diff --git a/csrc/jetson/pose/CMakeLists.txt b/csrc/jetson/pose/CMakeLists.txt new file mode 100644 index 0000000..41675f6 --- /dev/null +++ b/csrc/jetson/pose/CMakeLists.txt @@ -0,0 +1,60 @@ +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-pose 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/aarch64-linux-gnu) +set(TensorRT_LIBRARIES /usr/lib/aarch64-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(${PROJECT_NAME} + main.cpp + include/yolov8-pose.hpp + include/common.hpp + ) + +link_directories(${ALL_LIBS}) +target_link_libraries(${PROJECT_NAME} PRIVATE nvinfer nvinfer_plugin ${CUDA_LIBRARIES} ${OpenCV_LIBS}) + + +if(${OpenCV_VERSION} VERSION_GREATER_EQUAL 4.7.0) + message(STATUS "Build with -DBATCHED_NMS") + add_definitions(-DBATCHED_NMS) +endif() diff --git a/csrc/jetson/pose/include/common.hpp b/csrc/jetson/pose/include/common.hpp new file mode 100644 index 0000000..e23ac01 --- /dev/null +++ b/csrc/jetson/pose/include/common.hpp @@ -0,0 +1,157 @@ +// +// Created by ubuntu on 5/15/23. +// + +#ifndef JETSON_POSE_COMMON_HPP +#define JETSON_POSE_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 pose +{ + 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; + std::vector kps; + }; + + struct PreParam + { + float ratio = 1.0f; + float dw = 0.0f; + float dh = 0.0f; + float height = 0; + float width = 0; + }; +} +#endif //JETSON_POSE_COMMON_HPP diff --git a/csrc/jetson/pose/include/yolov8-pose.hpp b/csrc/jetson/pose/include/yolov8-pose.hpp new file mode 100644 index 0000000..4a04584 --- /dev/null +++ b/csrc/jetson/pose/include/yolov8-pose.hpp @@ -0,0 +1,515 @@ +// +// Created by ubuntu on 1/20/23. +// +#ifndef JETSON_POSE_YOLOV8_POSE_HPP +#define JETSON_POSE_YOLOV8_POSE_HPP + +#include "fstream" +#include "common.hpp" +#include "NvInferPlugin.h" + +using namespace pose; + +class YOLOv8_pose { +public: + explicit YOLOv8_pose(const std::string &engine_file_path); + + ~YOLOv8_pose(); + + 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 + ); + + static void draw_objects( + const cv::Mat &image, + cv::Mat &res, + const std::vector &objs, + const std::vector> &SKELETON, + const std::vector> &KPS_COLORS, + const std::vector> &LIMB_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_pose::YOLOv8_pose(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_pose::~YOLOv8_pose() { + 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_pose::make_pipe(bool warmup) { + + for (auto &bindings: this->input_bindings) { + void *d_ptr; + CHECK(cudaMalloc( + &d_ptr, + bindings.size * bindings.dsize + ) + ); + 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(cudaMalloc( + &d_ptr, + size) + ); + 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_pose::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_pose::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_pose::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_pose::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_pose::postprocess( + std::vector &objs, + float score_thres, + float iou_thres, + int topk +) { + 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; + std::vector> kpss; + + 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 kps_ptr = row_ptr + 5; + + float score = *scores_ptr; + if (score > score_thres) { + float x = *bboxes_ptr++ - dw; + float y = *bboxes_ptr++ - dh; + float w = *bboxes_ptr++; + float h = *bboxes_ptr; + + float x0 = clamp((x - 0.5f * w) * ratio, 0.f, width); + float y0 = clamp((y - 0.5f * h) * ratio, 0.f, height); + float x1 = clamp((x + 0.5f * w) * ratio, 0.f, width); + float y1 = clamp((y + 0.5f * h) * ratio, 0.f, height); + + cv::Rect_ bbox; + bbox.x = x0; + bbox.y = y0; + bbox.width = x1 - x0; + bbox.height = y1 - y0; + std::vector kps; + for (int k = 0; k < 17; k++) { + float kps_x = (*(kps_ptr + 3 * k) - dw) * ratio; + float kps_y = (*(kps_ptr + 3 * k + 1) - dh) * ratio; + float kps_s = *(kps_ptr + 3 * k + 2); + kps_x = clamp(kps_x, 0.f, width); + kps_y = clamp(kps_y, 