# 可视化器 YOLO

该示例设置了一个 DepthAI 管道，使用 RemoteConnection 流式传输 YOLOv6-Nano 目标检测结果和 512x288 NV12 摄像头，实现远程可视化。

这个示例需要DepthAI v3 API，参见[安装说明](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}}]]}}
```

## 源代码

#### 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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