# Visualizer YOLO

The example sets up a DepthAI pipeline to stream YOLOv6-Nano object detection results and 512x288 NV12 camera using
RemoteConnection, enabling remote visualization.

This example requires the DepthAI v3 API, see [installation instructions](https://docs.luxonis.com/software-v3/depthai.md).

## Pipeline

### examples/visualizer_yolo.pipeline.json

```json
{"pipeline": {"connections": [{"node1Id": 3, "node1Output": "out", "node1OutputGroup": "", "node2Id": 6, "node2Input": "in", "node2InputGroup": ""}, {"node1Id": 2, "node1Output": "passthrough", "node1OutputGroup": "", "node2Id": 4, "node2Input": "in", "node2InputGroup": ""}, {"node1Id": 2, "node1Output": "passthrough", "node1OutputGroup": "", "node2Id": 3, "node2Input": "imageIn", "node2InputGroup": ""}, {"node1Id": 2, "node1Output": "out", "node1OutputGroup": "", "node2Id": 3, "node2Input": "in", "node2InputGroup": ""}, {"node1Id": 0, "node1Output": "0", "node1OutputGroup": "dynamicOutputs", "node2Id": 2, "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": [[6, {"alias": "", "id": 6, "ioInfo": [[["", "in"], {"blocking": true, "group": "", "id": 11, "name": "in", "queueSize": 3, "type": 3, "waitForMessage": false}]], "logLevel": 3, "name": "XLinkOut", "parentId": -1, "properties": {"maxFpsLimit": -1.0, "metadataOnly": false, "streamName": "__x_3_out"}}], [4, {"alias": "", "id": 4, "ioInfo": [[["", "in"], {"blocking": true, "group": "", "id": 10, "name": "in", "queueSize": 3, "type": 3, "waitForMessage": false}]], "logLevel": 3, "name": "XLinkOut", "parentId": -1, "properties": {"maxFpsLimit": -1.0, "metadataOnly": false, "streamName": "__x_2_passthrough"}}], [3, {"alias": "detectionParser", "id": 3, "ioInfo": [[["", "out"], {"blocking": false, "group": "", "id": 9, "name": "out", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "imageIn"], {"blocking": false, "group": "", "id": 8, "name": "imageIn", "queueSize": 1, "type": 3, "waitForMessage": true}], [["", "in"], {"blocking": true, "group": "", "id": 7, "name": "in", "queueSize": 1, "type": 3, "waitForMessage": true}]], "logLevel": 3, "name": "DetectionParser", "parentId": 1, "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"}}}], [2, {"alias": "neuralNetwork", "id": 2, "ioInfo": [[["", "passthrough"], {"blocking": false, "group": "", "id": 6, "name": "passthrough", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "out"], {"blocking": false, "group": "", "id": 5, "name": "out", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "in"], {"blocking": true, "group": "", "id": 4, "name": "in", "queueSize": 3, "type": 3, "waitForMessage": true}]], "logLevel": 3, "name": "NeuralNetwork", "parentId": 1, "properties": {"backend": "", "backendProperties": {}, "blobSize": 8689834, "blobUri": "asset:__blob", "modelSource": 0, "modelUri": "", "numFrames": 8, "numNCEPerThread": 0, "numShavesPerThread": 0, "numThreads": 0}}], [0, {"alias": "", "id": 0, "ioInfo": [[["dynamicOutputs", "0"], {"blocking": false, "group": "dynamicOutputs", "id": 3, "name": "0", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "raw"], {"blocking": false, "group": "", "id": 2, "name": "raw", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "mockIsp"], {"blocking": true, "group": "", "id": 1, "name": "mockIsp", "queueSize": 8, "type": 3, "waitForMessage": false}], [["", "inputControl"], {"blocking": true, "group": "", "id": 0, "name": "inputControl", "queueSize": 3, "type": 3, "waitForMessage": false}]], "logLevel": 3, "name": "Camera", "parentId": -1, "properties": {"boardSocket": 0, "cameraName": "", "fps": -1.0, "imageOrientation": -1, "initialControl": {"aeLockMode": false, "aeMaxExposureTimeUs": 100663297, "aeRegion": {"height": 0, "priority": 1668351536, "width": 0, "x": 4163, "y": 1}, "afRegion": {"height": 0, "priority": 0, "width": 0, "x": 24829, "y": 0}, "antiBandingMode": 113, "autoFocusMode": 3, "awbLockMode": false, "awbMode": 120, "brightness": -105, "captureIntent": 99, "chromaDenoise": 0, "cmdMask": 0, "contrast": -33, "controlMode": 253, "effectMode": 96, "enableHdr": false, "expCompensation": 39, "expManual": {"exposureTimeUs": 24829, "frameDurationUs": 24829, "sensitivityIso": 1668351644}, "frameSyncMode": 0, "lensPosAutoInfinity": 176, "lensPosAutoMacro": 6, "lensPosition": 0, "lensPositionRaw": 0.0, "lowPowerNumFramesBurst": 0, "lowPowerNumFramesDiscard": 0, "lumaDenoise": 0, "miscControls": [], "saturation": -34, "sceneMode": 6, "sharpness": 0, "strobeConfig": {"activeLevel": 216, "enable": 0, "gpioNumber": 12}, "strobeTimings": {"durationUs": 16, "exposureBeginOffsetUs": 24829, "exposureEndOffsetUs": 17}, "wbColorTemp": 0}, "isp3aFps": 0, "mockIspHeight": -1, "mockIspWidth": -1, "numFramesPoolIsp": 3, "numFramesPoolPreview": 4, "numFramesPoolRaw": 3, "numFramesPoolStill": 4, "numFramesPoolVideo": 4, "outputRequests": [{"enableUndistortion": null, "fps": {"value": null}, "resizeMode": 0, "size": {"value": {"index": 0, "value": [416, 416]}}, "type": 8}], "resolutionHeight": -1, "resolutionWidth": -1}}]]}}
```

