fix veri
This commit is contained in:
Daniel
parent
6bd48bac2b
commit
dc0d10137d
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@ -74,10 +74,10 @@ namespace sv
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bool VeriDetectorCUDAImpl::cudaSetup()
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{
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#ifdef WITH_CUDA
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std::string trt_model_fn = get_home() + SV_MODEL_DIR + "model.engine";
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std::string trt_model_fn = get_home() + SV_MODEL_DIR + "veri.engine";
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if (!is_file_exist(trt_model_fn))
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{
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throw std::runtime_error("SpireCV (104) Error loading the VERI TensorRT model (File Not Exist)");
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throw std::runtime_error("SpireCV (104) Error loading the VeriDetector TensorRT model (File Not Exist)");
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}
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char *trt_model_stream{nullptr};
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size_t trt_model_size{0};
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@ -107,7 +107,7 @@ namespace sv
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delete[] trt_model_stream;
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const ICudaEngine &cu_engine = this->_trt_context->getEngine();
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assert(cu_engine.getNbBindings() == 2);
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assert(cu_engine.getNbBindings() == 3);
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this->_input_index = cu_engine.getBindingIndex("input");
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this->_output_index1 = cu_engine.getBindingIndex("output");
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@ -123,7 +123,6 @@ namespace sv
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this->_p_data = new float[2 * 3 * 224 * 224];
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this->_p_prob1 = new float[2 * 576];
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this->_p_prob2 = new float[2 * 1280];
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this->_p_prob3 = new float[2 * 1280];
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// Input
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TRTCHECK(cudaMemcpyAsync(_p_buffers[this->_input_index], this->_p_data, 2 * 3 * 224 * 224 * sizeof(float), cudaMemcpyHostToDevice, this->_cu_stream));
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// this->_trt_context->enqueue(1, _p_buffers, this->_cu_stream, nullptr);
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@ -139,11 +138,12 @@ namespace sv
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void VeriDetectorCUDAImpl::cudaRoiCNN(
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std::vector<cv::Mat> &input_rois_,
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std::vector<int> &output_labels_)
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std::vector<float> &output_labels_)
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{
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#ifdef WITH_CUDA
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for (int i = 0; i < 2; ++i)
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for (int i = 0; i < 2; i++)
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{
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for (int row = 0; row < 224; ++row)
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{
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@ -151,14 +151,15 @@ namespace sv
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for (int col = 0; col < 224; ++col)
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{
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// mean=[136.20, 141.50, 145.41], std=[44.77, 44.20, 44.30]
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this->_p_data[224 * 224 * 3 * i + col + row * 224] = ((float)uc_pixel[0] - 136.20f) / 44.77f;
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this->_p_data[224 * 224 * 3 * i + col + row * 224 + 224 * 224] = ((float)uc_pixel[1] - 141.50f) / 44.20f;
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this->_p_data[224 * 224 * 3 * i + col + row * 224 + 224 * 224 * 2] = ((float)uc_pixel[2] - 145.41f) / 44.30f;
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this->_p_data[col + row * 224 + 224 * 224 * 3 * i] = ((float)uc_pixel[0] - 136.20f) / 44.77f;
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this->_p_data[col + row * 224 + 224 * 224 + 224 * 224 * 3 * i] = ((float)uc_pixel[1] - 141.50f) / 44.20f;
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this->_p_data[col + row * 224 + 224 * 224 * 2 + 224 * 224 * 3 * i] = ((float)uc_pixel[2] - 145.41f) / 44.30f;
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uc_pixel += 3;
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}
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}
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}
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// Input
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TRTCHECK(cudaMemcpyAsync(_p_buffers[this->_input_index], this->_p_data, 2 * 3 * 224 * 224 * sizeof(float), cudaMemcpyHostToDevice, this->_cu_stream));
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// this->_trt_context->enqueue(1, _p_buffers, this->_cu_stream, nullptr);
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@ -180,10 +181,9 @@ namespace sv
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}
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}
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// 计算两个数组的余弦相似性
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float similarity = cosineSimilarity(_p_prob2, _p_prob2 + 1280, 1280);
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std::cout << "余弦相似性: " << similarity << std::endl;
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std::cout << "VERI LABEL: " << label << std::endl;
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float similarity = cosineSimilarity(this->_p_prob2, this->_p_prob2 + 1280, 1280);
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output_labels_.push_back(label);
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output_labels_.push_back(similarity);
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}
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#endif
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}
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@ -29,14 +29,13 @@ public:
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bool cudaSetup();
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void cudaRoiCNN(
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std::vector<cv::Mat>& input_rois_,
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std::vector<int>& output_labels_
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std::vector<float>& output_labels_
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);
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#ifdef WITH_CUDA
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float *_p_data;
