#include "VPTProcess.h"

#ifndef _MSC_VER
#include <sys/time.h>
#endif

#include <stdlib.h>
#include <cuda_runtime.h>
#include "ObjCls.h"
#include <time.h>
#include "ErrorInfo.h"
#include "fstream"
#include <boost/thread/thread.hpp>
//#include "mobileshift0.6_mergeBNALL.h"
//#include "ga_mobile_10_SSD10_416x224_416x224_mobile_shift2_mindim06_iter_46107_mergeBNALL.h"

//#include "test_mergeBNALLGPU.h"
//#include "ga_mobile_10cls_SSD_512x512_mobile_10clsv1_iter_200000_mergeBNALL.h"

#include "../model/vptModeTrt/ga_vpt_init_net.h"
#include "../model/vptModeTrt/ga_vpt_predict_net.h"
#include "../model/vptModeTrt/ga_trt_fpn_vpt_calibrator.h"
#include "../model/hidemodel_caffe_1108/ga_vpt_init_net_caffe2.h"
#include "../model/hidemodel_caffe_1108/ga_vpt_predict_net_caffe2.h"
#include "vpt_fpn_plugin_factory.h"
//#include "vpt.h"
#include "MutliSourceVideoProcess.h"
//struct vpt_handle
//{
//	void* det_handle;
//	vector<TaskTracker> taskTrackers;
//};

typedef struct objDetector {

	void* det_handle;
	float threshold;
	VPT_FPNPluginFactory tensorrt_plugin_factory;

	int licence_status = -1;//鎺堟潈鐩稿叧鍙傛暟
	int thrd_status = -1;	//鎺堟潈鐩稿叧鍙傛暟
	boost::thread thrd;		//鎺堟潈鐩稿叧鍙傛暟
	vector<TaskTracker> taskTrackers;
	objDetector()
	{
		det_handle = NULL;
		threshold = 0.6;
	}
}objDetector;


