Object tracker on video¶

This example shows how to run MobileNetv2SSD on video input frame, and perform object tracking on persons.

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 = ["person", ""]

nnPathDefault = str((Path(__file__).parent / Path('../models/person-detection-retail-0013_openvino_2021.4_7shave.blob')).resolve().absolute())
videoPathDefault = str((Path(__file__).parent / Path('../models/construction_vest.mp4')).resolve().absolute())
parser = argparse.ArgumentParser()
parser.add_argument('-nnPath', help="Path to mobilenet detection network blob", default=nnPathDefault)
parser.add_argument('-v', '--videoPath', help="Path to video frame", default=videoPathDefault)

args = parser.parse_args()

# Create pipeline
pipeline = dai.Pipeline()

# Define sources and outputs
manip = pipeline.create(dai.node.ImageManip)
objectTracker = pipeline.create(dai.node.ObjectTracker)
detectionNetwork = pipeline.create(dai.node.MobileNetDetectionNetwork)

manipOut = pipeline.create(dai.node.XLinkOut)
xinFrame = pipeline.create(dai.node.XLinkIn)
trackerOut = pipeline.create(dai.node.XLinkOut)
xlinkOut = pipeline.create(dai.node.XLinkOut)
nnOut = pipeline.create(dai.node.XLinkOut)

manipOut.setStreamName("manip")
xinFrame.setStreamName("inFrame")
xlinkOut.setStreamName("trackerFrame")
trackerOut.setStreamName("tracklets")
nnOut.setStreamName("nn")

# Properties
xinFrame.setMaxDataSize(1920*1080*3)

manip.initialConfig.setResizeThumbnail(544, 320)
# manip.initialConfig.setResize(384, 384)
# manip.initialConfig.setKeepAspectRatio(False) #squash the image to not lose FOV
# The NN model expects BGR input. By default ImageManip output type would be same as input (gray in this case)
manip.initialConfig.setFrameType(dai.ImgFrame.Type.BGR888p)
manip.inputImage.setBlocking(True)

# setting node configs
detectionNetwork.setBlobPath(args.nnPath)
detectionNetwork.setConfidenceThreshold(0.5)
detectionNetwork.input.setBlocking(True)

objectTracker.inputTrackerFrame.setBlocking(True)
objectTracker.inputDetectionFrame.setBlocking(True)
objectTracker.inputDetections.setBlocking(True)
objectTracker.setDetectionLabelsToTrack([1])  # 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
manip.out.link(manipOut.input)
manip.out.link(detectionNetwork.input)
xinFrame.out.link(manip.inputImage)
xinFrame.out.link(objectTracker.inputTrackerFrame)
detectionNetwork.out.link(nnOut.input)
detectionNetwork.out.link(objectTracker.inputDetections)
detectionNetwork.passthrough.link(objectTracker.inputDetectionFrame)
objectTracker.out.link(trackerOut.input)
objectTracker.passthroughTrackerFrame.link(xlinkOut.input)

# Connect and start the pipeline
with dai.Device(pipeline) as device:

    qIn = device.getInputQueue(name="inFrame")
    trackerFrameQ = device.getOutputQueue(name="trackerFrame", maxSize=4)
    tracklets = device.getOutputQueue(name="tracklets", maxSize=4)
    qManip = device.getOutputQueue(name="manip", maxSize=4)
    qDet = device.getOutputQueue(name="nn", maxSize=4)

    startTime = time.monotonic()
    counter = 0
    fps = 0
    detections = []
    frame = None

    def to_planar(arr: np.ndarray, shape: tuple) -> np.ndarray:
        return cv2.resize(arr, shape).transpose(2, 0, 1).flatten()

    # 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)

    def displayFrame(name, frame):
        for detection in detections:
            bbox = frameNorm(frame, (detection.xmin, detection.ymin, detection.xmax, detection.ymax))
            cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (255, 0, 0), 2)
            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.imshow(name, frame)

    cap = cv2.VideoCapture(args.videoPath)
    baseTs = time.monotonic()
    simulatedFps = 30
    inputFrameShape = (1920, 1080)

    while cap.isOpened():
        read_correctly, frame = cap.read()
        if not read_correctly:
            break

        img = dai.ImgFrame()
        img.setType(dai.ImgFrame.Type.BGR888p)
        img.setData(to_planar(frame, inputFrameShape))
        img.setTimestamp(baseTs)
        baseTs += 1/simulatedFps

        img.setWidth(inputFrameShape[0])
        img.setHeight(inputFrameShape[1])
        qIn.send(img)