0.f, height); + kps.push_back(kps_x); + kps.push_back(kps_y); + kps.push_back(kps_s); + } + + bboxes.push_back(bbox); + labels.push_back(0); + scores.push_back(score); + kpss.push_back(kps); + } + } + +#ifdef BATCHED_NMS + cv::dnn::NMSBoxesBatched( + bboxes, + scores, + labels, + score_thres, + iou_thres, + indices + ); +#else + cv::dnn::NMSBoxes( + bboxes, + scores, + score_thres, + iou_thres, + indices + ); +#endif + + int cnt = 0; + for (auto &i: indices) { + if (cnt >= topk) { + break; + } + Object obj; + obj.rect = bboxes[i]; + obj.prob = scores[i]; + obj.label = labels[i]; + obj.kps = kpss[i]; + objs.push_back(obj); + cnt += 1; + } +} + +void YOLOv8_pose::draw_objects( + const cv::Mat &image, + cv::Mat &res, + const std::vector &objs, + const std::vector> &SKELETON, + const std::vector> &KPS_COLORS, + const std::vector> &LIMB_COLORS +) { + res = image.clone(); + const int num_point = 17; + for (auto &obj: objs) { + cv::rectangle( + res, + obj.rect, + {0, 0, 255}, + 2 + ); + + char text[256]; + sprintf( + text, + "person %.1f%%", + obj.prob * 100 + ); + + int baseLine = 0; + cv::Size label_size = cv::getTextSize( + text, + cv::FONT_HERSHEY_SIMPLEX, + 0.4, + 1, + &baseLine + ); + + int x = (int) obj.rect.x; + int y = (int) obj.rect.y + 1; + + if (y > res.rows) + y = res.rows; + + cv::rectangle( + res, + cv::Rect(x, y, label_size.width, label_size.height + baseLine), + {0, 0, 255}, + -1 + ); + + cv::putText( + res, + text, + cv::Point(x, y + label_size.height), + cv::FONT_HERSHEY_SIMPLEX, + 0.4, + {255, 255, 255}, + 1 + ); + + auto &kps = obj.kps; + for (int k = 0; k < num_point + 2; k++) { + if (k < num_point) { + int kps_x = std::round(kps[k * 3]); + int kps_y = std::round(kps[k * 3 + 1]); + float kps_s = kps[k * 3 + 2]; + if (kps_s > 0.5f) { + cv::Scalar kps_color = cv::Scalar(KPS_COLORS[k][0], KPS_COLORS[k][1], KPS_COLORS[k][2]); + cv::circle(res, {kps_x, kps_y}, 5, kps_color, -1); + } + } + auto &ske = SKELETON[k]; + int pos1_x = std::round(kps[(ske[0] - 1) * 3]); + int pos1_y = std::round(kps[(ske[0] - 1) * 3 + 1]); + + int pos2_x = std::round(kps[(ske[1] - 1) * 3]); + int pos2_y = std::round(kps[(ske[1] - 1) * 3 + 1]); + + float pos1_s = kps[(ske[0] - 1) * 3 + 2]; + float pos2_s = kps[(ske[1] - 1) * 3 + 2]; + + + if (pos1_s > 0.5f && pos2_s > 0.5f) { + cv::Scalar limb_color = cv::Scalar(LIMB_COLORS[k][0], LIMB_COLORS[k][1], LIMB_COLORS[k][2]); + cv::line(res, {pos1_x, pos1_y}, {pos2_x, pos2_y}, limb_color, 2); + } + } + } +} + +#endif //JETSON_POSE_YOLOV8_POSE_HPP diff --git a/csrc/jetson/pose/main.cpp b/csrc/jetson/pose/main.cpp new file mode 100644 index 0000000..8a0720f --- /dev/null +++ b/csrc/jetson/pose/main.cpp @@ -0,0 +1,161 @@ +// +// Created by ubuntu on 4/7/23. +// +#include "chrono" +#include "yolov8-pose.hpp" +#include "opencv2/opencv.hpp" + + +const std::vector> KPS_COLORS = + {{0, 255, 0}, + {0, 255, 0}, + {0, 255, 0}, + {0, 255, 0}, + {0, 255, 0}, + {255, 128, 0}, + {255, 128, 0}, + {255, 128, 0}, + {255, 128, 0}, + {255, 128, 0}, + {255, 128, 0}, + {51, 153, 255}, + {51, 153, 255}, + {51, 153, 255}, + {51, 153, 255}, + {51, 153, 255}, + {51, 153, 255}}; + +const std::vector> SKELETON = {{16, 14}, + {14, 12}, + {17, 15}, + {15, 13}, + {12, 13}, + {6, 12}, + {7, 13}, + {6, 7}, + {6, 8}, + {7, 9}, + {8, 10}, + {9, 11}, + {2, 3}, + {1, 2}, + {1, 3}, + {2, 4}, + {3, 5}, + {4, 6}, + {5, 7}}; + +const std::vector> LIMB_COLORS = {{51, 153, 255}, + {51, 153, 255}, + {51, 153, 255}, + {51, 153, 255}, + {255, 51, 255}, + {255, 51, 255}, + {255, 51, 255}, + {255, 128, 0}, + {255, 128, 0}, + {255, 128, 0}, + {255, 128, 0}, + {255, 128, 0}, + {0, 255, 0}, + {0, 255, 0}, + {0, 255, 0}, + {0, 255, 0}, + {0, 255, 0}, + {0, 255, 0}, + {0, 255, 0}}; + +int main(int argc, char **argv) { + // cuda:0 + cudaSetDevice(0); + + const std::string engine_file_path{argv[1]}; + const std::string path{argv[2]}; + + std::vector imagePathList; + bool isVideo{false}; + + assert(argc == 3); + + auto yolov8_pose = new YOLOv8_pose(engine_file_path); + yolov8_pose->make_pipe(true); + + if (IsFile(path)) { + std::string suffix = path.substr(path.find_last_of('.') + 1); + if ( + suffix == "jpg" || + suffix == "jpeg" || + suffix == "png" + ) { + imagePathList.push_back(path); + } else if ( + suffix == "mp4" || + suffix == "avi" || + suffix == "m4v" || + suffix == "mpeg" || + suffix == "mov" || + suffix == "mkv" + ) { + isVideo = true; + } else { + printf("suffix %s is wrong !!!