triple Mu
2 years ago
committed by
GitHub
commit
ea727d2cf8
6 changed files with 969 additions and 1 deletions
@ -0,0 +1,59 @@ |
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cmake_minimum_required(VERSION 2.8.12) |
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set(CMAKE_CUDA_ARCHITECTURES 60 61 62 70 72 75 86) |
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set(CMAKE_CUDA_COMPILER /usr/local/cuda/bin/nvcc) |
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project(yolov8-pose LANGUAGES CXX CUDA) |
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++14 -O3 -g") |
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set(CMAKE_CXX_STANDARD 14) |
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set(CMAKE_BUILD_TYPE Release) |
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option(CUDA_USE_STATIC_CUDA_RUNTIME OFF) |
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|
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# CUDA |
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find_package(CUDA REQUIRED) |
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message(STATUS "CUDA Libs: \n${CUDA_LIBRARIES}\n") |
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message(STATUS "CUDA Headers: \n${CUDA_INCLUDE_DIRS}\n") |
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|
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# OpenCV |
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find_package(OpenCV REQUIRED) |
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message(STATUS "OpenCV Libs: \n${OpenCV_LIBS}\n") |
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message(STATUS "OpenCV Libraries: \n${OpenCV_LIBRARIES}\n") |
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message(STATUS "OpenCV Headers: \n${OpenCV_INCLUDE_DIRS}\n") |
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|
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# TensorRT |
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set(TensorRT_INCLUDE_DIRS /usr/include/x86_64-linux-gnu) |
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set(TensorRT_LIBRARIES /usr/lib/x86_64-linux-gnu) |
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message(STATUS "TensorRT Libs: \n${TensorRT_LIBRARIES}\n") |
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message(STATUS "TensorRT Headers: \n${TensorRT_INCLUDE_DIRS}\n") |
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list(APPEND INCLUDE_DIRS |
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${CUDA_INCLUDE_DIRS} |
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${OpenCV_INCLUDE_DIRS} |
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${TensorRT_INCLUDE_DIRS} |
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./include |
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) |
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list(APPEND ALL_LIBS |
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${CUDA_LIBRARIES} |
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${OpenCV_LIBRARIES} |
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${TensorRT_LIBRARIES} |
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) |
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include_directories(${INCLUDE_DIRS}) |
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add_executable(${PROJECT_NAME} |
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main.cpp |
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include/yolov8-pose.hpp |
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include/common.hpp |
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) |
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target_link_directories(${PROJECT_NAME} PUBLIC ${ALL_LIBS}) |
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target_link_libraries(${PROJECT_NAME} PRIVATE nvinfer nvinfer_plugin cudart ${OpenCV_LIBS}) |
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if (${OpenCV_VERSION} VERSION_GREATER_EQUAL 4.7.0) |
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message(STATUS "Build with -DBATCHED_NMS") |
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add_definitions(-DBATCHED_NMS) |
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endif () |
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@ -0,0 +1,157 @@ |
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//
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// Created by ubuntu on 4/7/23.
