add python demos
This commit is contained in:
dian.yuan
parent
3bdf2003ec
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97 changed files with 3250 additions and 290 deletions
13
README.md
13
README.md
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@ -25,6 +25,7 @@
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| Object Detection | [yolov11](https://github.com/ultralytics/ultralytics) | INT8 | A311D2/S905X5 |
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| Object Detection | [yoloworld](https://github.com/AILab-CVC/YOLO-World) | INT8 | A311D2/S905X5 |
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| Object Detection | [yoloe](https://github.com/ultralytics/ultralytics) | INT8 | A311D2/S905X5 |
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| Object Detection | [yolox](https://github.com/Megvii-BaseDetection/YOLOX/) | INT8 | A311D2/S905X5 |
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| Face Key Points | [retinaface](https://github.com/biubug6/Pytorch_Retinaface) | INT8 | A311D2/S905X5 |
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| Text Detection | ppocr-det | INT8 | A311D2/S905X5 |
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| Pose Estimation | blazepose_detect | INT8 | A311D2/S905X5 |
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@ -42,20 +43,26 @@
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| ------------------ | ------------ | ---------------- | ----- | ------ | ------ |
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| mobilenet | mobilenet_v2 | [1, 3, 224, 224] | INT8 |1047.54 | 798.94 |
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| resnet | resnet50-v2 | [1, 3, 224, 224] | INT8 | 106.78 | 128.91 |
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| yolov8 | yolov8l | [1, 3, 640, 640] | INT8 | 11.55 | 11.12 |
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| yolov8 | yolov8n | [1, 3, 640, 640] | INT8 | 101.72 | 95.14 |
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| | yolov8s | [1, 3, 640, 640] | INT8 | 42.33 | 42.77 |
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| | yolov8m | [1, 3, 640, 640] | INT8 | 19.67 | 19.82 |
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| | yolov8l | [1, 3, 640, 640] | INT8 | 10.53 | 10.12 |
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| yolov11 | yolov11n | [1, 3, 640, 640] | INT8 | 41.14 | 41.48 |
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| yoloworld | yoloworld | [1, 3, 480, 640] | INT8 | 19.38 | 19.04 |
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| yoloe | yoloe | [1, 3, 288, 512] | INT8 | 53.9 | 37.8 |
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| yolox | yolox_tiny | [1, 3, 640, 640] | INT8 | 42.9 | 35.89 |
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| | yolox_s | [1, 3, 640, 640] | INT8 | 35.12 | 33.31 |
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| | yolox_m | [1, 3, 640, 640] | INT8 | 18.55 | 17.98 |
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| retinaface | retinaface | [1, 3, 320, 320] | INT8 | 341.99 | 305.89 |
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| ppocr-det | paddleocrv4-det | [1, 3, 640, 640] | INT8 | 37.66 | 38.85 |
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| blazepose_detect | blazepose_detection | [1, 3, 224, 224] | INT8 | 476.29 | 461.74 |
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| blazepose_landmark | blazepose_landmark_full | [1, 3, 256, 256] | INT16 | 84.59 | 70.31 |
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| Whisper | encoder_tiny_en | [1, 80, 3000] | Hybrid | 0.71 | 0.58 |
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| Whisper | decoder_tiny_en | [1, 1500, 384]&[1, 48] | Hybrid | 10.35 | 9.22 |
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| | decoder_tiny_en | [1, 1500, 384]&[1, 48] | Hybrid | 10.35 | 9.22 |
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| Clip | clip-vit-base-patch32 | [1, 3, 224, 224] | Hybrid | 7.48 | 6.82 |
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- The performance data represents the runtime of the model on the NPU, as tested using the native case. Unless otherwise specified, it does not include the time spent on pre- and post-processing.
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- \ means currently supported.
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- means currently supported.
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# Examples Compile
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@ -1,7 +1,5 @@
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[简体中文](README_CN.md) | [English](README.md)
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# amlnn-model-playground
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# 简介
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@ -14,7 +12,7 @@
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# 依赖项
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- **amlnn-model-playground** 中的模型转换功能目前依赖于 Amlogic 提供的模型转换工具 **`adla-toolkit-binary-x.x.x.x`**，当前版本请联系您的销售代表获取该工具。
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- **amlnn-model-playground** 中的模型转换功能目前依赖于 Amlogic 提供的模型转换工具 **`adla-toolkit-binary-x.x.x.x`**，当前默认使用本工程的客户均已获取该工具。下一版本我们将通过 GitHub Release 公开发布该模型转换工具，计划于 2026 年第一季度（2026Q1）正式发布。
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- **Android编译**依赖NDK工具链，当前建议使用**r25c**版本，下载链接:https://github.com/android/ndk/wiki/Unsupported-Downloads
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- **Linux编译**依赖工具链:**gcc-arm-10.3-2021.07-x86_64-arm-none-linux-gnueabihf** ,下载链接：https://developer.arm.com/tools-and-software/open-source-software/developer-tools/gnu-toolchain/gnu-a/downloads/
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79
docs/Platform_compile_guide.md
Executable file
79
docs/Platform_compile_guide.md
Executable file
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@ -0,0 +1,79 @@
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# 1、Android Platform
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**Android compilation** depends on the NDK toolchain. Currently, version r25c is recommended. Download link: https://github.com/android/ndk/wiki/Unsupported-Downloads
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## NDK toolchain configuration
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- Verify whether the ndk-build compilation environment exists:
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Execute: ndk-build -v. If there is a version information prompt, it is successful. If not, refer to the following steps to configure the ndk-build compilation environment
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- Download android-ndk-r25c
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https://dl.google.com/android/repository/android-ndk-r25c-linux.zip
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- Unzip and get the path /xxxx/android-ndk-r25c
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- Set environment variables
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- Edit bash.bashrc file
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sudo vi ~/.bashrc
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Add at the end of the file: export PATH=/xxxx/android-ndk-r25c:$PATH
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- Update environment variables
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source ~/.bashrc
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- Check whether the environment has been successfully added
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ndk-build --version shows version information
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# 2、Linux Platform
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**Linux compilation** toolchain dependency: **gcc-arm-10.3-2021.07-x86_64-arm-none-linux-gnueabihf**, download link: https://developer.arm.com/tools-and-software/open-source-software/developer-tools/gnu-toolchain/gnu-a/downloads/
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## Linux toolchain configuration：
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1. Configure the cmake compilation environment:
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1. Verify whether there is a cmake environment:
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cmake --version shows the cmake version is successful. If cmake does not exist, perform the following steps to configure the cmake environment
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2. Install cmake
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pip3 install cmake==3.16.3
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3. Verify that the installation is successful
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cmake --version
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Displaying the cmake version is successful
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2. Configure the compilation toolchain
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1. Download the toolchain
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32-bit：https://developer.arm.com/tools-and-software/open-source-software/developer-tools/gnu-toolchain/gnu-a/downloads/ link，
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gcc-arm-10.3-2021.07-x86_64-arm-none-linux-gnueabihf.tar.xz
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64-bit: There is currently no 64-bit version, and it will not be added for the time being. If some customers use the corresponding version and did not add the toolchain path in time, please give feedback.
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2.arm-none-linux-gnueabihf install
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a.Unzip it and place it in the folder you need
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tar -xvJf ***.tar.xz
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b.Edit the bash.bashrc file
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sudo vi ~/.bashrc
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c.Add environment variables
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export PATH=path_to/gcc-arm-10.3-2021.07-x86_64-arm-none-linux-gnueabihf/bin:$PATH
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d.update environment variables
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source ~/.bashrc
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e.Check if the environment is successfully joined
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Execue：arm-none-linux-gnueabihf-gcc -v，View gcc 32bit version
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70
docs/Platform_compile_guide_CN.md
Executable file
70
docs/Platform_compile_guide_CN.md
Executable file
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# 1、Android 平台
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**Android编译**依赖NDK工具链，当前建议使用**r25c**版本，下载链接:https://github.com/android/ndk/wiki/Unsupported-Downloads
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## NDK工具链配置
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- 验证是否存在ndk-build编译环境：
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执行：ndk-build -v，有版本信息提示即为成功。如果没有，参照后面几步配置ndk-build编译环境
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- 下载android-ndk-r25c
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https://dl.google.com/android/repository/android-ndk-r25c-linux.zip
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- 解压，获得路径/xxxx/android-ndk-r25c
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- 配置环境变量
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- 编辑bash.bashrc文件
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sudo vi ~/.bashrc
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在文件末尾添加： export PATH=/xxxx/android-ndk-r25c:$PATH
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- 更新环境变量
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source ~/.bashrc
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- 检查环境变量是否加入成功
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ndk-build --version 有版本信息显示
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# 2、Linux平台
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**Linux编译**依赖工具链:**gcc-arm-10.3-2021.07-x86_64-arm-none-linux-gnueabihf** ,下载链接：https://developer.arm.com/tools-and-software/open-source-software/developer-tools/gnu-toolchain/gnu-a/downloads/
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## Linux工具链配置：
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一、配置cmake编译环境：
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1.验证是否存在cmake 环境：
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cmake --version 显示cmake 版本即为成功。如果不存在cmake，执行后面几步配置cmake环境
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2.安装cmake
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pip3 install cmake==3.16.3
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3.验证是否安装成功
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cmake --version
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显示cmake 版本即为成功
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二、配置编译工具链
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1.下载工具链
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32位：https://developer.arm.com/tools-and-software/open-source-software/developer-tools/gnu-toolchain/gnu-a/downloads/ 链接下
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gcc-arm-10.3-2021.07-x86_64-arm-none-linux-gnueabihf.tar.xz
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64位：当前暂时没有64bit版本，暂时不添加。如果有客户使用对应版本且未及时添加toolchain路径，请反馈。
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2.arm-none-linux-gnueabihf安装
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a.解压，并放置在自己需要的文件夹内
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tar -xvJf ***.tar.xz
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b.编辑bash.bashrc文件
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sudo vi ~/.bashrc
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c.添加环境变量
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export PATH=path_to/gcc-arm-10.3-2021.07-x86_64-arm-none-linux-gnueabihf/bin:$PATH
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d.更新环境变量
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source ~/.bashrc
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e.检查环境是否加入成功
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执行：arm-none-linux-gnueabihf-gcc -v，查看gcc 32bit版
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0
examples/resnet/README.md
Normal file → Executable file
0
examples/resnet/README.md
Normal file → Executable file
0
examples/resnet/cpp/.gitkeep
Normal file → Executable file
0
examples/resnet/cpp/.gitkeep
Normal file → Executable file
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@ -1,63 +1,19 @@
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#TODO
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#!/bin/bash
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set -e
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usage() {
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echo "Usage: $0 [-a <target_arch>]"
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echo " -a <target_arch> : Target architecture (default: aarch64)"
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echo " -h : Show this help message"
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exit 1
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}
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# Default values
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TARGET_ARCH=aarch64
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# Parse arguments
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while getopts 'a:h' opt; do
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case "$opt" in
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a)
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TARGET_ARCH=$OPTARG
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;;
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h)
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usage
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;;
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*)
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usage
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;;
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esac
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done
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# Default to aarch64-linux-gnu if GCC_COMPILER is not set
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GCC_COMPILER=${GCC_COMPILER:-aarch64-linux-gnu}
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# Set compilers
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export CC=${GCC_COMPILER}-gcc
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export CXX=${GCC_COMPILER}-g++
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# Validate compiler
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if ! command -v ${CC} &> /dev/null; then
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echo "Error: Compiler ${CC} not found."
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echo "Please set GCC_COMPILER environment variable to your cross-compiler path prefix."
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echo "Example: export GCC_COMPILER=/path/to/toolchain/bin/aarch64-linux-gnu"
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exit 1
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fi
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ROOT_PWD=$(cd "$(dirname $0)" && pwd)
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BUILD_DIR=${ROOT_PWD}/build/linux
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echo "Building for Linux..."
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echo "COMPILER: ${CC}"
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echo "TARGET_ARCH: ${TARGET_ARCH}"
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echo "BUILD_DIR: ${BUILD_DIR}"
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mkdir -p ${BUILD_DIR}
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cd ${BUILD_DIR}
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cmake ../../src \
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-DCMAKE_SYSTEM_NAME=Linux \
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-DCMAKE_SYSTEM_PROCESSOR=${TARGET_ARCH} \
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-DCMAKE_BUILD_TYPE=Release
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make -j4
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echo "Build complete. Executable in ${BUILD_DIR}/resnet_demo"
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#
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# Copyright (C) 2024–2025 Amlogic, Inc. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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### TO DO
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/*
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* Copyright (C) 2024–2025 Amlogic, Inc. All rights reserved.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
|
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
|
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* limitations under the License.
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*/
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#include <iostream>
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#include <vector>
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#include <string>
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/*
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* Copyright (C) 2024–2025 Amlogic, Inc. All rights reserved.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
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* limitations under the License.
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*/
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#include "postprocess.h"
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#include <iostream>
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#include <fstream>
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@ -1,3 +1,19 @@
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/*
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* Copyright (C) 2024–2025 Amlogic, Inc. All rights reserved.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
|
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* You may obtain a copy of the License at
|
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
|
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* distributed under the License is distributed on an "AS IS" BASIS,
|
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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||||
* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef RESNET_POSTPROCESS_H
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#define RESNET_POSTPROCESS_H
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0
examples/resnet/model/.gitkeep
Normal file → Executable file
0
examples/resnet/model/.gitkeep
Normal file → Executable file
0
examples/resnet/py/.gitkeep
Normal file → Executable file
0
examples/resnet/py/.gitkeep
Normal file → Executable file
115
examples/resnet/py/resnet.py
Executable file
115
examples/resnet/py/resnet.py
Executable file
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# -*- coding: utf-8 -*-
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"""
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Copyright (C) 2024–2025 Amlogic, Inc. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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||||
you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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||||
distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import os
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import argparse
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import glob
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import cv2
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import numpy as np
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from pathlib import Path
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from amlnnlite.api import AMLNNLite
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MEAN = np.array([123.675, 116.28, 103.53], dtype=np.float32)
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STD = np.array([58.395, 58.395, 58.395], dtype=np.float32)
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def preprocess(img_path):
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img = cv2.imread(img_path)
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if img is None:
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return None
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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img = cv2.resize(img, (224, 224), interpolation=cv2.INTER_LINEAR)
|
||||
img = img.astype(np.float32)
|
||||
|
||||
img = (img - MEAN) / STD
|
||||
img = np.expand_dims(img, axis=0)
|
||||
return img
|
||||
|
||||
def postprocess_topk(logits, labels, k=5):
|
||||
logits = logits.squeeze()
|
||||
idx = np.argsort(logits)[::-1][:k]
|
||||
|
||||
print(f"\n Top-{k} Results:")
|
||||
for i, c in enumerate(idx):
|
||||
name = labels[c] if c < len(labels) else f"Unknown({c})"
|
||||
score = logits[c]
|
||||
print(f" {i+1}. {name:20s} score={score:.6f}")
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Classification AMLNNLite Demo")
|
||||
|
||||
parser.add_argument('--board-work-path', default='/data/nn', help='Work path on board')
|
||||
parser.add_argument('--model-path', required=True, help='Path to .adla or .tflite model')
|
||||
parser.add_argument('--image-dir', required=True, help='Directory containing test images')
|
||||
parser.add_argument('--labels', required=True, help='Path to synset_words.txt or labels.txt')
|
||||
parser.add_argument('--run-cycles', type=int, default=1, help='Number of inference cycles')
|
||||
parser.add_argument('--loglevel', default='WARNING', choices=['DEBUG', 'INFO', 'WARNING', 'ERROR'])
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
amlnn = AMLNNLite()
|
||||
amlnn.config(
|
||||
board_work_path=args.board_work_path,
|
||||
model_path=args.model_path,
|
||||
run_cycles=args.run_cycles,
|
||||
loglevel=args.loglevel
|
||||
)
|
||||
amlnn.init()
|
||||
|
||||
if not os.path.exists(args.labels):
|
||||
print(f"Error: Label file not found: {args.labels}")
|
||||
amlnn.uninit(); return
|
||||
|
||||
with open(args.labels, "r") as f:
|
||||
labels = [line.strip() for line in f.readlines()]
|
||||
|
||||
image_files = []
|
||||
for ext in ['*.jpg', '*.jpeg', '*.png', '*.bmp']:
|
||||
image_files.extend(glob.glob(os.path.join(args.image_dir, ext)))
|
||||
image_files.extend(glob.glob(os.path.join(args.image_dir, ext.upper())))
|
||||
image_files.sort()
|
||||
|
||||
if not image_files:
|
||||
print(f"No image files found in: {args.image_dir}")
|
||||
amlnn.uninit(); return
|
||||
|
||||
total_images = len(image_files)
|
||||
|
||||
for idx, img_path in enumerate(image_files, start=1):
|
||||
print("=" * 60)
|
||||
print(f"Processing image {idx}/{total_images}: {os.path.basename(img_path)}")
|
||||
print("=" * 60)
|
||||
|
||||
inp = preprocess(img_path)
|
||||
if inp is None:
|
||||
print(f" Skip: Cannot read {img_path}")
|
||||
continue
|
||||
|
||||
for _ in range(args.run_cycles):
|
||||
outputs = amlnn.inference(
|
||||
inp,
|
||||
inputs_data_format='NHWC',
|
||||
outputs_data_format='NHWC'
|
||||
)
|
||||
|
||||
postprocess_topk(outputs[0], labels, k=5)
|
||||
|
||||
amlnn.visualize()
|
||||
amlnn.uninit()
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
0
examples/retinaface/README.md
Normal file → Executable file
0
examples/retinaface/README.md
Normal file → Executable file
0
examples/retinaface/cpp/.gitkeep
Normal file → Executable file
0
examples/retinaface/cpp/.gitkeep
Normal file → Executable file
|
|
@ -1,63 +1,19 @@
|
|||
#TODO
|
||||
#!/bin/bash
|
||||
set -e
|
||||
|
||||
usage() {
|
||||
echo "Usage: $0 [-a <target_arch>]"
|
||||
echo " -a <target_arch> : Target architecture (default: aarch64)"
|
||||
echo " -h : Show this help message"
|
||||
exit 1
|
||||
}
|
||||
|
||||
# Default values
|
||||
TARGET_ARCH=aarch64
|
||||
|
||||
# Parse arguments
|
||||
while getopts 'a:h' opt; do
|
||||
case "$opt" in
|
||||
a)
|
||||
TARGET_ARCH=$OPTARG
|
||||
;;
|
||||
h)
|
||||
usage
|
||||
;;
|
||||
*)
|
||||
usage
|
||||
;;
|
||||
esac
|
||||
done
|
||||
|
||||
# Default to aarch64-linux-gnu if GCC_COMPILER is not set
|
||||
GCC_COMPILER=${GCC_COMPILER:-aarch64-linux-gnu}
|
||||
|
||||
# Set compilers
|
||||
export CC=${GCC_COMPILER}-gcc
|
||||
export CXX=${GCC_COMPILER}-g++
|
||||
|
||||
# Validate compiler
|
||||
if ! command -v ${CC} &> /dev/null; then
|
||||
echo "Error: Compiler ${CC} not found."
|
||||
echo "Please set GCC_COMPILER environment variable to your cross-compiler path prefix."
|
||||
echo "Example: export GCC_COMPILER=/path/to/toolchain/bin/aarch64-linux-gnu"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
ROOT_PWD=$(cd "$(dirname $0)" && pwd)
|
||||
BUILD_DIR=${ROOT_PWD}/build/linux
|
||||
|
||||
echo "Building for Linux..."
