# Spatial object tracker on RGB This example shows how to run MobileNetv2SSD on the RGB input frame, and perform spatial object tracking on persons. setConfidenceThreshold() - confidence threshold above which objects are detected ### Similar samples: * [Object tracker on video](https://docs.luxonis.com/software/depthai/examples/object_tracker_video.md) * [Object tracker on RGB](https://docs.luxonis.com/software/depthai/examples/object_tracker.md) ## Setup Please run the [install script](https://github.com/luxonis/depthai-python/blob/main/examples/install_requirements.py) to download all required dependencies. Please note that this script must be ran from git context, so you have to download the [depthai-python](https://github.com/luxonis/depthai-python) repository first and then run the script ```bash git clone https://github.com/luxonis/depthai-python.git cd depthai-python/examples python3 install_requirements.py ``` For additional information, please follow the [installation guide](https://docs.luxonis.com/software/depthai/manual-install.md). ## Source code #### Python ```python #!/usr/bin/env python3 from pathlib import Path import cv2 import depthai as dai import numpy as np import time import argparse labelMap = ["background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"] nnPathDefault = str((Path(__file__).parent / Path('../models/mobilenet-ssd_openvino_2021.4_5shave.blob')).resolve().absolute()) parser = argparse.ArgumentParser() parser.add_argument('nnPath', nargs='?', help="Path to mobilenet detection network blob", default=nnPathDefault) parser.add_argument('-ff', '--full_frame', action="store_true", help="Perform tracking on full RGB frame", default=False) args = parser.parse_args() fullFrameTracking = args.full_frame # Create pipeline pipeline = dai.Pipeline() # Define sources and outputs camRgb = pipeline.create(dai.node.ColorCamera) spatialDetectionNetwork = pipeline.create(dai.node.MobileNetSpatialDetectionNetwork) monoLeft = pipeline.create(dai.node.MonoCamera) monoRight = pipeline.create(dai.node.MonoCamera) stereo = pipeline.create(dai.node.StereoDepth) objectTracker = pipeline.create(dai.node.ObjectTracker) xoutRgb = pipeline.create(dai.node.XLinkOut) trackerOut = pipeline.create(dai.node.XLinkOut) xoutRgb.setStreamName("preview") trackerOut.setStreamName("tracklets") # Properties camRgb.setPreviewSize(300, 300) camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P) camRgb.setInterleaved(False) camRgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.BGR) monoLeft.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P) monoLeft.setCamera("left") monoRight.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P) monoRight.setCamera("right") # setting node configs stereo.setDefaultProfilePreset(dai.node.StereoDepth.PresetMode.HIGH_DENSITY) # Align depth map to the perspective of RGB camera, on which inference is done stereo.setDepthAlign(dai.CameraBoardSocket.CAM_A) stereo.setOutputSize(monoLeft.getResolutionWidth(), monoLeft.getResolutionHeight()) spatialDetectionNetwork.setBlobPath(args.nnPath) spatialDetectionNetwork.setConfidenceThreshold(0.5) spatialDetectionNetwork.input.setBlocking(False) spatialDetectionNetwork.setBoundingBoxScaleFactor(0.5) spatialDetectionNetwork.setDepthLowerThreshold(100) spatialDetectionNetwork.setDepthUpperThreshold(5000) objectTracker.setDetectionLabelsToTrack([15]) # track only person # possible tracking types: ZERO_TERM_COLOR_HISTOGRAM, ZERO_TERM_IMAGELESS, SHORT_TERM_IMAGELESS, SHORT_TERM_KCF objectTracker.setTrackerType(dai.TrackerType.ZERO_TERM_COLOR_HISTOGRAM) # take the smallest ID when new object is tracked, possible options: SMALLEST_ID, UNIQUE_ID objectTracker.setTrackerIdAssignmentPolicy(dai.TrackerIdAssignmentPolicy.SMALLEST_ID) # Linking monoLeft.out.link(stereo.left) monoRight.out.link(stereo.right) camRgb.preview.link(spatialDetectionNetwork.input) objectTracker.passthroughTrackerFrame.link(xoutRgb.input) objectTracker.out.link(trackerOut.input) if fullFrameTracking: camRgb.setPreviewKeepAspectRatio(False) camRgb.video.link(objectTracker.inputTrackerFrame) objectTracker.inputTrackerFrame.setBlocking(False) # do not block the pipeline if it's too slow on full frame objectTracker.inputTrackerFrame.setQueueSize(2) else: spatialDetectionNetwork.passthrough.link(objectTracker.inputTrackerFrame) spatialDetectionNetwork.passthrough.link(objectTracker.inputDetectionFrame) spatialDetectionNetwork.out.link(objectTracker.inputDetections) stereo.depth.link(spatialDetectionNetwork.inputDepth) # Connect to device and start pipeline with dai.Device(pipeline) as device: preview = device.getOutputQueue("preview", 4, False) tracklets = device.getOutputQueue("tracklets", 4, False) startTime = time.monotonic() counter = 0 fps = 0 color = (255, 255, 255) while(True): imgFrame = preview.get() track = tracklets.get() counter+=1 current_time = time.monotonic() if (current_time - startTime) > 1 : fps = counter / (current_time - startTime) counter = 0 startTime = current_time frame = imgFrame.getCvFrame() trackletsData = track.tracklets for t in trackletsData: roi = t.roi.denormalize(frame.shape[1], frame.shape[0]) x1 = int(roi.topLeft().x) y1 = int(roi.topLeft().y) x2 = int(roi.bottomRight().x) y2 = int(roi.bottomRight().y) try: label = labelMap[t.label] except: label = t.label cv2.putText(frame, str(label), (x1 + 10, y1 + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255) cv2.putText(frame, f"ID: {[t.id]}", (x1 + 10, y1 + 35), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255) cv2.putText(frame, t.status.name, (x1 + 10, y1 + 50), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255) cv2.rectangle(frame, (x1, y1), (x2, y2), color, cv2.FONT_HERSHEY_SIMPLEX) cv2.putText(frame, f"X: {int(t.spatialCoordinates.x)} mm", (x1 + 10, y1 + 65), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255) cv2.putText(frame, f"Y: {int(t.spatialCoordinates.y)} mm", (x1 + 10, y1 + 80), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255) cv2.putText(frame, f"Z: {int(t.spatialCoordinates.z)} mm", (x1 + 10, y1 + 95), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255) cv2.putText(frame, "NN fps: {:.2f}".format(fps), (2, frame.shape[0] - 4), cv2.FONT_HERSHEY_TRIPLEX, 0.4, color) cv2.imshow("tracker", frame) if cv2.waitKey(1) == ord('q'): break ``` #### C++ ```cpp #include #include "utility.hpp" // Includes common necessary includes for development using depthai library #include "depthai/depthai.hpp" static const std::vector labelMap = {"background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"}; static std::atomic fullFrameTracking{false}; int main(int argc, char** argv) { using namespace std; using namespace std::chrono; std::string nnPath(BLOB_PATH); // If path to blob specified, use that if(argc > 1) { nnPath = std::string(argv[1]); } // Print which blob we are using printf("Using blob at path: %s\n", nnPath.c_str()); // Create pipeline dai::Pipeline pipeline; // Define sources and outputs auto camRgb = pipeline.create(); auto spatialDetectionNetwork = pipeline.create(); auto monoLeft = pipeline.create(); auto monoRight = pipeline.create(); auto stereo = pipeline.create(); auto objectTracker = pipeline.create(); auto xoutRgb = pipeline.create(); auto trackerOut = pipeline.create(); xoutRgb->setStreamName("preview"); trackerOut->setStreamName("tracklets"); // Properties camRgb->setPreviewSize(300, 300); camRgb->setResolution(dai::ColorCameraProperties::SensorResolution::THE_1080_P); camRgb->setInterleaved(false); camRgb->setColorOrder(dai::ColorCameraProperties::ColorOrder::BGR); monoLeft->setResolution(dai::MonoCameraProperties::SensorResolution::THE_400_P); monoLeft->setCamera("left"); monoRight->setResolution(dai::MonoCameraProperties::SensorResolution::THE_400_P); monoRight->setCamera("right"); // setting node configs stereo->setDefaultProfilePreset(dai::node::StereoDepth::PresetMode::HIGH_DENSITY); // Align depth map to the perspective of RGB camera, on which inference is done