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float *_p_prob1;
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float *_p_prob2;
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float *_p_prob3;
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nvinfer1::IExecutionContext *_trt_context;
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int _input_index;
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int _output_index1;
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@ -1,49 +1,70 @@
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#include "sv_veri_det.h"
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#include <cmath>
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#include <fstream>
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#include "gason.h"
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#include "sv_util.h"
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#ifdef WITH_CUDA
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#include <NvInfer.h>
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#include <cuda_runtime_api.h>
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#include "veri_det_cuda_impl.h"
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#endif
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#define SV_ROOT_DIR "/SpireCV/"
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namespace sv {
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VeriDetector::VeriDetector()
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namespace sv
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{
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VeriDetector::VeriDetector()
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{
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this->_cuda_impl = new VeriDetectorCUDAImpl;
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}
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VeriDetector::~VeriDetector()
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{
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}
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}
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VeriDetector::~VeriDetector()
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{
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}
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bool VeriDetector::setupImpl()
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{
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void VeriDetector::_load()
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{
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JsonValue all_value;
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JsonAllocator allocator;
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_load_all_json(this->alg_params_fn, all_value, allocator);
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JsonValue veriliner_params_value;
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_parser_algorithm_params("VeriDetector", all_value, veriliner_params_value);
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for (auto i : veriliner_params_value)
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{
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if ("vehicle_ID" == std::string(i->key))
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{
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this->vehicle_id = i->value.toString();
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std::cout << "vehicle_ID Load Sucess!" << std::endl;
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}
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}
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}
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bool VeriDetector::setupImpl()
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{
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#ifdef WITH_CUDA
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return this->_cuda_impl->cudaSetup();
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#endif
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return false;
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}
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void VeriDetector::roiCNN(
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std::vector<cv::Mat>& input_rois_,
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std::vector<int>& output_labels_
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)
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{
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return false;
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}
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void VeriDetector::roiCNN(
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std::vector<cv::Mat> &input_rois_,
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std::vector<float> &output_labels_)
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{
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#ifdef WITH_CUDA
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this->_cuda_impl->cudaRoiCNN(
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input_rois_,
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output_labels_
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);
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output_labels_);
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#endif
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}
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}
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void VeriDetector::detect(cv::Mat img1_, cv::Mat img2_, TargetsInFrame& tgts_)
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{
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void VeriDetector::detect(cv::Mat img_, const cv::Rect &bounding_box_, sv::Target &tgt)
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{
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if (!_params_loaded)
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{
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this->_load();
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@ -51,11 +72,79 @@ void VeriDetector::detect(cv::Mat img1_, cv::Mat img2_, TargetsInFrame& tgts_)
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_params_loaded = true;
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}
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std::vector<cv::Mat> e_roi = {img1_, img2_};
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// convert Rect2d from left-up to center.
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targetPos[0] = float(bounding_box_.x) + float(bounding_box_.width) * 0.5f;
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targetPos[1] = float(bounding_box_.y) + float(bounding_box_.height) * 0.5f;
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std::vector<int> output_labels;
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roiCNN(e_roi, output_labels);
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}
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targetSz[0] = float(bounding_box_.width);
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targetSz[1] = float(bounding_box_.height);
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// Extent the bounding box.
