931 lines
34 KiB
C++
931 lines
34 KiB
C++
#ifndef APP_COMMON_H
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#define APP_COMMON_H
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#include <stdio.h>
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#include <stdarg.h>
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#include <sys/time.h>
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#include <string.h>
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#include <vector>
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#include <memory>
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#include "CommonDataType/CommonDataType.h"
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//#include "DvppCommon/DvppCommon.h"
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#include <iostream>
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#include <fstream>
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#include <sstream>
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#include <assert.h>
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#include <vector>
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#include <numeric>
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#include <algorithm>
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#include <math.h>
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#include <ctime>
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#include <math.h>
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#include <chrono>
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#include <cmath>
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#include <functional>
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#include <sys/types.h>
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#include <sys/stat.h>
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#include <unistd.h>
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#include <NvInfer.h>
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#include "logging.h"
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#include <NvInferPlugin.h>
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#include <NvOnnxParser.h>
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#include <NvCaffeParser.h>
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#include <cuda.h>
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#include <cuda_runtime.h>
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#include <cuda_runtime_api.h>
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#include <opencv2/opencv.hpp>
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#include <opencv2/core/core.hpp>
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#include <opencv2/highgui/highgui.hpp>
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#include <opencv2/imgproc/imgproc.hpp>
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#include "preprocess.h"
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#ifdef ASCEND
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#include "DvppCommon.h"
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#else
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#include "nvidia_acl_datatype.h"
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#include "cuda_utils.h"
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#include "logging.h"
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#include "yolov5_common.h"
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#include "utils.h"
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#include "calibrator.h"
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#include "preprocess.h"
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#include "yololayer.h"
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using namespace Yolo;
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#endif
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using namespace std;
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using namespace cv;
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using namespace nvinfer1;
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using namespace nvcaffeparser1;
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#define USE_FP16 // set USE_INT8 or USE_FP16 or USE_FP32
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#define DEVICE 0 // GPU id
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#define BATCH_SIZE 1 //batch size
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#define MAX_WORKSPAXE_SIZE (16 * (1 << 20)) //工作空间大小
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#define MAX_IMAGE_INPUT_SIZE_THRESH 3000*3000
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#define INPUT_CHANNEL 3 //输入通道数
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//NVIDIA NX YOLOV5模型推理相关宏(因不与ASCEND冲突放到这)
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#define NMS_THRESH 0.4
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#define CONF_THRESH 0.5
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#define GET_INPUT_SIZE(w, h) (INPUT_CHANNEL * w *h)
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#define GET_OUTPUT_SIZE(w, h, cr , ca) (INPUT_CHANNEL * (((h / 32) * ( h / 32)) + ((h / 16) * ( h / 16)) + ((h / 8) * ( h / 8))) * (STEP1_POS_CONF + cr + ca))
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//STEP0
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#define STEP0_MAX_IMAGE_INPUT_SIZE_THRESH 3000*3000
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#define STEP0_BATCH_SIZE 1 //batch size