//int VPT_Init(void *&handle, VPTProcess_PARAM param)
//{
//	int ret = SUCCESS;
////#ifdef AUTHORIZATION
////#ifdef _WIN32
////	if (SUCCESS == (ret = sy_licence(productSN)))
////#elif __linux__
////	char* wtime = new char[15];
////	memset(wtime, 0, 15);
////	if (SUCCESS == (ret = sy_licence(productSN, &wtime)))
////#endif
////#else
////	ret = sy_time_check(2021, 4);
////	if (ret == SUCCESS)
////#endif
////	{
////		cuInit(0);
////		int device_count = 0;
////		cuDeviceGetCount(&device_count);
////
////		if (param.gpuid >= device_count)
////		{
////			printf("\nGPU_ID PARAM WRONG!\n");
////			return GPUID_PARAM_ERROR;
////		}
//
//		objDetector * tools = new objDetector;
//
//		ctools_init_params vpt_param;
//		vpt_param.thres_ = param.threshold;
//		vpt_param.log_level_ = 0;
//		vpt_param.device_type_ = param.mode;
//		vpt_param.device_id_ = param.gpuid;
//		vpt_param.engine_type_ = param.engine;
//		vpt_param.model_type_ = MODEL_FPN;
//
//#ifdef _MSC_VER
//		vpt_param.weight_file_ = NULL;
//		vpt_param.net_file_ = NULL;
//#else
//		vpt_param.weight_file_ = NULL;
//		vpt_param.net_file_ = NULL;
//#endif
//
//
//		vpt_param.data_process_str_ = param.preprocess_param;
//		vpt_param.need_im_info_ = 1; // true
//
//		if (vpt_param.engine_type_ == ENGINE_MCAFFE2)
//		{
//			//caffe2妯″瀷棰勫鐞嗗弬鏁?
//			vpt_param.data_process_str_ =
//				"CopyData_CPU2GPU_U8;"
//				"TypeConvert_U8_F32;"
//				"ResizePad_F32_F32,test_size,720,test_max_size,1280,fpn_coarsest_stride,32,"
//				"submean_b,103.94,submean_g,116.78,submean_r,123.68,"
//				"variance_rev_b,0.017,variance_rev_g,0.017,variance_rev_r,0.017;"
//				"NHWC2NCHW_F32"
//				;
//
//			vpt_param.weight_array_ = (unsigned char*)ga_vpt_init_net_caffe2;
//			vpt_param.weight_array_len_ = ga_vpt_init_net_len_caffe2;
//			vpt_param.net_array_ = (unsigned char*)ga_vpt_predict_net_caffe2;
//			vpt_param.net_array_len_ = ga_vpt_predict_net_len_caffe2;
//		}
//		else if (vpt_param.engine_type_ == ENGINE_TENSORRT)
//		{
//			vpt_param.weight_array_ = (uint8_t*)ga_vpt_init_net;
//			vpt_param.weight_array_len_ = ga_vpt_init_net_len;
//			vpt_param.net_array_ = (uint8_t*)ga_vpt_predict_net;
//			vpt_param.net_array_len_ = ga_vpt_predict_net_len;
//
//			vpt_param.trt_serialize_file_ = param.serialize_file;// NULL;// "FPN_VPT_ABCDEFGHIJKLMNOPQRSTUVWXYZ";
//
//																 //trt鐗堟湰棰勫鐞嗗弬鏁?
//			param.preprocess_param =
//				"CopyData_CPU2GPU_U8;"
//				"TypeConvert_U8_F32;"
//				"ResizeMaxPad_F32_F32,test_size,720,test_max_size,1280,max_height,736,max_width,1280,"
//				"submean_b,103.94,submean_g,116.78,submean_r,123.68,"
//				"variance_rev_b,0.017,variance_rev_g,0.017,variance_rev_r,0.017;"
//				"NHWC2NCHW_F32"
//				;
//
//			memset(vpt_param.tensorrt_param_str_, 0, sizeof(vpt_param.tensorrt_param_str_));
//
//			int batch_size = param.max_batch;
//			std::string g_data_mode = "FP32";
//			bool g_is_create_calibrator = false;
//
//			sprintf(vpt_param.tensorrt_param_str_, "max_batchsize %d,"
//				"data_mode %s,"
//				"is_create_calibrator %d,"
//				"input_names data im_info,"
//				"output_names cls_prob bbox_pred_final rois_count_each_img",
//				batch_size, g_data_mode.c_str(), g_is_create_calibrator);
//
//			vpt_param.tensorrt_calibrator_file_ = NULL;// "trt_fpn_vpt_calibrator";
//			vpt_param.tensorrt_calibrator_array_len_ = ga_trt_fpn_vpt_calibrator_len;// "trt_fpn_vpt_calibrator";
//			vpt_param.tensorrt_calibrator_array_ = (unsigned char*)ga_trt_fpn_vpt_calibrator;// "trt_fpn_vpt_calibrator";
//
//			vpt_param.tensorrt_plugin_factory_ptr_ = &(tools->tensorrt_plugin_factory);
//		}
//
//		tools->threshold = param.threshold;
//
//		int flag = ctools_init(&tools->det_handle, &vpt_param);
//
//		if (SUCCESS != flag)
//		{
//			printf("VPT_Init(ERROR): Init failed\n");
//			handle = NULL;
//		}
//		else
//			handle = (void*)tools;
//
//		return flag;
////	}
////	else
////	{
////		return AUTHOR_ERROR;
////	}
////#ifdef AUTHORIZATION
////#ifdef __linux__
////	if (wtime)
////	{
////		delete[] wtime;
////		wtime = NULL;
////	}
////#endif
////#endif
//}


int VPT_Init(void *&handle, VPTProcess_PARAM vparam)
{
	objDetector *tools = new objDetector;


	ctools_init_params param;
	param.thres_ = vparam.threshold;
	param.log_level_ = 0;
	param.device_type_ = vparam.mode;
	param.device_id_ = vparam.gpuid;
	param.engine_type_ = vparam.engine;
	param.model_type_ = MODEL_FPN;

	param.weight_array_ = (uint8_t*)ga_vpt_init_net;
	param.weight_array_len_ = ga_vpt_init_net_len;
	param.net_array_ = (uint8_t*)ga_vpt_predict_net;
	param.net_array_len_ = ga_vpt_predict_net_len;

	param.trt_serialize_file_ = vparam.serialize_file;// NULL;// "FPN_VPT_ABCDEFGHIJKLMNOPQRSTUVWXYZ";

