triple-Mu
2 years ago
parent
04ebf0deb2
commit
303c0ae8bb
16 changed files with 1740 additions and 4 deletions
@ -0,0 +1,54 @@ |
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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 LANGUAGES CXX CUDA) |
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++14 -O3 -g") |
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set(CMAKE_CXX_STANDARD 14) |
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set(CMAKE_BUILD_TYPE Release) |
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option(CUDA_USE_STATIC_CUDA_RUNTIME OFF) |
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# CUDA |
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find_package(CUDA REQUIRED) |
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message(STATUS "CUDA Libs: \n${CUDA_LIBRARIES}\n") |
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message(STATUS "CUDA Headers: \n${CUDA_INCLUDE_DIRS}\n") |
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# OpenCV |
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find_package(OpenCV REQUIRED) |
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message(STATUS "OpenCV Libs: \n${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.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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@ -0,0 +1,156 @@ |
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//
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// Created by ubuntu on 1/24/23.
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//
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#ifndef DETECTION_END2END_COMMON_HPP |
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#define DETECTION_END2END_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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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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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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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 det |
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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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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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}; |
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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 //DETECTION_END2END_COMMON_HPP
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@ -0,0 +1,423 @@ |
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//
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// Created by ubuntu on 1/20/23.
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//
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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 det; |
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class YOLOv8 |
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{ |
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public: |
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explicit YOLOv8(const std::string& engine_file_path); |
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~YOLOv8(); |
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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(std::vector<Object>& objs); |
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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::string>& CLASS_NAMES, |
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const std::vector<std::vector<unsigned int>>& 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::YOLOv8(const std::string& engine_file_path) |
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{ |
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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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{ |
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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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{ |
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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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} |
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else |
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{ |
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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::~YOLOv8() |
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{ |
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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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{ |
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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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{ |
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CHECK(cudaFreeHost(ptr)); |
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} |
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} |
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void YOLOv8::make_pipe(bool warmup) |
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{ |
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for (auto& bindings : this->input_bindings) |
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{ |
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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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for (auto& bindings : this->output_bindings) |
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{ |
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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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if (warmup) |
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{ |
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for (int i = 0; i < 10; i++) |
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{ |
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for (auto& bindings : this->input_bindings) |
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{ |
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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::letterbox(const cv::Mat& image, cv::Mat& out, 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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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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cv::Mat tmp; |
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if ((int)width != padw || (int)height != padh) |
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{ |
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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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} |
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else |
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{ |
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tmp = image.clone(); |
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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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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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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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void YOLOv8::copy_from_Mat(const cv::Mat& image) |
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{ |
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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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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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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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void YOLOv8::copy_from_Mat(const cv::Mat& image, cv::Size& size) |
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{ |
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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::infer() |
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{ |
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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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{ |
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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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void YOLOv8::postprocess(std::vector<Object>& objs) |
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{ |
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objs.clear(); |
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int* num_dets = static_cast<int*>(this->host_ptrs[0]); |
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auto* boxes = static_cast<float*>(this->host_ptrs[1]); |
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auto* scores = static_cast<float*>(this->host_ptrs[2]); |
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int* labels = static_cast<int*>(this->host_ptrs[3]); |
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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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for (int i = 0; i < num_dets[0]; i++) |
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{ |
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float* ptr = boxes + i * 4; |
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float x0 = *ptr++ - dw; |
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float y0 = *ptr++ - dh; |
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float x1 = *ptr++ - dw; |
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float y1 = *ptr - dh; |
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x0 = clamp(x0 * ratio, 0.f, width); |
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y0 = clamp(y0 * ratio, 0.f, height); |
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x1 = clamp(x1 * ratio, 0.f, width); |
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y1 = clamp(y1 * ratio, 0.f, height); |
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Object obj; |
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obj.rect.x = x0; |
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obj.rect.y = y0; |
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obj.rect.width = x1 - x0; |
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obj.rect.height = y1 - y0; |
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obj.prob = *(scores + i); |
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obj.label = *(labels + i); |
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objs.push_back(obj); |
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} |
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} |
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void YOLOv8::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::string>& CLASS_NAMES, |
||||
const std::vector<std::vector<unsigned int>>& COLORS |
||||
) |
||||
{ |
||||
res = image.clone(); |
||||
for (auto& obj : objs) |
||||
{ |
||||
cv::Scalar color = cv::Scalar( |
||||
COLORS[obj.label][0], |
||||
COLORS[obj.label][1], |
||||
COLORS[obj.label][2] |
||||
); |
||||
cv::rectangle( |
||||
res, |
||||
obj.rect, |
||||
color, |
||||
2 |
||||
); |
||||
|
||||
char text[256]; |
||||
sprintf( |
||||
text, |
||||
"%s %.1f%%", |
||||
CLASS_NAMES[obj.label].c_str(), |
||||
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 |
||||
); |
||||
} |
||||
} |
||||
@ -0,0 +1,161 @@ |
||||
//
|
||||
// Created by ubuntu on 1/20/23.