## Source code

#### Python

```python
#!/usr/bin/env python3
import depthai as dai
from argparse import ArgumentParser

parser = ArgumentParser()
parser.add_argument("--webSocketPort", type=int, default=8765)
parser.add_argument("--httpPort", type=int, default=8082)
args = parser.parse_args()

remoteConnector = dai.RemoteConnection(webSocketPort=args.webSocketPort, httpPort=args.httpPort)
# Create pipeline
with dai.Pipeline() as pipeline:
    cameraNode = pipeline.create(dai.node.Camera).build(dai.CameraBoardSocket.CAM_A)
    detectionNetwork = pipeline.create(dai.node.DetectionNetwork).build(
        cameraNode, dai.NNModelDescription("yolov6-nano")
    )

    remoteConnector.addTopic("detections", detectionNetwork.out, "img")
    remoteConnector.addTopic("images", detectionNetwork.passthrough, "img")

    pipeline.start()
    remoteConnector.registerPipeline(pipeline)

    while pipeline.isRunning():
        key = remoteConnector.waitKey(1)
        if key == ord("q"):
            print("Got q key from the remote connection!")
            break
```

#### C++

```cpp
#include <csignal>
#include <depthai/depthai.hpp>
#include <depthai/remote_connection/RemoteConnection.hpp>
#include <iostream>

#include "depthai/modelzoo/Zoo.hpp"

// Signal handling for clean shutdown
static bool isRunning = true;
void signalHandler(int signum) {
    isRunning = false;
}

int main(int argc, char** argv) {
    // Default port values
    int webSocketPort = 8765;
    int httpPort = 8082;

    // Register signal handler
    std::signal(SIGINT, signalHandler);

    // Create RemoteConnection
    dai::RemoteConnection remoteConnector(dai::RemoteConnection::DEFAULT_ADDRESS, webSocketPort, true, httpPort);

    // Create Pipeline
    dai::Pipeline pipeline;
    auto cameraNode = pipeline.create<dai::node::Camera>()->build(dai::CameraBoardSocket::CAM_A);

    // Create and configure Detection Network
    auto detectionNetwork = pipeline.create<dai::node::DetectionNetwork>()->build(cameraNode, dai::NNModelDescription{"yolov6-nano"});

    // Configure output for visualization
    auto* cameraOutputVisualize = cameraNode->requestOutput(std::make_pair(512, 288), dai::ImgFrame::Type::NV12);
    if(cameraOutputVisualize == nullptr) throw std::runtime_error("Error creating camera output");
    // Set up topics for remote connection
    remoteConnector.addTopic("detections", detectionNetwork->out);
    remoteConnector.addTopic("images", *cameraOutputVisualize);
    pipeline.start();

    remoteConnector.registerPipeline(pipeline);
    // Main loop
    while(isRunning && pipeline.isRunning()) {
        int key = remoteConnector.waitKey(1);
        if(key == 'q') {
            std::cout << "Got 'q' key from the remote connection!" << std::endl;
            break;
        }
    }

    std::cout << "Pipeline stopped." << std::endl;
    return 0;
}
```

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