# Object tracker on RGB

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

### Similar samples:

 * [Object tracker on video](https://docs.luxonis.com/software/depthai/examples/object_tracker_video.md)
 * [Spatial object tracker on RGB](https://docs.luxonis.com/software/depthai/examples/spatial_object_tracker.md)

## Demo

## 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_6shave.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)
detectionNetwork = pipeline.create(dai.node.MobileNetDetectionNetwork)
objectTracker = pipeline.create(dai.node.ObjectTracker)

xlinkOut = pipeline.create(dai.node.XLinkOut)
trackerOut = pipeline.create(dai.node.XLinkOut)
xinTrackerConfig = pipeline.create(dai.node.XLinkIn)

xlinkOut.setStreamName("preview")
trackerOut.setStreamName("tracklets")
xinTrackerConfig.setStreamName("trackerConfig")

# Properties
camRgb.setPreviewSize(300, 300)
camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)
camRgb.setInterleaved(False)
camRgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.BGR)
camRgb.setFps(40)

# testing MobileNet DetectionNetwork
detectionNetwork.setBlobPath(args.nnPath)
detectionNetwork.setConfidenceThreshold(0.5)
detectionNetwork.input.setBlocking(False)

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
camRgb.preview.link(detectionNetwork.input)
objectTracker.passthroughTrackerFrame.link(xlinkOut.input)

if fullFrameTracking:
    camRgb.video.link(objectTracker.inputTrackerFrame)
else:
    detectionNetwork.passthrough.link(objectTracker.inputTrackerFrame)

detectionNetwork.passthrough.link(objectTracker.inputDetectionFrame)
detectionNetwork.out.link(objectTracker.inputDetections)
objectTracker.out.link(trackerOut.input)

# set tracking parameters
objectTracker.setOcclusionRatioThreshold(0.4)
objectTracker.setTrackletMaxLifespan(120)
objectTracker.setTrackletBirthThreshold(3)

xinTrackerConfig.out.link(objectTracker.inputConfig)

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

    preview = device.getOutputQueue("preview", 4, False)
    tracklets = device.getOutputQueue("tracklets", 4, False)
    trackerConfigQueue = device.getInputQueue("trackerConfig")

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

    while(True):
        latestTrackedIds = []
        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

        color = (255, 0, 0)
        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)

            if t.status == dai.Tracklet.TrackingStatus.TRACKED:
                latestTrackedIds.append(t.id)

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

        cv2.imshow("tracker", frame)

        key = cv2.waitKey(1)
        if key == ord('q'):
            break
        elif key == ord('g'):
            # send tracker config to device
            config = dai.ObjectTrackerConfig()

            # take a random ID from the latest tracked IDs
            if len(latestTrackedIds) > 0:
                idToRemove = (np.random.choice(latestTrackedIds))
                print(f"Force removing ID: {idToRemove}")
                config.forceRemoveID(idToRemove)
                trackerConfigQueue.send(config)
            else:
                print("No tracked IDs available to force remove")
```

#### C++

```cpp
#include <chrono>
#include <iostream>

// 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 detectionNetwork = pipeline.create<dai::node::MobileNetDetectionNetwork>();
    auto objectTracker = pipeline.create<dai::node::ObjectTracker>();

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

    xlinkOut->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);
    camRgb->setFps(40);

    // testing MobileNet DetectionNetwork
    detectionNetwork->setBlobPath(nnPath);
    detectionNetwork->setConfidenceThreshold(0.5f);
    detectionNetwork->input.setBlocking(false);

    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
    camRgb->preview.link(detectionNetwork->input);
    objectTracker->passthroughTrackerFrame.link(xlinkOut->input);

    if(fullFrameTracking) {
        camRgb->video.link(objectTracker->inputTrackerFrame);
    } else {
        detectionNetwork->passthrough.link(objectTracker->inputTrackerFrame);
    }

    detectionNetwork->passthrough.link(objectTracker->inputDetectionFrame);
    detectionNetwork->out.link(objectTracker->inputDetections);
    objectTracker->out.link(trackerOut->input);

    // 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;

    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;
        }

        auto color = cv::Scalar(255, 0, 0);
        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, color);

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

        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' || key == 'Q') {
            return 0;
        }
    }
    return 0;
}
```

## Pipeline

### examples/object_tracker.pipeline.json

```json
{"pipeline": {"connections": [{"node1Id": 7, "node1Output": "passthroughTrackerFrame", "node1OutputGroup": "", "node2Id": 10, "node2Input": "in", "node2InputGroup": ""}, {"node1Id": 7, "node1Output": "out", "node1OutputGroup": "", "node2Id": 8, "node2Input": "in", "node2InputGroup": ""}, {"node1Id": 4, "node1Output": "out", "node1OutputGroup": "", "node2Id": 7, "node2Input": "inputDetections", "node2InputGroup": ""}, {"node1Id": 5, "node1Output": "passthrough", "node1OutputGroup": "", "node2Id": 7, "node2Input": "inputDetectionFrame", "node2InputGroup": ""}, {"node1Id": 5, "node1Output": "passthrough", "node1OutputGroup": "", "node2Id": 7, "node2Input": "inputTrackerFrame", "node2InputGroup": ""}, {"node1Id": 5, "node1Output": "passthrough", "node1OutputGroup": "", "node2Id": 3, "node2Input": "inputAlignTo", "node2InputGroup": ""}, {"node1Id": 5, "node1Output": "passthrough", "node1OutputGroup": "", "node2Id": 6, "node2Input": "imageIn", "node2InputGroup": ""}, {"node1Id": 5, "node1Output": "passthrough", "node1OutputGroup": "", "node2Id": 4, "node2Input": "inputImg", "node2InputGroup": ""}, {"node1Id": 5, "node1Output": "out", "node1OutputGroup": "", "node2Id": 6, "node2Input": "in", "node2InputGroup": ""}, {"node1Id": 6, "node1Output": "out", "node1OutputGroup": "", "node2Id": 4, "node2Input": "inputDetections", "node2InputGroup": ""}, {"node1Id": 3, "node1Output": "depth", "node1OutputGroup": "", "node2Id": 4, "node2Input": "inputDepth", "node2InputGroup": ""}, {"node1Id": 2, "node1Output": "0", "node1OutputGroup": "dynamicOutputs", "node2Id": 3, "node2Input": "right", "node2InputGroup": ""}, {"node1Id": 1, "node1Output": "0", "node1OutputGroup": "dynamicOutputs", "node2Id": 3, "node2Input": "left", "node2InputGroup": ""}, {"node1Id": 0, "node1Output": "0", "node1OutputGroup": "dynamicOutputs", "node2Id": 5, "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": [[10, {"alias": "", "id": 10, "ioInfo": [[["", "in"], {"blocking": true, "group": "", "id": 50, "name": "in", "queueSize": 3, "type": 3, "waitForMessage": false}]], "logLevel": 3, "name": "XLinkOut", "parentId": -1, "properties": {"maxFpsLimit": -1.0, "metadataOnly": false, "streamName": "__x_7_passthroughTrackerFrame"}}], [8, {"alias": "", "id": 8, "ioInfo": [[["", "in"], {"blocking": true, "group": "", "id": 49, "name": "in", "queueSize": 3, "type": 3, "waitForMessage": false}]], "logLevel": 3, "name": "XLinkOut", "parentId": -1, "properties": {"maxFpsLimit": -1.0, "metadataOnly": false, "streamName": "__x_7_out"}}], [7, {"alias": "", "id": 7, "ioInfo": [[["", "passthroughDetectionFrame"], {"blocking": false, "group": "", "id": 47, "name": "passthroughDetectionFrame", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "passthroughDetections"], {"blocking": false, "group": "", "id": 48, "name": "passthroughDetections", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "passthroughTrackerFrame"], {"blocking": false, "group": "", "id": 46, "name": "passthroughTrackerFrame", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "out"], {"blocking": false, "group": "", "id": 45, "name": "out", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "inputDetectionFrame"], {"blocking": false, "group": "", "id": 43, "name": "inputDetectionFrame", "queueSize": 4, "type": 3, "waitForMessage": true}], [["", "inputDetections"], {"blocking": false, "group": "", "id": 44, "name": "inputDetections", "queueSize": 4, "type": 3, "waitForMessage": true}], [["", "inputTrackerFrame"], {"blocking": false, "group": "", "id": 42, "name": "inputTrackerFrame", "queueSize": 4, "type": 3, "waitForMessage": true}]], "logLevel": 3, "name": "ObjectTracker", "parentId": -1, "properties": {"detectionLabelsToTrack": [0], "maxObjectsToTrack": 60, "trackerIdAssignmentPolicy": 1, "trackerThreshold": 0.0, "trackerType": 6, "trackingPerClass": true}}], [6, {"alias": "detectionParser", "id": 6, "ioInfo": [[["", "out"], {"blocking": false, "group": "", "id": 41, "name": "out", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "imageIn"], {"blocking": false, "group": "", "id": 40, "name": "imageIn", "queueSize": 1, "type": 3, "waitForMessage": true}], [["", "in"], {"blocking": true, "group": "", "id": 39, "name": "in", "queueSize": 1, "type": 3, "waitForMessage": true}]], "logLevel": 3, "name": "DetectionParser", "parentId": 4, "properties": {"networkInputs": {"images": {"dataType": 1, "dims": [416, 416, 3, 1], "name": "images", "numDimensions": 4, "offset": 0, "order": 17185, "qpScale": 1.0, "qpZp": 0.0, "quantization": false, "strides": []}}, "numFramesPool": 8, "parser": {"anchorMasks": {}, "anchors": [], "anchorsV2": [], "classNames": ["person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "classes": 80, "confidenceThreshold": 0.5, "coordinates": 4, "iouThreshold": 0.5, "nnFamily": 0, "subtype": "yolov6"}}}], [5, {"alias": "neuralNetwork", "id": 5, "ioInfo": [[["", "passthrough"], {"blocking": false, "group": "", "id": 38, "name": "passthrough", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "out"], {"blocking": false, "group": "", "id": 37, "name": "out", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "in"], {"blocking": false, "group": "", "id": 36, "name": "in", "queueSize": 3, "type": 3, "waitForMessage": true}]], "logLevel": 3, "name": "NeuralNetwork", "parentId": 4, "properties": {"backend": "", "backendProperties": {}, "blobSize": 8689834, "blobUri": "asset:__blob", "modelSource": 0, "modelUri": "", "numFrames": 8, "numNCEPerThread": 0, "numShavesPerThread": 0, "numThreads": 0}}], [4, {"alias": "", "id": 4, "ioInfo": [[["", "passthroughDepth"], {"blocking": false, "group": "", "id": 34, "name": "passthroughDepth", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "boundingBoxMapping"], {"blocking": false, "group": "", "id": 33, "name": "boundingBoxMapping", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "spatialLocationCalculatorOutput"], {"blocking": false, "group": "", "id": 35, "name": "spatialLocationCalculatorOutput", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "out"], {"blocking": false, "group": "", "id": 32, "name": "out", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "inputDetections"], {"blocking": true, "group": "", "id": 31, "name": "inputDetections", "queueSize": 1, "type": 3, "waitForMessage": true}], [["", "inputImg"], {"blocking": true, "group": "", "id": 30, "name": "inputImg", "queueSize": 2, "type": 3, "waitForMessage": true}], [["", "inputDepth"], {"blocking": false, "group": "", "id": 29, "name": "inputDepth", "queueSize": 4, "type": 3, "waitForMessage": true}]], "logLevel": 3, "name": "SpatialDetectionNetwork", "parentId": -1, "properties": {"calculationAlgorithm": 4, "depthThresholds": {"lowerThreshold": 100, "upperThreshold": 5000}, "detectedBBScaleFactor": 0.5}}], [3, {"alias": "", "id": 3, "ioInfo": [[["", "confidenceMap"], {"blocking": false, "group": "", "id": 28, "name": "confidenceMap", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "debugDispCostDump"], {"blocking": false, "group": "", "id": 27, "name": "debugDispCostDump", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "debugExtDispLrCheckIt2"], {"blocking": false, "group": "", "id": 26, "name": "debugExtDispLrCheckIt2", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "debugDispLrCheckIt2"], {"blocking": false, "group": "", "id": 24, "name": "debugDispLrCheckIt2", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "debugExtDispLrCheckIt1"], {"blocking": false, "group": "", "id": 25, "name": "debugExtDispLrCheckIt1", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "debugDispLrCheckIt1"], {"blocking": false, "group": "", "id": 23, "name": "debugDispLrCheckIt1", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "outConfig"], {"blocking": false, "group": "", "id": 22, "name": "outConfig", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "rectifiedRight"], {"blocking": false, "group": "", "id": 21, "name": "rectifiedRight", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "rectifiedLeft"], {"blocking": false, "group": "", "id": 20, "name": "rectifiedLeft", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "depth"], {"blocking": false, "group": "", "id": 16, "name": "depth", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "right"], {"blocking": true, "group": "", "id": 15, "name": "right", "queueSize": 3, "type": 3, "waitForMessage": false}], [["", "left"], {"blocking": true, "group": "", "id": 14, "name": "left", "queueSize": 3, "type": 3, "waitForMessage": false}], [["", "syncedRight"], {"blocking": false, "group": "", "id": 19, "name": "syncedRight", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "syncedLeft"], {"blocking": false, "group": "", "id": 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```

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