# Events This example shows how to use the EventsManager to send custom events, including file data and RGB camera frames, with associated tags and metadata. > EventsManager node is currently in "early access preview". Please use the > [depthai-core develop](https://github.com/luxonis/depthai-core/tree/develop) > branch to have access to the latest features and fixes. This example requires the DepthAI v3 API, see [installation instructions](https://docs.luxonis.com/software-v3/depthai.md). ## Pipeline ### examples/events.pipeline.json ```json {"pipeline": {"connections": [{"node1Id": 0, "node1Output": "0", "node1OutputGroup": "dynamicOutputs", "node2Id": 1, "node2Input": "in", "node2InputGroup": ""}], "globalProperties": {"calibData": null, "cameraTuningBlobSize": null, "cameraTuningBlobUri": "", "leonCssFrequencyHz": 700000000.0, "leonMssFrequencyHz": 700000000.0, "pipelineName": null, "pipelineVersion": null, "sippBufferSize": 18432, "sippDmaBufferSize": 16384, "xlinkChunkSize": -1}, "nodes": [[1, {"alias": "", "id": 1, "ioInfo": [[["", "in"], {"blocking": true, "group": "", "id": 4, "name": "in", "queueSize": 3, "type": 3, "waitForMessage": false}]], "logLevel": 3, "name": "XLinkOut", "parentId": -1, "properties": {"maxFpsLimit": -1.0, "metadataOnly": false, "streamName": "__x_0_0"}}], [0, {"alias": "", "id": 0, "ioInfo": [[["dynamicOutputs", "0"], {"blocking": false, "group": "dynamicOutputs", "id": 3, "name": "0", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "raw"], {"blocking": false, "group": "", "id": 2, "name": "raw", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "mockIsp"], {"blocking": true, "group": "", "id": 1, "name": "mockIsp", "queueSize": 8, "type": 3, "waitForMessage": false}], [["", "inputControl"], {"blocking": true, "group": "", "id": 0, "name": "inputControl", "queueSize": 3, "type": 3, "waitForMessage": false}]], "logLevel": 3, "name": "Camera", "parentId": -1, "properties": {"boardSocket": 0, "cameraName": "", "fps": -1.0, "imageOrientation": -1, "initialControl": {"aeLockMode": false, "aeMaxExposureTimeUs": 100663297, "aeRegion": {"height": 0, "priority": 3545344224, "width": 0, "x": 4163, "y": 1}, "afRegion": {"height": 0, "priority": 0, "width": 0, "x": 23088, "y": 0}, "antiBandingMode": 81, "autoFocusMode": 3, "awbLockMode": false, "awbMode": 40, "brightness": -86, "captureIntent": 211, "chromaDenoise": 0, "cmdMask": 0, "contrast": 43, "controlMode": 48, "effectMode": 90, "enableHdr": false, "expCompensation": -41, "expManual": {"exposureTimeUs": 23088, "frameDurationUs": 23088, "sensitivityIso": 3545344332}, "frameSyncMode": 0, "lensPosAutoInfinity": 96, "lensPosAutoMacro": 169, "lensPosition": 0, "lensPositionRaw": 0.0, "lowPowerNumFramesBurst": 0, "lowPowerNumFramesDiscard": 0, "lumaDenoise": 0, "miscControls": [], "saturation": 25, "sceneMode": 169, "sharpness": 0, "strobeConfig": {"activeLevel": 136, "enable": 0, "gpioNumber": -81}, "strobeTimings": {"durationUs": 4, "exposureBeginOffsetUs": 23088, "exposureEndOffsetUs": 17}, "wbColorTemp": 0}, "isp3aFps": 0, "mockIspHeight": -1, "mockIspWidth": -1, "numFramesPoolIsp": 3, "numFramesPoolPreview": 4, "numFramesPoolRaw": 3, "numFramesPoolStill": 4, "numFramesPoolVideo": 4, "outputRequests": [{"enableUndistortion": null, "fps": {"value": null}, "resizeMode": 0, "size": {"value": {"index": 0, "value": [256, 256]}}, "type": null}], "resolutionHeight": -1, "resolutionWidth": -1}}]]}} ``` ## Source code #### Python ```python #!