# 主机显示

本示例演示如何在DepthAI流水线中使用HostDisplay节点，通过OpenCV显示从设备获取的帧。HostDisplay节点是一个自定义主机节点，它从DepthAI流水线接收图像帧，并使用OpenCV的imshow函数在主机上显示。

该示例作为手动调用.get()从流水线队列中获取帧并使用OpenCV显示的替代方案。HostDisplay节点处理帧获取和显示过程，使得可视化流水线输出更加简便。这是自定义主机节点最基本的示例。

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/display.pipeline.json

```json
{"pipeline": {"connections": [{"node1Id": 0, "node1Output": "0", "node1OutputGroup": "dynamicOutputs", "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": [[3, {"alias": "", "id": 3, "ioInfo": [[["", "in"], {"blocking": true, "group": "", "id": 4, "name": "in", "queueSize": 3, "type": 3, "waitForMessage": false}]], "logLevel": 3, "name": "XLinkOut", "parentId": -1, "properties": {"maxFpsLimit": -1.0, "metadataOnly": false, "streamName": "__x_0_dynamicOutputs_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": 0, "aeRegion": {"height": 0, "priority": 0, "width": 0, "x": 0, "y": 0}, "afRegion": {"height": 0, "priority": 0, "width": 0, "x": 0, "y": 0}, "antiBandingMode": 0, "autoFocusMode": 3, "awbLockMode": false, "awbMode": 0, "brightness": 0, "captureIntent": 0, "chromaDenoise": 0, "cmdMask": 0, "contrast": 0, "controlMode": 0, "effectMode": 0, "enableHdr": false, "expCompensation": 0, "expManual": {"exposureTimeUs": 0, "frameDurationUs": 0, "sensitivityIso": 0}, "frameSyncMode": 0, "lensPosAutoInfinity": 0, "lensPosAutoMacro": 0, "lensPosition": 0, "lensPositionRaw": 0.0, "lowPowerNumFramesBurst": 0, "lowPowerNumFramesDiscard": 0, "lumaDenoise": 0, "miscControls": [], "saturation": 0, "sceneMode": 0, "sharpness": 0, "strobeConfig": {"activeLevel": 0, "enable": 0, "gpioNumber": 0}, "strobeTimings": {"durationUs": 0, "exposureBeginOffsetUs": 0, "exposureEndOffsetUs": 0}, "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": [300, 300]}}, "type": null}], "resolutionHeight": -1, "resolutionWidth": -1}}]]}}
```

## 源代码

#### Python

```python
import depthai as dai
import cv2

class HostDisplay(dai.node.HostNode):
    def build(self, frameOutput: dai.Node.Output):
        self.link_args(frameOutput) # Has to match the inputs to the `process` method

        # This sends all the processing to the pipeline where it's executed by the `pipeline.runTasks()` or implicitly by `pipeline.run()` method.
        # It's needed as the GUI window needs to be updated in the main thread, and the `process` method is by default called in a separate thread.
        self.sendProcessingToPipeline(True)
        return self

    def process(self, message: dai.ImgFrame):
        cv2.imshow("HostDisplay", message.getCvFrame())
        key = cv2.waitKey(1)
        if key == ord('q'):
            print("Detected 'q' - stopping the pipeline...")
            self.stopPipeline()

# with dai.Pipeline() as p:
p = dai.Pipeline()
with p:
    camera = p.create(dai.node.Camera).build()
    hostDisplay = p.create(HostDisplay).build(camera.requestOutput((300, 300)))

    p.run() # Will block until the pipeline is stopped by someone else (in this case it's the display node)
```

#### C++

```cpp
#include <atomic>
#include <csignal>
#include <iostream>
#include <memory>
#include <opencv2/opencv.hpp>

#include "depthai/depthai.hpp"

std::atomic<bool> quitEvent(false);

void signalHandler(int) {
    quitEvent = true;
}

// Custom host node for display
class HostDisplay : public dai::node::CustomNode<HostDisplay> {
   public:
    HostDisplay() {
        sendProcessingToPipeline(true);
    }

    std::shared_ptr<dai::Buffer> processGroup(std::shared_ptr<dai::MessageGroup> message) override {
        if(quitEvent) {
            stopPipeline();
            return nullptr;
        }
        if(message == nullptr) return nullptr;

        auto frame = message->get<dai::ImgFrame>("frame");
        if(frame == nullptr) return nullptr;

        cv::imshow("HostDisplay", frame->getCvFrame());
        int key = cv::waitKey(1);
        if(key == 'q') {
            std::cout << "Detected 'q' - stopping the pipeline..." << std::endl;
            stopPipeline();
        }

        return nullptr;
    }
};

int main() {
    signal(SIGTERM, signalHandler);
    signal(SIGINT, signalHandler);

    // Create device
    std::shared_ptr<dai::Device> device = std::make_shared<dai::Device>();

    // Create pipeline
    dai::Pipeline pipeline(device);

    // Create nodes
    auto camera = pipeline.create<dai::node::Camera>()->build();
    auto output = camera->requestOutput(std::make_pair(300, 300));

    // Create display node
    auto display = pipeline.create<HostDisplay>();
    output->link(display->inputs["frame"]);

    // Start pipeline
    pipeline.run();

    return 0;
}
```

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