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//
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#ifndef POSE_NORMAL_COMMON_HPP |
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#define POSE_NORMAL_COMMON_HPP |
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#include "opencv2/opencv.hpp" |
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#include <sys/stat.h> |
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#include <unistd.h> |
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#include "NvInfer.h" |
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#define CHECK(call) \ |
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do \
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{ \
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const cudaError_t error_code = call; \
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if (error_code != cudaSuccess) \
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{ \
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printf("CUDA Error:\n"); \
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printf(" File: %s\n", __FILE__); \
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printf(" Line: %d\n", __LINE__); \
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printf(" Error code: %d\n", error_code); \
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printf(" Error text: %s\n", \
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cudaGetErrorString(error_code)); \
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exit(1); \
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} \
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} while (0) |
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class Logger : public nvinfer1::ILogger |
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{ |
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public: |
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nvinfer1::ILogger::Severity reportableSeverity; |
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explicit Logger(nvinfer1::ILogger::Severity severity = nvinfer1::ILogger::Severity::kINFO) : |
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reportableSeverity(severity) |
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{ |
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} |
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|
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void log(nvinfer1::ILogger::Severity severity, const char* msg) noexcept override |
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{ |
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if (severity > reportableSeverity) |
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{ |
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return; |
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} |
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switch (severity) |
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{ |
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case nvinfer1::ILogger::Severity::kINTERNAL_ERROR: |
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std::cerr << "INTERNAL_ERROR: "; |
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break; |
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case nvinfer1::ILogger::Severity::kERROR: |
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std::cerr << "ERROR: "; |
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break; |
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case nvinfer1::ILogger::Severity::kWARNING: |
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std::cerr << "WARNING: "; |
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break; |
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case nvinfer1::ILogger::Severity::kINFO: |
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std::cerr << "INFO: "; |
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break; |
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default: |
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std::cerr << "VERBOSE: "; |
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break; |
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} |
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std::cerr << msg << std::endl; |
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} |
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}; |
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inline int get_size_by_dims(const nvinfer1::Dims& dims) |
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{ |
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int size = 1; |
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for (int i = 0; i < dims.nbDims; i++) |
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{ |
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size *= dims.d[i]; |
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} |
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return size; |
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} |
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|
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inline int type_to_size(const nvinfer1::DataType& dataType) |
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{ |
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switch (dataType) |
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{ |
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case nvinfer1::DataType::kFLOAT: |
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return 4; |
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case nvinfer1::DataType::kHALF: |
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return 2; |
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case nvinfer1::DataType::kINT32: |
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return 4; |
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case nvinfer1::DataType::kINT8: |
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return 1; |
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case nvinfer1::DataType::kBOOL: |
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return 1; |
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default: |
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return 4; |
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} |
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} |
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|
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inline static float clamp(float val, float min, float max) |
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{ |
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return val > min ? (val < max ? val : max) : min; |
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} |
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inline bool IsPathExist(const std::string& path) |
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{ |
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if (access(path.c_str(), 0) == F_OK) |
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{ |
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return true; |
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} |
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return false; |
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} |
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inline bool IsFile(const std::string& path) |
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{ |
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if (!IsPathExist(path)) |
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{ |
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printf("%s:%d %s not exist\n", __FILE__, __LINE__, path.c_str()); |
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return false; |
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} |
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struct stat buffer; |
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return (stat(path.c_str(), &buffer) == 0 && S_ISREG(buffer.st_mode)); |
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} |
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inline bool IsFolder(const std::string& path) |
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{ |
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if (!IsPathExist(path)) |
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{ |
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return false; |
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} |
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struct stat buffer; |
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return (stat(path.c_str(), &buffer) == 0 && S_ISDIR(buffer.st_mode)); |
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} |
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namespace pose |
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{ |
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struct Binding |
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{ |
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size_t size = 1; |
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size_t dsize = 1; |
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nvinfer1::Dims dims; |
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std::string name; |
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}; |
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|
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struct Object |
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{ |
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cv::Rect_<float> rect; |
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int label = 0; |
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float prob = 0.0; |
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std::vector<float> kps; |
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}; |
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|
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struct PreParam |
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{ |
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float ratio = 1.0f; |
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float dw = 0.0f; |
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float dh = 0.0f; |
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float height = 0; |
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float width = 0; |
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}; |
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} |
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#endif //POSE_NORMAL_COMMON_HPP
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@ -0,0 +1,516 @@ |
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//
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// Created by ubuntu on 1/20/23.