/usr/bin/env python3 import cv2 import depthai as dai import numpy as np import time # Callback functions def uploadSuccessCallback(sendSnapResult): assert (sendSnapResult.uploadStatus == dai.SendSnapCallbackStatus.SUCCESS) print(f"Successfully uploaded Snap: ({sendSnapResult.snapName}, {sendSnapResult.snapTimestamp}, {sendSnapResult.snapHubID}) to the hub.") def uploadFailureCallback(sendSnapResult): assert (sendSnapResult.uploadStatus != dai.SendSnapCallbackStatus.SUCCESS) print(f"Upload of Snap: ({sendSnapResult.snapName}, {sendSnapResult.snapTimestamp}, {sendSnapResult.snapLocalID}) to the hub has failed.") status = sendSnapResult.uploadStatus if status == dai.SendSnapCallbackStatus.FILE_BATCH_PREPARATION_FAILED: print("File batch preparation failed!") elif status == dai.SendSnapCallbackStatus.GROUP_CONTAINS_REJECTED_FILES: print("Snap's file group contains rejected files!") elif status == dai.SendSnapCallbackStatus.FILE_UPLOAD_FAILED: print("File upload was unsuccessful!") elif status == dai.SendSnapCallbackStatus.SEND_EVENT_FAILED: print("Snap could not be sent to the hub, following successful file upload!") # Create pipeline with dai.Pipeline() as pipeline: # Set your Hub team's api-key using the environment variable DEPTHAI_HUB_API_KEY. Or pass the key when creating the EventsManager instance. eventMan = dai.EventsManager("") cameraNode = pipeline.create(dai.node.Camera).build() detectionNetwork = pipeline.create(dai.node.DetectionNetwork).build(cameraNode, dai.NNModelDescription("yolov6-nano")) labelMap = detectionNetwork.getClasses() # Create output queues qRgb = detectionNetwork.passthrough.createOutputQueue() qDet = detectionNetwork.out.createOutputQueue() pipeline.start() # nn data, being the bounding box locations, are in <0..1> range - they need to be normalized with frame width/height def frameNorm(frame, bbox): normVals = np.full(len(bbox), frame.shape[0]) normVals[::2] = frame.shape[1] return (np.clip(np.array(bbox), 0, 1) * normVals).astype(int) print("Press 's' to send a snap, and 'q' to quit") while pipeline.isRunning(): key = cv2.waitKey(1) if key == ord("q"): pipeline.stop() break inRgb: dai.ImgFrame = qRgb.get() inDet: dai.ImgDetections = qDet.get() if inRgb is None or inDet is None: continue # Display the video stream and detections color = (255, 0, 0) frame = inRgb.getCvFrame() if frame is not None: for detection in inDet.detections: bbox = frameNorm( frame, (detection.xmin, detection.ymin, detection.xmax, detection.ymax), ) cv2.putText( frame, labelMap[detection.label], (bbox[0] + 10, bbox[1] + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255, ) cv2.putText( frame, f"{int(detection.confidence * 100)}%", (bbox[0] + 10, bbox[1] + 40), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255, ) cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color, 2) # Show the frame cv2.imshow("rgb", frame) # Trigger sendSnap() if key == ord("s"): localSnapID = eventMan.sendSnap("ImageDetection", None, inRgb, inDet, ["EventsExample", "Python"], {"key_0" : "value_0", "key_1" : "value_1"}, uploadSuccessCallback, uploadFailureCallback) if localSnapID != None: print(f"Snap with a localID: {localSnapID} has been successfully added to the EventsManager") else: print("Snap was not successfully added to the EventsManager") cv2.destroyAllWindows() # Wait for pending snaps to be uploaded if eventMan.waitForPendingUploads(timeoutMs=10000): print("Pending uploads have been successfully uploaded") else: print("Pending uploads were discarded due to timeout (10s) or dropped connection") ``` #### C++ ```cpp #include #include #include #include "depthai/depthai.hpp" #include "depthai/utility/EventsManager.hpp" // 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)); } // Callback functions void uploadSuccessCallback(dai::utility::SendSnapCallbackResult sendSnapResult) { std::cout << "Successfully uploaded Snap: (" << sendSnapResult.snapName << ", " << sendSnapResult.snapTimestamp << ", " << sendSnapResult.snapHubID.value_or("") << ") to the hub." << std::endl; } void uploadFailureCallback(dai::utility::SendSnapCallbackResult sendSnapResult) { std::cout << "Upload of Snap: (" << sendSnapResult.snapName << ", " << sendSnapResult.snapTimestamp << ", " << sendSnapResult.snapLocalID << ") to the hub has failed." << std::endl; switch(sendSnapResult.uploadStatus) { case dai::utility::SendSnapCallbackStatus::FILE_BATCH_PREPARATION_FAILED: std::cout << "File batch preparation failed!" << std::endl; break; case dai::utility::SendSnapCallbackStatus::GROUP_CONTAINS_REJECTED_FILES: std::cout << "Snap's file group contains rejected files!" << std::endl; break; case dai::utility::SendSnapCallbackStatus::FILE_UPLOAD_FAILED: std::cout << "File upload was unsuccessful!" << std::endl; break; case dai::utility::SendSnapCallbackStatus::SEND_EVENT_FAILED: std::cout << "Snap could not be sent to the hub, following successful file upload!" << std::endl; break; default: break; } } int main() { dai::Pipeline pipeline(true); // Set your Hub team's api-key using the environment variable DEPTHAI_HUB_API_KEY. Or pass the key when creating the EventsManager instance. auto eventsManager = std::make_shared(""); auto camRgb = pipeline.create()->build(); auto detectionNetwork = pipeline.create(); dai::NNModelDescription modelDescription; modelDescription.model = "yolov6-nano"; detectionNetwork->build(camRgb, modelDescription); auto labelMap = detectionNetwork->getClasses(); // Create output queues auto qRgb = detectionNetwork->passthrough.createOutputQueue(); auto qDet = detectionNetwork->out.createOutputQueue(); pipeline.start(); std::cout << "Press 's' to send a snap, and 'q' to quit" << std::endl; while(pipeline.isRunning()) { auto key = cv::waitKey(1); if(key == 'q') { pipeline.stop(); break; } auto inRgb = qRgb->get(); auto inDet = qDet->get(); if(inRgb == nullptr || inDet == nullptr) { continue; } // 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)); // 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)); // Draw confidence cv::putText(frame, std::to_string(static_cast(detection.confidence * 100)) + "%", cv::Point(bbox.x + 10, bbox.y + 40), cv::FONT_HERSHEY_TRIPLEX, 0.5, cv::Scalar(255, 255, 255)); // Draw rectangle cv::rectangle(frame, bbox, cv::Scalar(255, 0, 0), 2); } // Show the frame cv::imshow("rgb", frame); } // Trigger sendSnap() if(key == 's') { auto localSnapID = eventsManager->sendSnap("ImageDetection", std::nullopt, inRgb, inDet, {"EventsExample", "C++"}, {{"key_0", "value_0"}, {"key_1", "value_1"}}, uploadSuccessCallback, uploadFailureCallback); if(localSnapID.has_value()) { std::cout << "Snap with a localID: " << localSnapID.value() << " has been successfully added to the EventsManager" << std::endl; } else { std::cout << "Snap was not successfully added to the EventsManager" << std::endl; } } } cv::destroyAllWindows(); // Wait for pending snaps to be uploaded if(eventsManager->waitForPendingUploads(10000)) { std::cout << "Pending uploads have been successfully uploaded" << std::endl; } else { std::cout << "Pending uploads were discarded due to timeout (10s) or dropped connection" << std::endl; } return EXIT_SUCCESS; } ``` ### Need assistance? 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