|
||||
//
|
||||
#include "chrono" |
||||
#include "yolov8.hpp" |
||||
#include "opencv2/opencv.hpp" |
||||
|
||||
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" }; |
||||
|
||||
const std::vector<std::vector<unsigned int>> COLORS = { |
||||
{ 0, 114, 189 }, { 217, 83, 25 }, { 237, 177, 32 }, |
||||
{ 126, 47, 142 }, { 119, 172, 48 }, { 77, 190, 238 }, |
||||
{ 162, 20, 47 }, { 76, 76, 76 }, { 153, 153, 153 }, |
||||
{ 255, 0, 0 }, { 255, 128, 0 }, { 191, 191, 0 }, |
||||
{ 0, 255, 0 }, { 0, 0, 255 }, { 170, 0, 255 }, |
||||
{ 85, 85, 0 }, { 85, 170, 0 }, { 85, 255, 0 }, |
||||
{ 170, 85, 0 }, { 170, 170, 0 }, { 170, 255, 0 }, |
||||
{ 255, 85, 0 }, { 255, 170, 0 }, { 255, 255, 0 }, |
||||
{ 0, 85, 128 }, { 0, 170, 128 }, { 0, 255, 128 }, |
||||
{ 85, 0, 128 }, { 85, 85, 128 }, { 85, 170, 128 }, |
||||
{ 85, 255, 128 }, { 170, 0, 128 }, { 170, 85, 128 }, |
||||
{ 170, 170, 128 }, { 170, 255, 128 }, { 255, 0, 128 }, |
||||
{ 255, 85, 128 }, { 255, 170, 128 }, { 255, 255, 128 }, |
||||
{ 0, 85, 255 }, { 0, 170, 255 }, { 0, 255, 255 }, |
||||
{ 85, 0, 255 }, { 85, 85, 255 }, { 85, 170, 255 }, |
||||
{ 85, 255, 255 }, { 170, 0, 255 }, { 170, 85, 255 }, |
||||
{ 170, 170, 255 }, { 170, 255, 255 }, { 255, 0, 255 }, |
||||
{ 255, 85, 255 }, { 255, 170, 255 }, { 85, 0, 0 }, |
||||
{ 128, 0, 0 }, { 170, 0, 0 }, { 212, 0, 0 }, |
||||
{ 255, 0, 0 }, { 0, 43, 0 }, { 0, 85, 0 }, |
||||
{ 0, 128, 0 }, { 0, 170, 0 }, { 0, 212, 0 }, |
||||
{ 0, 255, 0 }, { 0, 0, 43 }, { 0, 0, 85 }, |
||||
{ 0, 0, 128 }, { 0, 0, 170 }, { 0, 0, 212 }, |
||||
{ 0, 0, 255 }, { 0, 0, 0 }, { 36, 36, 36 }, |
||||
{ 73, 73, 73 }, { 109, 109, 109 }, { 146, 146, 146 }, |
||||
{ 182, 182, 182 }, { 219, 219, 219 }, { 0, 114, 189 }, |
||||
{ 80, 183, 189 }, { 128, 128, 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 = new YOLOv8(engine_file_path); |
||||
yolov8->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 }; |
||||
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->copy_from_Mat(image, size); |
||||
auto start = std::chrono::system_clock::now(); |
||||
yolov8->infer(); |
||||
auto end = std::chrono::system_clock::now(); |
||||
yolov8->postprocess(objs); |
||||
yolov8->draw_objects(image, res, objs, CLASS_NAMES, 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->copy_from_Mat(image, size); |
||||
auto start = std::chrono::system_clock::now(); |
||||
yolov8->infer(); |
||||
auto end = std::chrono::system_clock::now(); |
||||
yolov8->postprocess(objs); |
||||
yolov8->draw_objects(image, res, objs, CLASS_NAMES, 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; |
||||
return 0; |
||||
} |
||||
@ -0,0 +1,59 @@ |
||||
cmake_minimum_required(VERSION 2.8.12) |
||||
|
||||
set(CMAKE_CUDA_ARCHITECTURES 60 61 62 70 72 75 86) |
||||
set(CMAKE_CUDA_COMPILER /usr/local/cuda/bin/nvcc) |
||||
|
||||
project(yolov8 LANGUAGES CXX CUDA) |
||||
|
||||
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++14 -O3 -g") |
||||
set(CMAKE_CXX_STANDARD 14) |
||||
set(CMAKE_BUILD_TYPE Release) |
||||
option(CUDA_USE_STATIC_CUDA_RUNTIME OFF) |
||||
|
||||
# CUDA |
||||
find_package(CUDA REQUIRED) |
||||
message(STATUS "CUDA Libs: \n${CUDA_LIBRARIES}\n") |
||||
message(STATUS "CUDA Headers: \n${CUDA_INCLUDE_DIRS}\n") |
||||
|
||||
# OpenCV |
||||
find_package(OpenCV REQUIRED) |
||||
message(STATUS "OpenCV Libs: \n${OpenCV_LIBS}\n") |
||||
message(STATUS "OpenCV Libraries: \n${OpenCV_LIBRARIES}\n") |
||||
