ncnn_yolop.h
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//
// Created by DefTruth on 2021/10/18.
//
#ifndef LITE_AI_TOOLKIT_NCNN_CV_NCNN_YOLOP_H
#define LITE_AI_TOOLKIT_NCNN_CV_NCNN_YOLOP_H
#include "lite/ncnn/core/ncnn_core.h"
namespace ncnncv
{
class LITE_EXPORTS NCNNYOLOP
{
private:
ncnn::Net *net = nullptr;
const char *log_id = nullptr;
const char *param_path = nullptr;
const char *bin_path = nullptr;
std::vector<const char*> input_names;
std::vector<const char*> output_names;
std::vector<int> input_indexes;
std::vector<int> output_indexes;
public:
explicit NCNNYOLOP(const std::string &_param_path,
const std::string &_bin_path,
unsigned int _num_threads = 1,
int _input_height = 640,
int _input_width = 640); //
~NCNNYOLOP();
private:
// nested classes
typedef struct
{
int grid0;
int grid1;
int stride;
float width;
float height;
} YOLOPAnchor;
typedef struct
{
float r;
int dw;
int dh;
int new_unpad_w;
int new_unpad_h;
bool flag;
} YOLOPScaleParams;
private:
const unsigned int num_threads; // initialize at runtime.
// target image size after resize
const int input_height; // 640/320/1280
const int input_width; // 640/320/1280
const float mean_vals[3] = {255.f * 0.485f, 255.f * 0.456, 255.f * 0.406f}; // RGB
const float norm_vals[3] = {1.f / (255.f * 0.229f), 1.f / (255.f * 0.224f), 1.f / (255.f * 0.225f)};
enum NMS
{
HARD = 0, BLEND = 1, OFFSET = 2
};
static constexpr const unsigned int nms_pre = 1000;
static constexpr const unsigned int max_nms = 30000;
std::vector<unsigned int> strides = {8, 16, 32};
std::unordered_map<unsigned int, std::vector<YOLOPAnchor>> center_anchors;
bool center_anchors_is_update = false;
protected:
NCNNYOLOP(const NCNNYOLOP &) = delete; //
NCNNYOLOP(NCNNYOLOP &&) = delete; //
NCNNYOLOP &operator=(const NCNNYOLOP &) = delete; //
NCNNYOLOP &operator=(NCNNYOLOP &&) = delete; //
private:
void print_debug_string();
void transform(const cv::Mat &mat_rs, ncnn::Mat &in);
void resize_unscale(const cv::Mat &mat,
cv::Mat &mat_rs,
int target_height,
int target_width,
YOLOPScaleParams &scale_params);
// only generate once
void generate_anchors(unsigned int target_height, unsigned int target_width);
void generate_bboxes_single_stride(const YOLOPScaleParams &scale_params,
ncnn::Mat &det_pred,
unsigned int stride,
float score_threshold,
float img_height,
float img_width,
std::vector<types::Boxf> &bbox_collection);
void generate_bboxes_da_ll(const YOLOPScaleParams &scale_params,
ncnn::Extractor &extractor,
std::vector<types::Boxf> &bbox_collection,
types::SegmentContent &da_seg_content,
types::SegmentContent &ll_seg_content,
float score_threshold, float img_height,
float img_width); // det,da_seg,ll_seg
void nms(std::vector<types::Boxf> &input, std::vector<types::Boxf> &output,
float iou_threshold, unsigned int topk, unsigned int nms_type);
public:
void detect(const cv::Mat &mat,
std::vector<types::Boxf> &detected_boxes,
types::SegmentContent &da_seg_content,
types::SegmentContent &ll_seg_content,
float score_threshold = 0.25f, float iou_threshold = 0.45f,
unsigned int topk = 100, unsigned int nms_type = NMS::OFFSET);
};
}
#endif //LITE_AI_TOOLKIT_NCNN_CV_NCNN_YOLOP_H