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#define STEP0_NMS_THRESH 0.5 //step1 非极大值抑制阈值
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#define STEP0_SCORE_THRESH 0.4 //step1 置信度(得分)阈值
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#define STEP0_INPUT_CHANNEL 3 //输入通道数
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#define STEP0_INPUT_H 640 //step0 输入图像高
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#define STEP0_INPUT_W 640 //step0 输入图像宽
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//STEP1
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#define STEP1_POS_CONF 5
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#define STEP1_NMS_THRESH 0.5 //step1 非极大值抑制阈值
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#define STEP1_SCORE_THRESH 0.4 //step1 置信度(得分)阈值
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#define STEP1_CLASS_NUM 19 //step1 7分类
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#define STEP1_CLEAR_NUM 21 //step1 21个清晰度
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#define STEP1_INPUT_H 960 //step1 输入图像高
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#define STEP1_INPUT_W 960 //step1 输入图像宽
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#define STEP1_BATCH_SIZE 1
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#define STEP1_OUTPUT_HISTOGRAM_N 5 //不能超过139167
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#define STEP1_BBOX_SIZE1 (STEP1_INPUT_H/32)
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#define STEP1_BBOX_SIZE2 (STEP1_INPUT_H/16)
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#define STEP1_BBOX_SIZE3 (STEP1_INPUT_H/8)
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#define STEP1_INPUT_SIZE INPUT_CHANNEL*STEP1_INPUT_H*STEP1_INPUT_W //input
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#define STEP1_OUTPUT_SIZE INPUT_CHANNEL*(STEP1_BBOX_SIZE1*STEP1_BBOX_SIZE1+STEP1_BBOX_SIZE2*STEP1_BBOX_SIZE2+STEP1_BBOX_SIZE3*STEP1_BBOX_SIZE3)*(STEP1_CLEAR_NUM+STEP1_CLASS_NUM+STEP1_POS_CONF) //output
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//STEP1
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#define STEP1_CLASS_NUM_DATE 8
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#define STEP1_CLASS_NUM_CON 7
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//STEP2
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#define STEP2_POS_CONF 5
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#define STEP2_NMS_THRESH 0.5 //step2 非极大值抑制阈值
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#define STEP2_SCORE_THRESH 0.4 //step2 置信度(得分)阈值
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#define STEP2_CLASS_NUM 47 //step2 31分类
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#define STEP2_CLEAR_NUM 5 //step2 5个清晰度
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#define STEP2_INPUT_H 608 //step2 输入图像高
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#define STEP2_INPUT_W 608 //step2 输入图像宽
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#define STEP2_BATCH_SIZE 1
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#define STEP2_OUTPUT_HISTOGRAM_N 5 //step2 不能超过22743
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#define STEP2_BBOX_SIZE1 (STEP2_INPUT_H/32)
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#define STEP2_BBOX_SIZE2 (STEP2_INPUT_H/16)
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#define STEP2_BBOX_SIZE3 (STEP2_INPUT_H/8)
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#define STEP2_INPUT_SIZE INPUT_CHANNEL*STEP2_INPUT_H*STEP2_INPUT_W //input
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#define STEP2_OUTPUT_SIZE INPUT_CHANNEL*(STEP2_BBOX_SIZE1*STEP2_BBOX_SIZE1+STEP2_BBOX_SIZE2*STEP2_BBOX_SIZE2+STEP2_BBOX_SIZE3*STEP2_BBOX_SIZE3)*(STEP2_CLEAR_NUM+STEP2_CLASS_NUM+STEP2_POS_CONF) //output
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#define CONVERT_TO_ODD(NUM) (NUM) // Convert the input to odd num
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#define CONVERT_TO_EVEN(NUM) (NUM) // Convert the input to even num
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#define CHECK_ODD(num) (NUM)
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#define CHECK_EVEN(num) (NUM)
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//模型参数
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typedef struct _YoloV5ModelParam{
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unsigned int uiClassNum;
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unsigned int uiDetSize;
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float fScoreThreshold;
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float fNmsThreshold;
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}YoloV5ModelParam;
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typedef struct _YoloV5ClearityModelParam{
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unsigned int uiClassNum;
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unsigned int uiClearNum;
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unsigned int uiDetSize;
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float fScoreThreshold;
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float fNmsThreshold;
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}YoloV5ClearityModelParam;
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typedef struct _ModelCommonInfo{
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unsigned int uiModelWidth;
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unsigned int uiModelHeight;
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unsigned int uiInputSize;
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unsigned int uiOutputSize;