stereo->setDepthAlign(dai::CameraBoardSocket::CAM_A); stereo->setOutputSize(monoLeft->getResolutionWidth(), monoLeft->getResolutionHeight()); spatialDetectionNetwork->setBlobPath(nnPath); spatialDetectionNetwork->setConfidenceThreshold(0.5f); spatialDetectionNetwork->input.setBlocking(false); spatialDetectionNetwork->setBoundingBoxScaleFactor(0.5); spatialDetectionNetwork->setDepthLowerThreshold(100); spatialDetectionNetwork->setDepthUpperThreshold(5000); objectTracker->setDetectionLabelsToTrack({15}); // track only person // possible tracking types: ZERO_TERM_COLOR_HISTOGRAM, ZERO_TERM_IMAGELESS, SHORT_TERM_IMAGELESS, SHORT_TERM_KCF objectTracker->setTrackerType(dai::TrackerType::ZERO_TERM_COLOR_HISTOGRAM); // take the smallest ID when new object is tracked, possible options: SMALLEST_ID, UNIQUE_ID objectTracker->setTrackerIdAssignmentPolicy(dai::TrackerIdAssignmentPolicy::SMALLEST_ID); // Linking monoLeft->out.link(stereo->left); monoRight->out.link(stereo->right); camRgb->preview.link(spatialDetectionNetwork->input); objectTracker->passthroughTrackerFrame.link(xoutRgb->input); objectTracker->out.link(trackerOut->input); if(fullFrameTracking) { camRgb->setPreviewKeepAspectRatio(false); camRgb->video.link(objectTracker->inputTrackerFrame); objectTracker->inputTrackerFrame.setBlocking(false); // do not block the pipeline if it's too slow on full frame objectTracker->inputTrackerFrame.setQueueSize(2); } else { spatialDetectionNetwork->passthrough.link(objectTracker->inputTrackerFrame); } spatialDetectionNetwork->passthrough.link(objectTracker->inputDetectionFrame); spatialDetectionNetwork->out.link(objectTracker->inputDetections); stereo->depth.link(spatialDetectionNetwork->inputDepth); // Connect to device and start pipeline dai::Device device(pipeline); auto preview = device.getOutputQueue("preview", 4, false); auto tracklets = device.getOutputQueue("tracklets", 4, false); auto startTime = steady_clock::now(); int counter = 0; float fps = 0; auto color = cv::Scalar(255, 255, 255); while(true) { auto imgFrame = preview->get(); auto track = tracklets->get(); counter++; auto currentTime = steady_clock::now(); auto elapsed = duration_cast>(currentTime - startTime); if(elapsed > seconds(1)) { fps = counter / elapsed.count(); counter = 0; startTime = currentTime; } cv::Mat frame = imgFrame->getCvFrame(); auto trackletsData = track->tracklets; for(auto& t : trackletsData) { auto roi = t.roi.denormalize(frame.cols, frame.rows); int x1 = roi.topLeft().x; int y1 = roi.topLeft().y; int x2 = roi.bottomRight().x; int y2 = roi.bottomRight().y; uint32_t labelIndex = t.label; std::string labelStr = to_string(labelIndex); if(labelIndex < labelMap.size()) { labelStr = labelMap[labelIndex]; } cv::putText(frame, labelStr, cv::Point(x1 + 10, y1 + 20), cv::FONT_HERSHEY_TRIPLEX, 0.5, 255); std::stringstream idStr; idStr << "ID: " << t.id; cv::putText(frame, idStr.str(), cv::Point(x1 + 10, y1 + 35), cv::FONT_HERSHEY_TRIPLEX, 0.5, 255); std::stringstream statusStr; statusStr << "Status: " << t.status; cv::putText(frame, statusStr.str(), cv::Point(x1 + 10, y1 + 50), cv::FONT_HERSHEY_TRIPLEX, 0.5, 255); std::stringstream depthX; depthX << "X: " << (int)t.spatialCoordinates.x << " mm"; cv::putText(frame, depthX.str(), cv::Point(x1 + 10, y1 + 65), cv::FONT_HERSHEY_TRIPLEX, 0.5, 255); std::stringstream depthY; depthY << "Y: " << (int)t.spatialCoordinates.y << " mm"; cv::putText(frame, depthY.str(), cv::Point(x1 + 10, y1 + 80), cv::FONT_HERSHEY_TRIPLEX, 0.5, 255); std::stringstream depthZ; depthZ << "Z: " << (int)t.spatialCoordinates.z << " mm"; cv::putText(frame, depthZ.str(), cv::Point(x1 + 10, y1 + 95), cv::FONT_HERSHEY_TRIPLEX, 0.5, 255); cv::rectangle(frame, cv::Rect(cv::Point(x1, y1), cv::Point(x2, y2)), color, cv::FONT_HERSHEY_SIMPLEX); } std::stringstream fpsStr; fpsStr << "NN fps: " << std::fixed << std::setprecision(2) << fps; cv::putText(frame, fpsStr.str(), cv::Point(2, imgFrame->getHeight() - 4), cv::FONT_HERSHEY_TRIPLEX, 0.4, color); cv::imshow("tracker", frame); int key = cv::waitKey(1); if(key == 'q') { return 0; } } return 0; } ``` ## Pipeline ### examples/spatial_object_tracker.pipeline.json ```json { "pipeline": { 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