|
||||
echo "COMPILER: ${CC}"
|
||||
echo "TARGET_ARCH: ${TARGET_ARCH}"
|
||||
echo "BUILD_DIR: ${BUILD_DIR}"
|
||||
|
||||
mkdir -p ${BUILD_DIR}
|
||||
cd ${BUILD_DIR}
|
||||
|
||||
cmake ../../src \
|
||||
-DCMAKE_SYSTEM_NAME=Linux \
|
||||
-DCMAKE_SYSTEM_PROCESSOR=${TARGET_ARCH} \
|
||||
-DCMAKE_BUILD_TYPE=Release
|
||||
|
||||
make -j4
|
||||
|
||||
echo "Build complete. Executable in ${BUILD_DIR}/retinaface_demo"
|
||||
#
|
||||
# Copyright (C) 2024–2025 Amlogic, Inc. 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.
|
||||
#
|
||||
### TO DO
|
||||
|
|
|
|||
|
|
@ -1,3 +1,19 @@
|
|||
/*
|
||||
* Copyright (C) 2024–2025 Amlogic, Inc. 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 <iostream>
|
||||
#include <vector>
|
||||
#include <filesystem>
|
||||
|
|
@ -19,43 +35,87 @@ static void hwc_to_chw(const cv::Mat& src, float* dst) {
|
|||
}
|
||||
|
||||
int main(int argc, char** argv) {
|
||||
if (argc < 3) { std::cout << "Usage: " << argv[0] << " <model.adla> <image_dir>\n"; return 0; }
|
||||
if (argc < 3) {
|
||||
std::cout << "Usage: " << argv[0] << " <model.adla> <image_dir>\n";
|
||||
return 0;
|
||||
}
|
||||
|
||||
aml_config cfg{};
|
||||
cfg.typeSize = sizeof(cfg); cfg.modelType = ADLA_LOADABLE; cfg.nbgType = NN_ADLA_FILE; cfg.path = argv[1];
|
||||
cfg.typeSize = sizeof(cfg);
|
||||
cfg.modelType = ADLA_LOADABLE;
|
||||
cfg.nbgType = NN_ADLA_FILE;
|
||||
cfg.path = argv[1];
|
||||
void* ctx = aml_module_create(&cfg);
|
||||
if (!ctx) {
|
||||
std::cerr << "Failed to create aml_module\n";
|
||||
return -1;
|
||||
}
|
||||
|
||||
auto priors = generate_priors();
|
||||
size_t num_priors = priors.size();
|
||||
std::vector<float> chw_buffer(kInputW * kInputH * 3);
|
||||
|
||||
fs::create_directory("retinaface_result");
|
||||
const std::string out_dir = "retinaface_result";
|
||||
fs::create_directory(out_dir);
|
||||
|
||||
std::vector<fs::path> image_paths;
|
||||
for (auto& it : fs::directory_iterator(argv[2])) {
|
||||
cv::Mat img = cv::imread(it.path().string());
|
||||
std::string ext = it.path().extension().string();
|
||||
std::transform(ext.begin(), ext.end(), ext.begin(), ::tolower);
|
||||
if (ext == ".jpg" || ext == ".png" || ext == ".jpeg" || ext == ".bmp") {
|
||||
image_paths.push_back(it.path());
|
||||
}
|
||||
}
|
||||
std::sort(image_paths.begin(), image_paths.end());
|
||||
|
||||
int total = image_paths.size();
|
||||
if (total == 0) {
|
||||
std::cout << "No images found in " << argv[2] << "\n";
|
||||
aml_module_destroy(ctx);
|
||||
return 0;
|
||||
}
|
||||
|
||||
for (int i = 0; i < total; ++i) {
|
||||
const auto& path = image_paths[i];
|
||||
const std::string filename = path.filename().string();
|
||||
|
||||
std::cout << "============================================================\n";
|
||||
std::cout << "Processing image " << (i + 1) << "/" << total << ": " << filename << "\n";
|
||||
std::cout << "============================================================\n";
|
||||
|
||||
cv::Mat img = cv::imread(path.string());
|
||||
if (img.empty()) continue;
|
||||
|
||||
float scale = std::min((float)kInputW / img.cols, (float)kInputH / img.rows);
|
||||
int nw = img.cols * scale, nh = img.rows * scale;
|
||||
int px = (kInputW - nw) / 2, py = (kInputH - nh) / 2;
|
||||
cv::Mat res, canvas = cv::Mat::zeros(kInputH, kInputW, CV_32FC3);
|
||||
cv::resize(img, res, {nw, nh}); res.convertTo(res, CV_32FC3);
|
||||
cv::resize(img, res, {nw, nh});
|
||||
res.convertTo(res, CV_32FC3);
|
||||
res.copyTo(canvas(cv::Rect(px, py, nw, nh)));
|
||||
hwc_to_chw(canvas, chw_buffer.data());
|
||||
|
||||
nn_input in{}; in.typeSize = sizeof(in); in.input_type = BINARY_RAW_DATA;
|
||||
in.input = (unsigned char*)chw_buffer.data(); in.size = chw_buffer.size() * 4;
|
||||
in.info.valid = 1; in.info.input_format = AML_INPUT_MODEL_NCHW; in.info.input_data_type = AML_INPUT_FP32;
|
||||
nn_input in{};
|
||||
in.typeSize = sizeof(in);
|
||||
in.input_type = BINARY_RAW_DATA;
|
||||
in.input = (unsigned char*)chw_buffer.data();
|
||||
in.size = chw_buffer.size() * 4;
|
||||
in.info.valid = 1;
|
||||
in.info.input_format = AML_INPUT_MODEL_NCHW;
|
||||
in.info.input_data_type = AML_INPUT_FP32;
|
||||
aml_module_input_set(ctx, &in);
|
||||
|
||||
aml_output_config_t outcfg{}; outcfg.typeSize = sizeof(outcfg); outcfg.format = AML_OUTDATA_FLOAT32;
|
||||
aml_output_config_t outcfg{};
|
||||
outcfg.typeSize = sizeof(outcfg);
|
||||
outcfg.format = AML_OUTDATA_FLOAT32;
|
||||
nn_output* out = (nn_output*)aml_module_output_get(ctx, outcfg);
|
||||
if (!out) continue;
|
||||
|
||||
float *loc = nullptr, *conf = nullptr, *landm = nullptr;
|
||||
for (int i = 0; i < out->num; i++) {
|
||||
if (out->out[i].size == num_priors * 4 * 4) loc = (float*)out->out[i].buf;
|
||||
else if (out->out[i].size == num_priors * 2 * 4) conf = (float*)out->out[i].buf;
|
||||
else if (out->out[i].size == num_priors * 10 * 4) landm = (float*)out->out[i].buf;
|
||||
for (int j = 0; j < out->num; j++) {
|
||||
if (out->out[j].size == num_priors * 4 * 4) loc = (float*)out->out[j].buf;
|
||||
else if (out->out[j].size == num_priors * 2 * 4) conf = (float*)out->out[j].buf;
|
||||
else if (out->out[j].size == num_priors * 10 * 4) landm = (float*)out->out[j].buf;
|
||||
}
|
||||
if (!loc || !conf || !landm) continue;
|
||||
|
||||
|
|
@ -64,11 +124,11 @@ int main(int argc, char** argv) {
|
|||
std::vector<std::array<float, 10>> lms;
|
||||
std::vector<float> scores_vec;
|
||||
|
||||
for (size_t i = 0; i < num_priors; i++) {
|
||||
float sc = is_planar ? conf[num_priors + i] : conf[i * 2 + 1];
|
||||
for (size_t j = 0; j < num_priors; j++) {
|
||||
float sc = is_planar ? conf[num_priors + j] : conf[j * 2 + 1];
|
||||
if (sc > 0.5f) {
|
||||
boxes.push_back(decode_box(loc, i, num_priors, is_planar, priors[i]));
|
||||
lms.push_back(decode_landm(landm, i, num_priors, is_planar, priors[i]));
|
||||
boxes.push_back(decode_box(loc, j, num_priors, is_planar, priors[j]));
|
||||
lms.push_back(decode_landm(landm, j, num_priors, is_planar, priors[j]));
|
||||
scores_vec.push_back(sc);
|
||||
}
|
||||
}
|
||||
|
|
@ -83,7 +143,8 @@ int main(int argc, char** argv) {
|
|||
|
||||
char score_text[16];
|
||||
std::snprintf(score_text, sizeof(score_text), "%.2f", scores_vec[k]);
|
||||
cv::putText(img, score_text, {x1, std::max(y1 - 5, 5)}, cv::FONT_HERSHEY_SIMPLEX, 0.5, {0, 255, 0}, 1, cv::LINE_AA);
|
||||
cv::putText(img, score_text, {x1, std::max(y1 - 5, 5)},
|
||||
cv::FONT_HERSHEY_SIMPLEX, 0.5, {0, 255, 0}, 1, cv::LINE_AA);
|
||||
|
||||
auto& lm = lms[k];
|
||||
for (int j = 0; j < 5; j++) {
|
||||
|
|
@ -92,8 +153,14 @@ int main(int argc, char** argv) {
|
|||
cv::circle(img, {lx, ly}, 2, {0, 0, 255}, -1);
|
||||
}
|
||||
}
|
||||
cv::imwrite("retinaface_result/" + it.path().filename().string(), img);
|
||||
std::cout << "Detected: " << it.path().filename() << " (" << keep.size() << " faces)\n";
|
||||
|
||||
std::string save_path = out_dir + "/" + filename;
|
||||
cv::imwrite(save_path, img);
|
||||
|
||||
std::cout << " Detected " << keep.size() << " faces\n";
|
||||
std::cout << " Result saved to: " << save_path << "\n\n";
|
||||
}
|
||||
aml_module_destroy(ctx); return 0;
|
||||
|
||||
aml_module_destroy(ctx);
|
||||
return 0;
|
||||
}
|
||||
|
|
@ -1,3 +1,19 @@
|
|||
/*
|
||||
* Copyright (C) 2024–2025 Amlogic, Inc. 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 <cmath>
|
||||
#include <numeric>
|
||||
|
|
|
|||
|
|
@ -1,3 +1,19 @@
|
|||
/*
|
||||
* Copyright (C) 2024–2025 Amlogic, Inc. 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 RETINAFACE_POSTPROCESS_H
|
||||
#define RETINAFACE_POSTPROCESS_H
|
||||
|
||||
|
|
|
|||
0
examples/retinaface/model/.gitkeep
Normal file → Executable file
0
examples/retinaface/model/.gitkeep
Normal file → Executable file
0
examples/retinaface/py/.gitkeep
Normal file → Executable file
0
examples/retinaface/py/.gitkeep
Normal file → Executable file
173
examples/retinaface/py/RetinaFace.py
Executable file
173
examples/retinaface/py/RetinaFace.py
Executable file
|
|
@ -0,0 +1,173 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
|
||||
"""
|
||||
Copyright (C) 2024–2025 Amlogic, Inc. 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 os
|
||||
import cv2
|
||||
import glob
|
||||
import argparse
|
||||
import time
|
||||
import numpy as np
|
||||
from pathlib import Path
|
||||
from amlnnlite.api import AMLNNLite
|
||||
|
||||
class PriorBox:
|
||||
def __init__(self, image_size=(320, 320)):
|
||||
self.image_size = image_size
|
||||
self.steps = [8, 16, 32]
|
||||
self.min_sizes = [[16, 32], [64, 128], [256, 512]]
|
||||
|
||||
def forward(self):
|
||||
priors = []
|
||||
h, w = self.image_size
|
||||
for idx, step in enumerate(self.steps):
|
||||
fm_h, fm_w = int(np.ceil(h / step)), int(np.ceil(w / step))
|
||||
for i in range(fm_h):
|
||||
for j in range(fm_w):
|
||||
for min_size in self.min_sizes[idx]:
|
||||
cx, cy = (j + 0.5) * step / w, (i + 0.5) * step / h
|
||||
s_kx, s_ky = min_size / w, min_size / h
|
||||
priors.append([cx, cy, s_kx, s_ky])
|
||||
return np.array(priors, dtype=np.float32)
|
||||
|
||||
def decode_boxes(loc, priors, variances=(0.1, 0.2)):
|
||||
boxes = np.concatenate((
|
||||
priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:],
|
||||
priors[:, 2:] * np.exp(loc[:, 2:] * variances[1])
|
||||
), axis=1)
|
||||
boxes[:, :2] -= boxes[:, 2:] / 2
|
||||
boxes[:, 2:] += boxes[:, :2]
|
||||
return boxes
|
||||
|
||||
def decode_landmarks(pre, priors, variances=(0.1, 0.2)):
|
||||
landms = np.concatenate([
|
||||
priors[:, :2] + pre[:, i:i+2] * variances[0] * priors[:, 2:] for i in range(0, 10, 2)
|
||||
], axis=1)
|
||||
return landms
|
||||
|
||||
def nms(dets, thresh=0.4):
|
||||
x1, y1, x2, y2, scores = dets.T
|
||||
areas = (x2 - x1) * (y2 - y1)
|
||||
order = scores.argsort()[::-1]
|
||||
keep = []
|
||||
while order.size > 0:
|
||||
i = order[0]; keep.append(i)
|
||||
xx1, yy1 = np.maximum(x1[i], x1[order[1:]]), np.maximum(y1[i], y1[order[1:]])
|
||||
xx2, yy2 = np.maximum(y1[i], y1[order[1:]]), np.maximum(y1[i], y1[order[1:]]) # fix
|
||||
xx2, yy2 = np.minimum(x2[i], x2[order[1:]]), np.minimum(y2[i], y2[order[1:]])
|
||||
w, h = np.maximum(0.0, xx2 - xx1), np.maximum(0.0, yy2 - yy1)
|
||||
ovr = (w * h) / (areas[i] + areas[order[1:]] - (w * h))
|
||||
order = order[np.where(ovr <= thresh)[0] + 1]
|
||||
return keep
|
||||
|
||||
def postprocess_retinaface(outputs, priors, conf_thresh=0.5, nms_thresh=0.4):
|
||||
loc = conf = landms = None
|
||||
for out in outputs:
|
||||
out = np.squeeze(np.asarray(out))
|
||||
if out.shape[-1] == 4: loc = out
|
||||
elif out.shape[-1] == 2: conf = out
|
||||
elif out.shape[-1] == 10: landms = out
|
||||
|
||||
if loc is None or conf is None or landms is None: return [], [], []
|
||||
scores = conf[:, 1]
|
||||
mask = scores > conf_thresh
|
||||
if not np.any(mask): return [], [], []
|
||||
|
||||
boxes = decode_boxes(loc[mask], priors[mask])
|
||||
landms = decode_landmarks(landms[mask], priors[mask])
|
||||
scores = scores[mask]
|
||||
keep = nms(np.hstack((boxes, scores[:, None])), nms_thresh)
|
||||
return boxes[keep], landms[keep], scores[keep]
|
||||
|
||||
def preprocess(img_path, input_size=(320, 320)):
|
||||
img = cv2.imread(img_path)
|
||||
if img is None: return None, None, 0, 0, 0
|
||||
h0, w0 = img.shape[:2]
|
||||
scale = min(input_size[0] / w0, input_size[1] / h0)
|
||||
nw, nh = int(w0 * scale), int(h0 * scale)
|
||||
resized = cv2.resize(img, (nw, nh))
|
||||
canvas = np.full((input_size[1], input_size[0], 3), 128, dtype=np.uint8)
|
||||
pad_x, pad_y = (input_size[0] - nw) // 2, (input_size[1] - nh) // 2
|
||||
canvas[pad_y:pad_y + nh, pad_x:pad_x + nw] = resized
|
||||
return np.expand_dims(canvas.astype(np.float32), axis=0), img, scale, pad_x, pad_y
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="RetinaFace AMLNNLite Demo")
|
||||
parser.add_argument('--board-work-path', type=str, default='/data/nn')
|
||||
parser.add_argument('--model-path', required=True, help='Path to .adla model')
|
||||
parser.add_argument('--image-dir', required=True, help='Directory of test images')
|
||||
parser.add_argument('--run-cycles', type=int, default=1, help='Inference cycles')
|
||||
parser.add_argument('--loglevel', type=str, default='WARNING', choices=['DEBUG', 'INFO', 'WARNING', 'ERROR'])
|
||||
args = parser.parse_args()
|
||||
|
||||
amlnn = AMLNNLite()
|
||||
amlnn.config(board_work_path=args.board_work_path,
|
||||
model_path=args.model_path,
|
||||
run_cycles=args.run_cycles,
|
||||
loglevel=args.loglevel)
|
||||
amlnn.init()
|
||||
|
||||
priors = PriorBox((320, 320)).forward()
|
||||
image_files = sorted(glob.glob(os.path.join(args.image_dir, "*.[jp][pn][g]")))
|
||||
|
||||
if not image_files:
|
||||
print(f"No images found in {args.image_dir}")
|
||||
amlnn.uninit(); return
|
||||
|
||||
res_dir = "retinaface_result"
|
||||
os.makedirs(res_dir, exist_ok=True)
|
||||
|
||||
for idx, img_path in enumerate(image_files, start=1):
|
||||
print("=" * 60)
|
||||
print(f"Processing image {idx}/{len(image_files)}: {Path(img_path).name}")
|
||||
print("=" * 60)
|
||||
|
||||
inp, orig, scale, pad_x, pad_y = preprocess(img_path)
|
||||
if inp is None: continue
|
||||
|
||||
outputs = amlnn.inference(inp, inputs_data_format='NHWC')
|
||||
|
||||
boxes, landms, scores = postprocess_retinaface(outputs, priors)
|
||||
|
||||
if len(boxes) > 0:
|
||||
print(f" Detected {len(boxes)} objects:")
|
||||
for i, sc in enumerate(scores, 1):
|
||||
print(f" {i}. face ({sc:.2f})")
|
||||
else:
|
||||
print(" No objects detected")
|
||||
|
||||
for box, lm in zip(boxes, landms):
|
||||
x1 = int((box[0] * 320 - pad_x) / scale)
|
||||
y1 = int((box[1] * 320 - pad_y) / scale)
|
||||
x2 = int((box[2] * 320 - pad_x) / scale)
|
||||
y2 = int((box[3] * 320 - pad_y) / scale)
|
||||
cv2.rectangle(orig, (x1, y1), (x2, y2), (0, 255, 0), 2)
|
||||
for lx, ly in lm.reshape(5, 2):
|
||||
cv2.circle(orig, (int((lx*320-pad_x)/scale), int((ly*320-pad_y)/scale)), 2, (0, 0, 255), -1)
|
||||
|
||||
save_path = os.path.join(res_dir, Path(img_path).name)
|
||||
cv2.imwrite(save_path, orig)
|
||||
print(f" Result saved to: {save_path}")
|
||||
|
||||
if args.loglevel == 'INFO':
|
||||
print("\nI Performance analysis visualization starting...")
|
||||
|
||||
amlnn.visualize()
|
||||
amlnn.uninit()
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
0
examples/yolov11/README.md
Normal file → Executable file
0
examples/yolov11/README.md
Normal file → Executable file
0
examples/yolov11/cpp/.gitkeep
Normal file → Executable file
0
examples/yolov11/cpp/.gitkeep
Normal file → Executable file
|
|
@ -1,65 +1,19 @@
|
|||
#TODO
|
||||
#!/bin/bash
|
||||
set -e
|
||||
|
||||
usage() {
|
||||
echo "Usage: $0 [-a <target_arch>]"
|
||||
echo " -a <target_arch> : Target architecture (default: aarch64)"
|
||||
echo " -h : Show this help message"
|
||||
exit 1
|
||||
}
|
||||
|
||||
# Default values
|
||||
TARGET_ARCH=aarch64
|
||||
|
||||
# Parse arguments
|
||||
while getopts 'a:h' opt; do
|
||||
case "$opt" in
|
||||
a)
|
||||
TARGET_ARCH=$OPTARG
|
||||
;;
|
||||
h)
|
||||
usage
|
||||
;;
|
||||
*)
|
||||
usage
|
||||
;;
|
||||
esac
|
||||
done
|
||||
|
||||
# Default to aarch64-linux-gnu if GCC_COMPILER is not set
|
||||
GCC_COMPILER=${GCC_COMPILER:-aarch64-linux-gnu}
|
||||
|
||||
# Set compilers
|
||||
export CC=${GCC_COMPILER}-gcc
|
||||
export CXX=${GCC_COMPILER}-g++
|
||||
|
||||
# Validate compiler
|
||||
if ! command -v ${CC} &> /dev/null; then
|
||||
echo "Error: Compiler ${CC} not found."