        trackFrame = trackerFrameQ.tryGet()
        if trackFrame is None:
            continue

        track = tracklets.get()
        manip = qManip.get()
        inDet = qDet.get()

        counter+=1
        current_time = time.monotonic()
        if (current_time - startTime) > 1 :
            fps = counter / (current_time - startTime)
            counter = 0
            startTime = current_time

        detections = inDet.detections
        manipFrame = manip.getCvFrame()
        displayFrame("nn", manipFrame)

        color = (255, 0, 0)
        trackerFrame = trackFrame.getCvFrame()
        trackletsData = track.tracklets
        for t in trackletsData:
            roi = t.roi.denormalize(trackerFrame.shape[1], trackerFrame.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(trackerFrame, str(label), (x1 + 10, y1 + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
            cv2.putText(trackerFrame, f"ID: {[t.id]}", (x1 + 10, y1 + 35), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
            cv2.putText(trackerFrame, t.status.name, (x1 + 10, y1 + 50), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
            cv2.rectangle(trackerFrame, (x1, y1), (x2, y2), color, cv2.FONT_HERSHEY_SIMPLEX)

        cv2.putText(trackerFrame, "Fps: {:.2f}".format(fps), (2, trackerFrame.shape[0] - 4), cv2.FONT_HERSHEY_TRIPLEX, 0.4, color)

        cv2.imshow("tracker", trackerFrame)

        if cv2.waitKey(1) == ord('q'):
            break

Also available on GitHub

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#include <chrono>
#include <iostream>

#include "utility.hpp"

// Includes common necessary includes for development using depthai library
#include "depthai/depthai.hpp"

static const std::vector<std::string> labelMap = {"", "person"};

static std::atomic<bool> fullFrameTracking{false};

int main(int argc, char** argv) {
    using namespace std;
    using namespace std::chrono;
    std::string nnPath(BLOB_PATH);
    std::string videoPath(VIDEO_PATH);

    // If path to blob specified, use that
    if(argc > 2) {
        nnPath = std::string(argv[1]);
        videoPath = std::string(argv[2]);
    }

    // Print which blob we are using
    printf("Using blob at path: %s\n", nnPath.c_str());
    printf("Using video at path: %s\n", videoPath.c_str());

    // Create pipeline
    dai::Pipeline pipeline;

    // Define sources and outputs
    auto manip = pipeline.create<dai::node::ImageManip>();
    auto objectTracker = pipeline.create<dai::node::ObjectTracker>();
    auto detectionNetwork = pipeline.create<dai::node::MobileNetDetectionNetwork>();

    auto manipOut = pipeline.create<dai::node::XLinkOut>();
    auto xinFrame = pipeline.create<dai::node::XLinkIn>();
    auto trackerOut = pipeline.create<dai::node::XLinkOut>();
    auto xlinkOut = pipeline.create<dai::node::XLinkOut>();
    auto nnOut = pipeline.create<dai::node::XLinkOut>();

    manipOut->setStreamName("manip");
    xinFrame->setStreamName("inFrame");
    xlinkOut->setStreamName("trackerFrame");
    trackerOut->setStreamName("tracklets");
    nnOut->setStreamName("nn");

    // Properties
    xinFrame->setMaxDataSize(1920 * 1080 * 3);

    manip->initialConfig.setResizeThumbnail(544, 320);
    // manip->initialConfig.setResize(384, 384);
    // manip->initialConfig.setKeepAspectRatio(false); //squash the image to not lose FOV
    // The NN model expects BGR input. By default ImageManip output type would be same as input (gray in this case)
    manip->initialConfig.setFrameType(dai::ImgFrame::Type::BGR888p);
    manip->inputImage.setBlocking(true);

    // setting node configs
    detectionNetwork->setBlobPath(nnPath);
    detectionNetwork->setConfidenceThreshold(0.5);
    detectionNetwork->input.setBlocking(true);

    objectTracker->inputTrackerFrame.setBlocking(true);
    objectTracker->inputDetectionFrame.setBlocking(true);
    objectTracker->inputDetections.setBlocking(true);
    objectTracker->setDetectionLabelsToTrack({1});  // 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
    manip->out.link(manipOut->input);
    manip->out.link(detectionNetwork->input);
    xinFrame->out.link(manip->inputImage);
    xinFrame->out.link(objectTracker->inputTrackerFrame);
    detectionNetwork->out.link(nnOut->input);
    detectionNetwork->out.link(objectTracker->inputDetections);
    detectionNetwork->passthrough.link(objectTracker->inputDetectionFrame);
    objectTracker->out.link(trackerOut->input);
    objectTracker->passthroughTrackerFrame.link(xlinkOut->input);