message(STATUS "OpenCV Headers: \n${OpenCV_INCLUDE_DIRS}\n") |
||||
|
||||
# TensorRT |
||||
set(TensorRT_INCLUDE_DIRS /usr/include/x86_64-linux-gnu) |
||||
set(TensorRT_LIBRARIES /usr/lib/x86_64-linux-gnu) |
||||
|
||||
|
||||
message(STATUS "TensorRT Libs: \n${TensorRT_LIBRARIES}\n") |
||||
message(STATUS "TensorRT Headers: \n${TensorRT_INCLUDE_DIRS}\n") |
||||
|
||||
list(APPEND INCLUDE_DIRS |
||||
${CUDA_INCLUDE_DIRS} |
||||
${OpenCV_INCLUDE_DIRS} |
||||
${TensorRT_INCLUDE_DIRS} |
||||
include |
||||
) |
||||
|
||||
list(APPEND ALL_LIBS |
||||
${CUDA_LIBRARIES} |
||||
${OpenCV_LIBRARIES} |
||||
${TensorRT_LIBRARIES} |
||||
) |
||||
|
||||
include_directories(${INCLUDE_DIRS}) |
||||
|
||||
add_executable(${PROJECT_NAME} |
||||
main.cpp |
||||
include/yolov8.hpp |
||||
include/common.hpp |
||||
) |
||||
|
||||
target_link_directories(${PROJECT_NAME} PUBLIC ${ALL_LIBS}) |
||||
target_link_libraries(${PROJECT_NAME} PRIVATE nvinfer nvinfer_plugin cudart ${OpenCV_LIBS}) |
||||
|
||||
if (${OpenCV_VERSION} VERSION_GREATER_EQUAL 4.7.0) |
||||
message(STATUS "Build with -DBATCHED_NMS") |
||||
add_definitions(-DBATCHED_NMS) |
||||
endif () |
||||
@ -0,0 +1,156 @@ |
||||
//
|
||||
// Created by ubuntu on 1/24/23.
|
||||
//
|
||||
|
||||
#ifndef DETECTION_NORMAL_COMMON_HPP |
||||
#define DETECTION_NORMAL_COMMON_HPP |
||||
#include "opencv2/opencv.hpp" |
||||
#include <sys/stat.h> |
||||
#include <unistd.h> |
||||
#include "NvInfer.h" |
||||
|
||||
#define CHECK(call) \ |
||||
do \
|
||||
{ \
|
||||
const cudaError_t error_code = call; \
|
||||
if (error_code != cudaSuccess) \
|
||||
{ \
|
||||
printf("CUDA Error:\n"); \
|
||||
printf(" File: %s\n", __FILE__); \
|
||||
printf(" Line: %d\n", __LINE__); \
|
||||
printf(" Error code: %d\n", error_code); \
|
||||
printf(" Error text: %s\n", \
|
||||
cudaGetErrorString(error_code)); \
|
||||
exit(1); \
|
||||
} \
|
||||
} while (0) |
||||
|
||||
class Logger : public nvinfer1::ILogger |
||||
{ |
||||
public: |
||||
nvinfer1::ILogger::Severity reportableSeverity; |
||||
|
||||
explicit Logger(nvinfer1::ILogger::Severity severity = nvinfer1::ILogger::Severity::kINFO) : |
||||
reportableSeverity(severity) |
||||
{ |
||||
} |
||||
|
||||
void log(nvinfer1::ILogger::Severity severity, const char* msg) noexcept override |
||||
{ |
||||
if (severity > reportableSeverity) |
||||
{ |
||||
return; |
||||
} |
||||
switch (severity) |
||||
{ |
||||
case nvinfer1::ILogger::Severity::kINTERNAL_ERROR: |
||||
std::cerr << "INTERNAL_ERROR: "; |
||||
break; |
||||
case nvinfer1::ILogger::Severity::kERROR: |
||||
std::cerr << "ERROR: "; |
||||
break; |
||||
case nvinfer1::ILogger::Severity::kWARNING: |
||||
std::cerr << "WARNING: "; |
||||
break; |
||||
case nvinfer1::ILogger::Severity::kINFO: |
||||
std::cerr << "INFO: "; |
||||
break; |
||||
default: |
||||
std::cerr << "VERBOSE: "; |
||||
break; |
||||
} |
||||
std::cerr << msg << std::endl; |
||||
} |
||||
}; |
||||
|
||||
inline int get_size_by_dims(const nvinfer1::Dims& dims) |
||||
{ |
||||
int size = 1; |
||||
for (int i = 0; i < dims.nbDims; i++) |
||||
{ |
||||
size *= dims.d[i]; |
||||
} |
||||
return size; |
||||
} |
||||
|
||||
inline int type_to_size(const nvinfer1::DataType& dataType) |
||||
{ |
||||
switch (dataType) |
||||
{ |
||||
case nvinfer1::DataType::kFLOAT: |
||||
return 4; |
||||
case nvinfer1::DataType::kHALF: |
||||
return 2; |
||||
case nvinfer1::DataType::kINT32: |
||||
return 4; |