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//
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#ifndef POSE_NORMAL_YOLOv8_pose_HPP |
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#define POSE_NORMAL_YOLOv8_pose_HPP |
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#include "fstream" |
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#include "common.hpp" |
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#include "NvInferPlugin.h" |
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using namespace pose; |
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class YOLOv8_pose { |
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public: |
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explicit YOLOv8_pose(const std::string &engine_file_path); |
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~YOLOv8_pose(); |
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void make_pipe(bool warmup = true); |
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void copy_from_Mat(const cv::Mat &image); |
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void copy_from_Mat(const cv::Mat &image, cv::Size &size); |
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void letterbox( |
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const cv::Mat &image, |
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cv::Mat &out, |
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cv::Size &size |
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); |
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void infer(); |
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void postprocess( |
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std::vector<Object> &objs, |
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float score_thres = 0.25f, |
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float iou_thres = 0.65f, |
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int topk = 100 |
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); |
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static void draw_objects( |
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const cv::Mat &image, |
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cv::Mat &res, |
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const std::vector<Object> &objs, |
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const std::vector<std::vector<unsigned int>> &SKELETON, |
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const std::vector<std::vector<unsigned int>> &KPS_COLORS, |
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const std::vector<std::vector<unsigned int>> &LIMB_COLORS |
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); |
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int num_bindings; |
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int num_inputs = 0; |
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int num_outputs = 0; |
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std::vector<Binding> input_bindings; |
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std::vector<Binding> output_bindings; |
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std::vector<void *> host_ptrs; |
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std::vector<void *> device_ptrs; |
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PreParam pparam; |
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private: |
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nvinfer1::ICudaEngine *engine = nullptr; |
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nvinfer1::IRuntime *runtime = nullptr; |
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nvinfer1::IExecutionContext *context = nullptr; |
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cudaStream_t stream = nullptr; |
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Logger gLogger{nvinfer1::ILogger::Severity::kERROR}; |
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}; |
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YOLOv8_pose::YOLOv8_pose(const std::string &engine_file_path) { |
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std::ifstream file(engine_file_path, std::ios::binary); |
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assert(file.good()); |
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file.seekg(0, std::ios::end); |
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auto size = file.tellg(); |
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file.seekg(0, std::ios::beg); |
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char *trtModelStream = new char[size]; |
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assert(trtModelStream); |
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file.read(trtModelStream, size); |
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file.close(); |
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initLibNvInferPlugins(&this->gLogger, ""); |
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this->runtime = nvinfer1::createInferRuntime(this->gLogger); |
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assert(this->runtime != nullptr); |
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this->engine = this->runtime->deserializeCudaEngine(trtModelStream, size); |
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assert(this->engine != nullptr); |
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this->context = this->engine->createExecutionContext(); |
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assert(this->context != nullptr); |
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cudaStreamCreate(&this->stream); |
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this->num_bindings = this->engine->getNbBindings(); |
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for (int i = 0; i < this->num_bindings; ++i) { |
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Binding binding; |
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nvinfer1::Dims dims; |
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nvinfer1::DataType dtype = this->engine->getBindingDataType(i); |
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std::string name = this->engine->getBindingName(i); |
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binding.name = name; |
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binding.dsize = type_to_size(dtype); |
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bool IsInput = engine->bindingIsInput(i); |
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if (IsInput) { |
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this->num_inputs += 1; |
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dims = this->engine->getProfileDimensions( |
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i, |
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0, |
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nvinfer1::OptProfileSelector::kMAX); |
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binding.size = get_size_by_dims(dims); |
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binding.dims = dims; |
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this->input_bindings.push_back(binding); |