|
||||
echo "Please set GCC_COMPILER environment variable to your cross-compiler path prefix."
|
||||
echo "Example: export GCC_COMPILER=/path/to/toolchain/bin/aarch64-linux-gnu"
|
||||
# Proceeding anyway as user might have custom env setup
|
||||
else
|
||||
echo "Using compiler: ${CC}"
|
||||
fi
|
||||
|
||||
ROOT_PWD=$(cd "$(dirname $0)" && pwd)
|
||||
BUILD_DIR=${ROOT_PWD}/build/linux
|
||||
|
||||
echo "Building for Linux..."
|
||||
echo "COMPILER: ${CC}"
|
||||
echo "TARGET_ARCH: ${TARGET_ARCH}"
|
||||
echo "BUILD_DIR: ${BUILD_DIR}"
|
||||
|
||||
mkdir -p ${BUILD_DIR}
|
||||
cd ${BUILD_DIR}
|
||||
|
||||
cmake ../../src \
|
||||
-DCMAKE_SYSTEM_NAME=Linux \
|
||||
-DCMAKE_SYSTEM_PROCESSOR=${TARGET_ARCH} \
|
||||
-DCMAKE_BUILD_TYPE=Release \
|
||||
-DOpenCV_DIR=${ROOT_PWD}/../../../dependency/opencv/opencv-linux-aarch64/share/OpenCV
|
||||
make -j4
|
||||
|
||||
echo "Build complete. Executable in ${BUILD_DIR}/yolo11_demo"
|
||||
#
|
||||
# Copyright (C) 2024–2025 Amlogic, Inc. 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.
|
||||
#
|
||||
### TO DO
|
||||
|
|
@ -1,3 +1,19 @@
|
|||
/*
|
||||
* Copyright (C) 2024–2025 Amlogic, Inc. 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 <iostream>
|
||||
#include <vector>
|
||||
#include <filesystem>
|
||||
|
|
|
|||
|
|
@ -1,3 +1,19 @@
|
|||
/*
|
||||
* Copyright (C) 2024–2025 Amlogic, Inc. 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 <cmath>
|
||||
#include <numeric>
|
||||
|
|
|
|||
|
|
@ -1,3 +1,19 @@
|
|||
/*
|
||||
* Copyright (C) 2024–2025 Amlogic, Inc. 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 YOLO11_POSTPROCESS_H
|
||||
#define YOLO11_POSTPROCESS_H
|
||||
|
||||
|
|
|
|||
0
examples/yolov11/model/.gitkeep
Normal file → Executable file
0
examples/yolov11/model/.gitkeep
Normal file → Executable file
0
examples/yolov11/py/.gitkeep
Normal file → Executable file
0
examples/yolov11/py/.gitkeep
Normal file → Executable file
157
examples/yolov11/py/yolov11.py
Executable file
157
examples/yolov11/py/yolov11.py
Executable file
|
|
@ -0,0 +1,157 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Copyright (C) 2024–2025 Amlogic, Inc. 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 numpy as np
|
||||
import os
|
||||
import glob
|
||||
import argparse
|
||||
import cv2
|
||||
from pathlib import Path
|
||||
from amlnnlite.api import AMLNNLite
|
||||
|
||||
class_names = {0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', 11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat', 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear', 22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag', 27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard', 32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove', 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle', 40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl', 46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli', 51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake', 56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard', 67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink', 72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors', 77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'}
|
||||
|
||||
def letterbox(img, new_shape=(640, 640), color=(114, 114, 114)):
|
||||
shape = img.shape[:2]
|
||||
scale = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
|
||||
new_unpad = (int(round(shape[1] * scale)), int(round(shape[0] * scale)))
|
||||
dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]
|
||||
dw /= 2; dh /= 2
|
||||
img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
|
||||
top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
|
||||
left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
|
||||
img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)
|
||||
return img, scale, (left, top)
|
||||
|
||||
def preprocess(img_path, new_shape=(640,640)):
|
||||
original_img = cv2.imread(str(img_path))
|
||||
if original_img is None: return None, None, None, None
|
||||
processed_img, scale, pad = letterbox(original_img, new_shape)
|
||||
rgb_img = cv2.cvtColor(processed_img, cv2.COLOR_BGR2RGB)
|
||||
normalized_img = rgb_img.astype(np.float32) / 255.0
|
||||
input_tensor = np.expand_dims(np.transpose(normalized_img, (2,0,1)), 0) # NCHW
|
||||
return input_tensor, original_img, scale, pad
|
||||
|
||||
def postprocess(outputs, scale, pad, strides=[32,16,8], conf_threshold=0.25, iou_threshold=0.45):
|
||||
all_boxes, all_scores, all_class_ids = [], [], []
|
||||
for scale_idx, output in enumerate(outputs):
|
||||
stride = strides[scale_idx]
|
||||
feat = output[0].transpose(1, 2, 0) # H, W, C
|
||||
h, w, c = feat.shape
|
||||
dfl = feat[:, :, :64].reshape(h, w, 4, 16)
|
||||
cls_logits = feat[:, :, 64:]
|
||||
cls_scores = 1.0 / (1.0 + np.exp(-cls_logits)) # sigmoid
|
||||
|
||||
exp_x = np.exp(dfl - np.max(dfl, axis=-1, keepdims=True))
|
||||
p = exp_x / np.sum(exp_x, axis=-1, keepdims=True)
|
||||
bbox_deltas = np.sum(p * np.arange(16, dtype=np.float32), axis=-1)
|
||||
|
||||
grid_y, grid_x = np.meshgrid(np.arange(h), np.arange(w), indexing='ij')
|
||||
l, t, r, b = np.split(bbox_deltas, 4, axis=-1)
|
||||
x1, y1 = (grid_x + 0.5 - l[..., 0]) * stride, (grid_y + 0.5 - t[..., 0]) * stride
|
||||
x2, y2 = (grid_x + 0.5 + r[..., 0]) * stride, (grid_y + 0.5 + b[..., 0]) * stride
|
||||
|
||||
all_boxes.append(np.stack([x1, y1, x2, y2], axis=-1).reshape(-1, 4))
|
||||
all_scores.append(cls_scores.reshape(-1, cls_scores.shape[-1]))
|
||||
|
||||
final_boxes = np.concatenate(all_boxes, axis=0)
|
||||
final_scores_all = np.concatenate(all_scores, axis=0)
|
||||
final_class_ids = np.argmax(final_scores_all, axis=1)
|
||||
final_scores = np.max(final_scores_all, axis=1)
|
||||
|
||||
mask = final_scores > conf_threshold
|
||||
if not np.any(mask): return []
|
||||
|
||||
valid_boxes = final_boxes[mask]
|
||||
valid_boxes[:, [0, 2]] = (valid_boxes[:, [0, 2]] - pad[0]) / scale
|
||||
valid_boxes[:, [1, 3]] = (valid_boxes[:, [1, 3]] - pad[1]) / scale
|
||||
|
||||
indices = cv2.dnn.NMSBoxes(valid_boxes.tolist(), final_scores[mask].tolist(), conf_threshold, iou_threshold)
|
||||
detections = []
|
||||
if len(indices) > 0:
|
||||
for idx in indices.flatten():
|
||||
detections.append({
|
||||
'bbox': valid_boxes[idx].tolist(),
|
||||
'confidence': float(final_scores[mask][idx]),
|
||||
'class_name': class_names.get(int(final_class_ids[mask][idx]), 'unknown')
|
||||
})
|
||||
return detections
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="YOLOV11 AMLNNLite Demo")
|
||||
parser.add_argument('--board-work-path', default='/data/nn', help='Work path on board')
|
||||
parser.add_argument('-m', '--model-path', required=True, help='Path to .adla or .tflite model')
|
||||
parser.add_argument('--image-dir', required=True, help='Directory containing test images')
|
||||
parser.add_argument('--run-cycles', type=int, default=1, help='Inference cycles for profiling')
|
||||
parser.add_argument('--loglevel', default='WARNING', choices=['DEBUG', 'INFO', 'WARNING', 'ERROR'], help='Log level')
|
||||
args = parser.parse_args()
|
||||
|
||||
amlnn = AMLNNLite()
|
||||
amlnn.config(
|
||||
board_work_path=args.board_work_path,
|
||||
model_path=args.model_path,
|
||||
run_cycles=args.run_cycles,
|
||||
loglevel=args.loglevel
|
||||
)
|
||||
amlnn.init()
|
||||
|
||||
image_files = []
|
||||
for ext in ["*.jpg", "*.jpeg", "*.png"]:
|
||||
image_files.extend(glob.glob(os.path.join(args.image_dir, ext)))
|
||||
image_files.extend(glob.glob(os.path.join(args.image_dir, ext.upper())))
|
||||
image_files.sort()
|
||||
|
||||
if not image_files:
|
||||
print(f"No images found in {args.image_dir}")
|
||||
amlnn.uninit(); return
|
||||
|
||||
model_stem = Path(args.model_path).stem
|
||||
res_dir = f"{model_stem}_result"
|
||||
os.makedirs(res_dir, exist_ok=True)
|
||||
|
||||
for i, img_path in enumerate(image_files, 1):
|
||||
print("=" * 60)
|
||||
print(f"Processing image {i}/{len(image_files)}: {os.path.basename(img_path)}")
|
||||
print("=" * 60)
|
||||
|
||||
input_tensor, ori_img, scale, pad = preprocess(img_path)
|
||||
if input_tensor is None: continue
|
||||
|
||||
for _ in range(args.run_cycles):
|
||||
outputs = amlnn.inference(input_tensor, inputs_data_format='NCHW', outputs_data_format='NCHW')
|
||||
|
||||
detections = postprocess(outputs, scale, pad)
|
||||
|
||||
print(f" Detected {len(detections)} objects:")
|
||||
for idx, det in enumerate(detections, 1):
|
||||
print(f" {idx}. {det['class_name']} ({det['confidence']:.2f})")
|
||||
|
||||
for det in detections:
|
||||
x1, y1, x2, y2 = map(int, det['bbox'])
|
||||
cv2.rectangle(ori_img, (x1, y1), (x2, y2), (0, 255, 0), 2)
|
||||
cv2.putText(ori_img, f"{det['class_name']} {det['confidence']:.2f}", (x1, y1-10),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
|
||||
|
||||
save_path = os.path.join(res_dir, f"{Path(img_path).stem}_result.jpg")
|
||||
cv2.imwrite(save_path, ori_img)
|
||||
print(f" Result saved to: {save_path}")
|
||||
|
||||
amlnn.visualize()
|
||||
amlnn.uninit()
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -0,0 +1,133 @@
|
|||
# yolov8
|
||||
|
||||
## 1.Overview
|
||||
|
||||
YOLOv8 was released by Ultralytics on January 10, 2023, offering cutting-edge performance in terms of accuracy and speed. Building upon the advancements of previous YOLO versions, YOLOv8 introduced new features and optimizations that make it an ideal choice for various [object detection](https://www.ultralytics.com/blog/a-guide-to-deep-dive-into-object-detection-in-2025) tasks in a wide range of applications.
|
||||
|
||||
## 2.Model Download
|
||||
|
||||
- **Open Source model**
|
||||
|
||||
- **Open Source projects:** https://github.com/ultralytics/ultralytics/tree/v8.2.0
|
||||
|
||||
- **Export Model Step:**
|
||||
|
||||
- **Install ultralytics**
|
||||
|
||||
pip install torch==2.4.1
|
||||
|
||||
pip install torchvision==0.19.1
|
||||
|
||||
pip install ultralytics==8.2.0
|
||||
|
||||
- **Download weights**
|
||||
|
||||
wget https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m.pt
|
||||
|
||||
wget https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s.pt
|
||||
|
||||
wget https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt
|
||||
|
||||
- **Export Model**
|
||||
|
||||
```
|
||||
from ultralytics import YOLO
|
||||
|
||||
model = YOLO("yolov8m.pt")
|
||||
model.export(format="onnx", opset=12, simplify=True, dynamic=False, imgsz=640)
|
||||
```
|
||||
|
||||
|
||||
- **Exported Model**
|
||||
|
||||
link to amlogic server( **onnx model or quantized tflite**)
|
||||
|
||||
|
||||
|
||||
## 3. Model Conversion
|
||||
|
||||
```
|
||||
cd model
|
||||
Usage: ./adla_covnert.sh model_path adla_tookkit_path target_platform
|
||||
|
||||
example
|
||||
./adla_covnert.sh yolov8m.onnx /xxxx/adla-toolkit-binary-3.2.9.3 PRODUCT_PID0XA005
|
||||
./adla_covnert.sh yolov8s.onnx /xxxx/adla-toolkit-binary-3.2.9.3 PRODUCT_PID0XA005
|
||||
./adla_covnert.sh yolov8n.onnx /xxxx/adla-toolkit-binary-3.2.9.3 PRODUCT_PID0XA005
|
||||
```
|
||||
|
||||
| Parameter | Discription |
|
||||
| ----------------- | ------------------------------------------------------------ |
|
||||
| model_path | onnx model path |
|
||||
| adla_tookkit_path | path to adla_toolkit |
|
||||
| target_platform | Specify target platform. for A311D2 : PRODUCT_PID0XA003。for S905X5: PRODUCT_PID0XA005 |
|
||||
|
||||
|
||||
|
||||
## 4. Demo Run
|
||||
|
||||
### CPP
|
||||
|
||||
#### 1. Compile
|
||||
|
||||
**Prerequisites:**
|
||||
- Android NDK (r25e recommended)
|
||||
- `ANDROID_NDK_PATH` environment variable set
|
||||
|
||||
**Build:**
|
||||
```bash
|
||||
# Build for arm64-v8a
|
||||
cd examples/yolov8/cpp
|
||||
./build-android.sh -a arm64-v8a
|
||||
```
|
||||
|
||||
The executable will be generated at `build/android/yolov8_demo` (Note: executable name may vary, verify in build folder).
|
||||
|
||||
#### 2. Run
|
||||
|
||||
```bash
|
||||
# Push executable to device
|
||||
adb push build/android/yolov8_demo /data/local/tmp/
|
||||
adb push model/yolov8s_int8_A311D2.adla /data/local/tmp/
|
||||
adb push test_image.jpg /data/local/tmp/
|
||||
|
||||
# Run on device
|
||||
adb shell
|
||||
cd /data/local/tmp
|
||||
chmod +x yolov8_demo
|
||||
export LD_LIBRARY_PATH=/vendor/lib64 or (/vendor/lib)
|
||||
|
||||
# Usage: ./yolo_world_demo <model_path> <image_path>
|
||||
./yolov8_demo yolov8s_int8_A311D2.adla test_image.jpg"
|
||||
```
|
||||
|
||||
**Note:** Replace `yolov8s_int8_A311D2.adla` with your actual model file path.
|
||||
|
||||
### Python
|
||||
|
||||
**Prerequisites:**
|
||||
- Python 3.10
|
||||
- Required packages: `numpy`, `opencv-python`, `amlnnlite`
|
||||
|
||||
**Install dependencies:**
|
||||
```bash
|
||||
pip install numpy opencv-python amlnnlite-1.0.0-cp310-cp310-linux_aarch64.whl
|
||||
```
|
||||
|
||||
**Run on device:**
|
||||
```bash
|
||||
python yolov8.py --model-path ./yolov8s_int8_A311D2.adla
|
||||
```
|
||||
|
||||
The script will automatically process all image files (`.jpg`, `.jpeg`, `.png`, `.bmp`) in the current directory and save results to a `{model_name}_result` folder.
|
||||
|
||||
## 5.Results
|
||||
The program will print the detection count and inference time. The result image with bounding boxes will be saved to the specified output path (`result.jpg` by default).
|
||||
|
||||
|
||||
You can pull the result image back to view it:
|
||||
```bash
|
||||
adb pull result.jpg.
|
||||
```
|
||||
|
||||
|
||||
|
|
@ -62,8 +62,6 @@ int main(int argc, char** argv) {
|
|||
}
|
||||
|
||||
// 3. Preprocess
|
||||
auto start_time = std::chrono::high_resolution_clock::now();
|
||||
|
||||
auto [preprocessed, scale, pad] = preprocess(img, std::make_tuple(MODEL_INPUT_HEIGHT, MODEL_INPUT_WIDTH));
|
||||
|
||||
// Quantize to int8 (model expects quantized input)
|
||||
|
|
@ -88,6 +86,7 @@ int main(int argc, char** argv) {
|
|||
outconfig.typeSize = sizeof(aml_output_config_t);
|
||||
outconfig.format = AML_OUTDATA_FLOAT32;
|
||||
|
||||
auto start_time = std::chrono::high_resolution_clock::now();
|
||||
nn_output* outdata = (nn_output*)aml_module_output_get(context, outconfig);
|
||||
if (!outdata) {
|
||||
std::cerr << "Failed to run network." << std::endl;
|
||||
|
|
@ -103,8 +102,8 @@ int main(int argc, char** argv) {
|
|||
const int channels = 144; // 64 DFL + 80 classes
|
||||
|
||||
std::vector<Detection> detections = postprocess(
|
||||
std::make_tuple(outbuf0, std::make_tuple(MODEL_INPUT_HEIGHT / 16, MODEL_INPUT_WIDTH / 16, channels), 16),
|
||||
std::make_tuple(outbuf1, std::make_tuple(MODEL_INPUT_HEIGHT / 8, MODEL_INPUT_WIDTH / 8, channels), 8),
|
||||
std::make_tuple(outbuf0, std::make_tuple(MODEL_INPUT_HEIGHT / 8, MODEL_INPUT_WIDTH / 8, channels), 8),
|
||||
std::make_tuple(outbuf1, std::make_tuple(MODEL_INPUT_HEIGHT / 16, MODEL_INPUT_WIDTH / 16, channels), 16),
|
||||
std::make_tuple(outbuf2, std::make_tuple(MODEL_INPUT_HEIGHT / 32, MODEL_INPUT_WIDTH / 32, channels), 32),
|
||||
std::make_tuple(preprocessed, scale, pad),
|
||||
SCORE_THRESHOLD,
|
||||
|
|
|
|||
24
examples/yolov8/model/adla_convert.sh
Executable file
24
examples/yolov8/model/adla_convert.sh
Executable file
|
|
@ -0,0 +1,24 @@
|
|||
# 1. $1: set ADLA_TOOL_PATH
|
||||
# 2. $2: set target-plaftorm
|
||||
# for A311D2 target-platform is PRODUCT_PID0XA003
|
||||
# for S905X5 target-platform is PRODUCT_PID0XA005
|
||||
# Usage: ./adla_covnert.sh yolov8m.onnx /XXX/adla-toolkit-binary-3.2.9.3 PRODUCT_PID0XA005
|
||||
|
||||
model_path=$1
|
||||
ADLA_TOOL_PATH=$2
|
||||
target_platform=$3
|
||||
|
||||
echo "model_path:[$model_path]"
|
||||
echo "ADLA_TOOL_PATH:[$ADLA_TOOL_PATH]"
|
||||
echo "target-plaftorm:[$target_platform]"
|
||||
|
||||
adla_convert=${ADLA_TOOL_PATH}/bin/adla_convert
|
||||
|
||||
$adla_convert --model-type onnx \
|
||||
--model $model_path \
|
||||
--inputs images --input-shapes "1,3,640,640" \
|
||||
--quantize-dtype int8 \
|
||||
--source-file dataset_coco.txt \
|
||||
--channel-mean-value "0,0,0,255" \
|
||||
--outputs "/model.22/Concat_output_0 /model.22/Concat_1_output_0 /model.22/Concat_2_output_0" \
|
||||
--target-platform $target_platform
|
||||
50
examples/yolov8/model/dataset_coco.txt
Executable file
50
examples/yolov8/model/dataset_coco.txt
Executable file
|
|
@ -0,0 +1,50 @@
|
|||
../../../resource/coco_dataset/000000000139.jpg
|
||||
../../../resource/coco_dataset/000000000285.jpg
|
||||
../../../resource/coco_dataset/000000000632.jpg
|
||||
../../../resource/coco_dataset/000000000724.jpg
|
||||
../../../resource/coco_dataset/000000000776.jpg
|
||||
../../../resource/coco_dataset/000000000785.jpg
|
||||
../../../resource/coco_dataset/000000000802.jpg
|
||||
../../../resource/coco_dataset/000000000872.jpg
|
||||
../../../resource/coco_dataset/000000000885.jpg
|
||||
../../../resource/coco_dataset/000000001000.jpg
|
||||
../../../resource/coco_dataset/000000001268.jpg
|
||||
../../../resource/coco_dataset/000000001296.jpg
|
||||
../../../resource/coco_dataset/000000001353.jpg
|
||||
../../../resource/coco_dataset/000000001425.jpg
|
||||
../../../resource/coco_dataset/000000001490.jpg
|
||||
../../../resource/coco_dataset/000000001503.jpg
|
||||
../../../resource/coco_dataset/000000001532.jpg
|
||||
../../../resource/coco_dataset/000000001584.jpg
|
||||
../../../resource/coco_dataset/000000001675.jpg
|
||||
../../../resource/coco_dataset/000000001761.jpg
|
||||
../../../resource/coco_dataset/000000001818.jpg
|
||||
../../../resource/coco_dataset/000000001993.jpg
|
||||
../../../resource/coco_dataset/000000002006.jpg
|
||||
../../../resource/coco_dataset/000000002149.jpg
|
||||
../../../resource/coco_dataset/000000002153.jpg
|
||||
../../../resource/coco_dataset/000000002157.jpg
|
||||
../../../resource/coco_dataset/000000002261.jpg
|
||||
../../../resource/coco_dataset/000000002299.jpg
|
||||
../../../resource/coco_dataset/000000002431.jpg
|
||||
../../../resource/coco_dataset/000000002473.jpg
|
||||
../../../resource/coco_dataset/000000002532.jpg
|
||||
../../../resource/coco_dataset/000000002587.jpg
|
||||
../../../resource/coco_dataset/000000002592.jpg
|
||||
../../../resource/coco_dataset/000000002685.jpg
|
||||
../../../resource/coco_dataset/000000002923.jpg
|
||||
../../../resource/coco_dataset/000000003156.jpg
|
||||
../../../resource/coco_dataset/000000003255.jpg
|
||||
../../../resource/coco_dataset/000000003501.jpg
|
||||
../../../resource/coco_dataset/000000003553.jpg
|
||||
../../../resource/coco_dataset/000000003661.jpg
|
||||
../../../resource/coco_dataset/000000003845.jpg
|
||||
../../../resource/coco_dataset/000000003934.jpg
|
||||
../../../resource/coco_dataset/000000004134.jpg
|
||||
../../../resource/coco_dataset/000000004395.jpg
|
||||
../../../resource/coco_dataset/000000004495.jpg
|
||||
../../../resource/coco_dataset/000000004765.jpg
|
||||
../../../resource/coco_dataset/000000004795.jpg
|
||||
../../../resource/coco_dataset/000000005001.jpg
|
||||
../../../resource/coco_dataset/000000005037.jpg
|
||||
../../../resource/coco_dataset/000000005060.jpg
|
||||
281
examples/yolov8/py/yolov8.py
Executable file
281
examples/yolov8/py/yolov8.py
Executable file
|
|
@ -0,0 +1,281 @@
|
|||
import numpy as np
|
||||
import os
|
||||
import glob
|
||||
import argparse
|
||||
import cv2
|
||||
from pathlib import Path
|
||||
from amlnnlite.api import AMLNNLite
|
||||
|
||||
|
||||
class_names = {
|
||||
0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane',
|
||||
5: 'bus', 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light',
|
||||
10: 'fire hydrant', 11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird',
|
||||
15: 'cat', 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow',
|
||||
20: 'elephant', 21: 'bear', 22: 'zebra', 23: 'giraffe', 24: 'backpack',
|
||||
25: 'umbrella', 26: 'handbag', 27: 'tie', 28: 'suitcase', 29: 'frisbee',
|
||||
30: 'skis', 31: 'snowboard', 32: 'sports ball', 33: 'kite', 34: 'baseball bat',
|
||||
35: 'baseball glove', 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle',
|
||||
40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon',
|
||||
45: 'bowl', 46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange',
|
||||
50: 'broccoli', 51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut',
|
||||
55: 'cake', 56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed',
|
||||
60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse',
|
||||
65: 'remote', 66: 'keyboard', 67: 'cell phone', 68: 'microwave', 69: 'oven',
|
||||
70: 'toaster', 71: 'sink', 72: 'refrigerator', 73: 'book', 74: 'clock',
|
||||
75: 'vase', 76: 'scissors', 77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'
|
||||
}
|
||||
|
||||
def letterbox(img, new_shape=(640, 640), color=(114, 114, 114)):
|
||||
shape = img.shape[:2] # [height, width]
|
||||
scale = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
|
||||
new_unpad = (int(round(shape[1] * scale)), int(round(shape[0] * scale)))
|
||||
pad_w = (new_shape[1] - new_unpad[0]) / 2
|
||||
pad_h = (new_shape[0] - new_unpad[1]) / 2
|
||||
|
||||
if shape[::-1] != new_unpad:
|
||||
img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
|
||||
|
||||
top, bottom = int(round(pad_h - 0.1)), int(round(pad_h + 0.1))
|
||||
left, right = int(round(pad_w - 0.1)), int(round(pad_w + 0.1))
|
||||
img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)
|
||||
|
||||
return img, scale, (left, top)
|
||||
|
||||
def preprocess(img_path, new_shape=(640, 640), data_format='NCHW', s=0.003921568859368563, zp=-128):
|
||||
original_img = cv2.imread(str(img_path))
|
||||
if original_img is None:
|
||||
raise ValueError(f"can't read image: {img_path}")
|
||||
|
||||
processed_img, scale, pad = letterbox(original_img, new_shape)
|
||||
rgb_img = cv2.cvtColor(processed_img, cv2.COLOR_BGR2RGB)
|
||||
normalized_img = rgb_img.astype(np.float32) / 255.0
|
||||
|
||||
if data_format == 'NCHW':
|
||||
# HWC -> CHW -> BCHW (ONNX default format)
|
||||
input_tensor = np.transpose(normalized_img, (2, 0, 1))
|
||||
input_tensor = np.expand_dims(input_tensor, axis=0)
|
||||
elif data_format == 'NHWC':
|
||||
# HWC -> BHWC (TFLITE default format)
|
||||
input_tensor = np.expand_dims(normalized_img, axis=0)
|
||||
else:
|
||||
raise ValueError(f"Unsupported data format: {data_format}. Only 'NCHW' and 'NHWC' are supported.")
|
||||
|
||||
# Quantize to int8
|
||||
input_tensor = np.round(input_tensor / s + zp).astype(np.int8)
|
||||
|
||||
return input_tensor, original_img, scale, pad
|
||||
|
||||
def postprocess(outputs, scale, pad, data_format='NCHW', strides=[8, 16, 32], conf_threshold=0.25, iou_threshold=0.45):
|
||||
all_boxes = []
|
||||
all_scores = []
|
||||
all_class_ids = []
|
||||
|
||||
for scale_idx, output in enumerate(outputs):
|
||||
stride = strides[scale_idx]
|
||||
|
||||
if data_format == 'NCHW':
|
||||
# (1, 144, H, W) → (H*W, 144)
|
||||
batch_size, channels, height, width = output.shape
|
||||
output_reshaped = output.transpose(0, 2, 3, 1).reshape(-1, channels)
|
||||
elif data_format == 'NHWC':
|
||||
# (1, H, W, 144) → (H*W, 144)
|
||||
batch_size, height, width, channels = output.shape
|
||||
output_reshaped = output.reshape(-1, channels)
|
||||
else:
|
||||
raise ValueError(f"Unsupported data format: {data_format}. Only 'NCHW' and 'NHWC' are supported.")
|
||||
|
||||
# Separate DFL and classification: 144 = 64(DFL) + 80(Classes)
|
||||
dfl_predictions = output_reshaped[:, :64]
|
||||
class_predictions = output_reshaped[:, 64:]
|
||||
|
||||
# Apply sigmoid activation to class scores
|
||||
class_scores = 1.0 / (1.0 + np.exp(-class_predictions))
|
||||
max_class_scores = np.max(class_scores, axis=1)
|
||||
class_ids = np.argmax(class_scores, axis=1)
|
||||
|
||||
# Generate grid coordinates
|
||||
grid_y, grid_x = np.meshgrid(np.arange(height), np.arange(width), indexing='ij')
|
||||
grid_x = grid_x.flatten().astype(np.float32)
|
||||
grid_y = grid_y.flatten().astype(np.float32)
|
||||
|
||||
# DFL decoding
|
||||
dfl_reshaped = dfl_predictions.reshape(-1, 4, 16)
|
||||
dfl_softmax = np.exp(dfl_reshaped) / np.sum(np.exp(dfl_reshaped), axis=-1, keepdims=True)
|
||||
regression_range = np.arange(16, dtype=np.float32)
|
||||
bbox_deltas = np.sum(dfl_softmax * regression_range[None, None, :], axis=-1)
|
||||
|
||||
# Convert to absolute coordinates
|
||||
anchor_x = (grid_x + 0.5) * stride
|
||||
anchor_y = (grid_y + 0.5) * stride
|
||||
|
||||
left, top, right, bottom = bbox_deltas.T
|
||||
x1 = anchor_x - left * stride
|
||||
y1 = anchor_y - top * stride
|
||||
x2 = anchor_x + right * stride
|
||||
y2 = anchor_y + bottom * stride
|
||||
|
||||
boxes = np.stack([x1, y1, x2, y2], axis=1)
|
||||
|
||||
all_boxes.append(boxes)
|
||||
all_scores.append(max_class_scores)
|
||||
all_class_ids.append(class_ids)
|
||||
|
||||
# Merge all scales
|
||||
final_boxes = np.concatenate(all_boxes, axis=0)
|
||||
final_scores = np.concatenate(all_scores, axis=0)
|
||||
final_class_ids = np.concatenate(all_class_ids, axis=0)
|
||||
|
||||
# Filter by confidence threshold
|
||||
valid_mask = final_scores > conf_threshold
|
||||
if not np.any(valid_mask):
|
||||
return []
|
||||
|
||||
valid_boxes = final_boxes[valid_mask]
|
||||
valid_scores = final_scores[valid_mask]
|
||||
valid_class_ids = final_class_ids[valid_mask]
|
||||
|
||||
# Map coordinates back to original image
|
||||
pad_x, pad_y = pad
|
||||
valid_boxes[:, [0, 2]] = (valid_boxes[:, [0, 2]] - pad_x) / scale
|
||||
valid_boxes[:, [1, 3]] = (valid_boxes[:, [1, 3]] - pad_y) / scale
|
||||
valid_boxes = np.maximum(valid_boxes, 0)
|
||||
|
||||
# NMS
|
||||
if len(valid_boxes) > 0:
|
||||
nms_indices = cv2.dnn.NMSBoxes(
|
||||
valid_boxes.tolist(), valid_scores.tolist(), conf_threshold, iou_threshold
|
||||
)
|
||||
|
||||
if len(nms_indices) > 0:
|
||||
nms_indices = nms_indices.flatten()
|
||||
detections = []
|
||||
|
||||
for idx in nms_indices:
|
||||
x1, y1, x2, y2 = valid_boxes[idx]
|
||||
confidence = valid_scores[idx]
|
||||
class_id = valid_class_ids[idx]
|
||||
|
||||
detections.append({
|
||||
'bbox': [float(x1), float(y1), float(x2), float(y2)],
|
||||
'confidence': float(confidence),
|
||||
'class_id': int(class_id),
|
||||
'class_name': class_names.get(int(class_id), f'class_{class_id}')
|
||||
})
|
||||
|
||||
return detections
|
||||
|
||||
return []
|
||||
|
||||
def get_class_color(class_id):
|
||||
import colorsys
|
||||
hue = (class_id * 137.508) % 360
|
||||
rgb = colorsys.hsv_to_rgb(hue/360.0, 0.8, 0.9)
|
||||
bgr = (int(rgb[2]*255), int(rgb[1]*255), int(rgb[0]*255))
|
||||
return bgr
|
||||
|
||||
def draw_detections(img, detections, save_path):
|
||||
result_img = img.copy()
|
||||
|
||||
for det in detections:
|
||||
x1, y1, x2, y2 = [int(coord) for coord in det['bbox']]
|
||||
confidence = det['confidence']
|
||||
class_name = det['class_name']
|
||||
class_id = det['class_id']
|
||||
|
||||
color = get_class_color(class_id)
|
||||
|
||||
cv2.rectangle(result_img, (x1, y1), (x2, y2), color, 2)
|
||||
|
||||
label = f"{class_name}: {confidence:.2f}"
|
||||
(label_w, label_h), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.6, 1)
|
||||
cv2.rectangle(result_img, (x1, y1 - label_h - 10), (x1 + label_w, y1), color, -1)
|
||||
text_color = (255, 255, 255) if sum(color) < 400 else (0, 0, 0)
|
||||
cv2.putText(result_img, label, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.6, text_color, 1)
|
||||
|
||||
cv2.imwrite(save_path, result_img)
|
||||
return result_img
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--model-path', default='./yolov8s_int8_A311D2.adla')
|
||||
parser.add_argument('--run-cycles', default= 1, type=int)
|
||||
args = parser.parse_args()
|
||||
|
||||
# Initialize AMLNNLite
|
||||
amlnn = AMLNNLite()
|
||||
amlnn.config(
|
||||
model_path=args.model_path, # Model file path, Support ADLD and quantized TFlite models
|
||||
run_cycles=args.run_cycles
|
||||
)
|
||||
amlnn.init()
|
||||
|
||||
# Find all image files in the 01_export_model directory
|
||||
image_dir = "./"
|
||||
image_extensions = ["*.jpg", "*.jpeg", "*.png", "*.bmp"]
|
||||
image_files = []
|
||||
for ext in image_extensions:
|
||||
image_files.extend(glob.glob(os.path.join(image_dir, ext)))
|
||||
image_files.extend(glob.glob(os.path.join(image_dir, ext.upper())))
|
||||
|
||||
if not image_files:
|
||||
print("No image files found in", image_dir)
|
||||
amlnn.uninit()
|
||||
return
|
||||
|
||||
print(f"Found {len(image_files)} image files to process:")
|
||||
for img_file in image_files:
|
||||
print(f" - {os.path.basename(img_file)}")
|
||||
print()
|
||||
|
||||
# Process each image
|
||||
for i, image_path in enumerate(image_files, 1):
|
||||
print(f"=" * 60)
|
||||
print(f"Processing image {i}/{len(image_files)}: {os.path.basename(image_path)}")
|
||||
print(f"=" * 60)
|
||||
|
||||
try:
|
||||
# Preprocess input
|
||||
input_tensor, original_img, scale, pad = preprocess(image_path, new_shape=(640, 640), data_format='NHWC', s=0.003921568859368563, zp=-128)
|
||||
|
||||
# Run inference
|
||||
outputs = amlnn.inference(
|
||||
inputs=[input_tensor]
|
||||
)
|
||||
|
||||
# Postprocess results
|
||||
detections = postprocess(outputs, scale, pad, data_format='NHWC', strides=[8, 16, 32], conf_threshold=0.25, iou_threshold=0.45)
|
||||
|
||||
# Print detection results
|
||||
if detections:
|
||||
print(f" Detected {len(detections)} objects:")
|
||||
for i, det in enumerate(detections, 1):
|
||||
print(f" {i}. {det['class_name']} ({det['confidence']:.2f})")
|
||||
else:
|
||||
print(" No objects detected")
|
||||
|
||||
# Save result image
|
||||
model_name = Path(args.model_path).stem
|
||||
result_dir = f"{model_name}_result"
|
||||
os.makedirs(result_dir, exist_ok=True)
|
||||
img_name = Path(image_path).stem
|
||||
save_path = os.path.join(result_dir, f"{img_name}_result.jpg")
|
||||
draw_detections(original_img, detections, str(save_path))
|
||||
print(f" Result saved to: {save_path}")
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error processing {os.path.basename(image_path)}: {e}")
|
||||
|
||||
print()
|
||||
|
||||
# Optional visualization
|
||||
amlnn.visualize()
|
||||
|
||||
# Release resources
|
||||
amlnn.uninit()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
||||
BIN
examples/yolov8/result.jpg
Executable file
BIN
examples/yolov8/result.jpg
Executable file
Binary file not shown.
|
After Width: | Height: | Size: 528 KiB |
|
|
@ -91,7 +91,6 @@ int main(int argc, char** argv) {
|
|||
int num_classes = CLASS_NAMES.size();
|
||||
int channels = 87;
|
||||
|
||||
// Using standard stride logic assuming standard YOLOv8/World export
|
||||
std::vector<Detection> detections = postprocess(
|
||||
std::make_tuple(outbuf0, std::make_tuple(MODEL_INPUT_HEIGHT / 8, MODEL_INPUT_WIDTH / 8, channels), 8),
|
||||
std::make_tuple(outbuf1, std::make_tuple(MODEL_INPUT_HEIGHT / 16, MODEL_INPUT_WIDTH / 16, channels), 16),
|
||||
|
|
|
|||
509
examples/yoloworld/py/yoloworld.py
Executable file
509
examples/yoloworld/py/yoloworld.py
Executable file
|
|
@ -0,0 +1,509 @@
|
|||
import numpy as np
|
||||
import os
|
||||
import glob
|
||||
import argparse
|
||||
import cv2
|
||||
from pathlib import Path
|
||||
from amlnnlite.api import AMLNNLite
|
||||
|
||||
|
||||
class_names = [
|
||||
"handbag", "backpack", "wallet",
|
||||
"watch", "necklace", "bracelet", "earrings", "finger ring", "sunglass", "hat", "shoes", "belt",
|
||||
"makeup palette", "lipstick tube",
|
||||
"car", "truck", "bicycle", "motorcycle",
|
||||
"phone", "laptop", "camera", "wine bottle", "stuffed toy"
|
||||
]
|
||||
|
||||
MODEL_INPUT_WIDTH = 640
|
||||
MODEL_INPUT_HEIGHT = 480
|
||||
NUM_CLASSES = len(class_names)
|
||||
CHANNELS = 87 # 4*16 (DFL) + 23 (classes)
|
||||
STRIDES = [8, 16, 32]
|
||||
SCORE_THRESHOLD = 0.3
|
||||
NMS_THRESHOLD = 0.45
|
||||
|
||||
def letterbox(img, new_shape=(480, 640), color=(114, 114, 114)):
|
||||
"""Resize and pad image with letterbox method"""
|
||||
shape = img.shape[:2] # [height, width]
|
||||
scale = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
|
||||
new_unpad = (int(round(shape[1] * scale)), int(round(shape[0] * scale)))
|
||||
pad_w = (new_shape[1] - new_unpad[0]) / 2
|
||||
pad_h = (new_shape[0] - new_unpad[1]) / 2
|
||||
|
||||
if shape[::-1] != new_unpad:
|
||||
img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
|
||||
|
||||
top, bottom = int(round(pad_h - 0.1)), int(round(pad_h + 0.1))
|
||||
left, right = int(round(pad_w - 0.1)), int(round(pad_w + 0.1))
|
||||
img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)
|
||||
|
||||
return img, scale, (left, top)
|
||||
|
||||
def preprocess(img_path, new_shape=(480, 640), data_format='NHWC'):
|
||||
"""Preprocess image for YOLOWorld model (float32 input/output)"""
|
||||
original_img = cv2.imread(str(img_path))
|
||||
if original_img is None:
|
||||
raise ValueError(f"can't read image: {img_path}")
|
||||
|
||||
processed_img, scale, pad = letterbox(original_img, new_shape)
|
||||
rgb_img = cv2.cvtColor(processed_img, cv2.COLOR_BGR2RGB)
|
||||
normalized_img = rgb_img.astype(np.float32) / 255.0
|
||||
|
||||
if data_format == 'NCHW':
|
||||
# HWC -> CHW -> BCHW
|
||||
input_tensor = np.transpose(normalized_img, (2, 0, 1))
|
||||
input_tensor = np.expand_dims(input_tensor, axis=0)
|
||||
elif data_format == 'NHWC':
|
||||
# HWC -> BHWC
|
||||
input_tensor = np.expand_dims(normalized_img, axis=0)
|
||||
else:
|
||||
raise ValueError(f"Unsupported data format: {data_format}. Only 'NCHW' and 'NHWC' are supported.")
|
||||
|
||||
# Keep as float32 (no quantization for float32 models)
|
||||
input_tensor = input_tensor.astype(np.float32)
|
||||
|
||||
return input_tensor, original_img, scale, pad
|
||||
|
||||
def sigmoid(x):
|
||||
"""Sigmoid activation function"""
|
||||
return 1.0 / (1.0 + np.exp(-np.clip(x, -250, 250)))
|
||||
|
||||
def compute_iou(box1, box2):
|
||||
"""Compute IoU between two boxes"""
|
||||
x1_1, y1_1, x2_1, y2_1 = box1
|
||||
x1_2, y1_2, x2_2, y2_2 = box2
|
||||
|
||||
xx1 = max(x1_1, x1_2)
|
||||
yy1 = max(y1_1, y1_2)
|
||||
xx2 = min(x2_1, x2_2)
|
||||
yy2 = min(y2_1, y2_2)
|
||||
|
||||
w = max(0.0, xx2 - xx1)
|
||||
h = max(0.0, yy2 - yy1)
|
||||
inter = w * h
|
||||
|
||||
area1 = (x2_1 - x1_1) * (y2_1 - y1_1)
|
||||
area2 = (x2_2 - x1_2) * (y2_2 - y1_2)
|
||||
|
||||
return inter / (area1 + area2 - inter + 1e-6)
|
||||
|
||||
def nms_by_class(detections, iou_threshold):
|
||||
"""NMS within each class"""
|
||||
if len(detections) == 0:
|
||||
return []
|
||||
|
||||
# Group by class
|
||||
class_detections = {}
|
||||
for det in detections:
|
||||
class_id = det['class_id']
|
||||
if class_id not in class_detections:
|
||||
class_detections[class_id] = []
|
||||
class_detections[class_id].append(det)
|
||||
|
||||
final_detections = []
|
||||
for class_id, cls_dets in class_detections.items():
|
||||
# Sort by score
|
||||
cls_dets.sort(key=lambda x: x['confidence'], reverse=True)
|
||||
|
||||
removed = [False] * len(cls_dets)
|
||||
for i in range(len(cls_dets)):
|
||||
if removed[i]:
|
||||
continue
|
||||
final_detections.append(cls_dets[i])
|
||||
|
||||
for j in range(i + 1, len(cls_dets)):
|
||||
if removed[j]:
|
||||
continue
|
||||
iou = compute_iou(cls_dets[i]['bbox'], cls_dets[j]['bbox'])
|
||||
if iou > iou_threshold:
|
||||
removed[j] = True
|
||||
|
||||
return final_detections
|
||||
|
||||
def suppress_cross_class_iou_conflicts(detections, iou_threshold):
|
||||
"""Suppress cross-class IOU conflicts"""
|
||||
if len(detections) == 0:
|
||||
return []
|
||||
|
||||
# Sort by score
|
||||
detections.sort(key=lambda x: x['confidence'], reverse=True)
|
||||
|
||||
removed = [False] * len(detections)
|
||||
final_detections = []
|
||||
|
||||
for i in range(len(detections)):
|
||||
if removed[i]:
|
||||
continue
|
||||
final_detections.append(detections[i])
|
||||
|
||||
for j in range(i + 1, len(detections)):
|
||||
if removed[j]:
|
||||
continue
|
||||
if detections[i]['class_id'] != detections[j]['class_id']:
|
||||
iou = compute_iou(detections[i]['bbox'], detections[j]['bbox'])
|
||||
if iou > iou_threshold:
|
||||
removed[j] = True
|
||||
|
||||
return final_detections
|
||||
|
||||
def get_detections(output, output_shape, stride, conf_thresh, num_classes, reverse=1, data_format='NHWC'):
|
||||
"""Extract detections from a single output layer using vectorized operations"""
|
||||
coords = 4 * 16 # DFL coords: 64
|
||||
|
||||
if data_format == 'NCHW':
|
||||
batch_size, channels, height, width = output_shape
|
||||
# Remove batch dimension and reshape: (channels, height, width) -> (height * width, channels)
|
||||
output_reshaped = output[0].transpose(1, 2, 0).reshape(-1, channels)
|
||||
elif data_format == 'NHWC':
|
||||
batch_size, height, width, channels = output_shape
|
||||
# Remove batch dimension and reshape: (height, width, channels) -> (height * width, channels)
|
||||
output_reshaped = output[0].reshape(-1, channels)
|
||||
else:
|
||||
raise ValueError(f"Unsupported data format: {data_format}")
|
||||
|
||||
# reverse=0: standard YOLO [classes + box]
|
||||
# reverse>0: YOLOWorld [box + classes]
|
||||
cls_offset = coords if reverse > 0 else 0
|
||||
dfl_offset = 0 if reverse > 0 else num_classes
|
||||
|
||||
# Extract class predictions and apply sigmoid
|
||||
class_predictions = output_reshaped[:, cls_offset:cls_offset + num_classes]
|
||||
class_scores = sigmoid(class_predictions)
|
||||
|
||||
# Get max class scores and class IDs
|
||||
max_class_scores = np.max(class_scores, axis=1)
|
||||
class_ids = np.argmax(class_scores, axis=1)
|
||||
|
||||
# Filter by confidence threshold
|
||||
valid_mask = max_class_scores > conf_thresh
|
||||
if not np.any(valid_mask):
|
||||
return []
|
||||
|
||||
# Extract DFL predictions for valid detections
|
||||
dfl_predictions = output_reshaped[valid_mask, dfl_offset:dfl_offset + coords]
|
||||
valid_scores = max_class_scores[valid_mask]
|
||||
valid_class_ids = class_ids[valid_mask]
|
||||
|
||||
# Reshape DFL: (N, 64) -> (N, 4, 16)
|
||||
dfl_reshaped = dfl_predictions.reshape(-1, 4, 16)
|
||||
|
||||
# DFL decoding with softmax
|
||||
max_logits = np.max(dfl_reshaped, axis=-1, keepdims=True)
|
||||
dfl_exp = np.exp(dfl_reshaped - max_logits)
|
||||
dfl_softmax = dfl_exp / np.sum(dfl_exp, axis=-1, keepdims=True)
|
||||
|
||||
# Weighted sum: regression_range = [0, 1, 2, ..., 15]
|
||||
regression_range = np.arange(16, dtype=np.float32)
|
||||
bbox_deltas = np.sum(dfl_softmax * regression_range[None, :], axis=-1) # (N, 4)
|
||||
|
||||
# Generate grid coordinates
|
||||
grid_y, grid_x = np.meshgrid(np.arange(height), np.arange(width), indexing='ij')
|
||||
grid_x = grid_x.flatten()
|
||||
grid_y = grid_y.flatten()
|
||||
|
||||
# Filter grid coordinates
|
||||
valid_grid_x = grid_x[valid_mask]
|
||||
valid_grid_y = grid_y[valid_mask]
|
||||
|
||||
# Convert to absolute coordinates
|
||||
anchor_x = (valid_grid_x + 0.5) * stride
|
||||
anchor_y = (valid_grid_y + 0.5) * stride
|
||||
|
||||
left, top, right, bottom = bbox_deltas.T
|
||||
x1 = anchor_x - left * stride
|
||||
y1 = anchor_y - top * stride
|
||||
x2 = anchor_x + right * stride
|
||||
y2 = anchor_y + bottom * stride
|
||||
|
||||
boxes = np.stack([x1, y1, x2, y2], axis=1)
|
||||
|
||||
# Create detections list
|
||||
detections = []
|
||||
for i in range(len(boxes)):
|
||||
detections.append({
|
||||
'bbox': [float(boxes[i, 0]), float(boxes[i, 1]), float(boxes[i, 2]), float(boxes[i, 3])],
|
||||
'confidence': float(valid_scores[i]),
|
||||
'class_id': int(valid_class_ids[i])
|
||||
})
|
||||
|
||||
return detections
|
||||
|
||||
def postprocess(outputs, scale, pad, data_format='NHWC', strides=[8, 16, 32],
|
||||
conf_threshold=0.4, iou_threshold=0.45, num_classes=23, reverse=1):
|
||||
"""Postprocess YOLOWorld outputs"""
|
||||
all_detections = []
|
||||
|
||||
# Process each output scale
|
||||
for scale_idx, output in enumerate(outputs):
|
||||
stride = strides[scale_idx]
|
||||
|
||||
# Output should already be float32 (no dequantization needed)
|
||||
if output.dtype != np.float32:
|
||||
output = output.astype(np.float32)
|
||||
|
||||
if data_format == 'NCHW':
|
||||
batch_size, channels, height, width = output.shape
|
||||
output_shape = (batch_size, channels, height, width)
|
||||
elif data_format == 'NHWC':
|
||||
batch_size, height, width, channels = output.shape
|
||||
output_shape = (batch_size, height, width, channels)
|
||||
else:
|
||||
raise ValueError(f"Unsupported data format: {data_format}")
|
||||
|
||||
dets = get_detections(output, output_shape, stride, conf_threshold,
|
||||
num_classes, reverse, data_format)
|
||||
all_detections.extend(dets)
|
||||
|
||||
if len(all_detections) == 0:
|
||||
return []
|
||||
|
||||
# Map coordinates back to original image
|
||||
pad_x, pad_y = pad
|
||||
detections_orig = []
|
||||
for det in all_detections:
|
||||
x1, y1, x2, y2 = det['bbox']
|
||||
x1_orig = (x1 - pad_x) / scale
|
||||
y1_orig = (y1 - pad_y) / scale
|
||||
x2_orig = (x2 - pad_x) / scale
|
||||
y2_orig = (y2 - pad_y) / scale
|
||||
|
||||
detections_orig.append({
|
||||
'bbox': [float(x1_orig), float(y1_orig), float(x2_orig), float(y2_orig)],
|
||||
'confidence': det['confidence'],
|
||||
'class_id': det['class_id'],
|
||||
'class_name': class_names[det['class_id']] if det['class_id'] < len(class_names) else f'class_{det["class_id"]}'
|
||||
})
|
||||
|
||||
# NMS by class
|
||||
detections_nms = nms_by_class(detections_orig, iou_threshold)
|
||||
|
||||
# Suppress cross-class IOU conflicts
|
||||
final_detections = suppress_cross_class_iou_conflicts(detections_nms, 0.8)
|
||||
|
||||
return final_detections
|
||||
|
||||
def get_class_color(class_id):
|
||||
"""Generate a color for each class"""
|
||||
import colorsys
|
||||
hue = (class_id * 137.508) % 360
|
||||
rgb = colorsys.hsv_to_rgb(hue/360.0, 0.8, 0.9)
|
||||
bgr = (int(rgb[2]*255), int(rgb[1]*255), int(rgb[0]*255))
|
||||
return bgr
|
||||
|
||||
def draw_detections(img, detections, save_path):
|
||||
"""Draw detection results on image"""
|
||||
result_img = img.copy()
|
||||
|
||||
for det in detections:
|
||||
x1, y1, x2, y2 = [int(coord) for coord in det['bbox']]
|
||||
confidence = det['confidence']
|
||||
class_name = det['class_name']
|
||||
class_id = det['class_id']
|
||||
|
||||
color = get_class_color(class_id)
|
||||
|
||||
cv2.rectangle(result_img, (x1, y1), (x2, y2), color, 2)
|
||||
|
||||
label = f"{class_name}: {confidence:.2f}"
|
||||
(label_w, label_h), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.6, 1)
|
||||
cv2.rectangle(result_img, (x1, y1 - label_h - 10), (x1 + label_w, y1), color, -1)
|
||||
text_color = (255, 255, 255) if sum(color) < 400 else (0, 0, 0)
|
||||
cv2.putText(result_img, label, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.6, text_color, 1)
|
||||
|
||||
cv2.imwrite(save_path, result_img)
|
||||
return result_img
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(
|
||||
description='YOLOWorld object detection demo using AMLNNLite',
|
||||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||||
epilog='''
|
||||
Examples:
|
||||
# Use default model path
|
||||
python yoloworld.py
|
||||
|
||||
# Specify model path
|
||||
python yoloworld.py --model-path ./model.adla
|
||||
|
||||
# Run multiple cycles for performance testing
|
||||
python yoloworld.py --run-cycles 100
|
||||
'''
|
||||
)
|
||||
parser.add_argument('--model-path',
|
||||
default='./yolo_world_480_640.adla',
|
||||
help='Path to the model file (.adla or .tflite)')
|
||||
parser.add_argument('--run-cycles',
|
||||
default=1,
|
||||
type=int,
|
||||
help='Number of inference cycles to run (for performance testing)')
|
||||
parser.add_argument('--image-dir',
|
||||
default='./',
|
||||
help='Directory containing images to process')
|
||||
parser.add_argument('--conf-threshold',
|
||||
type=float,
|
||||
default=SCORE_THRESHOLD,
|
||||
help=f'Confidence threshold for detection (default: {SCORE_THRESHOLD})')
|
||||
parser.add_argument('--nms-threshold',
|
||||
type=float,
|
||||
default=NMS_THRESHOLD,
|
||||
help=f'NMS IoU threshold (default: {NMS_THRESHOLD})')
|
||||
parser.add_argument('--no-visualize',
|
||||
action='store_true',
|
||||
help='Skip visualization chart generation')
|
||||
args = parser.parse_args()
|
||||
|
||||
# Validate model path
|
||||
if not os.path.exists(args.model_path):
|
||||
print(f"Error: Model file not found: {args.model_path}")
|
||||
return 1
|
||||
|
||||
if not os.path.isfile(args.model_path):
|
||||
print(f"Error: Model path is not a file: {args.model_path}")
|
||||
return 1
|
||||
|
||||
# Validate thresholds
|
||||
if not 0.0 < args.conf_threshold <= 1.0:
|
||||
print(f"Error: Confidence threshold must be in (0, 1], got {args.conf_threshold}")
|
||||
return 1
|
||||
|
||||
if not 0.0 < args.nms_threshold <= 1.0:
|
||||
print(f"Error: NMS threshold must be in (0, 1], got {args.nms_threshold}")
|
||||
return 1
|
||||
|
||||
# Initialize AMLNNLite with error handling
|
||||
print("Initializing AMLNNLite...")
|
||||
amlnn = None
|
||||
try:
|
||||
amlnn = AMLNNLite()
|
||||
print(f"Loading model: {args.model_path}")
|
||||
amlnn.config(
|
||||
model_path=args.model_path,
|
||||
run_cycles=args.run_cycles
|
||||
)
|
||||
print("Initializing model...")
|
||||
amlnn.init()
|
||||
print("Model initialized successfully!\n")
|
||||
except Exception as e:
|
||||
print(f"Error initializing AMLNNLite: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
return 1
|
||||
|
||||
# Use try-finally to ensure resources are released
|
||||
try:
|
||||
# Find all image files in the specified directory
|
||||
image_dir = args.image_dir
|
||||
if not os.path.exists(image_dir):
|
||||
print(f"Error: Image directory not found: {image_dir}")
|
||||
return 1
|
||||
|
||||
if not os.path.isdir(image_dir):
|
||||
print(f"Error: Image path is not a directory: {image_dir}")
|
||||
return 1
|
||||
|
||||
image_extensions = ["*.jpg", "*.jpeg", "*.png", "*.bmp"]
|
||||
image_files = []
|
||||
for ext in image_extensions:
|
||||
image_files.extend(glob.glob(os.path.join(image_dir, ext)))
|
||||
image_files.extend(glob.glob(os.path.join(image_dir, ext.upper())))
|
||||
|
||||
if not image_files:
|
||||
print(f"No image files found in {image_dir}")
|
||||
return 0
|
||||
|
||||
print(f"Found {len(image_files)} image file(s) to process:")
|
||||
for img_file in image_files:
|
||||
print(f" - {os.path.basename(img_file)}")
|
||||
print()
|
||||
|
||||
# Process each image
|
||||
for i, image_path in enumerate(image_files, 1):
|
||||
print(f"=" * 60)
|
||||
print(f"Processing image {i}/{len(image_files)}: {os.path.basename(image_path)}")
|
||||
print(f"=" * 60)
|
||||
|
||||
try:
|
||||
# Preprocess input (float32 model, no quantization)
|
||||
input_tensor, original_img, scale, pad = preprocess(
|
||||
image_path,
|
||||
new_shape=(MODEL_INPUT_HEIGHT, MODEL_INPUT_WIDTH),
|
||||
data_format='NHWC'
|
||||
)
|
||||
|
||||
# Validate input tensor shape and dtype
|
||||
expected_shape = (1, MODEL_INPUT_HEIGHT, MODEL_INPUT_WIDTH, 3)
|
||||
if input_tensor.shape != expected_shape:
|
||||
raise ValueError(f"Input tensor shape mismatch: expected {expected_shape}, got {input_tensor.shape}")
|
||||
if input_tensor.dtype != np.float32:
|
||||
raise ValueError(f"Input tensor dtype must be float32, got {input_tensor.dtype}")
|
||||
|
||||
# Run inference
|
||||
outputs = amlnn.inference(inputs=[input_tensor])
|
||||
|
||||
# Validate outputs
|
||||
if outputs is None:
|
||||
raise ValueError("Inference returned None")
|
||||
if len(outputs) != 3:
|
||||
raise ValueError(f"Expected 3 output tensors, got {len(outputs)}")
|
||||
|
||||
# Postprocess results
|
||||
detections = postprocess(
|
||||
outputs,
|
||||
scale,
|
||||
pad,
|
||||
data_format='NHWC',
|
||||
strides=STRIDES,
|
||||
conf_threshold=args.conf_threshold,
|
||||
iou_threshold=args.nms_threshold,
|
||||
num_classes=NUM_CLASSES,
|
||||
reverse=1 # YOLOWorld format
|
||||
)
|
||||
|
||||
# Print detection results
|
||||
if detections:
|
||||
print(f" Detected {len(detections)} object(s):")
|
||||
for j, det in enumerate(detections, 1):
|
||||
bbox = det['bbox']
|
||||
print(f"{j}. {det['class_name']} ({det['confidence']:.2f})")
|
||||
else:
|
||||
print(" No objects detected")
|
||||
|
||||
# Save result image
|
||||
model_name = Path(args.model_path).stem
|
||||
result_dir = f"{model_name}_result"
|
||||
os.makedirs(result_dir, exist_ok=True)
|
||||
img_name = Path(image_path).stem
|
||||
save_path = os.path.join(result_dir, f"{img_name}_result.jpg")
|
||||
draw_detections(original_img, detections, str(save_path))
|
||||
print(f" Result saved to: {save_path}")
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error processing {os.path.basename(image_path)}: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
# Continue processing other images
|
||||
continue
|
||||
|
||||
print()
|
||||
|
||||
# Optional visualization
|
||||
if not args.no_visualize:
|
||||
print("Generating visualization charts...")
|
||||
amlnn.visualize()
|
||||
print("Visualization charts saved.")
|
||||
finally:
|
||||
# Always release resources
|
||||
if amlnn is not None:
|
||||
print("\nReleasing resources...")
|
||||
amlnn.uninit()
|
||||
print("Resources released.")
|
||||
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import sys
|
||||
sys.exit(main())
|
||||
|
||||
|
||||
77
examples/yolox/cpp/build-android.sh
Executable file
77
examples/yolox/cpp/build-android.sh
Executable file
|
|
@ -0,0 +1,77 @@
|
|||
#!/bin/bash
|
||||
set -e
|
||||
|
||||
#
|
||||
# Copyright (C) 2024–2025 Amlogic, Inc. 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.
|
||||
#
|
||||
|
||||
usage() {
|
||||
echo "Usage: $0 [-a <target_abi>]"
|
||||
echo " -a <target_abi> : Target ABI (default: arm64-v8a)"
|
||||
echo " -h : Show this help message"
|
||||
exit 1
|
||||
}
|
||||
|
||||
# Default values
|
||||
TARGET_ABI=arm64-v8a
|
||||
|
||||
# Parse arguments
|
||||
while getopts 'a:h' opt; do
|
||||
case "$opt" in
|
||||
a)
|
||||
TARGET_ABI=$OPTARG
|
||||
;;
|
||||
h)
|
||||
usage
|
||||
;;
|
||||
*)
|
||||
usage
|
||||
;;
|
||||
esac
|
||||
done
|
||||
|
||||
if [ -z "${ANDROID_NDK_PATH}" ]; then
|
||||
if [ -n "${ANDROID_NDK}" ]; then
|
||||
ANDROID_NDK_PATH=${ANDROID_NDK}
|
||||
elif [ -n "${ANDROID_NDK_HOME}" ]; then
|
||||
ANDROID_NDK_PATH=${ANDROID_NDK_HOME}
|
||||
else
|
||||
echo "Error: ANDROID_NDK_PATH is not set."
|
||||
echo "Please set ANDROID_NDK_PATH to your Android NDK directory."
|
||||
exit 1
|
||||
fi
|
||||
fi
|
||||
|
||||
ROOT_PWD=$(cd "$(dirname $0)" && pwd)
|
||||
BUILD_DIR=${ROOT_PWD}/build/android
|
||||
|
||||
echo "Building for Android..."
|
||||
echo "NDK_PATH: ${ANDROID_NDK_PATH}"
|
||||
echo "TARGET_ABI: ${TARGET_ABI}"
|
||||
echo "BUILD_DIR: ${BUILD_DIR}"
|
||||
|
||||
mkdir -p ${BUILD_DIR}
|
||||
cd ${BUILD_DIR}
|
||||
|
||||
cmake ../../src \
|
||||
-DCMAKE_TOOLCHAIN_FILE=${ANDROID_NDK_PATH}/build/cmake/android.toolchain.cmake \
|
||||
-DANDROID_ABI=${TARGET_ABI} \
|
||||
-DANDROID_PLATFORM=android-24 \
|
||||
-DCMAKE_BUILD_TYPE=Release \
|
||||
-DOpenCV_DIR=${ROOT_PWD}/../../../dependency/opencv/opencv-android-sdk-build/sdk/native/jni/abi-${TARGET_ABI}
|
||||
|
||||
make -j4
|
||||
|
||||
echo "Build complete. Executable in ${BUILD_DIR}/yolox_demo"
|
||||
65
examples/yolox/cpp/build-linux.sh
Executable file
65
examples/yolox/cpp/build-linux.sh
Executable file
|
|
@ -0,0 +1,65 @@
|
|||
#TODO
|
||||
#!/bin/bash
|
||||
set -e
|
||||
|
||||
usage() {
|
||||
echo "Usage: $0 [-a <target_arch>]"
|
||||
echo " -a <target_arch> : Target architecture (default: aarch64)"
|
||||
echo " -h : Show this help message"
|
||||
exit 1
|
||||
}
|
||||
|
||||
# Default values
|
||||
TARGET_ARCH=aarch64
|
||||
|
||||
# Parse arguments
|
||||
while getopts 'a:h' opt; do
|
||||
case "$opt" in
|
||||
a)
|
||||
TARGET_ARCH=$OPTARG
|
||||
;;
|
||||
h)
|
||||
usage
|
||||
;;
|
||||
*)
|
||||
usage
|
||||
;;
|
||||
esac
|
||||
done
|
||||
|
||||
# Default to aarch64-linux-gnu if GCC_COMPILER is not set
|
||||
GCC_COMPILER=${GCC_COMPILER:-aarch64-linux-gnu}
|
||||
|
||||
# Set compilers
|
||||
export CC=${GCC_COMPILER}-gcc
|
||||
export CXX=${GCC_COMPILER}-g++
|
||||
|
||||
# Validate compiler
|
||||
if ! command -v ${CC} &> /dev/null; then
|
||||
echo "Error: Compiler ${CC} not found."
|
||||
echo "Please set GCC_COMPILER environment variable to your cross-compiler path prefix."
|
||||
echo "Example: export GCC_COMPILER=/path/to/toolchain/bin/aarch64-linux-gnu"
|
||||
# Proceeding anyway as user might have custom env setup
|
||||
else
|
||||
echo "Using compiler: ${CC}"
|
||||
fi
|
||||
|
||||
ROOT_PWD=$(cd "$(dirname $0)" && pwd)
|
||||
BUILD_DIR=${ROOT_PWD}/build/linux
|
||||
|
||||
echo "Building for Linux..."
|
||||
echo "COMPILER: ${CC}"
|
||||
echo "TARGET_ARCH: ${TARGET_ARCH}"
|
||||
echo "BUILD_DIR: ${BUILD_DIR}"
|
||||
|
||||
mkdir -p ${BUILD_DIR}
|
||||
cd ${BUILD_DIR}
|
||||
|
||||
cmake ../../src \
|
||||
-DCMAKE_SYSTEM_NAME=Linux \
|
||||
-DCMAKE_SYSTEM_PROCESSOR=${TARGET_ARCH} \
|
||||
-DCMAKE_BUILD_TYPE=Release \
|
||||
-DOpenCV_DIR=${ROOT_PWD}/../../../dependency/opencv/opencv-linux-aarch64/share/OpenCV
|
||||
make -j4
|
||||
|
||||
echo "Build complete. Executable in ${BUILD_DIR}/yolo11_demo"
|
||||
41
examples/yolox/cpp/src/CMakeLists.txt
Executable file
41
examples/yolox/cpp/src/CMakeLists.txt
Executable file
|
|
@ -0,0 +1,41 @@
|
|||
cmake_minimum_required(VERSION 3.5)
|
||||
project(yolo11_demo)
|
||||
|
||||
set(CMAKE_CXX_STANDARD 17)
|
||||
|
||||
# Set NNSDK path
|
||||
set(NNSDK_ROOT "${CMAKE_SOURCE_DIR}/../../../../dependency/nnsdk")
|
||||
include_directories(${NNSDK_ROOT}/include)
|
||||
include_directories(${CMAKE_SOURCE_DIR}/../../../../common)
|
||||
|
||||
# Set dependency path
|
||||
set(3RDPARTY_DIR "${CMAKE_SOURCE_DIR}/../../../../dependency")
|
||||
|
||||
if(CMAKE_SYSTEM_NAME STREQUAL "Android")
|
||||
if (ANDROID_ABI STREQUAL "arm64-v8a")
|
||||
link_directories(${NNSDK_ROOT}/lib/android/arm64-v8a)
|
||||
else()
|
||||
link_directories(${NNSDK_ROOT}/lib/android/armeabi-v7a)
|
||||
endif()
|
||||
# Android needs log
|
||||
link_libraries(log)
|
||||
elseif(CMAKE_SYSTEM_NAME STREQUAL "Linux")
|
||||
link_directories(${NNSDK_ROOT}/lib/linux/lib64_yocto)
|
||||
endif()
|
||||
|
||||
# Find OpenCV
|
||||
message(STATUS "OpenCV_DIR: ${OpenCV_DIR}")
|
||||
find_package(OpenCV REQUIRED)
|
||||
include_directories(${OpenCV_INCLUDE_DIRS})
|
||||
|
||||
|
||||
add_executable(yolox_demo
|
||||
main.cpp
|
||||
postprocess.cpp
|
||||
${CMAKE_SOURCE_DIR}/../../../../common/model_loader.cpp
|
||||
)
|
||||
|
||||
target_link_libraries(yolox_demo
|
||||
${OpenCV_LIBS}
|
||||
nnsdk
|
||||
)
|
||||
153
examples/yolox/cpp/src/main.cpp
Executable file
153
examples/yolox/cpp/src/main.cpp
Executable file
|
|
@ -0,0 +1,153 @@
|
|||
/*
|
||||
* Copyright (C) 2024–2025 Amlogic, Inc. 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 <iostream>
|
||||
#include <string>
|
||||
#include <vector>
|
||||
#include <chrono>
|
||||
#include <cmath>
|
||||
#include <opencv2/opencv.hpp>
|
||||
#include "postprocess.h"
|
||||
#include "model_loader.h"
|
||||
|
||||
static float sigmoid(float x) {
|
||||
return 1.0f / (1.0f + std::exp(-x));
|
||||
}
|
||||
|
||||
const std::string DEFAULT_OUTPUT_PATH = "./result.jpg";
|
||||
const int MODEL_INPUT_WIDTH = 640;
|
||||
const int MODEL_INPUT_HEIGHT = 640;
|
||||
const float SCORE_THRESHOLD = 0.25f;
|
||||
const float NMS_THRESHOLD = 0.45f;
|
||||
const float CONF_THRESHOLD = 0.45f;
|
||||
|
||||
const std::vector<std::string> CLASS_NAMES = {
|
||||
"person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat",
|
||||
"traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog",
|
||||
"horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella",
|
||||
"handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite",
|
||||
"baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle",
|
||||
"wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich",
|
||||
"orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch",
|
||||
"potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote",
|
||||
"keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book",
|
||||
"clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"
|
||||
};
|
||||
|
||||
int main(int argc, char** argv) {
|
||||
if (argc < 3) {
|
||||
printf("Usage: %s <model_path> <image_path> [output_path]\n", argv[0]);
|
||||
return -1;
|
||||
}
|
||||
|
||||
std::string model_path = argv[1];
|
||||
std::string image_path = argv[2];
|
||||
std::string output_path = (argc > 3) ? argv[3] : DEFAULT_OUTPUT_PATH;
|
||||
|
||||
std::cout << "YOLOX C++ Demo" << std::endl;
|
||||
std::cout << "Model: " << model_path << std::endl;
|
||||
std::cout << "Image: " << image_path << std::endl;
|
||||
std::cout << "Output: " << output_path << std::endl;
|
||||
|
||||
// 1. Load Image
|
||||
cv::Mat origin_img = cv::imread(image_path);
|
||||
if (origin_img.empty()) {
|
||||
std::cerr << "Failed to load image from " << image_path << std::endl;
|
||||
return -1;
|
||||
}
|
||||
|
||||
// 2. Initialize Network
|
||||
void* context = init_network(model_path.c_str());
|
||||
if (!context) {
|
||||
std::cerr << "Failed to initialize network." << std::endl;
|
||||
return -1;
|
||||
}
|
||||
|
||||
// 3. Preprocess
|
||||
cv::Mat img;
|
||||
float scale;
|
||||
std::tuple<int, int> pad;
|
||||
std::tie(img, scale, pad) = preproc(origin_img, std::make_tuple(MODEL_INPUT_HEIGHT, MODEL_INPUT_WIDTH));
|
||||
int pad_left = std::get<0>(pad);
|
||||
int pad_top = std::get<1>(pad);
|
||||
|
||||
// 4. Run Network
|
||||
std::tuple<cv::Mat, float, std::tuple<int, int>> input_tuple =
|
||||
std::make_tuple(img, scale, pad);
|
||||
|
||||
auto start_time = std::chrono::high_resolution_clock::now();
|
||||
|
||||
void* output_ptr = run_network(context, {input_tuple});
|
||||
if (!output_ptr) {
|
||||
std::cerr << "Failed to run network." << std::endl;
|
||||
uninit_network(context);
|
||||
return -1;
|
||||
}
|
||||
nn_output* outdata = (nn_output*)output_ptr;
|
||||
|
||||
// 5. Postprocess
|
||||
int num_classes = CLASS_NAMES.size();
|
||||
std::vector<Detection> detections;
|
||||
|
||||
if (outdata->num == 1) {
|
||||
// Single output YOLOX model [1, 8400, 85]
|
||||
float* output = (float*)outdata->out[0].buf;
|
||||
|
||||
int num_boxes = 8400; // Default for YOLOX
|
||||
if (outdata->out[0].param && outdata->out[0].param->num_of_dims >= 2) {
|
||||
if (outdata->out[0].param->num_of_dims == 3) {
|
||||
num_boxes = outdata->out[0].param->sizes[1];
|
||||
} else if (outdata->out[0].param->num_of_dims == 2) {
|
||||
num_boxes = outdata->out[0].param->sizes[0];
|
||||
}
|
||||
}
|
||||
|
||||
demo_postprocess(output, num_boxes, std::make_tuple(MODEL_INPUT_HEIGHT, MODEL_INPUT_WIDTH), false);
|
||||
|
||||
// boxes/scores
|
||||
std::vector<cv::Rect2f> boxes;
|
||||
std::vector<std::vector<float>> scores;
|
||||
extract_boxes_and_scores(
|
||||
output, num_boxes, num_classes,
|
||||
scale, pad_left, pad_top,
|
||||
origin_img.cols, origin_img.rows,
|
||||
boxes, scores
|
||||
);
|
||||
|
||||
// multiclass_nms
|
||||
detections = multiclass_nms(boxes, scores, num_classes, NMS_THRESHOLD, 0.1f);
|
||||
} else {
|
||||
std::cerr << "Error: Unsupported output count: " << outdata->num << std::endl;
|
||||
uninit_network(context);
|
||||
return -1;
|
||||
}
|
||||
|
||||
auto end_time = std::chrono::high_resolution_clock::now();
|
||||
std::chrono::duration<double, std::milli> inference_time = end_time - start_time;
|
||||
std::cout << "Inference + Postprocess time: " << inference_time.count() << " ms" << std::endl;
|
||||
std::cout << "Detections found: " << detections.size() << std::endl;
|
||||
|
||||
// 6. Visualize and Save
|
||||
cv::Mat result_img = vis(origin_img, detections, CONF_THRESHOLD, CLASS_NAMES);
|
||||
cv::imwrite(output_path, result_img);
|
||||
std::cout << "Result saved to " << output_path << std::endl;
|
||||
|
||||
// 7. Cleanup
|
||||
uninit_network(context);
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
409
examples/yolox/cpp/src/postprocess.cpp
Executable file
409
examples/yolox/cpp/src/postprocess.cpp
Executable file
|
|
@ -0,0 +1,409 @@
|
|||
/*
|
||||
* Copyright (C) 2024–2025 Amlogic, Inc. 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 <algorithm>
|
||||
#include <cmath>
|
||||
#include <numeric>
|
||||
|
||||
std::tuple<cv::Mat, float, std::tuple<int, int>> preproc(const cv::Mat& img, std::tuple<int, int> input_size) {
|
||||
// 1. letterbox (resize + padding)
|
||||
// 2. BGR to RGB conversion
|
||||
// 3. Normalize to 0-1 (divide by 255.0)
|
||||
// Note: NNSDK's model_loader expects HWC format, so return HWC instead of CHW
|
||||
|
||||
int input_height = std::get<0>(input_size);
|
||||
int input_width = std::get<1>(input_size);
|
||||
|
||||
// letterbox: calculate scale and padding
|
||||
float scale = std::min(static_cast<float>(input_height) / img.rows,
|
||||
static_cast<float>(input_width) / img.cols);
|
||||
int new_w = static_cast<int>(std::round(img.cols * scale));
|
||||
int new_h = static_cast<int>(std::round(img.rows * scale));
|
||||
|
||||
// resize
|
||||
cv::Mat resized_img;
|
||||
if (img.size() != cv::Size(new_w, new_h)) {
|
||||
cv::resize(img, resized_img, cv::Size(new_w, new_h), 0, 0, cv::INTER_LINEAR);
|
||||
} else {
|
||||
resized_img = img.clone();
|
||||
}
|
||||
|
||||
// padding
|
||||
float pad_w = (input_width - new_w) / 2.0f;
|
||||
float pad_h = (input_height - new_h) / 2.0f;
|
||||
int top = static_cast<int>(std::round(pad_h - 0.1f));
|
||||
int bottom = static_cast<int>(std::round(pad_h + 0.1f));
|
||||
int left = static_cast<int>(std::round(pad_w - 0.1f));
|
||||
int right = static_cast<int>(std::round(pad_w + 0.1f));
|
||||
|
||||
cv::Mat padded_img;
|
||||
cv::copyMakeBorder(resized_img, padded_img, top, bottom, left, right,
|
||||
cv::BORDER_CONSTANT, cv::Scalar(114, 114, 114));
|
||||
|
||||
// BGR to RGB conversion
|
||||
cv::Mat rgb_img;
|
||||
cv::cvtColor(padded_img, rgb_img, cv::COLOR_BGR2RGB);
|
||||
|
||||
// Normalize to 0-1 range (divide by 255.0)
|
||||
cv::Mat normalized_img;
|
||||
rgb_img.convertTo(normalized_img, CV_32F, 1.0 / 255.0);
|
||||
|
||||
// mean = [0.485, 0.456, 0.406]
|
||||
// std = [0.229, 0.224, 0.225]
|
||||
// Note: Use cv::divide for per-channel division in OpenCV
|
||||
cv::Scalar mean(0.485f, 0.456f, 0.406f);
|
||||
cv::Scalar std(0.229f, 0.224f, 0.225f);
|
||||
normalized_img -= mean;
|
||||
cv::divide(normalized_img, std, normalized_img);
|
||||
|
||||
// Return HWC format, ImageNet normalized float32 image (RGB format)
|
||||
// Also return scale and padding (left, top) for coordinate mapping
|
||||
return std::make_tuple(normalized_img, scale, std::make_tuple(left, top));
|
||||
}
|
||||
|
||||
static float sigmoid(float x) {
|
||||
return 1.0f / (1.0f + std::exp(-x));
|
||||
}
|
||||
|
||||
void demo_postprocess(float* outputs, int num_boxes, std::tuple<int, int> img_size, bool p6) {
|
||||
int img_height = std::get<0>(img_size);
|
||||
int img_width = std::get<1>(img_size);
|
||||
|
||||
std::vector<int> strides;
|
||||
if (!p6) {
|
||||
strides = {8, 16, 32};
|
||||
} else {
|
||||
strides = {8, 16, 32, 64};
|
||||
}
|
||||
|
||||
// Calculate grid count for each stride
|
||||
std::vector<int> hsizes, wsizes;
|
||||
for (int stride : strides) {
|
||||
hsizes.push_back(img_height / stride);
|
||||
wsizes.push_back(img_width / stride);
|
||||
}
|
||||
|
||||
// Build grids and expanded_strides
|
||||
std::vector<std::vector<float>> grids_list;
|
||||
std::vector<std::vector<float>> strides_list;
|
||||
|
||||
int total_boxes = 0;
|
||||
for (size_t i = 0; i < strides.size(); ++i) {
|
||||
int hsize = hsizes[i];
|
||||
int wsize = wsizes[i];
|
||||
int stride = strides[i];
|
||||
int grid_size = hsize * wsize;
|
||||
|
||||
std::vector<float> grid(grid_size * 2);
|
||||
std::vector<float> expanded_stride(grid_size);
|
||||
|
||||
for (int h = 0; h < hsize; ++h) {
|
||||
for (int w = 0; w < wsize; ++w) {
|
||||
int idx = h * wsize + w;
|
||||
grid[idx * 2] = static_cast<float>(w);
|
||||
grid[idx * 2 + 1] = static_cast<float>(h);
|
||||
expanded_stride[idx] = static_cast<float>(stride);
|
||||
}
|
||||
}
|
||||
|
||||
grids_list.push_back(grid);
|
||||
strides_list.push_back(expanded_stride);
|
||||
total_boxes += grid_size;
|
||||
}
|
||||
|
||||
// Merge all grids and strides
|
||||
std::vector<float> all_grids(total_boxes * 2);
|
||||
std::vector<float> all_strides(total_boxes);
|
||||
|
||||
int offset = 0;
|
||||
for (size_t i = 0; i < grids_list.size(); ++i) {
|
||||
int grid_size = grids_list[i].size() / 2;
|
||||
for (int j = 0; j < grid_size; ++j) {
|
||||
all_grids[(offset + j) * 2] = grids_list[i][j * 2];
|
||||
all_grids[(offset + j) * 2 + 1] = grids_list[i][j * 2 + 1];
|
||||
all_strides[offset + j] = strides_list[i][j];
|
||||
}
|
||||
offset += grid_size;
|
||||
}
|
||||
|
||||
// Apply grid and stride decoding
|
||||
for (int i = 0; i < num_boxes && i < total_boxes; ++i) {
|
||||
float* box = outputs + i * 85;
|
||||
|
||||
// outputs[..., :2] = (outputs[..., :2] + grids) * expanded_strides
|
||||
box[0] = (box[0] + all_grids[i * 2]) * all_strides[i];
|
||||
box[1] = (box[1] + all_grids[i * 2 + 1]) * all_strides[i];
|
||||
|
||||
// outputs[..., 2:4] = np.exp(outputs[..., 2:4]) * expanded_strides
|
||||
box[2] = std::exp(box[2]) * all_strides[i];
|
||||
box[3] = std::exp(box[3]) * all_strides[i];
|
||||
}
|
||||
}
|
||||
|
||||
std::vector<int> nms(const std::vector<cv::Rect2f>& boxes, const std::vector<float>& scores, float nms_thr) {
|
||||
if (boxes.empty()) return {};
|
||||
|
||||
// Create indices and sort
|
||||
std::vector<int> indices(boxes.size());
|
||||
std::iota(indices.begin(), indices.end(), 0);
|
||||
std::sort(indices.begin(), indices.end(), [&scores](int a, int b) {
|
||||
return scores[a] > scores[b];
|
||||
});
|
||||
|
||||
std::vector<int> keep;
|
||||
std::vector<bool> suppressed(boxes.size(), false);
|
||||
|
||||
for (size_t i = 0; i < indices.size(); ++i) {
|
||||
int idx = indices[i];
|
||||
if (suppressed[idx]) continue;
|
||||
|
||||
keep.push_back(idx);
|
||||
|
||||
float x1_i = boxes[idx].x;
|
||||
float y1_i = boxes[idx].y;
|
||||
float x2_i = boxes[idx].x + boxes[idx].width;
|
||||
float y2_i = boxes[idx].y + boxes[idx].height;
|
||||
float area_i = boxes[idx].width * boxes[idx].height;
|
||||
|
||||
for (size_t j = i + 1; j < indices.size(); ++j) {
|
||||