||||
case nvinfer1::DataType::kINT8: |
||||
return 1; |
||||
case nvinfer1::DataType::kBOOL: |
||||
return 1; |
||||
default: |
||||
return 4; |
||||
} |
||||
} |
||||
|
||||
inline static float clamp(float val, float min, float max) |
||||
{ |
||||
return val > min ? (val < max ? val : max) : min; |
||||
} |
||||
|
||||
inline bool IsPathExist(const std::string& path) |
||||
{ |
||||
if (access(path.c_str(), 0) == F_OK) |
||||
{ |
||||
return true; |
||||
} |
||||
return false; |
||||
} |
||||
|
||||
inline bool IsFile(const std::string& path) |
||||
{ |
||||
if (!IsPathExist(path)) |
||||
{ |
||||
printf("%s:%d %s not exist\n", __FILE__, __LINE__, path.c_str()); |
||||
return false; |
||||
} |
||||
struct stat buffer; |
||||
return (stat(path.c_str(), &buffer) == 0 && S_ISREG(buffer.st_mode)); |
||||
} |
||||
|
||||
inline bool IsFolder(const std::string& path) |
||||
{ |
||||
if (!IsPathExist(path)) |
||||
{ |
||||
return false; |
||||
} |
||||
struct stat buffer; |
||||
return (stat(path.c_str(), &buffer) == 0 && S_ISDIR(buffer.st_mode)); |
||||
} |
||||
|
||||
namespace det |
||||
{ |
||||
struct Binding |
||||
{ |
||||
size_t size = 1; |
||||
size_t dsize = 1; |
||||
nvinfer1::Dims dims; |
||||
std::string name; |
||||
}; |
||||
|
||||
struct Object |
||||
{ |
||||
cv::Rect_<float> rect; |
||||
int label = 0; |
||||
float prob = 0.0; |
||||
}; |
||||
|
||||
struct PreParam |
||||
{ |
||||
float ratio = 1.0f; |
||||
float dw = 0.0f; |
||||
float dh = 0.0f; |
||||
float height = 0; |
||||
float width = 0; |
||||
}; |
||||
} |
||||
#endif //DETECTION_NORMAL_COMMON_HPP
|
||||
@ -0,0 +1,491 @@ |
||||
//
|
||||
// Created by ubuntu on 1/20/23.
|
||||
//
|
||||
#include "fstream" |
||||
#include "common.hpp" |
||||
#include "NvInferPlugin.h" |
||||
using namespace det; |
||||
|
||||
class YOLOv8 |
||||
{ |
||||
public: |
||||
explicit YOLOv8(const std::string& engine_file_path); |
||||
~YOLOv8(); |
||||
|
||||
void make_pipe(bool warmup = true); |
||||
void copy_from_Mat(const cv::Mat& image); |
||||
void copy_from_Mat(const cv::Mat& image, cv::Size& size); |
||||
void letterbox( |
||||
const cv::Mat& image, |
||||
cv::Mat& out, |
||||
cv::Size& size |
||||
); |
||||
void infer(); |
||||
void postprocess( |
||||
std::vector<Object>& objs, |
||||
float score_thres = 0.25f, |
||||
float iou_thres = 0.65f, |
||||
int topk = 100, |
||||
int num_labels = 80 |
||||
); |
||||
static void draw_objects( |
||||
const cv::Mat& image, |
||||
cv::Mat& res, |
||||
const std::vector<Object>& objs, |
||||
const std::vector<std::string>& CLASS_NAMES, |
||||
const std::vector<std::vector<unsigned int>>& COLORS |
||||
); |
||||
int num_bindings; |
||||
int num_inputs = 0; |
||||
int num_outputs = 0; |
||||
std::vector<Binding> input_bindings; |
||||
std::vector<Binding> output_bindings; |
||||
std::vector<void*> host_ptrs; |
||||
std::vector<void*> device_ptrs; |
||||
|
||||
PreParam pparam; |
||||
private: |
||||
nvinfer1::ICudaEngine* engine = nullptr; |
||||
nvinfer1::IRuntime* runtime = nullptr; |
||||
nvinfer1::IExecutionContext* context = nullptr; |
||||
cudaStream_t stream = nullptr; |
||||
Logger gLogger{ nvinfer1::ILogger::Severity::kERROR }; |
||||
|
||||
}; |
||||
|
||||
YOLOv8::YOLOv8(const std::string& engine_file_path) |
||||
{ |
||||
std::ifstream file(engine_file_path, std::ios::binary); |
||||
assert(file.good()); |
||||
file.seekg(0, std::ios::end); |
||||
auto size = file.tellg(); |