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unsigned int uiChannel;
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unsigned int uiBatchSize;
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std::string strInputBlobName;
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std::string strOutputBlobName;
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}ModelCommonInfo;
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typedef struct _YoloV5ModelInfo{
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YoloV5ModelParam yolov5ModelParam;
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ModelCommonInfo modelCommonInfo;
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}YoloV5ModelInfo;
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typedef struct _YoloV5ClearityModelInfo{
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YoloV5ClearityModelParam yolov5ClearityModelParam;
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ModelCommonInfo modelCommonInfo;
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}YoloV5ClearityModelInfo;
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struct ModelConfig
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{
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std::string strAnchorPath; //锚框文件路径
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std::string strClassPath; //类别名称集合
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std::string strOmPath; //om文件路径
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int iModelWidth; //模型输入宽度
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int iModelHeight; //模型输入高度
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float fScoreThreshold; //得分阈值
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float fNMSTreshold; //nms阈值
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int iClassNum; //类别个数
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int iLineNum; //字段代号
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};
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typedef struct
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{
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float fLTX = 0; //左上X坐标
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float fLTY = 0; //左上y坐标
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float fRBX = 0; //右下y坐标
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float fRBY = 0; //右下y坐标
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}Location;
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//耗时宏
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// #define CUDA_MALLOC_TIME_CONSUMING_TEST //CUDA内存分配耗时
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// #define CUDA_MEMCPY_TIME_CONSUMING_TEST //CUDA内存拷贝耗时
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#define INFERENCE_MODEL_TIME_CONSUMING_TEST //模型推理耗时
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// #define OPENCV_CVTCOLOR_YUV2RGB_TIME_CONSUMING_TEST //OpenCV YUV2RGB耗时
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// #define OPENCV_CVTCOLOR_RGB2YUV_TIME_CONSUMING_TEST //OpenCV RGB2YUV耗时
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// #define OPENCV_CVTCOLOR_BGR2RGB_TIME_CONSUMING_TEST //OpenCV BGR2RGB耗时
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#define ENCODE_H264_STREAM //编码H264
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#define PACKAGE_VIDEO_STREAM //封装视频(MP4,FLV)
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#define RTSP_PUSH_VIDEO_STREAM //RTSP推流(推送H264)
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#define IMAGE_WIDTH 1920 //1920 2560
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#define IMAGE_HEIGHT 1080 //1080 1440
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//////////基础画像////////////////////////////////////
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//来车检测功能ID 包含:来车,行车方向,停车,停车启动
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#define METHOD_BASE_WIDTH 1280
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//车厢划分功能ID///////////////////////////////////////
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#define METHOD_BASE_HEIGHT 720
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//相机帧率////////////////////////////////////////////
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#define FRAME_RATE 25
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//////////功能ID///////////////////////////////////////
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//来车检测功能ID 包含:来车,行车方向,停车,停车启动
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#define TRAIN_ACTION 1
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//车厢划分功能ID///////////////////////////////////////
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#define TRAIN_PARTION 2
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#define DIRECTION 0
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//////////算法配置//////////
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//算法参数数量
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#define IMAGEANALYSE_NUM 17
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//图像优化处理
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#define IMAGE_GRAY 0x00000001 //灰度处理
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#define IMAGE_EQUALIZE 0x00000002 //直方图均衡化处理
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#define IMAGE_GAMMA 0x00000004 //gamma变化
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#define IMAGE_SPEC 0x00000008 //自定义处理
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//图像对比标准
