0 = res.at(j, LEFTTOPY) * IMAGE_HEIGHT / height_resize_scale; - objInfo.x1 = res.at(j, RIGHTTOPX) * IMAGE_WIDTH / width_resize_scale; - objInfo.y1 = res.at(j, RIGHTTOPY) * IMAGE_HEIGHT / height_resize_scale; + objInfo.x0 = res.at(j, LEFTTOPX) * IMAGE_WIDTH / widthResizeScale; + objInfo.y0 = res.at(j, LEFTTOPY) * IMAGE_HEIGHT / heightResizeScale; + objInfo.x1 = res.at(j, RIGHTTOPX) * IMAGE_WIDTH / widthResizeScale; + objInfo.y1 = res.at(j, RIGHTTOPY) * IMAGE_HEIGHT / heightResizeScale; objInfo.classId = RECTANGLE_COLOR; objectInfo.push_back(objInfo); @@ -103,10 +103,10 @@ namespace MxBase { objectInfo[j].confidence = conf; objectInfoSorted.push_back(objectInfo[j]); - for (int k = 0; k < KEYPOINTNUM; k++) - { - float x = res.at(keypoint_Pos, RECTANGLEPOINT + k * DIM) * IMAGE_WIDTH / width_resize_scale; - float y = res.at(keypoint_Pos, RECTANGLEPOINT + k * DIM + 1) * IMAGE_HEIGHT / height_resize_scale; + for (int k = 0; k < KEYPOINTNUM; k++) { + float x = res.at(keypoint_Pos, RECTANGLEPOINT + k * DIM) * IMAGE_WIDTH / widthResizeScale; + float y = res.at(keypoint_Pos, RECTANGLEPOINT + k * DIM + 1) * + IMAGE_HEIGHT / heightResizeScale; // use [(x-2,y-2),(x+2,y+2)] object rectangle to draw keypoints ObjectInfo objInfo; objInfo.x0= x - POINT_SIZE; @@ -225,7 +225,8 @@ namespace MxBase { * @param resize_scale_factor: The factor of min(WidthOriginal/WidthResize, HeightOriginal/HeightResize) * @param boxes: The matrix which contains detection box coordinates(x0,y0,x1,y1), shape[21824,4] */ - cv::Mat TotalYunetPostProcess::decode_for_loc(cv::Mat &loc, cv::Mat &prior) { + cv::Mat TotalYunetPostProcess::decode_for_loc(cv::Mat &loc, cv::Mat &prior) + { cv::Mat loc_first = loc.colRange(0, 2); cv::Mat loc_last = loc.colRange(2, 4); cv::Mat prior_first = prior.colRange(0, 2); @@ -240,12 +241,14 @@ namespace MxBase { boxes2 = boxes2 + boxes1; cv::Mat boxes3; - for (int i = 0; i < KEYPOINTNUM; i++) - { + for (int i = 0; i < KEYPOINTNUM; i++) { cv::Mat singlepoint = facepoint.colRange(i * 2, (i + 1) * 2); singlepoint = prior_last + (singlepoint * VARIANCE[0]).mul(prior_first); - if (i == 0) boxes3 = singlepoint; - else cv::hconcat(boxes3, singlepoint, boxes3); + if (i == 0) { + boxes3 = singlepoint; + } else { + cv::hconcat(boxes3, singlepoint, boxes3); + } } cv::Mat boxes; diff --git a/contrib/yunet/plugin3/TotalYunetPostProcess.h b/contrib/yunet/plugin1/TotalYunetPostProcess.h similarity index 88% rename from contrib/yunet/plugin3/TotalYunetPostProcess.h rename to contrib/yunet/plugin1/TotalYunetPostProcess.h index 60753ed03..7b234592c 100644 --- a/contrib/yunet/plugin3/TotalYunetPostProcess.h +++ b/contrib/yunet/plugin1/TotalYunetPostProcess.h @@ -14,21 +14,23 @@ * limitations under the License. */ -#ifndef TotalYunet_POST_PROCESS_H -#define TotalYunet_POST_PROCESS_H +#ifndef TOTAL_YUNET_POST_PROCESS_H +#define TOTAL_YUNET_POST_PROCESS_H #include "MxBase/PostProcessBases/ObjectPostProcessBase.h" #include "MxBase/CV/ObjectDetection/Nms/Nms.h" #include "opencv2/opencv.hpp" -#define DEFAULT_OBJECT_CONF_TENSOR 1 -#define DEFAULT_OBJECT_INFO_TENSOR 0 -#define DEFAULT_IOU_THRESH 0.3 -#define DEFAULT_CONFIDENCE_THRESH 0.9 + +constexpr uint32_t DEFAULT_OBJECT_CONF_TENSOR = 1; +constexpr uint32_t DEFAULT_OBJECT_INFO_TENSOR = 0; +constexpr float DEFAULT_IOU_THRESH = 0.3; +constexpr float DEFAULT_CONFIDENCE_THRESH = 0.9; namespace MxBase { - bool operator<(const ObjectInfo &a, const ObjectInfo &b) { + bool operator<(const ObjectInfo &a, const ObjectInfo &b) + { return a.confidence < b.confidence; } - + class TotalYunetPostProcess : public ObjectPostProcessBase { public: TotalYunetPostProcess() = default; diff --git a/contrib/yunet/plugin2/build.sh b/contrib/yunet/plugin1/build.sh similarity index 100% rename from contrib/yunet/plugin2/build.sh rename to contrib/yunet/plugin1/build.sh diff --git a/contrib/yunet/plugin2/CMakeLists.txt b/contrib/yunet/plugin2/CMakeLists.txt deleted file mode 100644 index aa4df178c..000000000 --- a/contrib/yunet/plugin2/CMakeLists.txt +++ /dev/null @@ -1,31 +0,0 @@ -cmake_minimum_required(VERSION 3.5.2) -project(kpyunetpostprocess) - -add_definitions(-D_GLIBCXX_USE_CXX11_ABI=0) -add_definitions(-Dgoogle=mindxsdk_private) - -set(PLUGIN_NAME "kpyunetpostprocess") -set(TARGET_LIBRARY ${PLUGIN_NAME}) -set(MX_SDK_HOME "$ENV{MX_SDK_HOME}") - -include_directories(${MX_SDK_HOME}/include) -include_directories(${MX_SDK_HOME}/opensource/include) -include_directories(${MX_SDK_HOME}/opensource/include/gstreamer-1.0) -include_directories(${MX_SDK_HOME}/opensource/include/glib-2.0) -include_directories(${MX_SDK_HOME}/opensource/lib/glib-2.0/include) -include_directories(${MX_SDK_HOME}/opensource/include/opencv4) - -link_directories(${MX_SDK_HOME}/opensource/lib/) -link_directories(${MX_SDK_HOME}/lib) - -add_compile_options(-std=c++11 -fPIC -fstack-protector-all -pie -Wno-deprecated-declarations) -add_compile_options("-DPLUGIN_NAME=${PLUGIN_NAME}") - -add_definitions(-DENABLE_DVPP_INTERFACE) -add_library(${TARGET_LIBRARY} SHARED KPYunetPostProcess.cpp) - -target_link_libraries(${TARGET_LIBRARY} glib-2.0 gstreamer-1.0 gobject-2.0 gstbase-1.0 gmodule-2.0) -target_link_libraries(${TARGET_LIBRARY} plugintoolkit mxpidatatype mxbase opencv_world) -target_link_libraries(${TARGET_LIBRARY} -Wl,-z,relro,-z,now,-z,noexecstack -s) - -install(TARGETS ${TARGET_LIBRARY} LIBRARY DESTINATION ${MX_SDK_HOME}/lib/modelpostprocessors) diff --git a/contrib/yunet/plugin2/KPYunetPostProcess.cpp b/contrib/yunet/plugin2/KPYunetPostProcess.cpp deleted file mode 100644 index dd66886b8..000000000 --- a/contrib/yunet/plugin2/KPYunetPostProcess.cpp +++ /dev/null @@ -1,235 +0,0 @@ -/* - * Copyright (c) Huawei Technologies Co., Ltd. 2020-2021. 