    // Connect to device and start pipeline
    dai::Device device(pipeline);

    auto qIn = device.getInputQueue("inFrame", 4);
    auto trackerFrameQ = device.getOutputQueue("trackerFrame", 4);
    auto tracklets = device.getOutputQueue("tracklets", 4);
    auto qManip = device.getOutputQueue("manip", 4);
    auto qDet = device.getOutputQueue("nn", 4);

    auto startTime = steady_clock::now();
    int counter = 0;
    float fps = 0;
    cv::Mat frame;
    cv::Mat manipFrame;
    std::vector<dai::ImgDetection> detections;

    // Add bounding boxes and text to the frame and show it to the user
    auto displayFrame = [](std::string name, cv::Mat frame, std::vector<dai::ImgDetection>& detections) {
        auto color = cv::Scalar(255, 0, 0);
        // nn data, being the bounding box locations, are in <0..1> range - they need to be normalized with frame width/height
        for(auto& detection : detections) {
            int x1 = detection.xmin * frame.cols;
            int y1 = detection.ymin * frame.rows;
            int x2 = detection.xmax * frame.cols;
            int y2 = detection.ymax * frame.rows;

            uint32_t labelIndex = detection.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, color);
            std::stringstream confStr;
            confStr << std::fixed << std::setprecision(2) << detection.confidence * 100;
            cv::putText(frame, confStr.str(), cv::Point(x1 + 10, y1 + 40), cv::FONT_HERSHEY_TRIPLEX, 0.5, color);
            cv::rectangle(frame, cv::Rect(cv::Point(x1, y1), cv::Point(x2, y2)), color, cv::FONT_HERSHEY_SIMPLEX);
        }
        // Show the frame
        cv::imshow(name, frame);
    };

    cv::VideoCapture cap(videoPath);
    auto baseTs = steady_clock::now();
    float simulatedFps = 30;

    while(cap.isOpened()) {
        // Read frame from video
        cap >> frame;
        if(frame.empty()) break;

        auto img = std::make_shared<dai::ImgFrame>();
        frame = resizeKeepAspectRatio(frame, cv::Size(1920, 1080), cv::Scalar(0));
        toPlanar(frame, img->getData());
        img->setTimestamp(baseTs);
        baseTs += steady_clock::duration(static_cast<int64_t>((1000 * 1000 * 1000 / simulatedFps)));
        img->setWidth(1920);
        img->setHeight(1080);
        img->setType(dai::ImgFrame::Type::BGR888p);
        qIn->send(img);

        auto trackFrame = trackerFrameQ->tryGet<dai::ImgFrame>();
        if(!trackFrame) {
            continue;
        }

        auto track = tracklets->get<dai::Tracklets>();
        auto inManip = qManip->get<dai::ImgFrame>();
        auto inDet = qDet->get<dai::ImgDetections>();

        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;
        }

        detections = inDet->detections;
        manipFrame = inManip->getCvFrame();
        displayFrame("nn", manipFrame, detections);

        auto color = cv::Scalar(255, 0, 0);
        cv::Mat trackerFrame = trackFrame->getCvFrame();
        auto trackletsData = track->tracklets;
        for(auto& t : trackletsData) {
            auto roi = t.roi.denormalize(trackerFrame.cols, trackerFrame.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(trackerFrame, labelStr, cv::Point(x1 + 10, y1 + 20), cv::FONT_HERSHEY_TRIPLEX, 0.5, color);

            std::stringstream idStr;
            idStr << "ID: " << t.id;
            cv::putText(trackerFrame, idStr.str(), cv::Point(x1 + 10, y1 + 40), cv::FONT_HERSHEY_TRIPLEX, 0.5, color);
            std::stringstream statusStr;
            statusStr << "Status: " << t.status;
            cv::putText(trackerFrame, statusStr.str(), cv::Point(x1 + 10, y1 + 60), cv::FONT_HERSHEY_TRIPLEX, 0.5, color);

            cv::rectangle(trackerFrame, 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(trackerFrame, fpsStr.str(), cv::Point(2, trackFrame->getHeight() - 4), cv::FONT_HERSHEY_TRIPLEX, 0.4, color);

        cv::imshow("tracker", trackerFrame);

        int key = cv::waitKey(1);
        if(key == 'q' || key == 'Q') {
            return 0;
        }
    }
    return 0;
}

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