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people_counting.cpp
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people_counting.cpp
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#include "people_counting.h"
#include "nvdsparsebbox_Yolo.h"
#include "nvdsinfer_custom_impl.h"
#include "nvdsparsebbox_Yolo.h"
#include <chrono>
struct InferDeleter
{
template <typename T>
void operator()(T* obj) const
{
if (obj)
{
obj->destroy();
}
}
};
PeopleDetector::PeopleDetector(std::string cfg_path, std::string wts_path, int32_t batch_size, float cls_thres, float nms_thres, nvinfer1::ILogger& logger) {
this->batch_size = batch_size;
this->nms_thres = nms_thres;
this->cls_thres = cls_thres;
std::vector<std::string> output_blob_names = {
"yolo_83",
"yolo_95",
"yolo_107"
};
this->output_blob_names = output_blob_names;
auto builder = nvinfer1::createInferBuilder(logger);
this->engine = this->init_engine(cfg_path, wts_path, builder);
this->ctx = this->engine->createExecutionContext();
this->buffers = new UnifiedBufManager(std::shared_ptr<nvinfer1::ICudaEngine>(engine, InferDeleter()), batch_size);
for (int32_t i = 0; i < 3; i++) {
NvDsInferLayerInfo layer = this->buffers->getLayerInfo(output_blob_names[i]);
this->layer_info.emplace_back(layer);
}
}
PeopleDetector::PeopleDetector(std::string model_path, int32_t batch_size, float cls_thres, float nms_thres, nvinfer1::ILogger& logger) {
this->batch_size = batch_size;
this->nms_thres = nms_thres;
this->cls_thres = cls_thres;
std::vector<std::string> output_blob_names = {
"yolo_83",
"yolo_95",
"yolo_107"
};
this->output_blob_names = output_blob_names;
IRuntime* runtime = createInferRuntime(gLogger);
int64_t length;
std::ifstream model_file(model_path, std::ios::binary);
if (!model_file) {
std::cerr << "cannot open file: " << model_path << std::endl;
return;
}
model_file.read((char*)&length, sizeof(length));
std::cerr << "data length = " << length << std::endl;
char *buf = new char[length];
model_file.read(buf, length);
model_file.close();
this->engine = runtime->deserializeCudaEngine(buf, length, NULL);
delete buf;
this->ctx = this->engine->createExecutionContext();
this->buffers = new UnifiedBufManager(std::shared_ptr<nvinfer1::ICudaEngine>(engine, InferDeleter()), batch_size);
for (int32_t i = 0; i < 3; i++) {
NvDsInferLayerInfo layer = this->buffers->getLayerInfo(output_blob_names[i]);
this->layer_info.emplace_back(layer);
}
}
nvinfer1::ICudaEngine* PeopleDetector::init_engine(std::string cfg_path, std::string weight_path, nvinfer1::IBuilder* builder) {
NetworkInfo info;
info.networkType = "yolov3";
info.configFilePath = cfg_path;
info.wtsFilePath = weight_path;
info.deviceType = "kGPU";
info.inputBlobName = input_blob_name;
Yolo yolo(info, builder);
return yolo.createEngine();
}
std::vector<NvDsInferParseObjectInfo> PeopleDetector::detect(cv::Mat img) {
std::vector<NvDsInferParseObjectInfo> objs;
std::vector<NvDsInferParseObjectInfo> calib_objs;
auto beg_buf = std::chrono::system_clock::now();
float* p = (float*)this->buffers->getBuffer(std::string(input_blob_name));
if (NULL == p) {
std::cerr << "null pointer of input buffer" << std::endl;
}
mat_8u3c_to_darknet_blob(img, input_tensor_height, input_tensor_width, input_tensor_depth, p);
auto end_buf = std::chrono::system_clock::now();
auto beg_exe = std::chrono::system_clock::now();
auto status = this->ctx->execute(batch_size, buffers->getDeviceBindings().data());
if (!status) {
std::cerr << "execution failed!" << std::endl;
return objs;
}
auto end_exe = std::chrono::system_clock::now();
NvDsInferNetworkInfo networkInfo {
.width = input_tensor_width,
.height = input_tensor_height,
.channels = 3,
};
NvDsInferParseDetectionParams params {
.numClassesConfigured = NUM_CLASSES_YOLO,
};
auto beg_post = std::chrono::system_clock::now();
bool res = NvDsInferParseYoloV3(this->layer_info, networkInfo, params, objs, kANCHORS, kMASKS, this->cls_thres, this->nms_thres);
if (!res) {
std::cerr << "fail to call NvDsInferParseYoloV3" << std::endl;
}
auto end_post = std::chrono::system_clock::now();
float xScale = (float)img.cols / input_tensor_width;
float yScale = (float)img.rows / input_tensor_height;
calib_objs.resize(objs.size());
for (int32_t i = 0; i < (int32_t)objs.size(); i++) {
NvDsInferParseObjectInfo obj = objs[i];
NvDsInferParseObjectInfo nf{
.classId = obj.classId,
.left = (uint32_t)(obj.left * xScale),
.top = (uint32_t)(obj.top * yScale),
.width = (uint32_t)(obj.width * xScale),
.height = (uint32_t)(obj.height * yScale),
.detectionConfidence = obj.detectionConfidence,
};
calib_objs[i] = nf;
}
auto msecs = [](std::chrono::system_clock::time_point beg, std::chrono::system_clock::time_point end) -> int {
return std::chrono::duration_cast<std::chrono::milliseconds>(end - beg).count();
};
std::cerr << "[PC time cost] | buffer:" << msecs(beg_buf, end_buf) << "ms, exe:" << msecs(beg_exe, end_exe) << "ms, post:" << msecs(beg_post, end_post) << "ms" << std::endl;
return calib_objs;
}
int32_t PeopleDetector::detect_capi(cv::Mat img, NvDsInferParseObjectInfo* boxes, int32_t& num) {
auto res = this->detect(img);
if (0 == num) {
std::cerr << "param num is 0, cannot write any result to box buf" << std::endl;
}
if (num > res.size()) {
num = res.size();
}
for (int32_t i = 0; i < num; i++) {
boxes[i] = res[i];
}
return 0;
}
PeopleDetector::~PeopleDetector() {
}