int idx_j = indices[j];
|
||||
if (suppressed[idx_j]) continue;
|
||||
|
||||
float x1_j = boxes[idx_j].x;
|
||||
float y1_j = boxes[idx_j].y;
|
||||
float x2_j = boxes[idx_j].x + boxes[idx_j].width;
|
||||
float y2_j = boxes[idx_j].y + boxes[idx_j].height;
|
||||
|
||||
float xx1 = std::max(x1_i, x1_j);
|
||||
float yy1 = std::max(y1_i, y1_j);
|
||||
float xx2 = std::min(x2_i, x2_j);
|
||||
float yy2 = std::min(y2_i, y2_j);
|
||||
|
||||
float w = std::max(0.0f, xx2 - xx1);
|
||||
float h = std::max(0.0f, yy2 - yy1);
|
||||
float inter = w * h;
|
||||
|
||||
float area_j = boxes[idx_j].width * boxes[idx_j].height;
|
||||
float ovr = inter / (area_i + area_j - inter);
|
||||
|
||||
if (ovr > nms_thr) {
|
||||
suppressed[idx_j] = true;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return keep;
|
||||
}
|
||||
|
||||
std::vector<Detection> multiclass_nms(const std::vector<cv::Rect2f>& boxes,
|
||||
const std::vector<std::vector<float>>& scores,
|
||||
int num_classes,
|
||||
float nms_thr,
|
||||
float score_thr) {
|
||||
if (boxes.empty() || scores.empty()) return {};
|
||||
|
||||
// Find max class score and class ID for each box
|
||||
std::vector<float> cls_scores(boxes.size());
|
||||
std::vector<int> cls_inds(boxes.size());
|
||||
|
||||
for (size_t i = 0; i < boxes.size(); ++i) {
|
||||
float max_score = -1.0f;
|
||||
int max_idx = -1;
|
||||
for (int c = 0; c < num_classes; ++c) {
|
||||
if (scores[i][c] > max_score) {
|
||||
max_score = scores[i][c];
|
||||
max_idx = c;
|
||||
}
|
||||
}
|
||||
cls_scores[i] = max_score;
|
||||
cls_inds[i] = max_idx;
|
||||
}
|
||||
|
||||
// Filter low-score boxes
|
||||
std::vector<cv::Rect2f> valid_boxes;
|
||||
std::vector<float> valid_scores;
|
||||
std::vector<int> valid_cls_inds;
|
||||
std::vector<int> valid_indices;
|
||||
|
||||
for (size_t i = 0; i < boxes.size(); ++i) {
|
||||
if (cls_scores[i] > score_thr) {
|
||||
valid_boxes.push_back(boxes[i]);
|
||||
valid_scores.push_back(cls_scores[i]);
|
||||
valid_cls_inds.push_back(cls_inds[i]);
|
||||
valid_indices.push_back(i);
|
||||
}
|
||||
}
|
||||
|
||||
if (valid_boxes.empty()) return {};
|
||||
|
||||
// Execute NMS
|
||||
std::vector<int> keep = nms(valid_boxes, valid_scores, nms_thr);
|
||||
|
||||
// Build results
|
||||
std::vector<Detection> dets;
|
||||
for (int idx : keep) {
|
||||
Detection det;
|
||||
det.x1 = valid_boxes[idx].x;
|
||||
det.y1 = valid_boxes[idx].y;
|
||||
det.x2 = valid_boxes[idx].x + valid_boxes[idx].width;
|
||||
det.y2 = valid_boxes[idx].y + valid_boxes[idx].height;
|
||||
det.score = valid_scores[idx];
|
||||
det.class_id = valid_cls_inds[idx];
|
||||
dets.push_back(det);
|
||||
}
|
||||
|
||||
return dets;
|
||||
}
|
||||
|
||||
cv::Mat vis(const cv::Mat& img,
|
||||
const std::vector<Detection>& detections,
|
||||
float conf_thresh,
|
||||
const std::vector<std::string>& class_names) {
|
||||
cv::Mat result = img.clone();
|
||||
|
||||
// Adjust font size based on image size
|
||||
int img_height = img.rows;
|
||||
int img_width = img.cols;
|
||||
float font_scale = std::max(0.6f, std::min(1.2f,
|
||||
static_cast<float>(std::sqrt(img_height * img_height + img_width * img_width)) * 0.0015f));
|
||||
int thickness = std::max(2, static_cast<int>(font_scale * 2.5f));
|
||||
|
||||
// YOLOX color palette
|
||||
static const std::vector<cv::Scalar> colors = {
|
||||
cv::Scalar(0, 114, 189), cv::Scalar(217, 83, 25), cv::Scalar(237, 177, 32),
|
||||
cv::Scalar(126, 47, 142), cv::Scalar(119, 172, 48), cv::Scalar(77, 190, 238),
|
||||
cv::Scalar(162, 20, 47), cv::Scalar(77, 77, 77), cv::Scalar(153, 153, 153),
|
||||
cv::Scalar(255, 0, 0), cv::Scalar(255, 128, 0), cv::Scalar(191, 191, 0),
|
||||
cv::Scalar(0, 255, 0), cv::Scalar(0, 0, 255), cv::Scalar(170, 0, 255),
|
||||
cv::Scalar(85, 85, 0), cv::Scalar(85, 170, 0), cv::Scalar(85, 255, 0),
|
||||
cv::Scalar(170, 85, 0), cv::Scalar(170, 170, 0), cv::Scalar(170, 255, 0),
|
||||
cv::Scalar(255, 85, 0), cv::Scalar(255, 170, 0), cv::Scalar(255, 255, 0),
|
||||
cv::Scalar(0, 85, 128), cv::Scalar(0, 170, 128), cv::Scalar(0, 255, 128),
|
||||
cv::Scalar(85, 0, 128), cv::Scalar(85, 85, 128), cv::Scalar(85, 170, 128),
|
||||
cv::Scalar(85, 255, 128), cv::Scalar(170, 0, 128), cv::Scalar(170, 85, 128),
|
||||
cv::Scalar(170, 170, 128), cv::Scalar(170, 255, 128), cv::Scalar(255, 0, 128),
|
||||
cv::Scalar(255, 85, 128), cv::Scalar(255, 170, 128), cv::Scalar(255, 255, 128),
|
||||
cv::Scalar(0, 85, 255), cv::Scalar(0, 170, 255), cv::Scalar(0, 255, 255),
|
||||
cv::Scalar(85, 0, 255), cv::Scalar(85, 85, 255), cv::Scalar(85, 170, 255),
|
||||
cv::Scalar(85, 255, 255), cv::Scalar(170, 0, 255), cv::Scalar(170, 85, 255),
|
||||
cv::Scalar(170, 170, 255), cv::Scalar(170, 255, 255), cv::Scalar(255, 0, 255),
|
||||
cv::Scalar(255, 85, 255), cv::Scalar(255, 170, 255), cv::Scalar(85, 0, 0),
|
||||
cv::Scalar(128, 0, 0), cv::Scalar(170, 0, 0), cv::Scalar(213, 0, 0),
|
||||
cv::Scalar(255, 0, 0), cv::Scalar(0, 43, 0), cv::Scalar(0, 85, 0),
|
||||
cv::Scalar(0, 128, 0), cv::Scalar(0, 170, 0), cv::Scalar(0, 213, 0),
|
||||
cv::Scalar(0, 255, 0), cv::Scalar(0, 0, 43), cv::Scalar(0, 0, 85),
|
||||
cv::Scalar(0, 0, 128), cv::Scalar(0, 0, 170), cv::Scalar(0, 0, 213),
|
||||
cv::Scalar(0, 0, 255), cv::Scalar(0, 0, 0), cv::Scalar(36, 36, 36),
|
||||
cv::Scalar(219, 219, 219), cv::Scalar(255, 255, 255)
|
||||
};
|
||||
|
||||
for (const auto& det : detections) {
|
||||
if (det.score < conf_thresh) continue;
|
||||
if (det.class_id < 0 || det.class_id >= static_cast<int>(class_names.size())) continue;
|
||||
|
||||
int x0 = static_cast<int>(det.x1);
|
||||
int y0 = static_cast<int>(det.y1);
|
||||
int x1 = static_cast<int>(det.x2);
|
||||
int y1 = static_cast<int>(det.y2);
|
||||
|
||||
cv::Scalar color = colors[det.class_id % colors.size()];
|
||||
|
||||
// Draw bounding box
|
||||
cv::rectangle(result, cv::Point(x0, y0), cv::Point(x1, y1), color, thickness);
|
||||
|
||||
// Prepare text
|
||||
std::string text = class_names[det.class_id] + ":" + cv::format("%.1f%%", det.score * 100);
|
||||
|
||||
// Calculate text size
|
||||
int baseline = 0;
|
||||
cv::Size txt_size = cv::getTextSize(text, cv::FONT_HERSHEY_SIMPLEX, font_scale, thickness, &baseline);
|
||||
|
||||
// Draw text background
|
||||
cv::Scalar txt_bk_color = color * 0.7;
|
||||
cv::rectangle(result,
|
||||
cv::Point(x0, y0 + 1),
|
||||
cv::Point(x0 + txt_size.width + 1, y0 + static_cast<int>(1.5 * txt_size.height)),
|
||||
txt_bk_color, -1);
|
||||
|
||||
// Draw text
|
||||
cv::Scalar txt_color = (cv::mean(color)[0] > 0.5) ? cv::Scalar(0, 0, 0) : cv::Scalar(255, 255, 255);
|
||||
cv::putText(result, text,
|
||||
cv::Point(x0, y0 + txt_size.height),
|
||||
cv::FONT_HERSHEY_SIMPLEX, font_scale, txt_color, thickness);
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
void extract_boxes_and_scores(
|
||||
float* output,
|
||||
int num_boxes,
|
||||
int num_classes,
|
||||
float scale,
|
||||
int pad_left,
|
||||
int pad_top,
|
||||
int img_width,
|
||||
int img_height,
|
||||
std::vector<cv::Rect2f>& boxes,
|
||||
std::vector<std::vector<float>>& scores)
|
||||
{
|
||||
boxes.clear();
|
||||
scores.clear();
|
||||
boxes.reserve(num_boxes);
|
||||
scores.reserve(num_boxes);
|
||||
|
||||
// Extract all boxes and scores
|
||||
for (int i = 0; i < num_boxes; ++i) {
|
||||
float* box_data = output + i * 85;
|
||||
|
||||
// Format after demo_postprocess: [cx, cy, w, h, obj_conf, class0, ..., class79]
|
||||
float cx = box_data[0];
|
||||
float cy = box_data[1];
|
||||
float w = box_data[2];
|
||||
float h = box_data[3];
|
||||
|
||||
// Python: boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2]/2.
|
||||
float x1 = cx - w / 2.0f;
|
||||
float y1 = cy - h / 2.0f;
|
||||
float x2 = cx + w / 2.0f;
|
||||
float y2 = cy + h / 2.0f;
|
||||
|
||||
// Python: valid_boxes[:, [0, 2]] = (valid_boxes[:, [0, 2]] - pad_x) / scale
|
||||
// Python: valid_boxes[:, [1, 3]] = (valid_boxes[:, [1, 3]] - pad_y) / scale
|
||||
x1 = (x1 - pad_left) / scale;
|
||||
y1 = (y1 - pad_top) / scale;
|
||||
x2 = (x2 - pad_left) / scale;
|
||||
y2 = (y2 - pad_top) / scale;
|
||||
|
||||
// Ensure coordinates are within image bounds
|
||||
x1 = std::max(0.0f, std::min(static_cast<float>(img_width), x1));
|
||||
y1 = std::max(0.0f, std::min(static_cast<float>(img_height), y1));
|
||||
x2 = std::max(0.0f, std::min(static_cast<float>(img_width), x2));
|
||||
y2 = std::max(0.0f, std::min(static_cast<float>(img_height), y2));
|
||||
|
||||
boxes.push_back(cv::Rect2f(x1, y1, x2 - x1, y2 - y1));
|
||||
|
||||
// Calculate class scores (obj_conf * cls_scores)
|
||||
float obj_conf = box_data[4];
|
||||
std::vector<float> cls_scores(num_classes);
|
||||
for (int c = 0; c < num_classes; ++c) {
|
||||
float cls_score = box_data[5 + c];
|
||||
cls_scores[c] = obj_conf * cls_score;
|
||||
}
|
||||
scores.push_back(cls_scores);
|
||||
}
|
||||
}
|
||||
112
examples/yolox/cpp/src/postprocess.h
Executable file
112
examples/yolox/cpp/src/postprocess.h
Executable file
|
|
@ -0,0 +1,112 @@
|
|||
/*
|
||||
* Copyright (C) 2024–2025 Amlogic, Inc. 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 YOLOX_POSTPROCESS_H
|
||||
#define YOLOX_POSTPROCESS_H
|
||||
|
||||
#include <opencv2/opencv.hpp>
|
||||
#include <vector>
|
||||
#include <tuple>
|
||||
#include <string>
|
||||
|
||||
/**
|
||||
* YOLOX preprocessing function
|
||||
* @param img Input image (BGR format)
|
||||
* @param input_size Target size (height, width)
|
||||
* @return Processed image (HWC format, float32, ImageNet normalized, RGB format), scale factor, padding (left, top)
|
||||
* Note: NNSDK's model_loader expects HWC format, so return HWC instead of CHW
|
||||
* Processing steps:
|
||||
* 1. letterbox (resize + padding with 114)
|
||||
* 2. BGR to RGB conversion
|
||||
* 3. Normalize to 0-1 (divide by 255.0)
|
||||
* 4. ImageNet normalization (mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
|
||||
*/
|
||||
std::tuple<cv::Mat, float, std::tuple<int, int>> preproc(const cv::Mat& img, std::tuple<int, int> input_size);
|
||||
|
||||
// Detection result structure
|
||||
struct Detection {
|
||||
float x1, y1, x2, y2; // Bounding box coordinates
|
||||
float score; // Confidence score
|
||||
int class_id; // Predicted class ID
|
||||
};
|
||||
|
||||
/**
|
||||
* YOLOX official demo_postprocess function (C++ implementation)
|
||||
* Decode model output to absolute coordinates
|
||||
* @param outputs Model output [batch, num_boxes, 85]
|
||||
* @param num_boxes Number of detection boxes
|
||||
* @param img_size Input image size (height, width)
|
||||
* @param p6 Whether to use P6 (default false, use P5)
|
||||
* @return Decoded output [num_boxes, 85], format: [cx, cy, w, h, obj_conf, class0, ..., class79]
|
||||
*/
|
||||
void demo_postprocess(float* outputs, int num_boxes, std::tuple<int, int> img_size, bool p6 = false);
|
||||
|
||||
/**
|
||||
* Extract boxes and scores from output after demo_postprocess
|
||||
* @param output Model output (processed by demo_postprocess) [num_boxes * 85]
|
||||
* @param num_boxes Number of detection boxes
|
||||
* @param num_classes Number of classes
|
||||
* @param scale Scale factor from preprocessing
|
||||
* @param pad_left Left padding boundary
|
||||
* @param pad_top Top padding boundary
|
||||
* @param img_width Original image width
|
||||
* @param img_height Original image height
|
||||
* @param boxes Output boxes (xyxy format, mapped to original image size)
|
||||
* @param scores Output scores (class scores for each box, obj_conf * cls_scores)
|
||||
*/
|
||||
void extract_boxes_and_scores(
|
||||
float* output,
|
||||
int num_boxes,
|
||||
int num_classes,
|
||||
float scale,
|
||||
int pad_left,
|
||||
int pad_top,
|
||||
int img_width,
|
||||
int img_height,
|
||||
std::vector<cv::Rect2f>& boxes,
|
||||
std::vector<std::vector<float>>& scores
|
||||
);
|
||||
|
||||
/**
|
||||
* Single-class NMS
|
||||
*/
|
||||
std::vector<int> nms(const std::vector<cv::Rect2f>& boxes, const std::vector<float>& scores, float nms_thr);
|
||||
|
||||
/**
|
||||
* YOLOX official multiclass_nms function (class-agnostic version)
|
||||
* @param boxes Detection boxes [N, 4] (x1, y1, x2, y2)
|
||||
* @param scores Class scores [N, num_classes]
|
||||
* @param num_classes Number of classes
|
||||
* @param nms_thr NMS threshold
|
||||
* @param score_thr Score threshold
|
||||
* @return Detection results, each row is [x1, y1, x2, y2, score, class_id]
|
||||
*/
|
||||
std::vector<Detection> multiclass_nms(const std::vector<cv::Rect2f>& boxes,
|
||||
const std::vector<std::vector<float>>& scores,
|
||||
int num_classes,
|
||||
float nms_thr,
|
||||
float score_thr);
|
||||
|
||||
/**
|
||||
* Visualize detection results (consistent with Python version, supports adaptive font size)
|
||||
*/
|
||||
cv::Mat vis(const cv::Mat& img,
|
||||
const std::vector<Detection>& detections,
|
||||
float conf_thresh,
|
||||
const std::vector<std::string>& class_names);
|
||||
|
||||
#endif // YOLOX_POSTPROCESS_H
|
||||
|
||||
0
examples/yolox/py/.gitkeep
Normal file
0
examples/yolox/py/.gitkeep
Normal file
448
examples/yolox/py/yolox.py
Executable file
448
examples/yolox/py/yolox.py
Executable file
|
|
@ -0,0 +1,448 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
import numpy as np
|
||||
import os
|
||||
import glob
|
||||
import argparse
|
||||
import cv2
|
||||
from pathlib import Path
|
||||
from amlnnlite.api import AMLNNLite
|
||||
|
||||
# COCO 80 class names
|
||||
CLASS_NAMES = [
|
||||
"person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat",
|
||||
"traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog",
|
||||
"horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella",
|
||||
"handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite",
|
||||
"baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle",
|
||||
"wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich",
|
||||
"orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch",
|
||||
"potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote",
|
||||
"keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book",
|
||||
"clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"
|
||||
]
|
||||
|
||||
def letterbox(img, new_shape=(640, 640), color=(114, 114, 114)):
|
||||
shape = img.shape[:2] # [height, width]
|
||||
scale = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
|
||||
new_unpad = (int(round(shape[1] * scale)), int(round(shape[0] * scale)))
|
||||
pad_w = (new_shape[1] - new_unpad[0]) / 2
|
||||
pad_h = (new_shape[0] - new_unpad[1]) / 2
|
||||
|
||||
if shape[::-1] != new_unpad:
|
||||
img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
|
||||
|
||||
top, bottom = int(round(pad_h - 0.1)), int(round(pad_h + 0.1))
|
||||
left, right = int(round(pad_w - 0.1)), int(round(pad_w + 0.1))
|
||||
img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)
|
||||
|
||||
return img, scale, (left, top)
|
||||
|
||||
def demo_postprocess(outputs, img_size, p6=False):
|
||||
"""
|
||||
YOLOX official demo_postprocess function
|
||||
Decode model output to absolute coordinates
|
||||
"""
|
||||
grids = []
|
||||
expanded_strides = []
|
||||
|
||||
if not p6:
|
||||
strides = [8, 16, 32]
|
||||
else:
|
||||
strides = [8, 16, 32, 64]
|
||||
|
||||
hsizes = [img_size[0] // stride for stride in strides]
|
||||
wsizes = [img_size[1] // stride for stride in strides]
|
||||
|
||||
for hsize, wsize, stride in zip(hsizes, wsizes, strides):
|
||||
xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize))
|
||||
grid = np.stack((xv, yv), 2).reshape(1, -1, 2)
|
||||
grids.append(grid)
|
||||
shape = grid.shape[:2]
|
||||
expanded_strides.append(np.full((*shape, 1), stride))
|
||||
|
||||
grids = np.concatenate(grids, 1)
|
||||
expanded_strides = np.concatenate(expanded_strides, 1)
|
||||
outputs[..., :2] = (outputs[..., :2] + grids) * expanded_strides
|
||||
outputs[..., 2:4] = np.exp(outputs[..., 2:4]) * expanded_strides
|
||||
|
||||
return outputs
|
||||
|
||||
def preprocess(img_path, new_shape=(640, 640), data_format='NHWC'):
|
||||
"""
|
||||
YOLOX preprocessing function (with ImageNet normalization)
|
||||
Returns: processed image (HWC format for NHWC, float32, normalized), scale, pad
|
||||
"""
|
||||
original_img = cv2.imread(str(img_path))
|
||||
if original_img is None:
|
||||
raise ValueError(f"can't read image: {img_path}")
|
||||
|
||||
processed_img, scale, pad = letterbox(original_img, new_shape)
|
||||
rgb_img = cv2.cvtColor(processed_img, cv2.COLOR_BGR2RGB)
|
||||
|
||||
# Normalize to 0-1
|
||||
normalized_img = rgb_img.astype(np.float32) / 255.0
|
||||
|
||||
# ImageNet normalization
|
||||
mean = np.array([0.485, 0.456, 0.406], dtype=np.float32)
|
||||
std = np.array([0.229, 0.224, 0.225], dtype=np.float32)
|
||||
normalized_img = (normalized_img - mean) / std
|
||||
|
||||
if data_format == 'NCHW':
|
||||
# HWC -> CHW -> BCHW
|
||||
input_tensor = np.transpose(normalized_img, (2, 0, 1))
|
||||
input_tensor = np.expand_dims(input_tensor, axis=0)
|
||||
elif data_format == 'NHWC':
|
||||
# HWC -> BHWC
|
||||
input_tensor = np.expand_dims(normalized_img, axis=0)
|
||||
else:
|
||||
raise ValueError(f"Unsupported data format: {data_format}. Only 'NCHW' and 'NHWC' are supported.")
|
||||
|
||||
return input_tensor, original_img, scale, pad
|
||||
|
||||
def nms(boxes, scores, nms_thr):
|
||||
"""Single class NMS implemented in Numpy."""
|
||||
x1 = boxes[:, 0]
|
||||
y1 = boxes[:, 1]
|
||||
x2 = boxes[:, 2]
|
||||
y2 = boxes[:, 3]
|
||||
|
||||
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
|
||||
order = scores.argsort()[::-1]
|
||||
|
||||
keep = []
|
||||
while order.size > 0:
|
||||
i = order[0]
|
||||
keep.append(i)
|
||||
if order.size == 1:
|
||||
break
|
||||
xx1 = np.maximum(x1[i], x1[order[1:]])
|
||||
yy1 = np.maximum(y1[i], y1[order[1:]])
|
||||
xx2 = np.minimum(x2[i], x2[order[1:]])
|
||||
yy2 = np.minimum(y2[i], y2[order[1:]])
|
||||
|
||||
w = np.maximum(0.0, xx2 - xx1 + 1)
|
||||
h = np.maximum(0.0, yy2 - yy1 + 1)
|
||||
inter = w * h
|
||||
ovr = inter / (areas[i] + areas[order[1:]] - inter)
|
||||
|
||||
inds = np.where(ovr <= nms_thr)[0]
|
||||
order = order[inds + 1]
|
||||
|
||||
return keep
|
||||
|
||||
def multiclass_nms(boxes, scores, nms_thr, score_thr):
|
||||
"""
|
||||
YOLOX official multiclass_nms function (class-agnostic version)
|
||||
"""
|
||||
cls_inds = scores.argmax(1)
|
||||
cls_scores = scores[np.arange(len(cls_inds)), cls_inds]
|
||||
|
||||
valid_score_mask = cls_scores > score_thr
|
||||
if valid_score_mask.sum() == 0:
|
||||
return None
|
||||
valid_scores = cls_scores[valid_score_mask]
|
||||
valid_boxes = boxes[valid_score_mask]
|
||||
valid_cls_inds = cls_inds[valid_score_mask]
|
||||
keep = nms(valid_boxes, valid_scores, nms_thr)
|
||||
if keep:
|
||||
dets = np.concatenate(
|
||||
[valid_boxes[keep], valid_scores[keep, None], valid_cls_inds[keep, None]], 1
|
||||
)
|
||||
return dets
|
||||
return None
|
||||
|
||||
def postprocess(outputs, scale, pad, img_size=(640, 640), conf_threshold=0.25, iou_threshold=0.45, p6=False):
|
||||
"""
|
||||
YOLOX postprocessing (based on python_x.py)
|
||||
Assumes single output [1, 8400, 85] or multiple outputs that need to be concatenated
|
||||
"""
|
||||
# Handle multiple outputs (if AMLNNLite returns multiple scales)
|
||||
if isinstance(outputs, list):
|
||||
if len(outputs) == 1:
|
||||
output = outputs[0]
|
||||
else:
|
||||
# Concatenate multiple outputs if needed
|
||||
# This assumes outputs are already in the correct format
|
||||
output = outputs[0] # Use first output for now
|
||||
else:
|
||||
output = outputs
|
||||
|
||||
# Ensure output is in correct format [1, N, 85]
|
||||
if len(output.shape) == 2:
|
||||
# [N, 85] -> [1, N, 85]
|
||||
output = output[None, :, :]
|
||||
elif len(output.shape) == 3:
|
||||
# [1, N, 85] or [N, 1, 85]
|
||||
if output.shape[0] != 1:
|
||||
output = output.transpose(1, 0, 2)[None, :, :]
|
||||
elif len(output.shape) == 4:
|
||||
# [1, 1, N, 85] -> [1, N, 85]
|
||||
output = output[0, 0]
|
||||
output = output[None, :, :]
|
||||
|
||||
# Convert to float32 if needed (AMLNNLite might return int8)
|
||||
if output.dtype != np.float32:
|
||||
output = output.astype(np.float32)
|
||||
|
||||
# Use demo_postprocess to decode coordinates
|
||||
predictions = demo_postprocess(output, img_size, p6=p6)[0] # [8400, 85]
|
||||
|
||||
# Extract boxes and scores
|
||||
# Format after demo_postprocess: [cx, cy, w, h, obj_conf, class0, ..., class79]
|
||||
boxes = predictions[:, :4] # [cx, cy, w, h] (absolute coordinates)
|
||||
scores = predictions[:, 4:5] * predictions[:, 5:] # obj_conf * cls_scores
|
||||
|
||||
# Convert to xyxy format
|
||||
boxes_xyxy = np.ones_like(boxes)
|
||||
boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2] / 2.0
|
||||
boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3] / 2.0
|
||||
boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2] / 2.0
|
||||
boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3] / 2.0
|
||||
|
||||
# Map coordinates back to original image
|
||||
pad_x, pad_y = pad
|
||||
boxes_xyxy[:, [0, 2]] = (boxes_xyxy[:, [0, 2]] - pad_x) / scale
|
||||
boxes_xyxy[:, [1, 3]] = (boxes_xyxy[:, [1, 3]] - pad_y) / scale
|
||||
boxes_xyxy = np.maximum(boxes_xyxy, 0)
|
||||
|
||||
# Multiclass NMS (class-agnostic, score_thr=0.1 as in official YOLOX)
|
||||
dets = multiclass_nms(boxes_xyxy, scores, nms_thr=iou_threshold, score_thr=0.1)
|
||||
|
||||
if dets is None:
|
||||
return []
|
||||
|
||||
# Convert to detection format
|
||||
final_boxes = dets[:, :4]
|
||||
final_scores = dets[:, 4]
|
||||
final_cls_inds = dets[:, 5].astype(int)
|
||||
|
||||
detections = []
|
||||
for i in range(len(dets)):
|
||||
x1, y1, x2, y2 = final_boxes[i]
|
||||
confidence = final_scores[i]
|
||||
class_id = final_cls_inds[i]
|
||||
|
||||
if confidence >= conf_threshold:
|
||||
detections.append({
|
||||
'bbox': [float(x1), float(y1), float(x2), float(y2)],
|
||||
'confidence': float(confidence),
|
||||
'class_id': int(class_id),
|
||||
'class_name': CLASS_NAMES[class_id] if class_id < len(CLASS_NAMES) else f'class_{class_id}'
|
||||
})
|
||||
|
||||
return detections
|
||||
|
||||
# YOLOX color palette (consistent with python_x.py)
|
||||
_COLORS = (
|
||||
np.array(
|
||||
[
|
||||
0.000, 0.447, 0.741,
|
||||
0.850, 0.325, 0.098,
|
||||
0.929, 0.694, 0.125,
|
||||
0.494, 0.184, 0.556,
|
||||
0.466, 0.674, 0.188,
|
||||
0.301, 0.745, 0.933,
|
||||
0.635, 0.078, 0.184,
|
||||
0.300, 0.300, 0.300,
|
||||
0.600, 0.600, 0.600,
|
||||
1.000, 0.000, 0.000,
|
||||
1.000, 0.500, 0.000,
|
||||
0.749, 0.749, 0.000,
|
||||
0.000, 1.000, 0.000,
|
||||
0.000, 0.000, 1.000,
|
||||
0.667, 0.000, 1.000,
|
||||
0.333, 0.333, 0.000,
|
||||
0.333, 0.667, 0.000,
|
||||
0.333, 1.000, 0.000,
|
||||
0.667, 0.333, 0.000,
|
||||
0.667, 0.667, 0.000,
|
||||
0.667, 1.000, 0.000,
|
||||
1.000, 0.333, 0.000,
|
||||
1.000, 0.667, 0.000,
|
||||
1.000, 1.000, 0.000,
|
||||
0.000, 0.333, 0.500,
|
||||
0.000, 0.667, 0.500,
|
||||
0.000, 1.000, 0.500,
|
||||
0.333, 0.000, 0.500,
|
||||
0.333, 0.333, 0.500,
|
||||
0.333, 0.667, 0.500,
|
||||
0.333, 1.000, 0.500,
|
||||
0.667, 0.000, 0.500,
|
||||
0.667, 0.333, 0.500,
|
||||
0.667, 0.667, 0.500,
|
||||
0.667, 1.000, 0.500,
|
||||
1.000, 0.000, 0.500,
|
||||
1.000, 0.333, 0.500,
|
||||
1.000, 0.667, 0.500,
|
||||
1.000, 1.000, 0.500,
|
||||
0.000, 0.333, 1.000,
|
||||
0.000, 0.667, 1.000,
|
||||
0.000, 1.000, 1.000,
|
||||
0.333, 0.000, 1.000,
|
||||
0.333, 0.333, 1.000,
|
||||
0.333, 0.667, 1.000,
|
||||
0.333, 1.000, 1.000,
|
||||
0.667, 0.000, 1.000,
|
||||
0.667, 0.333, 1.000,
|
||||
0.667, 0.667, 1.000,
|
||||
0.667, 1.000, 1.000,
|
||||
1.000, 0.000, 1.000,
|
||||
1.000, 0.333, 1.000,
|
||||
1.000, 0.667, 1.000,
|
||||
0.333, 0.000, 0.000,
|
||||
0.500, 0.000, 0.000,
|
||||
0.667, 0.000, 0.000,
|
||||
0.833, 0.000, 0.000,
|
||||
1.000, 0.000, 0.000,
|
||||
0.000, 0.167, 0.000,
|
||||
0.000, 0.333, 0.000,
|
||||
0.000, 0.500, 0.000,
|
||||
0.000, 0.667, 0.000,
|
||||
0.000, 0.833, 0.000,
|
||||
0.000, 1.000, 0.000,
|
||||
0.000, 0.000, 0.167,
|
||||
0.000, 0.000, 0.333,
|
||||
0.000, 0.000, 0.500,
|
||||
0.000, 0.000, 0.667,
|
||||
0.000, 0.000, 0.833,
|
||||
0.000, 0.000, 1.000,
|
||||
0.000, 0.000, 0.000,
|
||||
0.143, 0.143, 0.143,
|
||||
0.857, 0.857, 0.857,
|
||||
1.000, 1.000, 1.000
|
||||
]
|
||||
).astype(np.float32).reshape(-1, 3)
|
||||
)
|
||||
|
||||
def vis(img, detections, conf=0.5, class_names=None):
|
||||
"""
|
||||
YOLOX official visualization function (based on python_x.py)
|
||||
"""
|
||||
if class_names is None:
|
||||
class_names = CLASS_NAMES
|
||||
|
||||
result_img = img.copy()
|
||||
|
||||
# Adjust font size based on image size
|
||||
img_height, img_width = img.shape[:2]
|
||||
font_scale = max(0.6, min(1.2, np.sqrt(img_height * img_height + img_width * img_width) * 0.0015))
|
||||
thickness = max(2, int(font_scale * 2.5))
|
||||
|
||||
for det in detections:
|
||||
if det['confidence'] < conf:
|
||||
continue
|
||||
|
||||
x1, y1, x2, y2 = [int(coord) for coord in det['bbox']]
|
||||
confidence = det['confidence']
|
||||
class_id = det['class_id']
|
||||
|
||||
if class_id >= len(_COLORS):
|
||||
class_id = class_id % len(_COLORS)
|
||||
|
||||
color = (_COLORS[class_id] * 255).astype(np.uint8).tolist()
|
||||
text = '{}:{:.1f}%'.format(det['class_name'], confidence * 100)
|
||||
txt_color = (0, 0, 0) if np.mean(_COLORS[class_id]) > 0.5 else (255, 255, 255)
|
||||
font = cv2.FONT_HERSHEY_SIMPLEX
|
||||
|
||||
txt_size = cv2.getTextSize(text, font, font_scale, thickness)[0]
|
||||
cv2.rectangle(result_img, (x1, y1), (x2, y2), color, thickness)
|
||||
|
||||
txt_bk_color = (_COLORS[class_id] * 255 * 0.7).astype(np.uint8).tolist()
|
||||
cv2.rectangle(
|
||||
result_img,
|
||||
(x1, y1 + 1),
|
||||
(x1 + txt_size[0] + 1, y1 + int(1.5 * txt_size[1])),
|
||||
txt_bk_color,
|
||||
-1
|
||||
)
|
||||
cv2.putText(result_img, text, (x1, y1 + txt_size[1]), font, font_scale, txt_color, thickness=thickness)
|
||||
|
||||
return result_img
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--model-path', default='./yolox_s_int8_A311D2.adla')
|
||||
parser.add_argument('--run-cycles', default= 1, type=int)
|
||||
parser.add_argument('--input-path', default='./', help='Input image path (file or directory)')
|
||||
args = parser.parse_args()
|
||||
|
||||
# Initialize AMLNNLite
|
||||
amlnn = AMLNNLite()
|
||||
amlnn.config(
|
||||
model_path=args.model_path, # Model file path, Support ADLD and quantized TFlite models
|
||||
run_cycles=args.run_cycles
|
||||
)
|
||||
amlnn.init()
|
||||
|
||||
# Find image files
|
||||
image_files = []
|
||||
if os.path.isfile(args.input_path):
|
||||
# Single image file
|
||||
image_files = [args.input_path]
|
||||
elif os.path.isdir(args.input_path):
|
||||
# Directory - find all image files
|
||||
image_extensions = ["*.jpg", "*.jpeg", "*.png", "*.bmp"]
|
||||
for ext in image_extensions:
|
||||
image_files.extend(glob.glob(os.path.join(args.input_path, ext)))
|
||||
image_files.extend(glob.glob(os.path.join(args.input_path, ext.upper())))
|
||||
else:
|
||||
print(f"Error: Input path '{args.input_path}' does not exist")
|
||||
amlnn.uninit()
|
||||
return
|
||||
|
||||
if not image_files:
|
||||
print(f"No image files found in {args.input_path}")
|
||||
amlnn.uninit()
|
||||
return
|
||||
|
||||
print(f"Found {len(image_files)} image files to process:")
|
||||
for img_file in image_files:
|
||||
print(f" - {os.path.basename(img_file)}")
|
||||
print()
|
||||
|
||||
# Process each image
|
||||
for i, image_path in enumerate(image_files, 1):
|
||||
print(f"=" * 60)
|
||||
print(f"Processing image {i}/{len(image_files)}: {os.path.basename(image_path)}")
|
||||
print(f"=" * 60)
|
||||
|
||||
try:
|
||||
# Preprocess input
|
||||
input_tensor, original_img, scale, pad = preprocess(image_path, new_shape=(640, 640), data_format='NHWC')
|
||||
|
||||
# Run inference
|
||||
outputs = amlnn.inference(
|
||||
inputs=[input_tensor]
|
||||
)
|
||||
|
||||
# Postprocess results
|
||||
detections = postprocess(outputs, scale, pad, img_size=(640, 640), conf_threshold=0.25, iou_threshold=0.45, p6=False)
|
||||
|
||||
# Print detection results
|
||||
if detections:
|
||||
print(f" Detected {len(detections)} objects:")
|
||||
for i, det in enumerate(detections, 1):
|
||||
print(f" {i}. {det['class_name']} ({det['confidence']:.2f})")
|
||||
else:
|
||||
print(" No objects detected")
|
||||
|
||||
# Save result image (save to current directory)
|
||||
img_name = Path(image_path).stem
|
||||
save_path = f"{img_name}_result.jpg"
|
||||
result_img = vis(original_img, detections, conf=0.25, class_names=CLASS_NAMES)
|
||||
cv2.imwrite(save_path, result_img)
|
||||
print(f" Result saved to: {save_path}")
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error processing {os.path.basename(image_path)}: {e}")
|
||||
|
||||
print()
|
||||
|
||||
# Optional visualization
|
||||
amlnn.visualize()
|
||||
|
||||
# Release resources
|
||||
amlnn.uninit()
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
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