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#define IMAGE_COMP_BY_TEMPLE 0x00000001 //物体形状
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#define IMAGE_COMP_BY_HIST 0x00000002 //图像直方图
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#define IMAGE_COMP_BY_SPEC 0x00000004 //
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//////////车厢状态////////////////////////////////////
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#define TRAIN_INIT_STATUS 0x01 //无车
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#define TRAIN_LEFT_RUN 0x02 //左侧来车
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#define TRAIN_RIGHT_RUN 0x03 //右侧来车
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#define TRAIN_UNKOWN_RUN 0x04 //来车(无法确认方向)
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#define TRAIN_PAUSE 0x05 //停车
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#define TRAIN_RESTART 0x06 //列车重新启动
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#define TRAIN_PASSED 0x07 //列车完全通过
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#define TRAIN_WAIT_INIT 0x08 //列车完全通过等待重新初始化
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//////////标准值(估算)////////////////////////////////////
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//车厢长度
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#define TRAIN_WIDTH 14.5
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//车钩在画面正中央时单个车厢所在图像中的长度
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#define TRAIN_IN_CAMERA_WIDTH 10
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//初始速度
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#define TRAIN_DEFAULT_SPEED (((TRAIN_WIDTH) / 5.0))
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enum cmd_enum : unsigned char
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{
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//开始识别
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//value[0]代表识别最大次数，识别不出来就返回"fail"
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cmd_start_recognize = 0x01,
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cmd_end_recognize = 0x02,
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};
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//识别目标
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enum Target
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{
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NUM = 0, // 编号 (包含 2:煤炭漏斗车(兖矿自备,枣矿自备); 3:敞车; 4:棚车; 5:罐车; 6:平车; 8:牲畜车; 15:毒品车)
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PRO = 1, // 属性
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HEAD = 2, // 车头
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CHKDATE = 3, // 定检期
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CONTAINER = 4, // 集装箱
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SPACE = 5, // 连接轴间隔 (包含 18:连接轴通用间隔)
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TRAINSPACE = 6, // 车厢间隔 (包含 9:敞车间隔; 10:自备车间隔; 11:平车间隔; 12:罐车间隔; 13:棚车车间隔; 14:牲畜车间隔; 16: 毒品车间隔; 17:混合车厢间隔;)
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TOPSPACE = 7 // 顶部间隔
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};
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//编号在前 或 属性在前
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enum FirstTarget
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{
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AUTO_FIRST = 0, //自动判断
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NUM_FIRST = 1, //编号在前
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PRO_FIRST = 2 //属性在前
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};
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//行驶方向
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enum RunDirection
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{
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DIRECTION_UNKNOWN = 0, //未知
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DIRECTION_LEFT = 1, //向左行驶
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DIRECTION_RIGHT = 2 //向右行驶
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};
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//车辆状态
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enum TrainStatus
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{
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TRAINSTATUS_NO = 0, //无车
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TRAINSTATUS_RUN = 1, //行驶
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TRAINSTATUS_STOP = 2, //停止
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TRAINSTATUS_BACK = 3 //倒车
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};
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enum RunDirectionAndFirst
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{
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// -1-不知道行驶方向和哪个框在前
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DO_NOT_KNOW_DIRECTION = -1,
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// 0-向左行驶，编号在前
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LEFT_RUN_AND_NUM_FIRST = 0,
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// 1-向左行驶，属性在前
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LEFT_RUN_AND_PRO_FIRST = 1,
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// 2-向右行驶，编号在前
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RIGHT_RUN_AND_NUM_FIRST = 2,
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// 3-向右行驶，属性在前
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RIGHT_RUN_AND_PRO_FIRST = 3
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};
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//模型识别种类