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 "KPYunetPostProcess.h" -#include "MxBase/Log/Log.h" - -namespace { - const uint32_t LEFTTOPX = 0; - const uint32_t LEFTTOPY = 1; - const uint32_t RIGHTTOPX = 2; - const uint32_t RIGHTTOPY = 3; - const int PRIOR_PARAMETERS[4][3] = {{10, 16, 24}, {32, 48, -1}, {64, 96, -1}, {128, 192, 256}}; - const int PRIOR_PARAMETERS_COUNT = 3; - const float IMAGE_WIDTH = 160.0; - const float IMAGE_HEIGHT = 120.0; - const float STEPS[4] = {8.0, 16.0, 32.0, 64.0}; - const float VARIANCE[2] = {0.1, 0.2}; - const uint32_t RECTANGLEPOINT = 4; - const uint32_t KEYPOINTNUM = 5; - const uint32_t DIM = 2; - const uint32_t RECTANGLE_COLOR = 1; - const uint32_t KEYPOINT_COLOR = 2; - const uint32_t DIV_TWO = 2; -} -namespace MxBase { - KPYunetPostProcess& KPYunetPostProcess::operator=(const KPYunetPostProcess& other) - { - if (this == &other) { - return *this; - } - KeypointPostProcessBase::operator=(other); - return *this; - } - - APP_ERROR KPYunetPostProcess::Init(const std::map & postConfig) - { - LogDebug << "Start to Init KPYunetPostProcess."; - APP_ERROR ret = KeypointPostProcessBase::Init(postConfig); - if (ret != APP_ERR_OK) { - LogError << GetError(ret) << "Fail to superInit in KeypointPostProcessBase."; - return ret; - } - LogDebug << "End to Init KPYunetPostProcess."; - return APP_ERR_OK; - } - - APP_ERROR KPYunetPostProcess::DeInit() - { - return APP_ERR_OK; - } - - void KPYunetPostProcess::generate_keypointInfos(const std::vector & tensors, - std::vector >& keypointInfos, - const std::vector & resizedImageInfos, - cv::Mat& res) - { - auto shape = tensors[0].GetShape(); - float width_resize_scale = (float)resizedImageInfos[0].widthResize / resizedImageInfos[0].widthOriginal; - float height_resize_scale = (float)resizedImageInfos[0].heightResize / resizedImageInfos[0].heightOriginal; - uint32_t batchSize = shape[0]; - uint32_t VectorNum = shape[1]; - - for (uint32_t i = 0; i < batchSize; i++) { - std::vector objectInfo, objectInfoSorted; - std::vector keypointInfoSorted; - auto dataPtr_Conf = (float *) tensors[1].GetBuffer() + i * tensors[1].GetByteSize() / batchSize; - for (uint32_t j = 0; j < VectorNum; j++) { - float* begin_Conf = dataPtr_Conf + j * 2; - float conf = *(begin_Conf + 1); - - if (conf> confThresh_) { - ObjectInfo objInfo; - objInfo.confidence = j; - objInfo.x0 = res.at(j, LEFTTOPX) * IMAGE_WIDTH / width_resize_scale; - objInfo.y0 = res.at(j, LEFTTOPY) * IMAGE_HEIGHT / height_resize_scale; - objInfo.x1 = res.at(j, RIGHTTOPX) * IMAGE_WIDTH / width_resize_scale; - objInfo.y1 = res.at(j, RIGHTTOPY) * IMAGE_HEIGHT / height_resize_scale; - objInfo.classId = RECTANGLE_COLOR; - - objectInfo.push_back(objInfo); - } - } - MxBase::NmsSort(objectInfo, iouThresh_); - for (uint32_t j = 0; j < objectInfo.size(); j++) { - KeyPointDetectionInfo kpInfo; - int keypoint_Pos = (int)objectInfo[j].confidence; - float* begin_Conf = dataPtr_Conf + keypoint_Pos * 