【毕业设计】使用C++部署RTMPose的onnx模型或者tensorrt模型源码+使用说明.zip

#include "rtmpose_onnxruntime.h" #include <iostream> #include <thread> #include "characterset_convert.h" #include "rtmpose_utils.h" #undef max RTMPoseOnnxruntime::RTMPoseOnnxruntime() { } RTMPoseOnnxruntime::~RTMPoseOnnxruntime() { } std::vector<PosePoint> RTMPoseOnnxruntime::Inference(const cv::Mat& input_mat, const DetectBox& box) { std::vector<PosePoint> pose_result; if (!box.IsValid()) return pose_result; std::pair<cv::Mat, cv::Mat> crop_result_pair = CropImageByDetectBox(input_mat, box); cv::Mat crop_mat = crop_result_pair.first; cv::Mat affine_transform_reverse = crop_result_pair.second; // deep copy // 深拷贝 cv::Mat crop_mat_copy; crop_mat.copyTo(crop_mat_copy); // BGR to RGB // BGR转RGB cv::Mat input_mat_copy_rgb; cv::cvtColor(crop_mat, input_mat_copy_rgb, CV_BGR2RGB); // image data，HWC->CHW，image_data - mean / std normalize // 图片数据，HWC->CHW，image_data - mean / std归一化 int image_height = input_mat_copy_rgb.rows; int image_width = input_mat_copy_rgb.cols; int image_channels = input_mat_copy_rgb.channels(); std::vector<float> input_image_array; input_image_array.resize(1 * image_channels * image_height * image_width); float* input_image = input_image_array.data(); for (int h = 0; h < image_height; ++h) { for (int w = 0; w < image_width; ++w) { for (int c = 0; c < image_channels; ++c) { int chw_index = c * image_height * image_width + h * image_width + w; float tmp = input_mat_copy_rgb.ptr<uchar>(h)[w * 3 + c]; input_image[chw_index] = (tmp - IMAGE_MEAN[c]) / IMAGE_STD[c]; } } } // inference // 推理 std::vector<const char*> m_onnx_input_names{ "input" }; std::vector<const char*> m_onnx_output_names{ "simcc_x","simcc_y" }; std::array<int64_t, 4> input_shape{ 1, image_channels, image_height, image_width }; auto memory_info = Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU); Ort::Value input_tensor = Ort::Value::CreateTensor<float>( memory_info, input_image_array.data(), input_image_array.size(), input_shape.data(), input_shape.size() ); assert(input_tensor.IsTensor()); auto output_tensors = m_session.Run( Ort::RunOptions{ nullptr }, m_onnx_input_names.data(), &input_tensor, 1, m_onnx_output_names.data(), m_onnx_output_names.size() ); // pose process // 后处理 std::vector<int64_t> simcc_x_dims = output_tensors[0].GetTensorTypeAndShapeInfo().GetShape(); std::vector<int64_t> simcc_y_dims = output_tensors[1].GetTensorTypeAndShapeInfo().GetShape(); assert(simcc_x_dims.size() == 3 && simcc_y_dims.size() == 3); int batch_size = simcc_x_dims[0] == simcc_y_dims[0] ? simcc_x_dims[0] : 0; int joint_num = simcc_x_dims[1] == simcc_y_dims[1] ? simcc_x_dims[1] : 0; int extend_width = simcc_x_dims[2]; int extend_height = simcc_y_dims[2]; float* simcc_x_result = output_tensors[0].GetTensorMutableData<float>(); float* simcc_y_result = output_tensors[1].GetTensorMutableData<float>(); for (int i = 0; i < joint_num; ++i) { // find the maximum and maximum indexes in the value of each Extend_width length // 在每一个extend_width长度的数值中找到最大值以及最大值的索引 auto x_biggest_iter = std::max_element(simcc_x_result + i * extend_width, simcc_x_result + i * extend_width + extend_width); int max_x_pos = std::distance(simcc_x_result + i * extend_width, x_biggest_iter); int pose_x = max_x_pos / 2; float score_x = *x_biggest_iter; // find the maximum and maximum indexes in the value of each exten_height length // 在每一个extend_height长度的数值中找到最大值以及最大值的索引 auto y_biggest_iter = std::max_element(simcc_y_result + i * extend_height, simcc_y_result + i * extend_height + extend_height); int max_y_pos = std::distance(simcc_y_result + i * extend_height, y_biggest_iter); int pose_y = max_y_pos / 2; float score_y = *y_biggest_iter; //float score = (score_x + score_y) / 2; float score = std::max(score_x, score_y); PosePoint temp_point; temp_point.x = int(pose_x); temp_point.y = int(pose_y); temp_point.score = score; pose_result.emplace_back(temp_point); } // anti affine transformation to obtain the coordinates on the original picture // 反仿射变换获取在原始图片上的坐标 for (int i = 0; i < pose_result.size(); ++i) { cv::Mat origin_point_Mat = cv::Mat::ones(3, 1, CV_64FC1); origin_point_Mat.at<double>(0, 0) = pose_result[i].x; origin_point_Mat.at<double>(1, 0) = pose_result[i].y; cv::Mat temp_result_mat = affine_transform_reverse * origin_point_Mat; pose_result[i].x = temp_result_mat.at<double>(0, 0); pose_result[i].y = temp_result_mat.at<double>(1, 0); } return pose_result; } std::pair<cv::Mat, cv::Mat> RTMPoseOnnxruntime::CropImageByDetectBox(const cv::Mat& input_image, const DetectBox& box) { std::pair<cv::Mat, cv::Mat> result_pair; if (!input_image.data) { return result_pair; } if (!box.IsValid()) { return result_pair; } // deep copy // 深拷贝 cv::Mat input_mat_copy; input_image.copyTo(input_mat_copy); // calculate the width, height and center points of the human detection box // 计算人体检测框的宽、高以及中心点 int box_width = box.right - box.left; int box_height = box.bottom - box.top; int box_center_x = box.left + box_width / 2; int box_center_y = box.top + box_height / 2; float aspect_ratio = 192.0 / 256.0; // adjust the width and height ratio of the size of the picture in the RTMPOSE input // 根据rtmpose输入图片大小的宽高比例进行调整 if (box_width > (aspect_ratio * box_height)) { box_height = box_width / aspect_ratio; } else if (box_width < (aspect_ratio * box_height)) { box_width = box_height * aspect_ratio; } float scale_image_width = box_width * 1.25; float scale_image_height = box_height * 1.25; // get the affine matrix // 获取仿射矩阵 cv::Mat affine_transform = GetAffineTransform( box_center_x, box_center_y, scale_image_width, scale_image_height, 192, 256 ); cv::Mat affine_transform_reverse = GetAffineTransform( box_center_x, box_center_y, scale_image_width, scale_image_height, 192, 256, true ); // affine transform // 进行仿射变换 cv::Mat affine_image; cv::warpAffine(input_mat_copy, affine_image, affine_transform, cv::Size(192, 256), cv::INTER_LINEAR); //cv::imwrite("affine_img.jpg", affine_image); result_pair = std::make_pair(affine_image, affine_transform_reverse); return result_pair; }