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enum ModelTarget
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{
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//车头上的编号
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TRAIN_HEAD = 0,
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//属性
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TRAIN_PRO = 1,
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//编号 矿车、煤炭漏斗车（兖矿自备、枣矿自备）
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K_TRAIN_NUM = 2,
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//编号 敞车
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C_TRAIN_NUM = 3,
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//编号 棚车
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P_TRAIN_NUM = 4,
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//编号 罐车
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G_TRAIN_NUM = 5,
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//编号 平车
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NX_TRAIN_NUM = 6,
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//定检期
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TRAIN_CHECK = 7,
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//编号 牲畜车
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J_TRAIN_NUM = 8,
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//车厢间隔 敞车
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C_TRAIN_SPACE = 9,
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//车厢间隔 矿车、煤炭漏斗车（兖矿自备、枣矿自备）
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K_TRAIN_SPACE = 10,
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//车厢间隔 平车
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NX_TRAIN_SPACE = 11,
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//车厢间隔 罐车
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G_TRAIN_SPACE = 12,
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//车厢间隔 棚车
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P_TRAIN_SPACE = 13,
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//车厢间隔 牲畜车
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J_TRAIN_SPACE = 14,
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//毒品车
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W_TRAIN_NUM = 15,
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//毒品车间隔
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W_TRAIN_SPACE = 16,
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//混合车厢间隔
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M_TRAIN_SPACE = 17,
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//连接轴通用间隔
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U_TRAIN_SPACE = 18
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};
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//识别区域
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enum IdentifyAreas
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{
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//不在识别区
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NO_THING = -1,
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//非平车识别区
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NOT_FLATCAR = 0,
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//平车识别区
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FLATCAR = 1
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};
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//选优模式
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enum SelectBestMode
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{
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FREQUENCY = 0, //频率优先
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LENGTH //长度优先
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};
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enum TargetMaxLen
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{
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TYPE_MAXLEN = 7, //车型
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NUM_MAXLEN = 7, //编号
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LOAD_MAXLEN = 3, //载重 （KM98车型载重为100）
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SELF_MAXLEN = 3, //自重
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VOLUME_MAXLEN = 3, //容积
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CHANGE_MAXLEN = 2, //换长
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VOLUMESURFACE_MAXLEN = 7, //罐车容量记表
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CHKDATE_MAXLEN = 8 //定检期
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};
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enum ModelType
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{
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MODELTYPE_NUM = 1, //车号模型
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MODELTYPE_CHKDATE = 2, //定检期模型
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MODELTYPE_CONTAINER = 3 //集装箱模型
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};
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enum ValidType
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{
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VALID_ALL = 0, //画面信息全部有效
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VALID_LEFT = 1, //画面间隔左边有效
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VALID_RIGHT = 2 //画面间隔右边有效
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};
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//来车检测模型结果
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enum MonitorModelState
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{
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//初始化状态
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MONITOR_MODEL_INIT_STATE = -1,
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//车头部分
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MONITOR_MODEL_TRAIN_HEAD = 0,
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//无车
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MONITOR_MODEL_NO_TRAIN = 1,
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//车尾部分
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MONITOR_MODEL_TRAIN_TAIL = 2,
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//车体部分
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MONITOR_MODEL_TRAIN_BODY = 3
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};
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//帧基本信息
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typedef struct
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{
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uint32_t iWidth = 0;
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uint32_t iHeight = 0;
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acldvppStreamFormat format;
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int iRate = 0; //帧率
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} FrameInfo;
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//原图上大框坐标(第二步还原坐标时用)
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typedef struct
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{
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float fLTX = 0; //左上X坐标
|
||
float fLTY = 0; //左上y坐标
|
||
float fRBX = 0; //右下y坐标
|
||
float fRBY = 0; //右下y坐标
|
||
}Step1Location;
|
||
|
||
//推理数据(NVIDIA)
|
||
typedef struct
|
||
{
|
||
int iDataSource = 0; //数据来源标识
|
||
uint32_t iFrameId = 0;
|
||
FrameInfo frameInfo;
|
||
uint32_t iSize = 0; // Size of memory, bytes
|
||
uint32_t iSrcSize = 0; // Size of memory, bytes
|
||
std::shared_ptr<void> pData; // Smart pointer of data
|
||
std::shared_ptr<void> pSrcData; // Smart pointer of data(源数据) 推理时,RGB数据会拷贝一份,源数据不变用于传送到后处理引擎画框,另一份数据用于resize成640x640并归一化到0-1
|
||
uint64_t i64TimeStamp = 0; //帧数据时间戳
|
||
Step1Location step1Location; //step2 use
|
||
int iTargetType; //目标类别 (0:车号; 1:属性; 2:车头; 3:定检期; 4:集装箱)
|
||
int iBigClassId = -1; //大框id (给第二步用 1-属性 23456-编号)
|
||
int iCarXH = 0; //当前大框所属车厢
|
||
|
||
} InferenceData;
|
||
|
||
|
||
//帧数据
|
||
typedef struct
|
||
{
|
||
int iDataSource = 0; //数据来源标识
|
||
uint32_t iFrameId = 0;
|
||
FrameInfo frameInfo;
|
||
uint32_t iSize = 0; // Size of memory, bytes
|
||
std::shared_ptr<void> pData; // Smart pointer of data
|
||
uint64_t i64TimeStamp = 0; //帧数据时间戳
|
||
} FrameData;
|
||
|
||
//帧解码后数据
|
||
typedef struct
|
||
{
|
||
int iDataSource = 0; //数据来源标识
|
||
uint32_t iFrameId = 0;
|
||
uint32_t iWidth = 0; // Width of image
|
||
uint32_t iHeight = 0; // Height of image
|
||
uint32_t iWidthStride = 0; // Width after align up
|
||
uint32_t iHeightStride = 0; // Height after align up
|
||
acldvppPixelFormat format = PIXEL_FORMAT_YUV_SEMIPLANAR_420; // Format of image
|
||
uint32_t iSize = 0; // Size of data in byte
|
||
std::shared_ptr<void> pData = nullptr; // Smart pointer of data
|
||
uint64_t i64TimeStamp = 0; //帧数据时间戳
|
||
bool bHostMemory = false; //数据解码后内存是否在Host侧
|
||
std::string strPicFilePath;
|
||
std::string strTrainDate; //过车日期 (格式：YYYY-MM-DD)
|
||
std::string strTrainName; //车次 (格式：HH-MM-SS)
|
||
bool bIsEnd = false; //列车结束标识
|
||
int iStatus = 0; //0-无车; 1-有车行驶; 2-有车停止
|
||
int iDirection = 0; //行驶方向(0-未知; 1-向左; 2-向右)
|
||
int iRate = 0; //帧率
|
||
} DecodedData;
|
||
|
||
//存图数据
|
||
typedef struct
|
||
{
|
||
uint32_t iFrameId = 0;
|
||
uint32_t iWidth = 0; //Width of image
|
||
uint32_t iHeight = 0; //Height of image
|
||
|
||
std::string strImgPath;
|
||
std::string strImgName;
|
||
uint32_t iSize = 0; // Size of memory, bytes
|
||
std::shared_ptr<void> pData = nullptr; // Smart pointer of data
|
||
bool bIsEnd = false; //列车结束标识
|
||
bool bSaveToFtp = false; //是否保存至ftp服务器
|
||
uint64_t i64TimeStamp = 0; //帧数据时间戳
|
||
std::shared_ptr<DecodedData> pDecodeData = nullptr;
|
||
int iDirection = 0; //行驶方向(0-未知; 1-向左; 2-向右)
|
||
int nMonitorState = MONITOR_MODEL_INIT_STATE;
|
||
} SaveImgData;
|
||
|
||
//识别处理数据
|
||
typedef struct
|
||
{
|
||
int iDataSource = 0; //数据来源标识
|
||
uint32_t iFrameId = 0; //帧号
|
||
uint64_t i64TimeStamp = 0; //帧数据时间戳
|
||
uint32_t iDataNO = 0; //数据编号
|
||
std::string strTrainDate; //过车日期 (格式：YYYY-MM-DD)
|
||
std::string strTrainName; //车次 (格式：HH-MM-SS)
|
||
int iDirection = 0; //行驶方向(0-未知; 1-向左; 2-向右)
|
||
int iStatus = 0; //车辆状态(0-无车; 1-有车行驶; 2-有车停止)
|
||
std::shared_ptr<DecodedData> pDecodeData = nullptr;
|
||
std::shared_ptr<void> pVoidData = nullptr;
|
||
std::shared_ptr<void> pData = nullptr;
|
||
|
||
uint32_t iSize = 0; // Size of memory, bytes
|
||
uint32_t iWidth = 0; //Width of image
|
||
uint32_t iHeight = 0; //Height of image
|
||
int iRate = 0; //帧率
|
||
int iTrainIndex = 0;
|
||
bool bIsEnd = false; //车厢结束标识
|
||
bool bIsTrainEnd = false; //列车结束标识
|
||
std::string strOrigTrainDate; //原过车日期 (格式：YYYY-MM-DD)
|
||
std::string strOrigTrainName; //原车次 (格式：HH-MM-SS)
|
||
uint32_t iOrigFrameId = 0; //原帧号
|
||
int nMonitorState = MONITOR_MODEL_INIT_STATE;
|
||
std::string strPicFilePath;
|
||
|
||
}ProcessData;
|
||
|
||
//预处理子数据
|
||
typedef struct
|
||
{
|
||
int iBigClassId = -1; //类别大类 （第二步用 1-属性 23456-编号）
|
||
int iCarXH = 0; //所属车厢序号 （第二步用）
|
||
uint32_t iWidth = 0;
|
||
uint32_t iHeight = 0;
|
||
uint32_t iWidthStride = 0; // Width after align up
|
||
uint32_t iHeightStride = 0; // Height after align up
|
||
acldvppPixelFormat format = PIXEL_FORMAT_YUV_SEMIPLANAR_420; // Format of image
|
||
uint32_t iSize = 0; // Size of memory, bytes
|
||
std::shared_ptr<void> pData = nullptr; // Smart pointer of data
|
||
std::string strPicFilePath;
|
||
Step1Location step1Location;
|
||
} PreSubData;
|
||
|
||
//预处理数据
|
||
typedef struct
|
||
{
|
||
std::vector<PreSubData> vecPreSubData;
|
||
} PreData;
|
||
|
||
//推理子数据
|
||
typedef struct
|
||
{
|
||
int iBigClassId = -1; //类别大类 （第二步用 1-属性 23456-编号）
|
||
int iCarXH = 0; //图像所属车厢序号（第二步用）
|
||
std::vector<RawData> vecInferOutput;
|
||
Step1Location step1Location;
|
||
} AclSubData;
|
||
|
||
typedef struct
|
||
{
|
||
int iBigClassId = -1; //类别大类 （第二步用 1-属性 23456-编号）
|
||
int iCarXH = 0; //图像所属车厢序号（第二步用）
|
||
std::vector<stDetection> vecInferOutput;
|
||
Step1Location step1Location;
|
||
} CudaAclSubData;
|
||
|
||
//推理数据
|
||
typedef struct
|
||
{
|
||
std::vector<AclSubData> vecAclSubData;
|
||
} AclData;
|
||
|
||
|
||
|
||
//推理数据(Cuda)
|
||
typedef struct
|
||
{
|
||
std::vector<InferenceData> vecAclSubData;
|
||
} CudaAclData;
|
||
|
||
//动态检测结果
|
||
typedef struct
|
||
{
|
||
uint32_t iFrameId = 0; //帧号
|
||
uint64_t i64TimeStamp = 0; //帧数据时间戳
|
||
bool bHasTrain = false; //有无车标志
|
||
bool bIsEnd = false; //列车结束标识
|
||
std::string strTrainDate; //过车日期 (格式：YYYY-MM-DD)
|
||
std::string strTrainName; //车次 (格式：HH-MM-SS)
|
||
int iDirection = 0; //行驶方向(0-未知; 1-向左; 2-向右)
|
||
int nMonitorState = MONITOR_MODEL_INIT_STATE;
|
||
} MoveData;
|
||
|
||
//模型具体结果
|
||
typedef struct
|
||
{
|
||
int iLine = -1; //字段代号（仅第二步使用）
|
||
int iClassId = -1; //大小框的类别
|
||
float fScore = 0; //分数
|
||
int iAnchorId = -1; //暂时仅记录
|
||
float fLTX = 0; //左上X坐标 Left Top x
|
||
float fLTY = 0; //左上y坐标 Left Top y
|
||
float fRBX = 0; //右下y坐标 Right Bottom x
|
||
float fRBY = 0; //右下y坐标 Right Bottom y
|
||
float fClear = 0; //清晰度
|
||
} SingleData;
|
||
|
||
//后处理子数据
|
||
typedef struct
|
||
{
|
||
int iTargetType; //目标类别 (0:车号; 1:属性; 2:车头; 3:定检期; 4:集装箱)
|
||
int iBigClassId = -1; //大框id (给第二步用 1-属性 23456-编号)
|
||
int iCarXH = 0; //当前大框所属车厢
|
||
std::vector<SingleData> vecSingleData; //模型具体结果
|
||
Step1Location step1Location; //原图上大框坐标(第二步坐标还原时用)
|
||
} PostSubData;
|
||
|
||
//后处理数据
|
||
typedef struct
|
||
{
|
||
int iModelType = -1; //(0:车号模型，1:定检期模型，2:集装箱模型)
|
||
std::vector<PostSubData> vecPostSubData; //模型后处理结果
|
||
uint32_t iFrameId = 0;
|
||
|
||
int nMonitorState = MONITOR_MODEL_INIT_STATE;
|
||
|
||
} PostData;
|
||
|
||
|
||
//转换具体内容
|
||
typedef struct
|
||
{
|
||
int iLine = -1; //字段代号
|
||
bool IsChkFlag = false; //是否符合基本数据校验
|
||
std::vector<std::string> vecValue; //转换后的每个字符内容
|
||
std::vector<float> vecScore; //每个字符对应的分数
|
||
} TransInfo;
|
||
|
||
//转换后子数据
|
||
typedef struct
|
||
{
|
||
int iBigClassId = -1; //类别大类(1-属性; 23456-编号; 7-定检期)
|
||
int iCarXH = 0; //当前大框所属车厢
|
||
float fScoreSum = 0; //大框内各个字符总得分, 取最优帧用
|
||
std::string strAllValue; //大框内所有字符, 集装箱校验,选优用
|
||
std::vector<TransInfo> vecTransInfo;//具体的转换信息
|
||
Step1Location step1Location; //原图上大框坐标
|
||
} TransSubData;
|
||
|
||
//转换后数据
|
||
typedef struct
|
||
{
|
||
std::vector<TransSubData> vecTransSubData;
|
||
} TransData;
|
||
|
||
|
||
//火车车号信息
|
||
typedef struct
|
||
{
|
||
int iDataSource = 0; //数据来源标识
|
||
int iCarXH = 0; //车厢序号
|
||
bool bIsEnd = false; //列车结束标识
|
||
uint64_t i64TrainEndTimeStamp = 0; //列车结束帧号时间戳(bIsEnd=true，该参数才有值)
|
||
std::string strTrainType; //车厢类型
|
||
std::string strTrainNum; //车厢编号
|
||
std::string strBestImg; //最优图片名称
|
||
float fScoreSum = 0; //车号最优图字符总得分
|
||
uint64_t i64TimeStamp = 0; //最优图片时间戳
|
||
Step1Location step1Location; //原图上大框坐标
|
||
std::string strTrainDate; //过车日期 (格式：YYYY-MM-DD)
|
||
std::string strTrainName; //车次 (格式：HH-MM-SS)
|
||
uint32_t iStartFrameId = 0; //当前车厢车号的开始帧号
|
||
uint32_t iEndFrameId = 0; //当前车厢车号的结束帧号
|
||
uint64_t i64StartTimeStamp = 0; //当前车厢车号的开始帧号时间戳
|
||
uint64_t i64EndTimeStamp = 0; //当前车厢车号的结束帧号时间戳
|
||
int iTrainTypeId = -1; //车厢类型ID(标识哪种类型的车)
|
||
int iDirection = 0; //行驶方向(0-未知; 1-向左; 2-向右)
|
||
} TrainNum;
|
||
|
||
//火车属性信息
|
||
typedef struct
|
||
{
|
||
int iDataSource = 0; //数据来源标识
|
||
int iCarXH = 0; //车厢序号
|
||
bool bIsEnd = false; //列车结束标识
|
||
uint64_t i64TrainEndTimeStamp = 0; //列车结束帧号时间戳(bIsEnd=true，该参数才有值)
|
||
std::string strLoad; //载重
|
||
std::string strSelf; //自重
|
||
std::string strVolume; //容积
|
||
std::string strChange; //换长
|
||
std::string strVolumeSurface; //罐车容量记表
|
||
std::string strBestImg; //最优图片名称
|
||
float fScoreSum = 0; //属性最优图字符总得分
|
||
uint64_t i64TimeStamp = 0; //最优图片时间戳
|
||
Step1Location step1Location; //原图上大框坐标
|
||
std::string strTrainDate; //过车日期 (格式：YYYY-MM-DD)
|
||
std::string strTrainName; //车次 (格式：HH-MM-SS)
|
||
uint32_t iStartFrameId = 0; //当前车厢属性的开始帧号
|
||
uint32_t iEndFrameId = 0; //当前车厢属性的结束帧号(即下节车厢属性的开始帧号)
|
||
uint64_t i64StartTimeStamp = 0; //当前车厢属性的开始帧号时间戳
|
||
uint64_t i64EndTimeStamp = 0; //当前车厢属性的结束帧号时间戳
|
||
int iDirection = 0; //行驶方向(0-未知; 1-向左; 2-向右)
|
||
} TrainPro;
|
||
|
||
//火车定检期信息
|
||
typedef struct
|
||
{
|
||
int iDataSource = 0; //数据来源标识
|
||
int iCarXH = 0; //车厢序号
|
||
bool bIsEnd = false; //列车结束标识
|
||
std::string strTrainDate; //过车日期 (格式：YYYY-MM-DD)
|
||
std::string strTrainName; //车次 (格式：HH-MM-SS)
|
||
std::string strChkDate1; //定检期第一行
|
||
std::string strChkDate2; //定检期第二行
|
||
std::string strBestImg; //定检期最优图
|
||
float fScoreSum = 0; //定检期最优图字符总得分
|
||
uint64_t i64TimeStamp = 0; //最优图片时间戳
|
||
Step1Location step1Location; //原图上大框坐标
|
||
uint32_t iStartFrameId = 0; //当前车厢定检期的开始帧号
|
||
uint32_t iEndFrameId = 0; //当前车厢定检期的结束帧号
|
||
uint64_t i64StartTimeStamp = 0; //当前车厢定检期的开始帧号时间戳
|
||
uint64_t i64EndTimeStamp = 0; //当前车厢定检期的结束帧号时间戳
|
||
std::string strChkDate1DeadLine;//定检期1截至日期
|
||
} ChkDate;
|
||
|
||
//火车集装箱信息
|
||
typedef struct
|
||
{
|
||
int iDataSource = 0; //数据来源标识
|
||
int iCarXH = 0; //车厢序号
|
||
bool bIsEnd = false; //列车结束标识
|
||
std::string strTrainDate; //过车日期 (格式：YYYY-MM-DD)
|
||
std::string strTrainName; //车次 (格式：HH-MM-SS)
|
||
std::string strContainerNo; //集装箱号
|
||
std::string strBestImg; //集装箱最优图
|
||
float fScoreSum = 0; //集装箱最优图总得分, 取最优帧用
|
||
uint64_t i64TimeStamp = 0; //集装箱最优图时间戳
|
||
Step1Location step1Location; //原图上大框坐标
|
||
uint32_t iStartFrameId = 0; //当前车厢集装箱的开始帧号
|
||
uint32_t iEndFrameId = 0; //当前车厢集装箱的结束帧号
|
||
bool bChkFlag = false; //集装箱是否校验通过
|
||
} Container;
|
||
|
||
//车厢的2个集装箱信息
|
||
typedef struct
|
||
{
|
||
Container container1;
|
||
Container container2;
|
||
} TrainContainer;
|
||
|
||
//火车信息
|
||
typedef struct
|
||
{
|
||
int iDataSource = 0; //数据来源标识
|
||
bool bIsEnd = false; //列车结束标识
|
||
uint64_t i64TrainEndTimeStamp = 0; //列车结束帧号时间戳(bIsEnd=true，该参数才有值)
|
||
std::string strTrainDate; //过车日期 (格式：YYYY-MM-DD)
|
||
std::string strTrainName; //车次 (格式：HH-MM-SS)
|
||
int iCarXH = 0; //车厢序号
|
||
uint64_t i64TimeStamp = 0; //当前车厢时间戳 = ((车号时间戳+属性时间戳) / 2)
|
||
TrainNum trainNum; //车厢车号信息
|
||
TrainPro trainPro; //车厢属性信息
|
||
ChkDate chkDate; //车厢定检期信息
|
||
Container container1; //车厢集装箱1信息
|
||
Container container2; //车厢集装箱2信息
|
||
uint32_t iStartFrameId = 0; //当前车厢开始帧号
|
||
uint32_t iEndFrameId = 0; //当前车厢结束帧号
|
||
uint64_t i64StartTimeStamp = 0; //当前车厢开始帧号时间戳(集装箱合并使用)
|
||
uint64_t i64EndTimeStamp = 0; //当前车厢结束帧号时间戳(集装箱合并使用)
|
||
bool bMergerFlag = false; //定检期集装箱合并完成
|
||
int iDirection = 0; //行驶方向(0-未知; 1-向左; 2-向右)
|
||
} Train;
|
||
|
||
//ftp上传数据
|
||
typedef struct
|
||
{
|
||
std::string strLocalFile; //本地文件
|
||
std::string strFtpFilePath; //上传路径
|
||
std::string strFtpFileName; //上传文件名
|
||
bool bIsEnd = false; //列车结束标识
|
||
} FtpData;
|
||
|
||
|
||
typedef struct
|
||
{
|
||
float fLTX = 0; //左上X坐标
|
||
float fLTY = 0; //左上y坐标
|
||
float fRBX = 0; //右下y坐标
|
||
float fRBY = 0; //右下y坐标
|
||
}Coordinates;
|
||
|
||
typedef struct
|
||
{
|
||
std::string strTrainDate; //过车日期 (格式：YYYY-MM-DD)
|
||
std::string strTrainName; //车次 (格式：HH-MM-SS)
|
||
uint32_t iStartFrameId = 0; //当前车厢开始帧号
|
||
uint64_t i64StartTimeStamp = 0; //当前车厢开始帧时间戳
|
||
uint32_t iEndFrameId = 0; //当前车厢结束帧号
|
||
uint64_t i64EndTimeStamp = 0; //当前车厢结束帧时间戳
|
||
int iTrainIndex = 0; //第几节车厢
|
||
bool bIsEnd = false; //是否最后一节
|
||
bool bmodelconfirmed; //是否是间隔模型切分的车厢
|
||
} TrainRange;
|
||
|
||
//模型识别配置结构体
|
||
//包含文件配置部分和内部处理变量
|
||
typedef struct imganalyse
|
||
{
|
||
// from file
|
||
/////////下述设定取自csv文件
|
||
int nType; //算法目的 0:动态检测 1:车厢划分
|
||
int nAreaX1; //算法监测区域 开始位置x坐标
|
||
int nAreaY1; //算法监测区域 开始位置y坐标
|
||
int nAreaX2; //算法监测区域 结束位置x坐标
|
||
int nAreaY2; //算法监测区域 结束位置y坐标
|
||
int bOrLevel; //算法结果融合等级 相同值进行与运算 不同值进行或运算
|
||
bool bDiff; //是否以不同作为判断条件 false:以匹配度相同作为判定条件 true：以匹配度不同作为判定条件
|
||
bool bOn; //是否启用改区域匹配监测(该算法是否有效) false：不启用(无效) true:启用(有效)
|
||
double dSameThresholdVal; //判定匹配相同的阈值
|
||
double dDiffThresholdVal; //判定匹配不同的阈值
|
||
int nPauseMaxCoumt; //判定为停车的最大计量(超过设定值判定为停车)
|
||
int npicimprovetype; //图像改善处理(包含 灰度化/均衡化直方图/gamma变换/自定义)
|
||
int templemethod; //模型比对模式
|
||
int histmethod; //直方图比对模式
|
||
int specmethod; //自定义比对模式
|
||
int compmethod; //对比类型(模型比对/模型比对/自定义比对,可多选)
|
||
std::string baseImagePath; //基准图片路径
|
||
/////////下述设定为处理识别变量
|
||
double dComparePoint; //比对值
|
||
double dPreComparePoint; //比对值
|
||
bool bChanged; //与基准图对比值 是否发生变化 true:发生变化 false:没有发生变化
|
||
bool bPreChanged; //上一帧与基准图对比值
|
||
bool fluctuationFlag; //最近100帧的最大值与最小值的波动标志
|
||
int nSameCount; //比对值没有发生变化的计数
|
||
int iIndex; //第几个配置项
|
||
}AnalyseInfo;
|
||
|
||
|
||
//车厢划分信息结构体
|
||
//文件配置
|
||
typedef struct trainpartion
|
||
{
|
||
std::string strTrainDate;
|
||
std::string strTrainName;
|
||
int nindex; //车厢顺位
|
||
uint64_t i64StartTimeStamp; //当节车厢出现的开始帧时间
|
||
uint64_t i64EndTimeStamp; //当节车厢出现的结束帧时间
|
||
float fspeed; //当节车厢的车速
|
||
int startframe; //当节车厢出现的开始帧帧号
|
||
int endframe; //当节车厢出现的结束帧帧号
|
||
int modelSpaceFrame;
|
||
bool bfuncconfirmed; //是否算法划分完成 true:是 false:否
|
||
bool bmodelconfirmed; //是否根据第一次识别结果确认完成 true:是 false:否
|
||
float fLTX; //车钩开始位置X值
|
||
float fLTY; //车钩开始位置y值
|
||
float fRBX; //车钩结束位置X值
|
||
float fRBY; //车钩结束位置y值
|
||
bool bIsEnd = false; //是否最后一节
|
||
int nStatus; //方向(0:未知，1:向左行驶，2:向右行驶)
|
||
int iRate; //帧率
|
||
}PartionInfo;
|
||
|
||
typedef struct
|
||
{
|
||
std::shared_ptr<ProcessData> processData;
|
||
std::string strAllClassType;
|
||
}TrainBackInfo;
|
||
|
||
//方向计算
|
||
typedef struct
|
||
{
|
||
int iBigClassId = -1; //大框id (给第二步用 1-属性 23456-编号)
|
||
uint32_t iFrameId = 0; //帧号
|
||
float fCenterX; //第一步识别目标中心点X坐标
|
||
} CalculateInfo;
|
||
|
||
|
||
typedef struct
|
||
{
|
||
int iDataSource = 0; //数据来源标识
|
||
uint32_t iFrameId = 0;
|
||
FrameInfo frameInfo;
|
||
uint32_t iSize = 0; // Size of memory, bytes
|
||
uint32_t iSrcSize = 0; // Size of memory, bytes
|
||
std::shared_ptr<void> pData; // Smart pointer of data
|
||
std::shared_ptr<void> pSrcData; // Smart pointer of data(源数据) 推理时,RGB数据会拷贝一份,源数据不变用于传送到后处理引擎画框,另一份数据用于resize成640x640并归一化到0-1
|
||
uint64_t i64TimeStamp = 0; //帧数据时间戳
|
||
} YoloModelInfo;
|
||
|
||
template<typename _T>
|
||
std::string join_dims(const std::vector<_T>& dims){
|
||
std::stringstream output;
|
||
char buf[64];
|
||
const char* fmts[] = {"%d", " x %d"};
|
||
for(int i = 0; i < dims.size(); ++i){
|
||
snprintf(buf, sizeof(buf), fmts[i != 0], dims[i]);
|
||
output << buf;
|
||
}
|
||
return output.str();
|
||
}
|
||
|
||
inline unsigned int getElementSize(nvinfer1::DataType t);
|
||
|
||
inline int64_t volume(const nvinfer1::Dims& d);
|
||
|
||
#endif
|