106 lines
2.5 KiB
C++
106 lines
2.5 KiB
C++
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#include "NanoDetector.h"
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#include "dnninfer.h"
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CNanoDetector::CNanoDetector(const char* strModelFile, int iBatchSize, int iInferMode, int iDeviceIndex)
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{
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m_bInit = false;
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m_strModelFile = strModelFile;
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m_pInfer = NULL;
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m_iBatchSize = iBatchSize;
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m_iInferMode = iInferMode;
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m_iDeviceInedx = 0;
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}
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CNanoDetector::~CNanoDetector()
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{
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UnInit();
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}
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bool CNanoDetector::Init()
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{
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bool bRet = false;
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{
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m_pInfer = DnnInfer_Init(m_strModelFile.c_str(), m_iBatchSize, m_iInferMode, m_iDeviceInedx);
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if (m_pInfer != NULL)
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{
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m_bInit = true;
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bRet = true;
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}
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}
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return bRet;
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}
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bool CNanoDetector::UnInit()
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{
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if (m_bInit)
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{
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m_bInit = false;
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if (m_pInfer != NULL)
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{
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DnnInfer_Close(m_pInfer);
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}
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}
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return true;
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}
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const char* CNanoDetector::GetType()
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{
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return "CNanoDetector";
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}
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bool CNanoDetector::Execute(cv::Mat& inputImage)
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{
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m_vecDetectResult.clear();
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if (m_pInfer == NULL)
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return false;
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if (!inputImage.isContinuous())
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inputImage = inputImage.clone();
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Datum inputData(inputImage.data, Shape(1, inputImage.channels(), inputImage.rows, inputImage.cols), 1);
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DetResult detResult[512];
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int iUseCount = 0;
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DnnInfer_Det_InferV2(m_pInfer, &inputData, detResult, 512, iUseCount, 0.4, 0.5);
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for (int i = 0; i < iUseCount; i++)
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{
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DetectorResult detectResult;
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detectResult.hasMask = false;
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detectResult.image_id = 0;
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detectResult.label = detResult[i].label;
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detectResult.score = detResult[i].score;
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strcpy_s(detectResult.name, detResult[i].name);
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char strLabelName[32] = { 0 };
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DnnInfer_GetLabelName(m_pInfer, strLabelName, detResult[i].label);
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//printf("LabelName:%s %d %d %d %d\n", strLabelName);
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//detectResult.name = detResult[i].name;
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detectResult.left = detResult[i].left;
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detectResult.top = detResult[i].top;
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detectResult.width = detResult[i].width;
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detectResult.height = detResult[i].height;
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m_vecDetectResult.emplace_back(detectResult);
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}
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//cv::Mat mask, predict;
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//int target_label = 1;
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////ʹmask = target_label<65><6C><EFBFBD><EFBFBD><EFBFBD><EFBFBD> = 1
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//cv::Mat target = mask == target_label;
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////ʹ predict = target_label<65><6C><EFBFBD><EFBFBD><EFBFBD><EFBFBD> = 1
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//cv::Mat result = predict = target_label;
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//
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////ʹ<><CAB9>ע<EFBFBD><D7A2><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>targetlabel<65><6C><EFBFBD><EFBFBD><EFBFBD><EFBFBD>=1
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//cv::Mat intersection = target == result;
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////<2F><>ע<EFBFBD><D7A2><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> - <20><>ע<EFBFBD><D7A2><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>Ϊ1<CEAA><31><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
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//cv::Mat unionSet = cv::sum(target + result) - intersection;
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////<2F><><EFBFBD>㽻<EFBFBD><E3BDBB>=1<><31><EFBFBD>صĺ<D8B5>/=1<><31><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ĺ<EFBFBD>
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//float iou = cv::sum(intersection)[0] / cv::sum(unionSet)[0];
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return true;
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}
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bool CNanoDetector::GetResults(std::vector<DetectorResult>& vecDetectorResults)
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{
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vecDetectorResults = m_vecDetectResult;
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return true;
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}
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