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// set max opt shape
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this->context->setBindingDimensions(i, dims); |
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} else { |
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dims = this->context->getBindingDimensions(i); |
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binding.size = get_size_by_dims(dims); |
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binding.dims = dims; |
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this->output_bindings.push_back(binding); |
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this->num_outputs += 1; |
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} |
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} |
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} |
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YOLOv8_pose::~YOLOv8_pose() { |
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this->context->destroy(); |
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this->engine->destroy(); |
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this->runtime->destroy(); |
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cudaStreamDestroy(this->stream); |
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for (auto &ptr: this->device_ptrs) { |
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CHECK(cudaFree(ptr)); |
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} |
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for (auto &ptr: this->host_ptrs) { |
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CHECK(cudaFreeHost(ptr)); |
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} |
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} |
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void YOLOv8_pose::make_pipe(bool warmup) { |
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for (auto &bindings: this->input_bindings) { |
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void *d_ptr; |
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CHECK(cudaMallocAsync( |
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&d_ptr, |
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bindings.size * bindings.dsize, |
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this->stream) |
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); |
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this->device_ptrs.push_back(d_ptr); |
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} |
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|
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for (auto &bindings: this->output_bindings) { |
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void *d_ptr, *h_ptr; |
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size_t size = bindings.size * bindings.dsize; |
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CHECK(cudaMallocAsync( |
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&d_ptr, |
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size, |
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this->stream) |
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); |
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CHECK(cudaHostAlloc( |
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&h_ptr, |
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size, |
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0) |
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); |
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this->device_ptrs.push_back(d_ptr); |
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this->host_ptrs.push_back(h_ptr); |
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} |
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|
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if (warmup) { |
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for (int i = 0; i < 10; i++) { |
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for (auto &bindings: this->input_bindings) { |
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size_t size = bindings.size * bindings.dsize; |
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void *h_ptr = malloc(size); |
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memset(h_ptr, 0, size); |
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CHECK(cudaMemcpyAsync( |
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this->device_ptrs[0], |
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h_ptr, |
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size, |
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cudaMemcpyHostToDevice, |
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this->stream) |
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); |
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free(h_ptr); |
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} |
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this->infer(); |
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} |
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printf("model warmup 10 times\n"); |
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} |
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} |
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void YOLOv8_pose::letterbox( |
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const cv::Mat &image, |
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cv::Mat &out, |
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cv::Size &size |
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) { |
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const float inp_h = size.height; |
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const float inp_w = size.width; |
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float height = image.rows; |
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float width = image.cols; |
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|
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float r = std::min(inp_h / height, inp_w / width); |
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int padw = std::round(width * r); |
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int padh = std::round(height * r); |
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|
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cv::Mat tmp; |
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if ((int) width != padw || (int) height != padh) { |
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cv::resize( |
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image, |
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tmp, |
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cv::Size(padw, padh) |
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); |
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} else { |
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tmp = image.clone(); |
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} |
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|
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float dw = inp_w - padw; |
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float dh = inp_h - padh; |
