Feature/snaps events v2 integration #1492
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,44 +1,100 @@ | ||
| #include <chrono> | ||
| #include <iostream> | ||
| #include <opencv2/opencv.hpp> | ||
| #include <string> | ||
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| #include "depthai/depthai.hpp" | ||
| #include "depthai/utility/EventsManager.hpp" | ||
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| // Helper function to normalize frame coordinates | ||
| cv::Rect frameNorm(const cv::Mat& frame, const dai::Point2f& topLeft, const dai::Point2f& bottomRight) { | ||
| float width = frame.cols, height = frame.rows; | ||
| return cv::Rect(cv::Point(topLeft.x * width, topLeft.y * height), cv::Point(bottomRight.x * width, bottomRight.y * height)); | ||
| } | ||
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| int main() { | ||
| dai::Pipeline pipeline(true); | ||
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| // Enter you hub team's api-key | ||
| auto eventsManager = std::make_shared<dai::utility::EventsManager>(); | ||
| eventsManager->setToken(""); | ||
| eventsManager->setLogResponse(false); | ||
| auto fileGroup = std::make_shared<dai::utility::FileGroup>(); | ||
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| auto camRgb = pipeline.create<dai::node::Camera>()->build(); | ||
| auto detectionNetwork = pipeline.create<dai::node::DetectionNetwork>(); | ||
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| dai::NNModelDescription modelDescription; | ||
| modelDescription.model = "yolov6-nano"; | ||
| detectionNetwork->build(camRgb, modelDescription); | ||
| auto labelMap = detectionNetwork->getClasses(); | ||
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| // Create output queues | ||
| auto qRgb = detectionNetwork->passthrough.createOutputQueue(); | ||
| auto qDet = detectionNetwork->out.createOutputQueue(); | ||
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| pipeline.start(); | ||
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| int counter = 0; | ||
| while(pipeline.isRunning()) { | ||
| auto inRgb = qRgb->get<dai::ImgFrame>(); | ||
| auto inDet = qDet->get<dai::ImgDetections>(); | ||
| if(inRgb == nullptr || inDet == nullptr) { | ||
| continue; | ||
| } | ||
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| // Display the video stream and detections | ||
| cv::Mat frame = inRgb->getCvFrame(); | ||
| if(!frame.empty()) { | ||
| // Display detections | ||
| for(const auto& detection : inDet->detections) { | ||
| auto bbox = frameNorm(frame, dai::Point2f(detection.xmin, detection.ymin), dai::Point2f(detection.xmax, detection.ymax)); | ||
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| // Draw label | ||
| cv::putText( | ||
| frame, labelMap.value()[detection.label], cv::Point(bbox.x + 10, bbox.y + 20), cv::FONT_HERSHEY_TRIPLEX, 0.5, cv::Scalar(255, 255, 255)); | ||
|
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| // Draw confidence | ||
| cv::putText(frame, | ||
| std::to_string(static_cast<int>(detection.confidence * 100)) + "%", | ||
| cv::Point(bbox.x + 10, bbox.y + 40), | ||
| cv::FONT_HERSHEY_TRIPLEX, | ||
| 0.5, | ||
| cv::Scalar(255, 255, 255)); | ||
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| // Draw rectangle | ||
| cv::rectangle(frame, bbox, cv::Scalar(255, 0, 0), 2); | ||
| } | ||
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| // Show the frame | ||
| cv::imshow("rgb", frame); | ||
| } | ||
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| // Suppose we are only interested in the detections with confidence between 50% and 60% | ||
| auto borderDetections = std::make_shared<dai::ImgDetections>(); | ||
| for(const auto& detection : inDet->detections) { | ||
| if(detection.confidence > 0.5f && detection.confidence < 0.6f) { | ||
| borderDetections->detections.emplace_back(detection); | ||
| } | ||
| } | ||
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| // Are there any border detections | ||
| if(borderDetections->detections.size() > 0) { | ||
| std::string fileName = "ImageDetection_"; | ||
| std::stringstream ss; | ||
| ss << fileName << counter; | ||
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| fileGroup->clearFiles(); | ||
| fileGroup->addImageDetectionsPair(ss.str(), inRgb, borderDetections); | ||
| eventsManager->sendSnap("ImageDetection", fileGroup, {"EventsExample", "C++"}, {{"key_0", "value_0"}, {"key_1", "value_1"}}, ""); | ||
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| counter++; | ||
| } | ||
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| if(cv::waitKey(1) == 'q') { | ||
| break; | ||
| } | ||
| } | ||
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| return EXIT_SUCCESS; | ||
| } |
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