2; - kpInfo.score = *(begin_Conf + 1); - - for (int k = 0; k < KEYPOINTNUM; k++) - { - float x = res.at(keypoint_Pos, RECTANGLEPOINT + k * DIM) * IMAGE_WIDTH / width_resize_scale; - float y = res.at(keypoint_Pos, RECTANGLEPOINT + k * DIM + 1) * IMAGE_HEIGHT / height_resize_scale; - kpInfo.keyPointMap[k].push_back(x); - kpInfo.keyPointMap[k].push_back(y); - } - keypointInfoSorted.push_back(kpInfo); - } - keypointInfos.push_back(keypointInfoSorted); - } - } - - void KPYunetPostProcess::KeypointDetectionOutput(const std::vector & tensors, - std::vector >& keypointInfos, - const std::vector & resizedImageInfos) - { - LogDebug << "KPYunetPostProcess start to write results."; - - auto loc = tensors[0].GetBuffer(); - auto conf = tensors[1].GetBuffer(); - auto shape = tensors[0].GetShape(); - auto iou = tensors[2].GetBuffer(); - - cv::Mat PriorBox; - cv::Mat location = cv::Mat(shape[1], shape[2], CV_32FC1, tensors[0].GetBuffer()); - GeneratePriorBox(PriorBox); - cv::Mat res = decode_for_loc(location, PriorBox); - generate_keypointInfos(tensors, keypointInfos, resizedImageInfos, res); - - LogDebug << "KPYunetPostProcess write results successed."; - } - APP_ERROR KPYunetPostProcess::Process(const std::vector &tensors, - std::vector> &KeypointInfos, - const std::vector &resizedImageInfos, - const std::map> &configParamMap) - { - LogDebug << "Start to Process KPYunetPostProcess."; - APP_ERROR ret = APP_ERR_OK; - auto inputs = tensors; - ret = CheckAndMoveTensors(inputs); - if (ret != APP_ERR_OK) { - LogError << "CheckAndMoveTensors failed. ret=" << ret; - return ret; - } - KeypointDetectionOutput(inputs, KeypointInfos, resizedImageInfos); - LogDebug << "End to Process KPYunetPostProcess."; - return APP_ERR_OK; - } - - /* - * @description: Generate prior boxes for detection boxes decoding - * @param anchors A Matrix used to save prior boxes that contains box coordinates(x0,y0,x1,y1), shape[21824,4] - */ - void KPYunetPostProcess::GeneratePriorBox(cv::Mat &anchors) - { - std::vector>feature_maps(RECTANGLEPOINT, std::vector(DIM)); - for (int i = 0; i < feature_maps.size(); i++) { - feature_maps[i][0] = IMAGE_HEIGHT / STEPS[i]; - feature_maps[i][1] = IMAGE_WIDTH / STEPS[i]; - } - for (int k = 0; k < feature_maps.size(); k++) { - auto f = feature_maps[k]; - float step = (float)STEPS[k]; - for (int i = 0; i < f[0]; i++) { - for (int j = 0; j < f[1]; j++) { - for (int l = 0; l < PRIOR_PARAMETERS_COUNT && PRIOR_PARAMETERS[k][l] != -1; l++) { - float min_size = PRIOR_PARAMETERS[k][l]; - cv::Mat anchor(1, RECTANGLEPOINT, CV_32F); - float center_x = (j + 0.5f) * step; - float center_y = (i + 0.5f) * step; - - float xmin = (center_x - min_size / 2.f) / IMAGE_WIDTH; - float ymin = (center_y - min_size / 2.f) / IMAGE_HEIGHT; - float xmax = (center_x + min_size / 2.f) / IMAGE_WIDTH; - float ymax = (center_y + min_size / 2.f) / IMAGE_HEIGHT; - - float prior_width = xmax - xmin; - float prior_height = ymax - ymin; - float prior_center_x = (xmin + xmax) / 2; - float prior_center_y = (ymin + ymax) / 2; - - anchor.at(0, LEFTTOPX) = prior_width; - anchor.at(0, LEFTTOPY) = prior_height; - anchor.at(0, RIGHTTOPX) = prior_center_x; - anchor.at(0, RIGHTTOPY) = prior_center_y; - - anchors.push_back(anchor); - } - } - } - } - } - - cv::Mat KPYunetPostProcess::decode_for_loc(cv::Mat &loc, cv::Mat &prior) { - cv::Mat loc_first = loc.colRange(0, 2); - cv::Mat loc_last = loc.colRange(2, 4); - cv::Mat prior_first = prior.colRange(0, 2); - cv::Mat prior_last = prior.colRange(2, 4); - cv::Mat facepoint = loc.colRange(4, 14); - - cv::Mat boxes1 = prior_last + (loc_first * VARIANCE[0]).mul(prior_first); - cv::Mat boxes2; - cv::exp(loc_last * VARIANCE[1], boxes2); - boxes2 = boxes2.mul(prior_first); - boxes1 = boxes1 - boxes2 / DIV_TWO; - boxes2 = boxes2 + boxes1; - - cv::Mat boxes3; - for (int i = 0; i < KEYPOINTNUM; i++) - { - cv::Mat singlepoint = facepoint.colRange(i * 2, (i + 1) * 2); - singlepoint = prior_last + (singlepoint * VARIANCE[0]).mul(prior_first); - if (i == 0) boxes3 = singlepoint; - else cv::hconcat(boxes3, singlepoint, boxes3); - } - - cv::Mat boxes; - cv::hconcat(boxes1, boxes2, boxes); - cv::hconcat(boxes, boxes3, boxes); - return boxes; - } - - extern "C" { - std::shared_ptr GetKeypointInstance() - { - LogInfo << "Begin to get KPYunetPostProcess instance."; - auto instance = std::make_shared(); - LogInfo << "End to get KPYunetPostProcess instance."; - return instance; - } - } -} \ No newline at end of file diff --git a/contrib/yunet/plugin2/KPYunetPostProcess.h b/contrib/yunet/plugin2/KPYunetPostProcess.h deleted file mode 100644 index 88496062b..000000000 --- a/contrib/yunet/plugin2/KPYunetPostProcess.h +++ /dev/null @@ -1,75 +0,0 @@ -/* - * Copyright (c) Huawei Technologies Co., Ltd. 2020-2021. 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. - */ - -#ifndef KPYunet_POST_PROCESS_H -#define KPYunet_POST_PROCESS_H -#include "MxBase/PostProcessBases/KeypointPostProcessBase.h" -#include "MxBase/PostProcessBases/ObjectPostProcessBase.h" -#include "MxBase/CV/ObjectDetection/Nms/Nms.h" -#include "opencv2/opencv.hpp" -#define DEFAULT_OBJECT_CONF_TENSOR 1 -#define DEFAULT_OBJECT_INFO_TENSOR 0 -#define DEFAULT_IOU_THRESH 0.3 -#define DEFAULT_CONFIDENCE_THRESH 0.9 - -namespace MxBase { - bool operator<(const KeyPointDetectionInfo &a, const KeyPointDetectionInfo &b) { - return a.score < b.score; - } - bool operator<(const ObjectInfo &a, const ObjectInfo &b) { - return a.confidence < b.confidence; - } - - class KPYunetPostProcess : public KeypointPostProcessBase { - public: - KPYunetPostProcess() = default; - - ~KPYunetPostProcess() = default; - - KPYunetPostProcess(const KPYunetPostProcess& other); - - KPYunetPostProcess& operator=(const KPYunetPostProcess& other); - - APP_ERROR Init(const std::map & postConfig) override; - - APP_ERROR DeInit() override; - - APP_ERROR Process(const std::vector &tensors, std::vector> &keypointInfos, - const std::vector &resizedImageInfos = {}, - const std::map> &configParamMap = {}) override; - - protected: - void GeneratePriorBox(cv::Mat &anchors); - cv::Mat decode_for_loc(cv::Mat &loc, cv::Mat &prior); - void KeypointDetectionOutput(const std::vector & tensors, - std::vector >& ketpointInfos, - const std::vector & resizedImageInfos = {}); - void generate_keypointInfos(const std::vector & tensors, - std::vector >& keypointInfos, - const std::vector & resizedImageInfos, - cv::Mat &res); - private: - uint32_t objectConfTensor_ = DEFAULT_OBJECT_CONF_TENSOR; - uint32_t objectInfoTensor_ = DEFAULT_OBJECT_INFO_TENSOR; - float iouThresh_ = DEFAULT_IOU_THRESH; - float confThresh_ = DEFAULT_CONFIDENCE_THRESH; - }; - - extern "C" { - std::shared_ptr GetKetpointInstance(); - } -} -#endif diff --git a/contrib/yunet/plugin3/build.sh b/contrib/yunet/plugin3/build.sh deleted file mode 100644 index 006f86ba8..000000000 --- a/contrib/yunet/plugin3/build.sh +++ /dev/null @@ -1,36 +0,0 @@ -#!/bin/bash -# Copyright 2021 Huawei Technologies Co., Ltd -# -# 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.mitations under the License. - -set -e - -current_folder="$( cd "$(dirname "$0")" ;pwd -P )" - -function build_plugin() { - build_path=$current_folder/build - if [ -d "$build_path" ]; then - rm -rf "$build_path" - else - echo "file $build_path is not exist." - fi - mkdir -p "$build_path" - cd "$build_path" - cmake .. - make -j - cd .. - exit 0 -} - -build_plugin -exit 0 \ No newline at end of file -- Gitee From d323e5acc9c1b5cf8d58041257f7bea73fb993fe Mon Sep 17 00:00:00 2001 From: xu Date: Wed, 4 Dec 2024 18:04:15 +0800 Subject: [PATCH 2/3] =?UTF-8?q?=E6=A3=80=E8=A7=86=E6=84=8F=E8=A7=81?= =?UTF-8?q?=E4=BF=AE=E6=94=B9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- contrib/VideoGestureRecognition/README.md | 2 +- contrib/yunet/README.md | 8 +++++--- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/contrib/VideoGestureRecognition/README.md b/contrib/VideoGestureRecognition/README.md index 8dd5ce45e..f16e9b9ad 100644 --- a/contrib/VideoGestureRecognition/README.md +++ b/contrib/VideoGestureRecognition/README.md @@ -26,7 +26,7 @@ | live555 | 1.10 | 实现视频转rstp进行推流 | [链接](https://gitee.com/ascend/mindxsdk-referenceapps/blob/master/docs/%E5%8F%82%E8%80%83%E8%B5%84%E6%96%99/Live555%E7%A6%BB%E7%BA%BF%E8%A7%86%E9%A2%91%E8%BD%ACRTSP%E8%AF%B4%E6%98%8E%E6%96%87%E6%A1%A3.md) | | ffmpeg | 4.2.1 | 实现mp4格式视频转为264格式视频 | [链接](https://gitee.com/ascend/mindxsdk-referenceapps/blob/master/docs/%E5%8F%82%E8%80%83%E8%B5%84%E6%96%99/pc%E7%AB%AFffmpeg%E5%AE%89%E8%A3%85%E6%95%99%E7%A8%8B.md#https://ffmpeg.org/download.html) | -**注意：** +**注意：** ffmpeg需要用户到相关网址下载源码编译安装。 