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dw /= 2.0f; |
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dh /= 2.0f; |
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int top = int(std::round(dh - 0.1f)); |
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int bottom = int(std::round(dh + 0.1f)); |
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int left = int(std::round(dw - 0.1f)); |
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int right = int(std::round(dw + 0.1f)); |
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|
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cv::copyMakeBorder( |
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tmp, |
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tmp, |
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top, |
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bottom, |
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left, |
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right, |
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cv::BORDER_CONSTANT, |
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{114, 114, 114} |
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); |
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|
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cv::dnn::blobFromImage(tmp, |
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out, |
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1 / 255.f, |
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cv::Size(), |
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cv::Scalar(0, 0, 0), |
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true, |
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false, |
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CV_32F |
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); |
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this->pparam.ratio = 1 / r; |
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this->pparam.dw = dw; |
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this->pparam.dh = dh; |
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this->pparam.height = height; |
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this->pparam.width = width;; |
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} |
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|
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void YOLOv8_pose::copy_from_Mat(const cv::Mat &image) { |
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cv::Mat nchw; |
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auto &in_binding = this->input_bindings[0]; |
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auto width = in_binding.dims.d[3]; |
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auto height = in_binding.dims.d[2]; |
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cv::Size size{width, height}; |
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this->letterbox( |
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image, |
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nchw, |
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size |
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); |
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|
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this->context->setBindingDimensions( |
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0, |
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nvinfer1::Dims |
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{ |
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4, |
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{1, 3, height, width} |
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} |
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); |
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|
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CHECK(cudaMemcpyAsync( |
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this->device_ptrs[0], |
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nchw.ptr<float>(), |
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nchw.total() * nchw.elemSize(), |
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cudaMemcpyHostToDevice, |
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this->stream) |
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); |
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} |
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|
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void YOLOv8_pose::copy_from_Mat(const cv::Mat &image, cv::Size &size) { |
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cv::Mat nchw; |
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this->letterbox( |
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image, |
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nchw, |
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size |
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); |
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this->context->setBindingDimensions( |
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0, |
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nvinfer1::Dims |
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{4, |
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{1, 3, size.height, size.width} |
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} |
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); |
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CHECK(cudaMemcpyAsync( |
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this->device_ptrs[0], |
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nchw.ptr<float>(), |
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nchw.total() * nchw.elemSize(), |
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cudaMemcpyHostToDevice, |
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this->stream) |
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); |
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} |
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|
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void YOLOv8_pose::infer() { |
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|
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this->context->enqueueV2( |
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this->device_ptrs.data(), |
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this->stream, |
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nullptr |
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); |
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for (int i = 0; i < this->num_outputs; i++) { |
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size_t osize = this->output_bindings[i].size * this->output_bindings[i].dsize; |
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CHECK(cudaMemcpyAsync(this->host_ptrs[i], |
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this->device_ptrs[i + this->num_inputs], |
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osize, |
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cudaMemcpyDeviceToHost, |
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this->stream) |