||||
file.seekg(0, std::ios::beg); |
||||
char* trtModelStream = new char[size]; |
||||
assert(trtModelStream); |
||||
file.read(trtModelStream, size); |
||||
file.close(); |
||||
initLibNvInferPlugins(&this->gLogger, ""); |
||||
this->runtime = nvinfer1::createInferRuntime(this->gLogger); |
||||
assert(this->runtime != nullptr); |
||||
|
||||
this->engine = this->runtime->deserializeCudaEngine(trtModelStream, size); |
||||
assert(this->engine != nullptr); |
||||
|
||||
this->context = this->engine->createExecutionContext(); |
||||
|
||||
assert(this->context != nullptr); |
||||
cudaStreamCreate(&this->stream); |
||||
this->num_bindings = this->engine->getNbBindings(); |
||||
|
||||
for (int i = 0; i < this->num_bindings; ++i) |
||||
{ |
||||
Binding binding; |
||||
nvinfer1::Dims dims; |
||||
nvinfer1::DataType dtype = this->engine->getBindingDataType(i); |
||||
std::string name = this->engine->getBindingName(i); |
||||
binding.name = name; |
||||
binding.dsize = type_to_size(dtype); |
||||
|
||||
bool IsInput = engine->bindingIsInput(i); |
||||
if (IsInput) |
||||
{ |
||||
this->num_inputs += 1; |
||||
dims = this->engine->getProfileDimensions( |
||||
i, |
||||
0, |
||||
nvinfer1::OptProfileSelector::kMAX); |
||||
binding.size = get_size_by_dims(dims); |
||||
binding.dims = dims; |
||||
this->input_bindings.push_back(binding); |
||||
// set max opt shape
|
||||
this->context->setBindingDimensions(i, dims); |
||||
|
||||
} |
||||
else |
||||
{ |
||||
dims = this->context->getBindingDimensions(i); |
||||
binding.size = get_size_by_dims(dims); |
||||
binding.dims = dims; |
||||
this->output_bindings.push_back(binding); |
||||
this->num_outputs += 1; |
||||
} |
||||
} |
||||
|
||||
} |
||||
|
||||
YOLOv8::~YOLOv8() |
||||
{ |
||||
this->context->destroy(); |
||||
this->engine->destroy(); |
||||
this->runtime->destroy(); |
||||
cudaStreamDestroy(this->stream); |
||||
for (auto& ptr : this->device_ptrs) |
||||
{ |
||||
CHECK(cudaFree(ptr)); |
||||
} |
||||
|
||||
for (auto& ptr : this->host_ptrs) |
||||
{ |
||||
CHECK(cudaFreeHost(ptr)); |
||||
} |
||||
|
||||
} |
||||
void YOLOv8::make_pipe(bool warmup) |
||||
{ |
||||
|
||||
for (auto& bindings : this->input_bindings) |
||||
{ |
||||
void* d_ptr; |
||||
CHECK(cudaMallocAsync( |
||||
&d_ptr, |
||||
bindings.size * bindings.dsize, |
||||
this->stream) |
||||
); |
||||
this->device_ptrs.push_back(d_ptr); |
||||
} |
||||
|
||||
for (auto& bindings : this->output_bindings) |
||||
{ |
||||
void* d_ptr, * h_ptr; |
||||
size_t size = bindings.size * bindings.dsize; |
||||
CHECK(cudaMallocAsync( |
||||
&d_ptr, |
||||
size, |
||||
this->stream) |
||||
); |
||||
CHECK(cudaHostAlloc( |
||||
&h_ptr, |
||||
size, |
||||
0) |
||||
); |
||||
this->device_ptrs.push_back(d_ptr); |
||||
this->host_ptrs.push_back(h_ptr); |
||||
} |
||||
|
||||
if (warmup) |
||||
{ |
||||
for (int i = 0; i < 10; i++) |
||||
{ |
||||
for (auto& bindings : this->input_bindings) |
||||
{ |
||||
size_t size = bindings.size * bindings.dsize; |
||||
void* h_ptr = malloc(size); |
||||
memset(h_ptr, 0, size); |
||||
CHECK(cudaMemcpyAsync( |
||||
this->device_ptrs[0], |
||||
h_ptr, |
||||
size, |
||||
cudaMemcpyHostToDevice, |
||||
this->stream) |
||||
); |
||||
free(h_ptr); |
||||
} |
||||
this->infer(); |
||||
} |
||||
printf("model warmup 10 times\n"); |
||||
|
||||
} |
||||
} |
||||
|
||||
void YOLOv8::letterbox(const cv::Mat& image, cv::Mat& out, cv::Size& size) |