第三方库默认全部安装到/usr/local/下面，全部安装完成后，请设置环境变量 ```bash diff --git a/contrib/yunet/README.md b/contrib/yunet/README.md index cb80a7709..f8f6b9196 100644 --- a/contrib/yunet/README.md +++ b/contrib/yunet/README.md @@ -44,6 +44,8 @@ yunet基于MindXSDK开发，在昇腾芯片上进行人脸检测，并实现可 | live555 | 1.09 | 实现视频转rstp进行推流 | [链接](https://gitee.com/ascend/mindxsdk-referenceapps/blob/master/docs/%E5%8F%82%E8%80%83%E8%B5%84%E6%96%99/Live555%E7%A6%BB%E7%BA%BF%E8%A7%86%E9%A2%91%E8%BD%ACRTSP%E8%AF%B4%E6%98%8E%E6%96%87%E6%A1%A3.md) | | ffmpeg | 4.2.1 | 实现mp4格式视频转为264格式视频 | [链接](https://gitee.com/ascend/mindxsdk-referenceapps/blob/master/docs/%E5%8F%82%E8%80%83%E8%B5%84%E6%96%99/pc%E7%AB%AFffmpeg%E5%AE%89%E8%A3%85%E6%95%99%E7%A8%8B.md#https://ffmpeg.org/download.html) | +注：ffmpeg需要用户到相关网址下载源码编译安装。 + ### 1.5 代码目录结构说明 本项目名为yunet实时人脸检测，项目目录如下所示： @@ -133,9 +135,9 @@ chmod 640 plugin/build/libyunetpostprocess.so cp plugin/build/libyunetpostprocess.so ${MX_SDK_HOME}/lib/modelpostprocessors/ ``` -**步骤6：** 运行。在项目根目录下，执行命令`bash run.sh`，即可在根目录下得到输出结果`result.264`。 +**步骤6：** 运行。在项目根目录下，执行命令`bash run.sh`，可手动执行`ctrl + c`终止程序运行。 -使用`ctrl + c`终止程序运行。 +**步骤7：** 查看结果。在根目录下得到输出结果`result.264`。 ## 5 性能验证 @@ -152,7 +154,7 @@ cp plugin1/build/libtotalyunetpostprocess.so ${MX_SDK_HOME}/lib/modelpostprocess **步骤3：** 性能测试。修改根目录下`run.sh`文件中第32行，由`python3 main.py`改为`python3 test.py`，再次在根目录下运行`bash run.sh`，即可获得`libtotalyunetpostprocess.so`的性能结果。 -因为性能检测结果实时输出，使用者在`test.py`运行过程中可以实时查看检测结果，并且在需要时可以在输出帧率后立刻按`ctrl + c`终止程序运行，以查看帧率。 +**步骤4：** 查看结果。因为性能检测结果实时输出，使用者在`test.py`运行过程中可以实时查看检测结果，并且在需要时可以在输出帧率后立刻按`ctrl + c`终止程序运行，以查看帧率。 ## 6 常见问题 -- Gitee From 4ebadc1a8922c9332af1e67ecb4c14831a9c38e4 Mon Sep 17 00:00:00 2001 From: xu Date: Wed, 4 Dec 2024 19:05:53 +0800 Subject: [PATCH 3/3] =?UTF-8?q?=E8=A1=A5=E5=85=85=E8=AF=B4=E6=98=8E?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- contrib/VideoGestureRecognition/README.md | 21 +++++---------------- contrib/yunet/README.md | 2 +- mxVision/InferOfflineVideo/README.md | 2 +- 3 files changed, 7 insertions(+), 18 deletions(-) diff --git a/contrib/VideoGestureRecognition/README.md b/contrib/VideoGestureRecognition/README.md index 91c8b4fad..065f60276 100644 --- a/contrib/VideoGestureRecognition/README.md +++ b/contrib/VideoGestureRecognition/README.md @@ -27,7 +27,7 @@ | live555 | 1.10 | 实现视频转rstp进行推流 | [链接](https://gitee.com/ascend/mindxsdk-referenceapps/blob/master/docs/%E5%8F%82%E8%80%83%E8%B5%84%E6%96%99/Live555%E7%A6%BB%E7%BA%BF%E8%A7%86%E9%A2%91%E8%BD%ACRTSP%E8%AF%B4%E6%98%8E%E6%96%87%E6%A1%A3.md) | | ffmpeg | 4.2.1 | 实现mp4格式视频转为264格式视频 | [链接](https://gitee.com/ascend/mindxsdk-referenceapps/blob/master/docs/%E5%8F%82%E8%80%83%E8%B5%84%E6%96%99/pc%E7%AB%AFffmpeg%E5%AE%89%E8%A3%85%E6%95%99%E7%A8%8B.md#https://ffmpeg.org/download.html) | -**注意：** ffmpeg需要用户到相关网址下载源码编译安装。 +**注意：** live555和ffmpeg需要用户到相关网址下载源码编译安装。 