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); |
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|
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} |
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cudaStreamSynchronize(this->stream); |
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|
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} |
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|
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void YOLOv8_pose::postprocess( |
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std::vector<Object> &objs, |
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float score_thres, |
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float iou_thres, |
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int topk |
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) { |
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objs.clear(); |
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auto num_channels = this->output_bindings[0].dims.d[1]; |
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auto num_anchors = this->output_bindings[0].dims.d[2]; |
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|
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auto &dw = this->pparam.dw; |
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auto &dh = this->pparam.dh; |
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auto &width = this->pparam.width; |
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auto &height = this->pparam.height; |
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auto &ratio = this->pparam.ratio; |
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|
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std::vector<cv::Rect> bboxes; |
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std::vector<float> scores; |
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std::vector<int> labels; |
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std::vector<int> indices; |
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std::vector<std::vector<float>> kpss; |
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|
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cv::Mat output = cv::Mat( |
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num_channels, |
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num_anchors, |
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CV_32F, |
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static_cast<float *>(this->host_ptrs[0]) |
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); |
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output = output.t(); |
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for (int i = 0; i < num_anchors; i++) { |
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auto row_ptr = output.row(i).ptr<float>(); |
||||
auto bboxes_ptr = row_ptr; |
||||
auto scores_ptr = row_ptr + 4; |
||||
auto kps_ptr = row_ptr + 5; |
||||
|
||||
float score = *scores_ptr; |
||||
if (score > score_thres) { |
||||
float x = *bboxes_ptr++ - dw; |
||||
float y = *bboxes_ptr++ - dh; |
||||
float w = *bboxes_ptr++; |
||||
float h = *bboxes_ptr; |
||||
|
||||
float x0 = clamp((x - 0.5f * w) * ratio, 0.f, width); |
||||
float y0 = clamp((y - 0.5f * h) * ratio, 0.f, height); |
||||
float x1 = clamp((x + 0.5f * w) * ratio, 0.f, width); |
||||
float y1 = clamp((y + 0.5f * h) * ratio, 0.f, height); |
||||
|
||||
cv::Rect_<float> bbox; |
||||
bbox.x = x0; |
||||
bbox.y = y0; |
||||
bbox.width = x1 - x0; |
||||
bbox.height = y1 - y0; |
||||
std::vector<float> kps; |
||||
for (int k = 0; k < 17; k++) { |
||||
float kps_x = (*(kps_ptr + 3 * k) - dw) * ratio; |
||||
float kps_y = (*(kps_ptr + 3 * k + 1) - dh) * ratio; |
||||
float kps_s = *(kps_ptr + 3 * k + 2); |
||||
kps_x = clamp(kps_x, 0.f, width); |
||||
kps_y = clamp(kps_y, 0.f, height); |
||||
kps.push_back(kps_x); |
||||
kps.push_back(kps_y); |
||||
kps.push_back(kps_s); |
||||
} |
||||
|
||||
bboxes.push_back(bbox); |
||||
labels.push_back(0); |
||||
scores.push_back(score); |
||||
kpss.push_back(kps); |
||||
} |
||||
} |
||||
|
||||
#ifdef BATCHED_NMS |
||||
cv::dnn::NMSBoxesBatched( |
||||
bboxes, |
||||
scores, |
||||
labels, |
||||
score_thres, |
||||
iou_thres, |
||||
indices |
||||
); |
||||
#elif |
||||
cv::dnn::NMSBoxes( |
||||
bboxes, |
||||
scores, |
||||
score_thres, |
||||
iou_thres, |
||||
indices |
||||
); |
||||
#endif |
||||
|
||||
int cnt = 0; |
||||
for (auto &i: indices) { |
||||
if (cnt >= topk) { |
||||
break; |
||||
} |
||||
Object obj; |
||||
obj.rect = bboxes[i]; |
||||
obj.prob = scores[i]; |
||||
obj.label = labels[i]; |
||||
obj.kps = kpss[i]; |
||||
objs.push_back(obj); |
||||
cnt += 1; |
||||
} |
||||
} |
||||
|
||||
void YOLOv8_pose::draw_objects( |
||||
const cv::Mat &image, |
||||
cv::Mat &res, |
||||
const std::vector<Object> &objs, |
||||
const std::vector<std::vector<unsigned int>> &SKELETON, |
||||
const std::vector<std::vector<unsigned int>> &KPS_COLORS, |
||||
const std::vector<std::vector<unsigned int>> &LIMB_COLORS |
||||
) { |
||||
res = image.clone(); |
||||
for (auto &obj: objs) { |
||||
cv::rectangle( |
||||
res, |
||||
obj.rect, |
||||
{0, 0, 255}, |
||||
2 |
||||
); |
||||
|
||||
char text[256]; |
||||
sprintf( |
||||
text, |
||||
"person %.1f%%", |
||||
obj.prob * 100 |
||||
); |
||||
|
||||
int baseLine = 0; |
||||
cv::Size label_size = cv::getTextSize( |
||||
text, |
||||
cv::FONT_HERSHEY_SIMPLEX, |
||||
0.4, |
||||
1, |
||||
&baseLine |
||||
); |
||||
|
||||
int x = (int) obj.rect.x; |
||||
int y = (int) obj.rect.y + 1; |
||||
|
||||
if (y > res.rows) |
||||
y = res.rows; |
||||
|
||||
cv::rectangle( |
||||
res, |
||||
cv::Rect(x, y, label_size.width, label_size.height + baseLine), |
||||
{0, 0, 255}, |
||||
-1 |
||||
); |
||||
|
||||
cv::putText( |
||||
res, |
||||
text, |
||||
cv::Point(x, y + label_size.height), |
||||
cv::FONT_HERSHEY_SIMPLEX, |
||||
0.4, |
||||
{255, 255, 255}, |
||||
1 |
||||
); |
||||
|
||||
auto &kps = obj.kps; |
||||
for (int k = 0; k < 17; k++) { |
||||
int kps_x = std::round(kps[k * 3]); |
||||
int kps_y = std::round(kps[k * 3 + 1]); |
||||
float kps_s = kps[k * 3 + 2]; |
||||
cv::Scalar kps_color = cv::Scalar(KPS_COLORS[k][0], KPS_COLORS[k][1], KPS_COLORS[k][2]); |
||||
if (kps_s > 0.5f) { |
||||
cv::circle(res, {kps_x, kps_y}, 5, kps_color, -1); |
||||
} |
||||
} |
||||
int sk_id = 0; |
||||
for (auto &ske: SKELETON) { |
||||
int pos1_x = std::round(kps[(ske[0] - 1) * 3]); |
||||
int pos1_y = std::round(kps[(ske[0] - 1) * 3 + 1]); |
||||
|
||||
int pos2_x = std::round(kps[(ske[1] - 1) * 3]); |
||||
int pos2_y = std::round(kps[(ske[1] - 1) * 3 + 1]); |
||||
|
||||
float pos1_s = kps[(ske[0] - 1) * 3 + 2]; |
||||
float pos2_s = kps[(ske[1] - 1) * 3 + 2]; |
||||
cv::Scalar limb_color = cv::Scalar(LIMB_COLORS[sk_id][0], LIMB_COLORS[sk_id][1], LIMB_COLORS[sk_id][2]); |
||||
sk_id += 1; |
||||
if (pos1_s < 0.5f || pos2_s < 0.5f) { |
||||
continue; |
||||
} |
||||
cv::line(res, {pos1_x, pos1_y}, {pos2_x, pos2_y}, limb_color, 2); |
||||
|
||||
} |
||||
} |
||||
} |
||||
|
||||
#endif //POSE_NORMAL_YOLOv8_pose_HPP
|
||||
@ -0,0 +1,162 @@ |
||||
//
|
||||
// Created by ubuntu on 4/7/23.
|
||||
//
|
||||
#include "chrono" |
||||
#include "yolov8-pose.hpp" |
||||
#include "opencv2/opencv.hpp" |
||||
|
||||
|
||||
const std::vector<std::vector<unsigned int>> KPS_COLORS = |
||||
{{0, 255, 0}, |
||||
{0, 255, 0}, |
||||
{0, 255, 0}, |
||||
{0, 255, 0}, |
||||
{0, 255, 0}, |
||||
{255, 128, 0}, |
||||
{255, 128, 0}, |
||||
{255, 128, 0}, |
||||
{255, 128, 0}, |
||||
{255, 128, 0}, |
||||
{255, 128, 0}, |
||||
{51, 153, 255}, |
||||
{51, 153, 255}, |
||||
{51, 153, 255}, |
||||
{51, 153, 255}, |
||||
{51, 153, 255}, |
||||
{51, 153, 255}}; |
||||
|
||||
const std::vector<std::vector<unsigned int>> SKELETON = {{16, 14}, |
||||
{14, 12}, |
||||
{17, 15}, |
||||
{15, 13}, |
||||
{12, 13}, |
||||
{6, 12}, |
||||
{7, 13}, |
||||
{6, 7}, |
||||
{6, 8}, |
||||
{7, 9}, |
||||
{8, 10}, |
||||
{9, 11}, |
||||
{2, 3}, |
||||
{1, 2}, |
||||
{1, 3}, |
||||
{2, 4}, |
||||
{3, 5}, |
||||
{4, 6}, |
||||
{5, 7}}; |
||||
|
||||
const std::vector<std::vector<unsigned int>> LIMB_COLORS = {{51, 153, 255}, |
||||
{51, 153, 255}, |
||||
{51, 153, 255}, |
||||
{51, 153, 255}, |
||||
{255, 51, 255}, |
||||
{255, 51, 255}, |
||||
{255, 51, 255}, |
||||
{255, 128, 0}, |
||||
{255, 128, 0}, |
||||
{255, 128, 0}, |
||||
{255, 128, 0}, |
||||
{255, 128, 0}, |
||||
{0, 255, 0}, |
||||
{0, 255, 0}, |
||||
{0, 255, 0}, |
||||
{0, 255, 0}, |
||||
{0, 255, 0}, |
||||
{0, 255, 0}, |
||||
{0, 255, 0}}; |
||||
|
||||
int main(int argc, char **argv) { |
||||
// cuda:0
|
||||
cudaSetDevice(0); |
||||
|
||||
const std::string engine_file_path{argv[1]}; |
||||
const std::string path{argv[2]}; |
||||
|
||||
std::vector<std::string> imagePathList; |
||||
bool isVideo{false}; |
||||
|
||||
assert(argc == 3); |
||||
|
||||
auto yolov8_pose = new YOLOv8_pose(engine_file_path); |
||||
yolov8_pose->make_pipe(true); |
||||
|
||||
if (IsFile(path)) { |
||||
std::string suffix = path.substr(path.find_last_of('.') + 1); |
||||
if ( |
||||
suffix == "jpg" || |
||||
suffix == "jpeg" || |
||||
suffix == "png" |
||||
) { |
||||
imagePathList.push_back(path); |
||||
} else if ( |
||||
suffix == "mp4" || |
||||
suffix == "avi" || |
||||
suffix == "m4v" || |
||||
suffix == "mpeg" || |
||||
suffix == "mov" || |
||||
suffix == "mkv" |
||||
) { |
||||
isVideo = true; |
||||
} else { |
||||
printf("suffix %s is wrong !!!\n", suffix.c_str()); |
||||
std::abort(); |
||||
} |
||||
} else if (IsFolder(path)) { |
||||
cv::glob(path + "/*.jpg", imagePathList); |
||||
} |
||||
|
||||
cv::Mat res, image; |
||||
cv::Size size = cv::Size{640, 640}; |
||||
int num_labels = 80; |
||||
int topk = 100; |
||||
float score_thres = 0.25f; |
||||
float iou_thres = 0.65f; |
||||
|
||||
std::vector<Object> objs; |
||||
|
||||
cv::namedWindow("result", cv::WINDOW_AUTOSIZE); |
||||
|
||||
if (isVideo) { |
||||
cv::VideoCapture cap(path); |
||||
|
||||
if (!cap.isOpened()) { |
||||
printf("can not open %s\n", path.c_str()); |
||||
return -1; |
||||
} |
||||
while (cap.read(image)) { |
||||
objs.clear(); |
||||
yolov8_pose->copy_from_Mat(image, size); |
||||
auto start = std::chrono::system_clock::now(); |
||||
yolov8_pose->infer(); |
||||
auto end = std::chrono::system_clock::now(); |
||||
yolov8_pose->postprocess(objs, score_thres, iou_thres, topk); |
||||
yolov8_pose->draw_objects(image, res, objs, SKELETON, KPS_COLORS, LIMB_COLORS); |