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float sumSz = targetSz[0] + targetSz[1];
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float wExtent = targetSz[0] + 0.5 * (sumSz);
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float hExtent = targetSz[1] + 0.5 * (sumSz);
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int sz = int(cv::sqrt(wExtent * hExtent));
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cv::Mat crop;
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getSubwindow(crop, img_, sz, 224);
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std::string img_ground_dir = get_home() + SV_ROOT_DIR + this->vehicle_id;
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cv::Mat img_ground = cv::imread(img_ground_dir);
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cv::resize(img_ground, img_ground, cv::Size(224, 224));
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std::vector<cv::Mat> input_rois_ = {crop, img_ground};
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#ifdef WITH_CUDA
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std::vector<float> output_labels;
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roiCNN(input_rois_, output_labels);
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#endif
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// auto t1 = std::chrono::system_clock::now();
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// tgts_.setFPS(1000.0 / std::chrono::duration_cast<std::chrono::milliseconds>(t1 - this->_t0).count());
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// this->_t0 = std::chrono::system_clock::now();
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// tgts_.setTimeNow();
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if (output_labels.size() > 0)
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{
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// tgt.category_id = output_labels[0];
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tgt.sim_score = output_labels[1];
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// tgts_.targets.push_back(tgt);
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}
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}
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void VeriDetector::getSubwindow(cv::Mat &dstCrop, cv::Mat &srcImg, int originalSz, int resizeSz)
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{
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cv::Scalar avgChans = mean(srcImg);
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cv::Size imgSz = srcImg.size();
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int c = (originalSz + 1) / 2;
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int context_xmin = (int)(targetPos[0]) - c;
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int context_xmax = context_xmin + originalSz - 1;
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int context_ymin = (int)(targetPos[1]) - c;
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int context_ymax = context_ymin + originalSz - 1;
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int left_pad = std::max(0, -context_xmin);
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int top_pad = std::max(0, -context_ymin);
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int right_pad = std::max(0, context_xmax - imgSz.width + 1);
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int bottom_pad = std::max(0, context_ymax - imgSz.height + 1);
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context_xmin += left_pad;
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context_xmax += left_pad;
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context_ymin += top_pad;
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context_ymax += top_pad;
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cv::Mat cropImg;
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if (left_pad == 0 && top_pad == 0 && right_pad == 0 && bottom_pad == 0)
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{
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// Crop image without padding.
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cropImg = srcImg(cv::Rect(context_xmin, context_ymin,
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context_xmax - context_xmin + 1, context_ymax - context_ymin + 1));
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}
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else // Crop image with padding, and the padding value is avgChans
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{
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cv::Mat tmpMat;
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cv::copyMakeBorder(srcImg, tmpMat, top_pad, bottom_pad, left_pad, right_pad, cv::BORDER_CONSTANT, avgChans);
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cropImg = tmpMat(cv::Rect(context_xmin, context_ymin, context_xmax - context_xmin + 1, context_ymax - context_ymin + 1));
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}
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resize(cropImg, dstCrop, cv::Size(resizeSz, resizeSz));
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}
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}
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@ -8,29 +8,36 @@
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#include <string>
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#include <chrono>
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namespace sv {
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class VeriDetectorCUDAImpl;
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class VeriDetector : public LandingMarkerDetectorBase
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namespace sv
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{
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public:
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class VeriDetectorCUDAImpl;
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class VeriDetector : public LandingMarkerDetectorBase
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{
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public:
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VeriDetector();
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~VeriDetector();
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void detect(cv::Mat img1_, cv::Mat img2_, TargetsInFrame &tgts_);
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void detect(cv::Mat img_, const cv::Rect &bounding_box_, sv::Target &tgt);
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protected:
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protected:
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void _load();
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bool setupImpl();
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void roiCNN(
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std::vector<cv::Mat>& input_rois_,
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std::vector<int>& output_labels_
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);
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std::vector<cv::Mat> &input_rois_,
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std::vector<float> &output_labels_);
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void getSubwindow(cv::Mat &dstCrop, cv::Mat &srcImg, int originalSz, int resizeSz);
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VeriDetectorCUDAImpl* _cuda_impl;
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};
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std::string vehicle_id;
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// Save the target bounding box for each frame.