第三方库默认全部安装到/usr/local/下面，全部安装完成后，请设置环境变量 ```bash @@ -75,21 +75,10 @@ export LD_LIBRARY_PATH=/usr/local/ffmpeg/lib:$LD_LIBRARY_PATH ## 2 设置环境变量 -``` -# 请确认install_path路径是否正确 -# Set environment PATH (Please confirm that the install_path is correct). - -export install_path=/usr/local/Ascend/ascend-toolkit/latest -export PATH=/usr/local/python3.9.2/bin:${install_path}/atc/ccec_compiler/bin:${install_path}/atc/bin:$PATH -export PYTHONPATH=${install_path}/atc/python/site-packages:${install_path}/atc/python/site-packages/auto_tune.egg/auto_tune:${install_path}/atc/python/site-packages/schedule_search.egg -export LD_LIBRARY_PATH=${install_path}/atc/lib64:$LD_LIBRARY_PATH -export ASCEND_OPP_PATH=${install_path}/opp - -export MX_SDK_HOME=${SDK安装路径} -export FFMPEG_PATH=${FFMPEG安装路径} -LD_LIBRARY_PATH=${MX_SDK_HOME}/lib:${MX_SDK_HOME}/opensource/lib:${MX_SDK_HOME}/opensource/lib64:${FFMPEG_PATH}/lib:/usr/local/Ascend/ascend-toolkit/latest/acllib/lib64:/usr/local/Ascend/driver/lib64/ -export GST_PLUGIN_SCANNER=${MX_SDK_HOME}/opensource/libexec/gstreamer-1.0/gst-plugin-scanner -export GST_PLUGIN_PATH=${MX_SDK_HOME}/opensource/lib/gstreamer-1.0:${MX_SDK_HOME}/lib/plugins +```bash +. /usr/local/Ascend/ascend-toolkit/set_env.sh # toolkit 默认安装路径，根据实际安装路径修改 +. ${SDK_INSTALL_PATH}/mxVision/set_env.sh # sdk 安装路径，根据实际安装路径修改 +export FFMPEG_PATH=/usr/local/ffmpeg # ffmpeg 默认安装路径，根据实际安装路径修改 ``` ## 3 准备模型 diff --git a/contrib/yunet/README.md b/contrib/yunet/README.md index f8f6b9196..5716e6442 100644 --- a/contrib/yunet/README.md +++ b/contrib/yunet/README.md @@ -44,7 +44,7 @@ yunet基于MindXSDK开发，在昇腾芯片上进行人脸检测，并实现可 | live555 | 1.09 | 实现视频转rstp进行推流 | [链接](https://gitee.com/ascend/mindxsdk-referenceapps/blob/master/docs/%E5%8F%82%E8%80%83%E8%B5%84%E6%96%99/Live555%E7%A6%BB%E7%BA%BF%E8%A7%86%E9%A2%91%E8%BD%ACRTSP%E8%AF%B4%E6%98%8E%E6%96%87%E6%A1%A3.md) | | ffmpeg | 4.2.1 | 实现mp4格式视频转为264格式视频 | [链接](https://gitee.com/ascend/mindxsdk-referenceapps/blob/master/docs/%E5%8F%82%E8%80%83%E8%B5%84%E6%96%99/pc%E7%AB%AFffmpeg%E5%AE%89%E8%A3%85%E6%95%99%E7%A8%8B.md#https://ffmpeg.org/download.html) | -注：ffmpeg需要用户到相关网址下载源码编译安装。 +注：live555和ffmpeg需要用户到相关网址下载源码编译安装。 ### 1.5 代码目录结构说明 diff --git a/mxVision/InferOfflineVideo/README.md b/mxVision/InferOfflineVideo/README.md index d871a7b95..c01eb228e 100644 --- a/mxVision/InferOfflineVideo/README.md +++ b/mxVision/InferOfflineVideo/README.md @@ -27,7 +27,7 @@ | live555 | 1.10 | 实现视频转rstp进行推流 | [链接](https://gitee.com/ascend/mindxsdk-referenceapps/blob/master/docs/%E5%8F%82%E8%80%83%E8%B5%84%E6%96%99/Live555%E7%A6%BB%E7%BA%BF%E8%A7%86%E9%A2%91%E8%BD%ACRTSP%E8%AF%B4%E6%98%8E%E6%96%87%E6%A1%A3.md) | | ffmpeg | 4.2.1 | 实现mp4格式视频转为264格式视频 | [链接](https://gitee.com/ascend/mindxsdk-referenceapps/blob/master/docs/%E5%8F%82%E8%80%83%E8%B5%84%E6%96%99/pc%E7%AB%AFffmpeg%E5%AE%89%E8%A3%85%E6%95%99%E7%A8%8B.md#https://ffmpeg.org/download.html) | -**注意：** +**注意：** live555和ffmpeg需要用户到相关网址下载源码编译安装。 第三方库默认全部安装到/usr/local/下面，全部安装完成后，请设置环境变量 ```bash -- Gitee