||||
auto tc = (double) |
||||
std::chrono::duration_cast<std::chrono::microseconds>(end - start).count() / 1000.; |
||||
printf("cost %2.4lf ms\n", tc); |
||||
cv::imshow("result", res); |
||||
if (cv::waitKey(10) == 'q') { |
||||
break; |
||||
} |
||||
} |
||||
} else { |
||||
for (auto &path: imagePathList) { |
||||
objs.clear(); |
||||
image = cv::imread(path); |
||||
yolov8_pose->copy_from_Mat(image, size); |
||||
auto start = std::chrono::system_clock::now(); |
||||
yolov8_pose->infer(); |
||||
auto end = std::chrono::system_clock::now(); |
||||
yolov8_pose->postprocess(objs, score_thres, iou_thres, topk); |
||||
yolov8_pose->draw_objects(image, res, objs, SKELETON, KPS_COLORS, LIMB_COLORS); |
||||
auto tc = (double) |
||||
std::chrono::duration_cast<std::chrono::microseconds>(end - start).count() / 1000.; |
||||
printf("cost %2.4lf ms\n", tc); |
||||
cv::imshow("result", res); |
||||
cv::waitKey(0); |
||||
} |
||||
} |
||||
cv::destroyAllWindows(); |
||||
delete yolov8_pose; |
||||
return 0; |
||||
} |
||||
@ -0,0 +1,74 @@ |
||||
# YOLOv8-pose Model with TensorRT |
||||
|
||||
The yolov8-pose model conversion route is : |
||||
YOLOv8 PyTorch model -> ONNX -> TensorRT Engine |
||||
|
||||
***Notice !!!*** We don't support TensorRT API building !!! |
||||
|
||||
# Export Orin ONNX model by ultralytics |
||||
|
||||
You can leave this repo and use the original `ultralytics` repo for onnx export. |
||||
|
||||
### 1. Python script |
||||
|
||||
Usage: |
||||
|
||||
```python |
||||
from ultralytics import YOLO |
||||
|
||||
# Load a model |
||||
model = YOLO("yolov8s-pose.pt") # load a pretrained model (recommended for training) |
||||
success = model.export(format="engine", device=0) # export the model to engine format |
||||
assert success |
||||
``` |
||||
|
||||
After executing the above script, you will get an engine named `yolov8s-pose.engine` . |
||||
|
||||
### 2. CLI tools |
||||
|
||||
Usage: |
||||
|
||||
```shell |
||||
yolo export model=yolov8s-pose.pt format=engine device=0 |
||||
``` |
||||
|
||||
After executing the above command, you will get an engine named `yolov8s-pose.engine` too. |
||||
|
||||
## Inference with c++ |
||||
|
||||
You can infer with c++ in [`csrc/pose/normal`](../csrc/pose/normal) . |
||||
|
||||
### Build: |
||||
|
||||
Please set you own librarys in [`CMakeLists.txt`](../csrc/pose/normal/CMakeLists.txt) and modify `KPS_COLORS` and `SKELETON` and `LIMB_COLORS` in [`main.cpp`](../csrc/pose/normal/main.cpp). |
||||
|
||||
Besides, you can modify the postprocess parameters such as `score_thres` and `iou_thres` and `topk` in [`main.cpp`](../csrc/pose/normal/main.cpp). |
||||
|
||||
```c++ |
||||
int topk = 100; |
||||
float score_thres = 0.25f; |
||||
float iou_thres = 0.65f; |
||||
``` |
||||
|
||||
And build: |
||||
|
||||
``` shell |
||||
export root=${PWD} |
||||
cd src/pose/normal |
||||
mkdir build |
||||
cmake .. |
||||
make |
||||
mv yolov8-pose ${root} |
||||
cd ${root} |
||||
``` |
||||
|
||||
Usage: |
||||
|
||||
``` shell |
||||
# infer image |
||||
./yolov8-pose yolov8s-pose.engine data/bus.jpg |
||||
# infer images |
||||
./yolov8-pose yolov8s-pose.engine data |
||||
# infer video |
||||
./yolov8-pose yolov8s-pose.engine data/test.mp4 # the video path |
||||
``` |
||||
Loading…
Reference in new issue