# 检测网络回放

此示例演示了使用DepthAI的RemoteConnection来流式传输并可视化来自回放视频文件的YOLOv6目标检测结果和视频帧。

这个示例需要DepthAI v3 API，参见[安装说明](https://docs.luxonis.com/software-v3/depthai.md)。

## 流水线

### 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}}]]}}
```

## 源代码

#### 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;
}
```

### 需要帮助？

请前往 [OAKChina 官网](https://www.oakchina.cn/) 获取技术支持或解答您的任何疑问。