diff --git a/README.md b/README.md index 7e41d10..287187c 100644 --- a/README.md +++ b/README.md @@ -39,9 +39,12 @@ wget https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8l.pt wget https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8x.pt ``` +# Normal Usage -# Build TensorRT Engine by ONNX +You can export ONNX or Engine using the origin [`ultralytics`](https://github.com/ultralytics/ultralytics) repo . +Please see more information in [`Normal.md`](docs/Normal.md). +# Build TensorRT Engine by ONNX ## Export ONNX by `ultralytics` API @@ -169,15 +172,15 @@ python3 infer.py \ ## 2. Infer with C++ -You can infer with c++ in [`csrc/detect`](csrc/detect) . +You can infer with c++ in [`csrc/detect/end2end`](csrc/detect/end2end) . ### Build: -Please set you own librarys in [`CMakeLists.txt`](csrc/detect/CMakeLists.txt) and modify you own config in [`config.h`](csrc/detect/include/config.h) such as `CLASS_NAMES` and `COLORS`. +Please set you own librarys in [`CMakeLists.txt`](csrc/detect/end2end/CMakeLists.txt) and modify `CLASS_NAMES` and `COLORS` in [`main.cpp`](csrc/detect/end2end/main.cpp). ``` shell export root=${PWD} -cd src/end2end +cd src/detect/end2end mkdir build cmake .. make diff --git a/csrc/detect/end2end/CMakeLists.txt b/csrc/detect/end2end/CMakeLists.txt new file mode 100644 index 0000000..bcdd8b0 --- /dev/null +++ b/csrc/detect/end2end/CMakeLists.txt @@ -0,0 +1,54 @@ +cmake_minimum_required(VERSION 2.8.12) + +set(CMAKE_CUDA_ARCHITECTURES 60 61 62 70 72 75 86) +set(CMAKE_CUDA_COMPILER /usr/local/cuda/bin/nvcc) + +project(yolov8 LANGUAGES CXX CUDA) + +set(CMAKE_CXX_FLAGS "${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}) diff --git a/csrc/detect/end2end/include/common.hpp b/csrc/detect/end2end/include/common.hpp new file mode 100644 index 0000000..ee8973f --- /dev/null +++ b/csrc/detect/end2end/include/common.hpp @@ -0,0 +1,156 @@ +// +// Created by ubuntu on 1/24/23. +// + +#ifndef DETECTION_END2END_COMMON_HPP +#define DETECTION_END2END_COMMON_HPP +#include "opencv2/opencv.hpp" +#include +#include +#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_ 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_END2END_COMMON_HPP diff --git a/csrc/detect/end2end/include/yolov8.hpp b/csrc/detect/end2end/include/yolov8.hpp new file mode 100644 index 0000000..08709b0 --- /dev/null +++ b/csrc/detect/end2end/include/yolov8.hpp @@ -0,0 +1,423 @@ +// +// 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& objs); + static void draw_objects( + const cv::Mat& image, + cv::Mat& res, + const std::vector& objs, + const std::vector& CLASS_NAMES, + const std::vector>& COLORS + ); + int num_bindings; + int num_inputs = 0; + int num_outputs = 0; + std::vector input_bindings; + std::vector output_bindings; + std::vector host_ptrs; + std::vector 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(), + 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(), + 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& objs) +{ + objs.clear(); + int* num_dets = static_cast(this->host_ptrs[0]); + auto* boxes = static_cast(this->host_ptrs[1]); + auto* scores = static_cast(this->host_ptrs[2]); + int* labels = static_cast(this->host_ptrs[3]); + 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; + for (int i = 0; i < num_dets[0]; i++) + { + float* ptr = boxes + i * 4; + + float x0 = *ptr++ - dw; + float y0 = *ptr++ - dh; + float x1 = *ptr++ - dw; + float y1 = *ptr - dh; + + x0 = clamp(x0 * ratio, 0.f, width); + y0 = clamp(y0 * ratio, 0.f, height); + x1 = clamp(x1 * ratio, 0.f, width); + y1 = clamp(y1 * ratio, 0.f, height); + Object obj; + obj.rect.x = x0; + obj.rect.y = y0; + obj.rect.width = x1 - x0; + obj.rect.height = y1 - y0; + obj.prob = *(scores + i); + obj.label = *(labels + i); + objs.push_back(obj); + } +} + +void YOLOv8::draw_objects( + const cv::Mat& image, + cv::Mat& res, + const std::vector& objs, + const std::vector& CLASS_NAMES, + const std::vector>& 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 + ); + } +} diff --git a/csrc/detect/end2end/main.cpp b/csrc/detect/end2end/main.cpp new file mode 100644 index 0000000..6730c58 --- /dev/null +++ b/csrc/detect/end2end/main.cpp @@ -0,0 +1,161 @@ +// +// Created by ubuntu on 1/20/23. +// +#include "chrono" +#include "yolov8.hpp" +#include "opencv2/opencv.hpp" + +const std::vector 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> 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 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 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(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(end - start).count() / 1000.; + printf("cost %2.4lf ms\n", tc); + cv::imshow("result", res); + cv::waitKey(0); + } + } + cv::destroyAllWindows(); + delete yolov8; + return 0; +} diff --git a/csrc/detect/normal/CMakeLists.txt b/csrc/detect/normal/CMakeLists.txt new file mode 100644 index 0000000..27cdcee --- /dev/null +++ b/csrc/detect/normal/CMakeLists.txt @@ -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 () diff --git a/csrc/detect/normal/include/common.hpp b/csrc/detect/normal/include/common.hpp new file mode 100644 index 0000000..6b2d570 --- /dev/null +++ b/csrc/detect/normal/include/common.hpp @@ -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 +#include +#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_ 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 diff --git a/csrc/detect/normal/include/yolov8.hpp b/csrc/detect/normal/include/yolov8.hpp new file mode 100644 index 0000000..274319e --- /dev/null +++ b/csrc/detect/normal/include/yolov8.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& 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& objs, + const std::vector& CLASS_NAMES, + const std::vector>& COLORS + ); + int num_bindings; + int num_inputs = 0; + int num_outputs = 0; + std::vector input_bindings; + std::vector output_bindings; + std::vector host_ptrs; + std::vector 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(), + 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(), + 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& 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 bboxes; + std::vector scores; + std::vector labels; + std::vector indices; + + cv::Mat output = cv::Mat( + num_channels, + num_anchors, + CV_32F, + static_cast(this->host_ptrs[0]) + ); + output = output.t(); + for (int i = 0; i < num_anchors; i++) + { + auto row_ptr = output.row(i).ptr(); + 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_ 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& objs, + const std::vector& CLASS_NAMES, + const std::vector>& 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 + ); + } +} diff --git a/csrc/detect/normal/main.cpp b/csrc/detect/normal/main.cpp new file mode 100644 index 0000000..b1226a5 --- /dev/null +++ b/csrc/detect/normal/main.cpp @@ -0,0 +1,166 @@ +// +// Created by ubuntu on 1/20/23. +// +#include "chrono" +#include "yolov8.hpp" +#include "opencv2/opencv.hpp" + +const std::vector 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> 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 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 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(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(end - start).count() / 1000.; + printf("cost %2.4lf ms\n", tc); + cv::imshow("result", res); + cv::waitKey(0); + } + } + cv::destroyAllWindows(); + delete yolov8; + return 0; +} diff --git a/csrc/detect/CMakeLists.txt b/csrc/detection/CMakeLists.txt similarity index 100% rename from csrc/detect/CMakeLists.txt rename to csrc/detection/CMakeLists.txt diff --git a/csrc/detect/include/config.h b/csrc/detection/include/config.h similarity index 100% rename from csrc/detect/include/config.h rename to csrc/detection/include/config.h diff --git a/csrc/detect/include/utils.h b/csrc/detection/include/utils.h similarity index 100% rename from csrc/detect/include/utils.h rename to csrc/detection/include/utils.h diff --git a/csrc/detect/include/yolov8.hpp b/csrc/detection/include/yolov8.hpp similarity index 100% rename from csrc/detect/include/yolov8.hpp rename to csrc/detection/include/yolov8.hpp diff --git a/csrc/detect/main.cpp b/csrc/detection/main.cpp similarity index 100% rename from csrc/detect/main.cpp rename to csrc/detection/main.cpp diff --git a/docs/Normal.md b/docs/Normal.md new file mode 100644 index 0000000..d69c041 --- /dev/null +++ b/docs/Normal.md @@ -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 +``` diff --git a/export.py b/export.py old mode 100644 new mode 100755