														 //trt鐗堟湰棰勫鐞嗗弬鏁?


	memset(param.tensorrt_param_str_, 0, sizeof(param.tensorrt_param_str_));

	int batch_size = vparam.max_batch;
	std::string g_data_mode = "INT8";
	bool g_is_create_calibrator = false;

	sprintf(param.tensorrt_param_str_, "max_batchsize %d,"
		"data_mode %s,"
		"is_create_calibrator %d,"
		"input_names data im_info,"
		"output_names cls_prob bbox_pred_final rois_count_each_img",
		batch_size, g_data_mode.c_str(), g_is_create_calibrator);

	param.tensorrt_calibrator_file_ = NULL;// "trt_fpn_vpt_calibrator";
	param.tensorrt_calibrator_array_len_ = ga_trt_fpn_vpt_calibrator_len;// "trt_fpn_vpt_calibrator";
	param.tensorrt_calibrator_array_ = (unsigned char*)ga_trt_fpn_vpt_calibrator;// "trt_fpn_vpt_calibrator";

	param.tensorrt_plugin_factory_ptr_ = &(tools->tensorrt_plugin_factory);


	//param.data_process_str_ =
	//	"TypeConvert_U8_F32;"
	//	"ResizePad_F32_F32,test_size,540,test_max_size,1280,fpn_coarsest_stride,32,"
	//	"submean_b,103.94,submean_g,116.78,submean_r,123.68,"
	//	"variance_rev_b,0.017,variance_rev_g,0.017,variance_rev_r,0.017;"
	//	"NHWC2NCHW_F32"
	//	;

	param.data_process_str_ =
		//"CopyData_CPU2GPU_U8;"
		"TypeConvert_U8_F32;"
		"ResizeMaxPad_F32_F32,test_size,720,test_max_size,1280,max_height,736,max_width,1280,"
		"submean_b,103.94,submean_g,116.78,submean_r,123.68,"
		"variance_rev_b,0.017,variance_rev_g,0.017,variance_rev_r,0.017;"
		"NHWC2NCHW_F32"
		;
	/*
	   param.data_process_str_ =
	  "CopyData_CPU2GPU_U8;"
	  "TypeConvert_U8_F32;"
	  "ResizePad_F32_F32,test_size,720,test_max_size,1280,fpn_coarsest_stride,32,"
	  "submean_b,103.94,submean_g,116.78,submean_r,123.68,"
	  "variance_rev_b,0.017,variance_rev_g,0.017,variance_rev_r,0.017;"
	  "NHWC2NCHW_F32"
	  ;
  */

	int flag = ctools_init(&(tools->det_handle), &param);
	if (SUCCESS != flag)
	{
		if (tools)
		{
			delete tools;
			tools = NULL;
		}
	}
	else
	{
		handle = tools;
	}
	return flag;
}


void tmp_test(void * handle)
{
	objDetector *tools = (objDetector*)handle;
 
const int batchsize = 5;
	sy_img images[batchsize];
	cv::Mat img[batchsize];

	char strpath[1024];
	memset(strpath, 0, sizeof(strpath));
	for (int b = 0; b < batchsize; b++)
	{
#ifdef _MSC_VER
		sprintf(strpath, "../../../data/%d.jpg", b);// b);
#else
		sprintf(strpath, "0.jpg", b);

#endif

		img[b] = cv::imread(strpath);
		images[b].set_data(img[b].cols, img[b].rows, img[b].channels(), img[b].data);
	}


	//vpt_batch(handle, images, batchsize, &result);

	int count = 1;
	while (count--)
	{

printf("begin vpt_batch\n");
		vpt_batch(tools->det_handle, images, batchsize, &result);

	}


	for (int b = 0; b < batchsize; b++)
	{
		std::cout << "b: " << b << ", count: " << result[b].obj_count_ << std::endl;
   }
}

int VPT_Process_GPU(void * handle, sy_img * batch_img, int startBatch, int batchsize, vector<VPT_Result>& result, vector<vector<int>>& deleteObjectID, vector<vector<VPT_Result>>& unUsedResult)
{



tmp_test(handle);




	objDetector *tools = (objDetector*)handle;
#ifndef _MSC_VER
	struct timeval first_time, second_time;
	gettimeofday(&first_time, NULL);
#endif

	bool isUseDet = true;
	int channels = 3;

#ifndef _MSC_VER
	if (0)
	{
		gettimeofday(&second_time, NULL);
		double time_val = (second_time.tv_sec - first_time.tv_sec) * 1000000 + second_time.tv_usec - first_time.tv_usec;
		printf("set data time_val(ms) = %lf\n", time_val / 1000.0);
		gettimeofday(&first_time, NULL);
	}
#endif


	ctools_result *detresult;

	sy_img * batch_img_cpu = new sy_img[batchsize];
	for (int i = 0; i < batchsize; ++i)
	{
		batch_img_cpu[i].data_ = (unsigned char *)malloc(batch_img[i].w_ * batch_img[i].h_ * batch_img[i].c_);
		cudaMemcpy(batch_img_cpu[i].data_, batch_img[i].data_, batch_img[i].w_ * batch_img[i].h_ * batch_img[i].c_, cudaMemcpyDeviceToHost);
   
   	cv::Mat newVideoImg(batch_img[i].h_,  batch_img[i].w_, CV_8UC3, batch_img_cpu[i].data_);
    
    char filename[260];
    sprintf(filename, "pic/%d.jpg", i);
						cv::imwrite(filename, newVideoImg);
						free(batch_img_cpu[i].data_);
                                    
	}

	int res_status = ctools_process(tools->det_handle, batch_img, batchsize, &detresult);
	//for (int i = 0; i < batchsize; ++i)
	//{
	//	free(batch_img_cpu[i].data_);	
	//}
	//delete[]batch_img_cpu;

#ifndef _MSC_VER

	if (0)
	{
		gettimeofday(&second_time, NULL);
		double time_val = (second_time.tv_sec - first_time.tv_sec) * 1000000 + second_time.tv_usec - first_time.tv_usec;
		printf("process time_val(ms) = %lf\n", time_val / 1000.0);
	}

#endif

	vector <vector< vector <float>>> detectResult(batchsize);	//杞寲涓鸿窡韪墍闇瑕佺殑杈撳叆
	int batch_size = batchsize;

#ifndef _MSC_VER

	if (0)
	{
		gettimeofday(&first_time, NULL);

	}

#endif

	for (int b = 0; b < batch_size; b++)
	{
		ctools_result &cur_result = detresult[b];
printf("b=%d, count=%d\n", b, cur_result.obj_count_);
		for (int c = 0; c < cur_result.obj_count_ && c < MAX_OBJ_COUNT; c++)
		{

			float x1 = cur_result.obj_results_[c].data_[2];
			float y1 = cur_result.obj_results_[c].data_[3];
			float x2 = cur_result.obj_results_[c].data_[4];
			float y2 = cur_result.obj_results_[c].data_[5];

			float class_id = cur_result.obj_results_[c].data_[0];
			float score = cur_result.obj_results_[c].data_[1];

			int imgid = b;
			if (score >= THRESHOLD)
			{
				vector <float> obj;

				obj.push_back(x1);
				obj.push_back(y1);
				obj.push_back(x2);
				obj.push_back(y2);
				obj.push_back(score);
				obj.push_back(class_id);
				detectResult[imgid].push_back(obj);
			}
		}
	}
	//exit(-1);
#ifndef _MSC_VER

	if (0)
	{
		gettimeofday(&second_time, NULL);
		double time_val = (second_time.tv_sec - first_time.tv_sec) * 1000000 + second_time.tv_usec - first_time.tv_usec;
		printf("save result time_val(ms) = %lf\n", time_val / 1000.0);
	}

#endif

	int resIndex = startBatch;
	int detectIndex = 0;

#ifndef _MSC_VER

	if (0)
	{
		gettimeofday(&first_time, NULL);
	}
#endif

	for (int i = startBatch; i < tools->taskTrackers.size(); i++)	//batch?直????貌?同?母?????
	{
 printf("i=%d\n", i);
		if (!tools->taskTrackers[i].tracker.GetState())
		{
			cout << "************************************ " << i << " pause" << endl;
			continue;
		}

		
   // cout << i << " " << taskTrackers[i].TaskID << " " << resIndex << endl;
		isUseDet = true;
		for (int j = 0; j < FusionInterval; j++)	//??帧??????一帧?????? (FusionInterval - 1)帧 
		{
			int width = 1920; int height = 1080;
			//int objCount = 0;
			if (j == 0)
			{
				int objCount = tools->taskTrackers[i].tracker.update(/*tools->param.w*/width* tools->taskTrackers[i].ratioWidth, /*tools->param.h*/height* tools->taskTrackers[i].ratioHeight, isUseDet, detectResult[detectIndex], result[resIndex].obj, deleteObjectID[detectIndex]);
				result[resIndex].objCount = objCount;
				vector<vector<float>>().swap(detectResult[detectIndex]);
				detectResult[detectIndex].clear();
				isUseDet = false;
			}
			else
			{
				VPT_Result unresult;
				VPT_ObjInfo obj[MAX_OBJ_COUNT];
				unresult.objCount = tools->taskTrackers[i].tracker.update(/*tools->param.w*/width* tools->taskTrackers[i].ratioWidth, /*tools->param.h*/height* tools->taskTrackers[i].ratioHeight, isUseDet, detectResult[detectIndex], unresult.obj, deleteObjectID[detectIndex]);
				unUsedResult[resIndex].push_back(unresult);
			}
		}
		std::cout<<"tracked obj Count : "<<result[resIndex].objCount<<"\n";
		resIndex++;
		detectIndex++;
		if (resIndex == startBatch + batchsize)
			break;
			}

	vector <vector< vector <float>>>().swap(detectResult);

#ifndef _MSC_VER

	if (0)
	{
		gettimeofday(&second_time, NULL);
		double time_val = (second_time.tv_sec - first_time.tv_sec) * 1000000 + second_time.tv_usec - first_time.tv_usec;
		printf("tracking time_val(ms) = %lf\n", time_val / 1000.0);
		}
#endif

	return SUCCESS;
}



void VPT_Release(void * handle)
{
	objDetector *tools = (objDetector*)handle;

	if (tools)
	{
		if (tools->det_handle)
		{
			ctools_release(&tools->det_handle);
			tools->det_handle = NULL;
		}
		
		vector<TaskTracker>().swap(tools->taskTrackers);

		delete tools;
		tools = NULL;
	}
}

void AddTaskTracker(void * handle, const int taskID, const double rWidth, const double rHeight)
{
	objDetector *tools = (objDetector*)handle;
	TaskTracker t;
	t.TaskID = taskID;
	t.ratioWidth = rWidth;
	t.ratioHeight = rHeight;
	tools->taskTrackers.push_back(t);
}

void FinishTaskTracker(void * handle, const int taskID)
{
	objDetector *tools = (objDetector*)handle;
	for (int i = 0; i < tools->taskTrackers.size(); i++)
	{
		if (tools->taskTrackers[i].TaskID == taskID)
		{
			tools->taskTrackers.erase(tools->taskTrackers.begin() + i);
			break;
		}
	}
}

void PauseTaskTracker(void * handle, const int taskID)
{
	objDetector *tools = (objDetector*)handle;
	for (int i = 0; i < tools->taskTrackers.size(); i++)
	{
		if (tools->taskTrackers[i].TaskID == taskID)
		{
			tools->taskTrackers[i].tracker.Pause();
			break;
		}
	}
}

void RestartTaskTraker(void * handle, const int taskID)
{
	objDetector *tools = (objDetector*)handle;
	for (int i = 0; i < tools->taskTrackers.size(); i++)
	{
		if (tools->taskTrackers[i].TaskID == taskID)
		{
			tools->taskTrackers[i].tracker.ReSet();
			break;
		}
	}
}

void DrawTracker(void * handle, const int taskID, cv::Mat *img)
{
	objDetector *tools = (objDetector*)handle;
	for (int i = 0; i <  tools->taskTrackers.size(); i++)
	{
		if (tools->taskTrackers[i].TaskID == taskID)
		{
			tools->taskTrackers[i].tracker.addTracker(img);
			break;
		}
	}
}



int VPT_Process(void * handle, unsigned char ** bgr, int batchsize, VPT_Result *result)
{
	//			isUseDet = false;
	//		}

	//	}
	//	resIndex++;
	//	if (resIndex == batchsize) break;
	//}
	//vector <vector< vector <float>>>().swap(detectResult);
	//vector<vector<float>>().swap(ssdResult);
	///*detectResult.clear();
	//ssdResult.clear();*/

	return SUCCESS;
}

//#include <windows.h>




void permute(float * image, int testWidth, int testHeight)
{
	//cv::Mat host_image;
	float * host_image;
	//host_image.create(testHeight, testWidth, CV_32FC3);

	host_image = (float *)malloc(testHeight*testWidth * 3 * sizeof(float));;

	float *Host_img = new float[3 * testWidth * testHeight]{};//?????诖?

	float* image_data_original = image;
	//NPP_CHECK_CUDA(cudaMemcpy(Host_img, image_data_original, testWidth*testHeight * 3 * sizeof(float), cudaMemcpyDeviceToHost));//?????钥???????图????????
	cudaMemcpy(Host_img, image_data_original, testWidth*testHeight * 3 * sizeof(float), cudaMemcpyDeviceToHost);//?????钥???????图????????

	for (int j = 0; j < testHeight; j++)
	{
		float *pts = host_image + j * testWidth * 3;
		for (int i = 0; i < testWidth; i++)
		{
			//pts[3 * i] = cv::saturate_cast<uchar>(Host_img[3 * (j*host_image.cols + i) + 0]);                                     //b
			//pts[3 * i + 1] = cv::saturate_cast<uchar>(Host_img[3 * (j*host_image.cols + i) + 1]);             //g
			//pts[3 * i + 2] = cv::saturate_cast<uchar>(Host_img[3 * (j*host_image.cols + i) + 2]);         //r

			pts[3 * i] = (Host_img[j * testWidth + i]);                                     //b
			pts[3 * i + 1] = (Host_img[testWidth * testHeight + j * testWidth + i]);             //g
			pts[3 * i + 2] = (Host_img[2 * testWidth * testHeight + j * testWidth + i]);         //r
		}
	}

	cudaMemcpy(image_data_original, host_image, testWidth*testHeight * 3 * sizeof(float), cudaMemcpyHostToDevice);
	free(host_image);
	//cv::Mat showImg;
	//cv::resize(host_image, showImg, cv::Size(640, 480));
	//cv::imshow("image", showImg);
	//cv::waitKey(0);
}

cv::Mat GpuMat2OpencvMat(unsigned char* image, int width, int height)
{
	int testWidth = width;
	int testHeight = height;
	cv::Mat host_image;
	host_image.create(testHeight, testWidth, CV_8UC3);
	unsigned char *Host_img = new unsigned char[3 * testWidth * testHeight]{};//?????诖?
	unsigned char* image_data_original = image;

	cudaError_t code = cudaMemcpy(Host_img, image_data_original, testWidth*testHeight * 3 * sizeof(unsigned char), cudaMemcpyDeviceToHost);//?????钥???????图????????
	if (code != 0)
	{
		printf("==========================================================error");
	}
	std::ofstream outfile("decode.bin", ios::out | ios::binary);
	outfile.write((char*)Host_img, int(sizeof(char) * 1080 * 1920 * 3));
	outfile.close();

	cudaMemcpy(host_image.data, image_data_original, 1920 * testHeight * 3 * sizeof(unsigned char), cudaMemcpyDeviceToHost);//?????钥???????图????????