\n", suffix.c_str()); + std::abort(); + } + } else if (IsFolder(path)) { + cv::glob(path + "/*.jpg", imagePathList); + } + + cv::Mat res, image; + cv::Size size = cv::Size{640, 640}; + int 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_pose->copy_from_Mat(image, size); + auto start = std::chrono::system_clock::now(); + yolov8_pose->infer(); + auto end = std::chrono::system_clock::now(); + yolov8_pose->postprocess(objs, score_thres, iou_thres, topk); + yolov8_pose->draw_objects(image, res, objs, SKELETON, KPS_COLORS, LIMB_COLORS); + auto tc = (double) + std::chrono::duration_cast(end - start).count() / 1000.; + printf("cost %2.4lf ms\n", tc); + cv::imshow("result", res); + if (cv::waitKey(10) == 'q') { + break; + } + } + } else { + for (auto &path: imagePathList) { + objs.clear(); + image = cv::imread(path); + yolov8_pose->copy_from_Mat(image, size); + auto start = std::chrono::system_clock::now(); + yolov8_pose->infer(); + auto end = std::chrono::system_clock::now(); + yolov8_pose->postprocess(objs, score_thres, iou_thres, topk); + yolov8_pose->draw_objects(image, res, objs, SKELETON, KPS_COLORS, LIMB_COLORS); + auto tc = (double) + std::chrono::duration_cast(end - start).count() / 1000.; + printf("cost %2.4lf ms\n", tc); + cv::imshow("result", res); + cv::waitKey(0); + } + } + cv::destroyAllWindows(); + delete yolov8_pose; + return 0; +} diff --git a/csrc/pose/normal/include/yolov8-pose.hpp b/csrc/pose/normal/include/yolov8-pose.hpp index ca31310..fa3541e 100644 --- a/csrc/pose/normal/include/yolov8-pose.hpp +++ b/csrc/pose/normal/include/yolov8-pose.hpp @@ -1,5 +1,5 @@ // -// Created by ubuntu on 1/20/23. +// Created by ubuntu on 4/7/23. // #ifndef POSE_NORMAL_YOLOv8_pose_HPP #define POSE_NORMAL_YOLOv8_pose_HPP @@ -401,7 +401,7 @@ void YOLOv8_pose::postprocess( iou_thres, indices ); -#elif +#else cv::dnn::NMSBoxes( bboxes, scores, diff --git a/docs/Jetson.md b/docs/Jetson.md index c0b49b9..e10b613 100644 --- a/docs/Jetson.md +++ b/docs/Jetson.md @@ -139,3 +139,70 @@ Usage: # infer video ./yolov8-seg yolov8s-seg.engine data/test.mp4 # the video path ``` + + + +## Normal Posture + +### 1. Export Posture Normal ONNX + +`yolov8s-pose.pt` is your trained pytorch model, or the official pre-trained model. + +Do not use any model other than pytorch model. +Do not use [`build.py`](../build.py) to export engine if you don't know how to install pytorch and other environments on jetson. + +***!!! Please use the PC to execute the following script !!!*** + +```shell +# Export yolov8s-pose.pt to yolov8s-pose.onnx +yolo export model=yolov8s-pose.pt format=onnx simplify=True +``` + +***!!! Please use the Jetson to execute the following script !!!*** + +```shell +# Using trtexec tools for export engine +/usr/src/tensorrt/bin/trtexec \ +--onnx=yolov8s-pose.onnx \ +--saveEngine=yolov8s-pose.engine +``` + +After executing the above command, you will get an engine named `yolov8s-pose.engine` . + +### 2. Inference with c++ + +It is highly recommended to use C++ inference on Jetson. +Here is a demo: [`csrc/jetson/pose`](../csrc/jetson/pose) . + +#### Build: + +Please modify `KPS_COLORS` and `SKELETON` and `LIMB_COLORS` and postprocess parameters in [`main.cpp`](../csrc/jetson/pose/main.cpp) for yourself. + +```c++ +int topk = 100; +float score_thres = 0.25f; +float iou_thres = 0.65f; +``` + +And build: + +``` shell +export root=${PWD} +cd src/jetson/pose +mkdir build +cmake .. +make +mv yolov8-pose ${root} +cd ${root} +``` + +Usage: + +``` shell +# infer image +./yolov8-pose yolov8s-pose.engine data/bus.jpg +# infer images +./yolov8-pose yolov8s-pose.engine data +# infer video +./yolov8-pose yolov8s-pose.engine data/test.mp4 # the video path +```