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std::vector<float> targetSz = {0, 0}; // H and W of bounding box
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std::vector<float> targetPos = {0, 0}; // center point of bounding box (x, y)
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VeriDetectorCUDAImpl *_cuda_impl;
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};
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}
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#endif
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@ -91,6 +91,9 @@ public:
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//! The angle of the target in the image coordinate system, (unit: degree) [-180, 180].
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double yaw_a;
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//! Similarity, Confidence, (0, 1]
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double sim_score;
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//! Whether the height&width of the target can be obtained.
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bool has_hw;
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//! Whether the category of the target can be obtained.
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@ -0,0 +1,140 @@
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#include <iostream>
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#include <arpa/inet.h>
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#include <netinet/in.h>
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#include <sys/socket.h>
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#include <unistd.h>
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#include <string.h>
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#define PORT 12346 // UDP端口号
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// 枚举定义消息类型
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enum MessageType {
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TIMESTAMP = 0x00,
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CLICK_COORDINATES = 0x01,
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TARGET_BOX_COORDINATES = 0x02,
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CLICK_COORDINATES_FRAME_ID = 0x03,
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TARGET_BOX_COORDINATES_FRAME_ID = 0x04
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};
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// 定义消息结构体
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#pragma pack(1) // 使用1字节对齐方式
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struct Message {
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unsigned char header[2]; // 帧头
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unsigned char type; // 消息类型
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unsigned char reserved; // 保留字段
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unsigned int requestID; // 请求ID
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unsigned int dataLength; // 数据长度
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unsigned char* data; // 数据段
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};
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#pragma pack() // 恢复默认对齐方式
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// 解析时间戳消息
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void parseTimestampMessage(unsigned char* data) {
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unsigned short year = ntohs(*reinterpret_cast<unsigned short*>(data));
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unsigned char month = *(data + 2);
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unsigned char day = *(data + 3);
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unsigned char hour = *(data + 4);
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unsigned char minute = *(data + 5);
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unsigned char second = *(data + 6);
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unsigned short millisecond = ntohs(*reinterpret_cast<unsigned short*>(data + 7));
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std::cout << "Timestamp: " << static_cast<int>(year) << "-" << static_cast<int>(month)
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<< "-" << static_cast<int>(day) << " " << static_cast<int>(hour) << ":"
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<< static_cast<int>(minute) << ":" << static_cast<int>(second) << "."