																															//    for (int j = 0; j < host_image.rows; j++)
																															//    {
																															//        uchar *pts = host_image.ptr<uchar>(j);
																															//        for (int i = 0; i < host_image.cols; i++)
																															//        {
																															//            //pts[3 * i] = cv::saturate_cast<uchar>(Host_img[3 * (j*host_image.cols + i) + 0]);                                     //b
																															//            //pts[3 * i + 1] = cv::saturate_cast<uchar>(Host_img[3 * (j*host_image.cols + i) + 1]);             //g
																															//            //pts[3 * i + 2] = cv::saturate_cast<uchar>(Host_img[3 * (j*host_image.cols + i) + 2]);         //r
																															//            pts[3 * i] = cv::saturate_cast<uchar>(Host_img[j* host_image.cols*3 + 3 * i]);                                     //b
																															//            pts[3 * i + 1] = cv::saturate_cast<uchar>(Host_img[j* host_image.cols*3 + 3 * i + 1]);             //g
																															//            pts[3 * i + 2] = cv::saturate_cast<uchar>(Host_img[j* host_image.cols*3 + 3 * i + 2]);         //r
																															//        }
																															//    }
	cv::imwrite("input3.jpg", host_image);
	return host_image;
}


int VPT_ProcessImage(void * handle, unsigned char ** bgr, int batchsize, VPT_Result * result)
{
	//objDetector* tools = (objDetector*)handle;
	//if (bgr == NULL)	//图?????荽???
	//	return IMG_DATA_ERROR;

	//vector <Mat> imgs;
	////int datasize = tools->param.w * tools->param.h * tools->param.c;
	//for (int i = 0; i < batchsize; i++)
	//{
	//	Mat img(tools->param.h, tools->param.w, tools->param.c, bgr[i]);
	//	imgs.push_back(img);
	//}

	//vector<vector<float>> ssdResult;
	////clock_t begin = clock();
	//ssdResult = SSD_Detect(tools->detector, batchsize, imgs);	//
	//vector <vector< vector <float>>> detectResult(batchsize);	//转??为????????要??????
	//for (int i = 0; i < ssdResult.size(); ++i)
	//{
	//	const vector<float>& d = ssdResult[i];// Detection format: [image_id, label, score, xmin, ymin, xmax, ymax)
	//	const float score = d[2];
	//	int imgid = d[0];
	//	if (score >= THRESHOLD)  //????为left top right bottom score id
	//	{
	//		vector <float> obj;
	//		obj.push_back(d[3] * imgs[d[0]].cols);
	//		obj.push_back(d[4] * imgs[d[0]].rows);
	//		obj.push_back(d[5] * imgs[d[0]].cols);
	//		obj.push_back(d[6] * imgs[d[0]].rows);
	//		obj.push_back(d[2]);
	//		obj.push_back(d[1]);
	//		detectResult[imgid].push_back(obj);
	//	}
	//}
	//for (int i = 0; i < batchsize; i++)
	//{
	//	for (int j = 0; j < detectResult[i].size(); j++)//left top right bottom score id
	//	{
	//		result[i].obj[j].id = 0;
	//		result[i].obj[j].left = detectResult[i][j][0];
	//		result[i].obj[j].top = detectResult[i][j][1];
	//		result[i].obj[j].right = detectResult[i][j][2];
	//		result[i].obj[j].bottom = detectResult[i][j][3];
	//		result[i].obj[j].confidence = detectResult[i][j][4];
	//		result[i].obj[j].index = detectResult[i][j][5] - 1;
	//	}
	//	
	//}

	return SUCCESS;
}