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

Python

C++

Also available on GitHub

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#!/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

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#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;
}

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