SOMS/test/XuAilibTest/NanoDetector.cpp

#include "NanoDetector.h"
#include "dnninfer.h"

CNanoDetector::CNanoDetector(const char* strModelFile, int iBatchSize, int iInferMode, int iDeviceIndex)
{
	m_bInit = false;
	m_strModelFile = strModelFile;
	m_pInfer = NULL;
	m_iBatchSize = iBatchSize;
	m_iInferMode = iInferMode;
	m_iDeviceInedx = 0;
}
CNanoDetector::~CNanoDetector()
{
	UnInit();
}

bool CNanoDetector::Init()
{
	bool bRet = false;
	{
		m_pInfer = DnnInfer_Init(m_strModelFile.c_str(), m_iBatchSize, m_iInferMode, m_iDeviceInedx);
		if (m_pInfer != NULL)
		{
			m_bInit = true;
			bRet = true;
		}
	}
	return bRet;
}

bool CNanoDetector::UnInit()
{
	if (m_bInit)
	{
		m_bInit = false;
		if (m_pInfer != NULL)
		{
			DnnInfer_Close(m_pInfer);
		}
	}
	return true;
}

const char* CNanoDetector::GetType()
{
	return "CNanoDetector";
}

bool CNanoDetector::Execute(cv::Mat& inputImage)
{
	m_vecDetectResult.clear();
	if (m_pInfer == NULL)
		return false;
	if (!inputImage.isContinuous())
		inputImage = inputImage.clone();
	Datum inputData(inputImage.data, Shape(1, inputImage.channels(), inputImage.rows, inputImage.cols), 1);

	DetResult detResult[512];
	int iUseCount = 0;

	DnnInfer_Det_InferV2(m_pInfer, &inputData, detResult, 512, iUseCount, 0.4, 0.5);

	for (int i = 0; i < iUseCount; i++)
	{
		DetectorResult detectResult;
		detectResult.hasMask = false;
		detectResult.image_id = 0;
		detectResult.label = detResult[i].label;
		detectResult.score = detResult[i].score;
		strcpy_s(detectResult.name, detResult[i].name);
		char strLabelName[32] = { 0 };
		DnnInfer_GetLabelName(m_pInfer, strLabelName, detResult[i].label);
		//printf("LabelName:%s %d %d %d %d\n", strLabelName);
		//detectResult.name = detResult[i].name;
		detectResult.left = detResult[i].left;
		detectResult.top = detResult[i].top;
		detectResult.width = detResult[i].width;
		detectResult.height = detResult[i].height;
		m_vecDetectResult.emplace_back(detectResult);
	}

	//cv::Mat mask, predict;
	//int target_label = 1;
	////使mask = target_label的像素 = 1
	//cv::Mat target = mask == target_label;
	////使 predict = target_label的像素 = 1
	//cv::Mat result = predict = target_label;
	//
	////使标注和推理都等于targetlabel的像素=1
	//cv::Mat intersection = target == result;
	////标注和推理非零像素相加 - 标注和推理都为1的像素
	//cv::Mat unionSet = cv::sum(target + result) - intersection;
	////计算交集=1像素的和/=1的相机并集的和
	//float iou = cv::sum(intersection)[0] / cv::sum(unionSet)[0];


	return true;
}

bool CNanoDetector::GetResults(std::vector<DetectorResult>& vecDetectorResults)
{
	vecDetectorResults = m_vecDetectResult;
	return true;
}