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<< static_cast<int>(millisecond) << std::endl;
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}
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// 解析点击坐标消息
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void parseClickCoordinatesMessage(unsigned char* data) {
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float x = *reinterpret_cast<float*>(data);
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float y = *reinterpret_cast<float*>(data + 4);
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std::cout << "Click coordinates: (" << x << ", " << y << ")" << std::endl;
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}
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// 解析目标框坐标消息
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void parseTargetBoxCoordinatesMessage(unsigned char* data) {
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float x1 = *reinterpret_cast<float*>(data);
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float y1 = *reinterpret_cast<float*>(data + 4);
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float x2 = *reinterpret_cast<float*>(data + 8);
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float y2 = *reinterpret_cast<float*>(data + 12);
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std::cout << "Target box coordinates: (" << x1 << ", " << y1 << ") - ("
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<< x2 << ", " << y2 << ")" << std::endl;
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}
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// 解析UDP数据包
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void parseUDPData(unsigned char* buffer, int length) {
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if (length < 13) {
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std::cout << "Invalid UDP data format" << std::endl;
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return;
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}
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Message message;
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memcpy(&message, buffer, sizeof(Message));
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// 解析消息类型
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MessageType messageType = static_cast<MessageType>(message.type);
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// 根据消息类型处理数据
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switch (messageType) {
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case TIMESTAMP:
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if (length != 9) {
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std::cout << "Invalid timestamp message length" << std::endl;
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return;
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}
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parseTimestampMessage(message.data);
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break;
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case CLICK_COORDINATES:
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if (length != 13) {
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std::cout << "Invalid click coordinates message length" << std::endl;
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return;
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}
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parseClickCoordinatesMessage(message.data);
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break;
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case TARGET_BOX_COORDINATES:
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if (length != 21) {
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std::cout << "Invalid target box coordinates message length" << std::endl;
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return;
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}
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parseTargetBoxCoordinatesMessage(message.data);
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break;
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default:
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std::cout << "Unsupported message type" << std::endl;
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break;
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}
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}
|
||||
|
