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:
Demo¶
Setup¶
Please run the install script to download all required dependencies. Please note that this script must be ran from git context, so you have to download the depthai-python repository first and then run the script
git clone https://github.com/luxonis/depthai-python.git
cd depthai-python/examples
python3 install_requirements.py
For additional information, please follow installation guide
This example script requires external file(s) to run. If you are using:
depthai-python, run
python3 examples/install_requirements.py
to download required file(s)dephtai-core, required file(s) will get downloaded automatically when building the example
Source code¶
Also available on GitHub
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 | #!/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.setBoardSocket(dai.CameraBoardSocket.LEFT) monoRight.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P) monoRight.setBoardSocket(dai.CameraBoardSocket.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.RGB) 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 |
Also available on GitHub
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 | #include <chrono> #include "utility.hpp" // Includes common necessary includes for development using depthai library #include "depthai/depthai.hpp" static const std::vector<std::string> 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<bool> 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<dai::node::ColorCamera>(); auto spatialDetectionNetwork = pipeline.create<dai::node::MobileNetSpatialDetectionNetwork>(); auto monoLeft = pipeline.create<dai::node::MonoCamera>(); auto monoRight = pipeline.create<dai::node::MonoCamera>(); auto stereo = pipeline.create<dai::node::StereoDepth>(); auto objectTracker = pipeline.create<dai::node::ObjectTracker>(); auto xoutRgb = pipeline.create<dai::node::XLinkOut>(); auto 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->setBoardSocket(dai::CameraBoardSocket::LEFT); monoRight->setResolution(dai::MonoCameraProperties::SensorResolution::THE_400_P); monoRight->setBoardSocket(dai::CameraBoardSocket::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::RGB); 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<dai::ImgFrame>(); auto track = tracklets->get<dai::Tracklets>(); counter++; auto currentTime = steady_clock::now(); auto elapsed = duration_cast<duration<float>>(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; } |
Got questions?
We’re always happy to help with code or other questions you might have.