||||
{ |
||||
const float inp_h = size.height; |
||||
const float inp_w = size.width; |
||||
float height = image.rows; |
||||
float width = image.cols; |
||||
|
||||
float r = std::min(inp_h / height, inp_w / width); |
||||
int padw = std::round(width * r); |
||||
int padh = std::round(height * r); |
||||
|
||||
cv::Mat tmp; |
||||
if ((int)width != padw || (int)height != padh) |
||||
{ |
||||
cv::resize( |
||||
image, |
||||
tmp, |
||||
cv::Size(padw, padh) |
||||
); |
||||
} |
||||
else |
||||
{ |
||||
tmp = image.clone(); |
||||
} |
||||
|
||||
float dw = inp_w - padw; |
||||
float dh = inp_h - padh; |
||||
|
||||
dw /= 2.0f; |
||||
dh /= 2.0f; |
||||
int top = int(std::round(dh - 0.1f)); |
||||
int bottom = int(std::round(dh + 0.1f)); |
||||
int left = int(std::round(dw - 0.1f)); |
||||
int right = int(std::round(dw + 0.1f)); |
||||
|
||||
cv::copyMakeBorder( |
||||
tmp, |
||||
tmp, |
||||
top, |
||||
bottom, |
||||
left, |
||||
right, |
||||
cv::BORDER_CONSTANT, |
||||
{ 114, 114, 114 } |
||||
); |
||||
|
||||
cv::dnn::blobFromImage(tmp, |
||||
out, |
||||
1 / 255.f, |
||||
cv::Size(), |
||||
cv::Scalar(0, 0, 0), |
||||
true, |
||||
false, |
||||
CV_32F |
||||
); |
||||
this->pparam.ratio = 1 / r; |
||||
this->pparam.dw = dw; |
||||
this->pparam.dh = dh; |
||||
this->pparam.height = height; |
||||
this->pparam.width = width;; |
||||
} |
||||
|
||||
void YOLOv8::copy_from_Mat(const cv::Mat& image) |
||||
{ |
||||
cv::Mat nchw; |
||||
auto& in_binding = this->input_bindings[0]; |
||||
auto width = in_binding.dims.d[3]; |
||||
auto height = in_binding.dims.d[2]; |
||||
cv::Size size{ width, height }; |
||||
this->letterbox( |
||||
image, |
||||
nchw, |
||||
size |
||||
); |
||||
|
||||
this->context->setBindingDimensions( |
||||
0, |
||||
nvinfer1::Dims |
||||
{ |
||||
4, |
||||
{ 1, 3, height, width } |
||||
} |
||||
); |
||||
|
||||
CHECK(cudaMemcpyAsync( |
||||
this->device_ptrs[0], |
||||
nchw.ptr<float>(), |
||||
nchw.total() * nchw.elemSize(), |
||||
cudaMemcpyHostToDevice, |
||||
this->stream) |
||||
); |
||||
} |
||||
|
||||
void YOLOv8::copy_from_Mat(const cv::Mat& image, cv::Size& size) |
||||
{ |
||||
cv::Mat nchw; |
||||
this->letterbox( |
||||
image, |
||||
nchw, |
||||
size |
||||
); |
||||
this->context->setBindingDimensions( |
||||
0, |
||||
nvinfer1::Dims |
||||
{ 4, |
||||
{ 1, 3, size.height, size.width } |
||||
} |
||||
); |
||||
CHECK(cudaMemcpyAsync( |
||||
this->device_ptrs[0], |
||||
nchw.ptr<float>(), |
||||
nchw.total() * nchw.elemSize(), |
||||
cudaMemcpyHostToDevice, |
||||
this->stream) |
||||
); |
||||
} |
||||
|
||||
void YOLOv8::infer() |
||||
{ |
||||
|
||||
this->context->enqueueV2( |
||||
this->device_ptrs.data(), |
||||
this->stream, |
||||
nullptr |
||||
); |
||||
for (int i = 0; i < this->num_outputs; i++) |
||||
{ |
||||
size_t osize = this->output_bindings[i].size * this->output_bindings[i].dsize; |
||||
CHECK(cudaMemcpyAsync(this->host_ptrs[i], |
||||
this->device_ptrs[i + this->num_inputs], |
||||
osize, |
||||
cudaMemcpyDeviceToHost, |
||||
this->stream) |
||||
); |
||||
|
||||
} |
||||
cudaStreamSynchronize(this->stream); |
||||
|
||||
} |
||||
|
||||
void YOLOv8::postprocess( |
||||
std::vector<Object>& objs, |
||||
float score_thres, |
||||
float iou_thres, |
||||
int topk, |
||||
int num_labels |
||||
) |
||||
{ |
||||
objs.clear(); |
||||
auto num_channels = this->output_bindings[0].dims.d[1]; |
||||
auto num_anchors = this->output_bindings[0].dims.d[2]; |
||||
|
||||
auto& dw = this->pparam.dw; |
||||
auto& dh = this->pparam.dh; |
||||
auto& width = this->pparam.width; |
||||
auto& height = this->pparam.height; |
||||
auto& ratio = this->pparam.ratio; |
||||
|
||||
std::vector<cv::Rect> bboxes; |
||||
std::vector<float> scores; |
||||
std::vector<int> labels; |
||||
std::vector<int> indices; |
||||
|
||||
cv::Mat output = cv::Mat( |
||||
num_channels, |
||||
num_anchors, |
||||
CV_32F, |
||||
static_cast<float*>(this->host_ptrs[0]) |
||||
); |
||||
output = output.t(); |
||||
for (int i = 0; i < num_anchors; i++) |
||||
{ |
||||
auto row_ptr = output.row(i).ptr<float>(); |
||||
auto bboxes_ptr = row_ptr; |
||||
auto scores_ptr = row_ptr + 4; |
||||
auto max_s_ptr = std::max_element(scores_ptr, scores_ptr + num_labels); |
||||
float score = *max_s_ptr; |
||||
if (score > score_thres) |
||||
{ |
||||
std::cout << score << std::endl; |
||||
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); |
||||
|
||||
int label = max_s_ptr - scores_ptr; |
||||
cv::Rect_<float> bbox; |
||||
bbox.x = x0; |
||||
bbox.y = y0; |
||||
bbox.width = x1 - x0; |
||||
bbox.height = y1 - y0; |
||||
|
||||
bboxes.push_back(bbox); |
||||
labels.push_back(label); |
||||
scores.push_back(score); |
||||
} |
||||
} |
||||
|
||||
#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]; |
||||
objs.push_back(obj); |
||||
cnt += 1; |
||||
} |
||||
} |
||||
|
||||
void YOLOv8::draw_objects( |
||||
const cv::Mat& image, |
||||
cv::Mat& res, |
||||
const std::vector<Object>& objs, |
||||
const std::vector<std::string>& CLASS_NAMES, |
||||
const std::vector<std::vector<unsigned int>>& COLORS |
||||
) |
||||
{ |
||||
res = image.clone(); |
||||
for (auto& obj : objs) |
||||
{ |
||||
cv::Scalar color = cv::Scalar( |
||||
COLORS[obj.label][0], |
||||
COLORS[obj.label][1], |
||||
COLORS[obj.label][2] |
||||
); |
||||
cv::rectangle( |
||||
res, |
||||
obj.rect, |
||||
color, |
||||
2 |
||||
); |
||||
|
||||
char text[256]; |
||||
sprintf( |
||||
text, |
||||
"%s %.1f%%", |
||||
CLASS_NAMES[obj.label].c_str(), |
||||
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 |
||||
); |
||||
} |
||||
} |
||||
@ -0,0 +1,166 @@ |
||||
//
|
||||
// Created by ubuntu on 1/20/23.
|
||||
//
|
||||
#include "chrono" |
||||
#include "yolov8.hpp" |
||||
#include "opencv2/opencv.hpp" |
||||
|
||||
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" }; |
||||
|
||||
const std::vector<std::vector<unsigned int>> COLORS = { |
||||
{ 0, 114, 189 }, { 217, 83, 25 }, { 237, 177, 32 }, |
||||
{ 126, 47, 142 }, { 119, 172, 48 }, { 77, 190, 238 }, |
||||
{ 162, 20, 47 }, { 76, 76, 76 }, { 153, 153, 153 }, |
||||
{ 255, 0, 0 }, { 255, 128, 0 }, { 191, 191, 0 }, |
||||
{ 0, 255, 0 }, { 0, 0, 255 }, { 170, 0, 255 }, |
||||
{ 85, 85, 0 }, { 85, 170, 0 }, { 85, 255, 0 }, |
||||
{ 170, 85, 0 }, { 170, 170, 0 }, { 170, 255, 0 }, |
||||
{ 255, 85, 0 }, { 255, 170, 0 }, { 255, 255, 0 }, |
||||
{ 0, 85, 128 }, { 0, 170, 128 }, { 0, 255, 128 }, |
||||
{ 85, 0, 128 }, { 85, 85, 128 }, { 85, 170, 128 }, |
||||
{ 85, 255, 128 }, { 170, 0, 128 }, { 170, 85, 128 }, |
||||
{ 170, 170, 128 }, { 170, 255, 128 }, { 255, 0, 128 }, |
||||
{ 255, 85, 128 }, { 255, 170, 128 }, { 255, 255, 128 }, |
||||
{ 0, 85, 255 }, { 0, 170, 255 }, { 0, 255, 255 }, |
||||
{ 85, 0, 255 }, { 85, 85, 255 }, { 85, 170, 255 }, |
||||
{ 85, 255, 255 }, { 170, 0, 255 }, { 170, 85, 255 }, |
||||
{ 170, 170, 255 }, { 170, 255, 255 }, { 255, 0, 255 }, |
||||
{ 255, 85, 255 }, { 255, 170, 255 }, { 85, 0, 0 }, |
||||
{ 128, 0, 0 }, { 170, 0, 0 }, { 212, 0, 0 }, |
||||
{ 255, 0, 0 }, { 0, 43, 0 }, { 0, 85, 0 }, |
||||
{ 0, 128, 0 }, { 0, 170, 0 }, { 0, 212, 0 }, |
||||
{ 0, 255, 0 }, { 0, 0, 43 }, { 0, 0, 85 }, |
||||
{ 0, 0, 128 }, { 0, 0, 170 }, { 0, 0, 212 }, |
||||
{ 0, 0, 255 }, { 0, 0, 0 }, { 36, 36, 36 }, |
||||
{ 73, 73, 73 }, { 109, 109, 109 }, { 146, 146, 146 }, |
||||
{ 182, 182, 182 }, { 219, 219, 219 }, { 0, 114, 189 }, |
||||
{ 80, 183, 189 }, { 128, 128, 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 = new YOLOv8(engine_file_path); |
||||
yolov8->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->copy_from_Mat(image, size); |
||||
auto start = std::chrono::system_clock::now(); |
||||
yolov8->infer(); |
||||
auto end = std::chrono::system_clock::now(); |
||||
yolov8->postprocess(objs, score_thres, iou_thres, topk, num_labels); |
||||
yolov8->draw_objects(image, res, objs, CLASS_NAMES, 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->copy_from_Mat(image, size); |
||||
auto start = std::chrono::system_clock::now(); |
||||
yolov8->infer(); |
||||
auto end = std::chrono::system_clock::now(); |
||||
yolov8->postprocess(objs, score_thres, iou_thres, topk, num_labels); |
||||
yolov8->draw_objects(image, res, objs, CLASS_NAMES, 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; |
||||
return 0; |
||||
} |
||||
@ -0,0 +1,67 @@ |
||||
# Normal Usage of [`ultralytics`](https://github.com/ultralytics/ultralytics) |
||||
|
||||
## Export TensorRT Engine |
||||
|
||||
### 1. Python script |
||||
|
||||
Usage: |
||||
|
||||
```python |
||||
|
||||
from ultralytics import YOLO |
||||
|
||||
# Load a model |
||||
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training) |
||||
success = model.export(format="engine") # export the model to engine format |
||||
assert success |
||||
``` |
||||
|
||||
After executing the above script, you will get an engine named `yolov8n.engine` . |
||||
|
||||
### 2. CLI tools |
||||
|
||||
```shell |
||||
yolo export model=yolov8n.pt format=engine |
||||
``` |
||||
|
||||
After executing the above command, you will get an engine named `yolov8n.engine` too. |
||||
|
||||
## Inference with c++ |
||||
|
||||
You can infer with c++ in [`csrc/detect/normal`](../csrc/detect/normal) . |
||||
|
||||
### Build: |
||||
|
||||
Please set you own librarys in [`CMakeLists.txt`](../csrc/detect/normal/CMakeLists.txt) and modify `CLASS_NAMES` and `COLORS` in [`main.cpp`](../csrc/detect/normal/main.cpp). |
||||
|
||||
Besides, you can modify the postprocess parameters such as `num_labels` and `score_thres` and `iou_thres` and `topk` in [`main.cpp`](../csrc/detect/normal/main.cpp). |
||||
|
||||
```c++ |
||||
int num_labels = 80; |
||||
int topk = 100; |
||||
float score_thres = 0.25f; |
||||
float iou_thres = 0.65f; |
||||
``` |
||||
|
||||
And build: |
||||
|
||||
``` shell |
||||
export root=${PWD} |
||||
cd src/detect/normal |
||||
mkdir build |
||||
cmake .. |
||||
make |
||||
mv yolov8 ${root} |
||||
cd ${root} |
||||
``` |
||||
|
||||
Usage: |
||||
|
||||
``` shell |
||||
# infer image |
||||
./yolov8 yolov8s.engine data/bus.jpg |
||||
# infer images |
||||
./yolov8 yolov8s.engine data |
||||
# infer video |
||||
./yolov8 yolov8s.engine data/test.mp4 # the video path |
||||
``` |
||||
Loading…
Reference in new issue