||||
int main() {
|
||||
int sockfd;
|
||||
struct sockaddr_in servaddr, cliaddr;
|
||||
unsigned char buffer[4096];
|
||||
|
||||
// 创建UDP套接字
|
||||
sockfd = socket(AF_INET, SOCK_DGRAM, 0);
|
||||
|
||||
memset(&servaddr, 0, sizeof(servaddr));
|
||||
memset(&cliaddr, 0, sizeof(cliaddr));
|
||||
|
||||
servaddr.sin_family = AF_INET;
|
||||
servaddr.sin_addr.s_addr = htonl(INADDR_ANY);
|
||||
servaddr.sin_port = htons(PORT);
|
||||
|
||||
// 绑定UDP套接字到端口
|
||||
bind(sockfd, (const struct sockaddr*)&servaddr, sizeof(servaddr));
|
||||
|
||||
while (true) {
|
||||
socklen_t len = sizeof(cliaddr);
|
||||
|
||||
// 接收UDP数据包
|
||||
ssize_t n = recvfrom(sockfd, buffer, sizeof(buffer), MSG_WAITALL,
|
||||
(struct sockaddr*)&cliaddr, &len);
|
||||
|
||||
// 解析UDP数据包
|
||||
parseUDPData(buffer, n);
|
||||
}
|
||||
|
||||
close(sockfd);
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
|
@ -5,41 +5,85 @@
|
|||
|
||||
using namespace std;
|
||||
|
||||
int main(int argc, char *argv[]) {
|
||||
// 打开摄像头
|
||||
struct node
|
||||
{
|
||||
double x, y;
|
||||
};
|
||||
|
||||
node p1;
|
||||
|
||||
// 框选到的矩形
|
||||
cv::Rect rect_sel;
|
||||
|
||||
int main(int argc, char *argv[])
|
||||
{
|
||||
// 实例化 框选目标跟踪类
|
||||
sv::VeriDetector veri;
|
||||
// 手动导入相机参数，如果使用Amov的G1等吊舱或相机，则可以忽略该步骤，将自动下载相机参数文件
|
||||
veri.loadCameraParams(sv::get_home() + "/SpireCV/calib_webcam_640x480.yaml");
|
||||
|
||||
cv::VideoCapture cap1("/home/amov/Videos/com/FlyVideo_2023-09-02_11-36-00.avi");
|
||||
cv::VideoCapture cap2("/home/amov/Videos/com/FlyVideo_2023-09-02_11-41-55.avi");
|
||||
sv::CommonObjectDetector cod;
|
||||
cod.loadCameraParams(sv::get_home() + "/SpireCV/calib_webcam_640x480.yaml");
|
||||
|
||||
// 打开摄像头
|
||||
sv::Camera cap;
|
||||
// cap.setWH(640, 480);
|
||||
// cap.setFps(30);
|
||||
//cap.open(sv::CameraType::WEBCAM, 0); // CameraID 0
|
||||
cap.open(sv::CameraType::WEBCAM, 0); // CameraID 0
|
||||
// 实例化OpenCV的Mat类，用于内存单帧图像
|
||||
|
||||
|
||||
cv::Mat img1,img2;
|
||||
cv::Mat img;
|
||||
int frame_id = 0;
|
||||
while (1)
|
||||
{
|
||||
|
||||
// 实例化SpireCV的 单帧检测结果 接口类 TargetsInFrame
|
||||
sv::TargetsInFrame tgts(frame_id++);
|
||||
|
||||
// 读取一帧图像到img
|
||||
cap1.read(img1);
|
||||
cap2.read(img2);
|
||||
cap.read(img);
|
||||
cv::resize(img, img, cv::Size(cod.image_width, cod.image_height));
|
||||
|
||||
cv::resize(img1, img1, cv::Size(224, 224));
|
||||
cv::resize(img2, img2, cv::Size(224, 224));
|
||||
// 执行通用目标检测
|
||||
cod.detect(img, tgts);
|
||||
|
||||
veri.detect(img1, img2, tgts);
|
||||
// 可视化检测结果，叠加到img上
|
||||
sv::drawTargetsInFrame(img, tgts);
|
||||
|
||||
// 控制台打印通用目标检测结果
|
||||
printf("Frame-[%d]\n", frame_id);
|
||||
// 打印当前检测的FPS
|
||||
printf(" FPS = %.2f\n", tgts.fps);
|
||||
// 打印当前相机的视场角（degree）
|
||||
printf(" FOV (fx, fy) = (%.2f, %.2f)\n", tgts.fov_x, tgts.fov_y);
|
||||
for (int i = 0; i < tgts.targets.size(); i++)
|
||||
{
|
||||
printf("Frame-[%d], Object-[%d]\n", frame_id, i);
|
||||
// 打印每个目标的中心位置，cx，cy的值域为[0, 1]
|
||||
printf(" Object Center (cx, cy) = (%.3f, %.3f)\n", tgts.targets[i].cx, tgts.targets[i].cy);
|
||||
// 打印每个目标的外接矩形框的宽度、高度，w，h的值域为(0, 1]
|
||||
printf(" Object Size (w, h) = (%.3f, %.3f)\n", tgts.targets[i].w, tgts.targets[i].h);
|
||||
// 打印每个目标的置信度
|
||||
printf(" Object Score = %.3f\n", tgts.targets[i].score);
|
||||
// 打印每个目标的类别，字符串类型
|
||||
printf(" Object Category = %s, Category ID = [%d]\n", tgts.targets[i].category.c_str(), tgts.targets[i].category_id);
|
||||
// 打印每个目标的视线角，跟相机视场相关
|
||||
printf(" Object Line-of-sight (ax, ay) = (%.3f, %.3f)\n", tgts.targets[i].los_ax, tgts.targets[i].los_ay);
|
||||
// 打印每个目标的3D位置（在相机坐标系下），跟目标实际长宽、相机参数相关
|
||||
printf(" Object Position = (x, y, z) = (%.3f, %.3f, %.3f)\n", tgts.targets[i].px, tgts.targets[i].py, tgts.targets[i].pz);
|
||||
|
||||
|
||||
// 显示img
|
||||
// cv::imshow("img", img);
|
||||
// cv::waitKey(10);
|
||||
if (tgts.targets[i].category_id == 2)
|
||||
{
|
||||
p1.x = tgts.targets[i].cx * tgts.width - tgts.targets[i].w * tgts.width / 2;
|
||||
p1.y = tgts.targets[i].cy * tgts.height - tgts.targets[i].h * tgts.height / 2;
|
||||
rect_sel = cv::Rect(p1.x, p1.y, tgts.targets[i].w * tgts.width, tgts.targets[i].h * tgts.height);
|
||||
veri.detect(img, rect_sel, tgts.targets[i]);
|
||||
// 打印每个目标的CosineSimilarity
|
||||
printf(" CosineSimilarity Score = %.3f\n", tgts.targets[i].sim_score);
|
||||
}
|
||||
}
|
||||
|
||||
// 显示检测结果img
|
||||
cv::imshow("img", img);
|
||||
cv::waitKey(10);
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue