# Detection network Replay

This example demonstrates using DepthAI's RemoteConnection to stream and visualize YOLOv6 object detection results and video
frames from a replayed video file.

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

## Pipeline

### examples/detection_network_replay.pipeline.json

```json
{"pipeline": {"connections": [{"node1Id": 5, "node1Output": "out", "node1OutputGroup": "", "node2Id": 2, "node2Input": "in", "node2InputGroup": ""}, {"node1Id": 3, "node1Output": "out", "node1OutputGroup": "", "node2Id": 6, "node2Input": "in", "node2InputGroup": ""}, {"node1Id": 2, "node1Output": "passthrough", "node1OutputGroup": "", "node2Id": 3, "node2Input": "imageIn", "node2InputGroup": ""}, {"node1Id": 2, "node1Output": "out", "node1OutputGroup": "", "node2Id": 3, "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": 7, "name": "in", "queueSize": 3, "type": 3, "waitForMessage": false}]], "logLevel": 3, "name": "XLinkOut", "parentId": -1, "properties": {"maxFpsLimit": -1.0, "metadataOnly": false, "streamName": "__x_3_out"}}], [5, {"alias": "", "id": 5, "ioInfo": [[["", "out"], {"blocking": false, "group": "", "id": 6, "name": "out", "queueSize": 8, "type": 0, "waitForMessage": false}]], "logLevel": 3, "name": "XLinkIn", "parentId": -1, "properties": {"maxDataSize": 5242880, "numFrames": 8, "streamName": "__x_2__in"}}], [3, {"alias": "detectionParser", "id": 3, "ioInfo": [[["", "out"], {"blocking": false, "group": "", "id": 5, "name": "out", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "imageIn"], {"blocking": false, "group": "", "id": 4, "name": "imageIn", "queueSize": 1, "type": 3, "waitForMessage": true}], [["", "in"], {"blocking": true, "group": "", "id": 3, "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": 2, "name": "passthrough", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "out"], {"blocking": false, "group": "", "id": 1, "name": "out", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "in"], {"blocking": true, "group": "", "id": 0, "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}}]]}}
```

## Source code

#### Python

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

scriptDir = Path(__file__).resolve().parent
examplesRoot = (scriptDir / Path('../')).resolve()  # This resolves the parent directory correctly
models = examplesRoot / 'models'
videoPath = models / 'construction_vest.mp4'

parser = ArgumentParser()
parser.add_argument("--webSocketPort", type=int, default=8765)
parser.add_argument("--httpPort", type=int, default=8082)
parser.add_argument("-i", "--inputVideo", default=videoPath, help="Input video name")
args = parser.parse_args()

remoteConnector = dai.RemoteConnection(webSocketPort=args.webSocketPort, httpPort=args.httpPort)
# Create pipeline
with dai.Pipeline() as pipeline:
    replay = pipeline.create(dai.node.ReplayVideo)
    replay.setReplayVideoFile(Path(args.inputVideo))
    detectionNetwork = pipeline.create(dai.node.DetectionNetwork).build(
        replay, dai.NNModelDescription("yolov6-nano")
    )

    remoteConnector.addTopic("detections", detectionNetwork.out, "img")
    remoteConnector.addTopic("images", replay.out, "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 replay = pipeline.create<dai::node::ReplayVideo>();
    replay->setReplayVideoFile(VIDEO_PATH);

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

    // Set up topics for remote connection
    remoteConnector.addTopic("detections", detectionNetwork->out);
    remoteConnector.addTopic("images", replay->out);
    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;
}
```

### Need assistance?

Head over to [Discussion Forum](https://discuss.luxonis.com/) for technical support or any other questions you might have.