# 延迟测量 本示例展示了如何使用 [ImgFrame](https://docs.luxonis.com/software/depthai-components/messages/img_frame.md) 的 .getTimestamp() 函数结合 dai.Clock.now() 来测量从图像被捕获（更准确地说，是从图像被 ISP 处理并附上时间戳开始）到帧在主机上被接收之间的延迟。 如果您想了解更多关于低延迟的内容，请参阅文档页面 [这里](https://docs.luxonis.com/software/depthai/optimizing.md)。 ## 演示 本示例测量了 [ColorCamera](https://docs.luxonis.com/software/depthai-components/nodes/color_camera.md) 以 60FPS 运行时 isp 1080P 输出（YUV420 编码帧）的延迟。我们测得的延迟约为 33ms，这与在 [优化延迟](https://docs.luxonis.com/software/depthai/optimizing.md) 文档页面中测量的结果一致。 ```bash UsbSpeed.SUPER Latency: 33.49 ms, Average latency: 33.49 ms, Std: 0.00 Latency: 34.92 ms, Average latency: 34.21 ms, Std: 0.71 Latency: 33.23 ms, Average latency: 33.88 ms, Std: 0.74 Latency: 33.70 ms, Average latency: 33.84 ms, Std: 0.65 Latency: 33.94 ms, Average latency: 33.86 ms, Std: 0.58 Latency: 34.18 ms, Average latency: 33.91 ms, Std: 0.54 ``` ## 设置 请运行[安装脚本](https://github.com/luxonis/depthai-python/blob/main/examples/install_requirements.py)以下载所有必需的依赖项。请注意，此脚本必须在 git 上下文中运行，因此您需要先下载 [depthai-python](https://github.com/luxonis/depthai-python) 仓库，然后运行该脚本。 ```bash git clone https://github.com/luxonis/depthai-python.git cd depthai-python/examples python3 install_requirements.py ``` 更多信息，请参考[安装指南](https://docs.luxonis.com/software/depthai/manual-install.md)。 ## 源代码 #### Python ```python import depthai as dai import numpy as np # Create pipeline pipeline = dai.Pipeline() # This might improve reducing the latency on some systems pipeline.setXLinkChunkSize(0) # Define source and output camRgb = pipeline.create(dai.node.ColorCamera) camRgb.setFps(60) camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P) xout = pipeline.create(dai.node.XLinkOut) xout.setStreamName("out") camRgb.isp.link(xout.input) # Connect to device and start pipeline with dai.Device(pipeline) as device: print(device.getUsbSpeed()) q = device.getOutputQueue(name="out") diffs = np.array([]) while True: imgFrame = q.get() # Latency in miliseconds latencyMs = (dai.Clock.now() - imgFrame.getTimestamp()).total_seconds() * 1000 diffs = np.append(diffs, latencyMs) print('Latency: {:.2f} ms, Average latency: {:.2f} ms, Std: {:.2f}'.format(latencyMs, np.average(diffs), np.std(diffs))) # Not relevant for this example # cv2.imshow('frame', imgFrame.getCvFrame()) ``` #### C++ WIP ## 管线 ### examples/latency_measurement.pipeline.json ```json { "pipeline": { "connections": [ { "node1Id": 0, "node1Output": "isp", "node1OutputGroup": "", "node2Id": 1, "node2Input": "in", "node2InputGroup": "" } ], "globalProperties": { "calibData": null, "cameraTuningBlobSize": null, "cameraTuningBlobUri": "", "leonCssFrequencyHz": 700000000.0, "leonMssFrequencyHz": 700000000.0, "pipelineName": null, "pipelineVersion": null, "sippBufferSize": 18432, "sippDmaBufferSize": 16384, "xlinkChunkSize": 0 }, "nodes": [ [ 0, { "id": 0, "ioInfo": [ [ [ "", "inputConfig" ], { "blocking": false, "group": "", "id": 1, "name": "inputConfig", "queueSize": 8, "type": 3, "waitForMessage": false } ], [ [ "", "raw" ], { "blocking": false, "group": "", "id": 6, "name": "raw", "queueSize": 8, "type": 0, "waitForMessage": false } ], [ [ "", "still" ], { "blocking": false, "group": "", "id": 7, "name": "still", "queueSize": 8, "type": 0, "waitForMessage": false } ], [ [ "", "inputControl" ], { "blocking": true, "group": "", "id": 2, "name": "inputControl", "queueSize": 8, "type": 3, "waitForMessage": false } ], [ [ "", "video" ], { "blocking": false, "group": "", "id": 3, "name": "video", "queueSize": 8, "type": 0, "waitForMessage": false } ], [ [ "", "isp" ], { "blocking": false, "group": "", "id": 4, "name": "isp", "queueSize": 8, "type": 0, "waitForMessage": false } ], [ [ "", "preview" ], { "blocking": false, "group": "", "id": 5, "name": "preview", "queueSize": 8, "type": 0, "waitForMessage": false } ], [ [ "", "frameEvent" ], { "blocking": false, "group": "", "id": 8, "name": "frameEvent", "queueSize": 8, "type": 0, "waitForMessage": false } ] ], "name": "ColorCamera", "properties": { "boardSocket": -1, "cameraName": "", "colorOrder": 0, "fp16": false, "fps": 60.0, "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, "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, "saturation": 0, "sceneMode": 0, "sharpness": 0, "strobeConfig": { "activeLevel": 0, "enable": 0, "gpioNumber": 0 }, "strobeTimings": { "durationUs": 0, "exposureBeginOffsetUs": 0, "exposureEndOffsetUs": 0 }, "wbColorTemp": 0 }, "interleaved": true, "isp3aFps": 0, "ispScale": { "horizDenominator": 0, "horizNumerator": 0, "vertDenominator": 0, "vertNumerator": 0 }, "numFramesPoolIsp": 3, "numFramesPoolPreview": 4, "numFramesPoolRaw": 3, "numFramesPoolStill": 4, "numFramesPoolVideo": 4, "previewHeight": 300, "previewKeepAspectRatio": true, "previewWidth": 300, "rawPacked": null, "resolution": 0, "sensorCropX": -1.0, "sensorCropY": -1.0, "stillHeight": -1, "stillWidth": -1, "videoHeight": -1, "videoWidth": -1 } } ], [ 1, { "id": 1, "ioInfo": [ [ [ "", "in" ], { "blocking": true, "group": "", "id": 9, "name": "in", "queueSize": 8, "type": 3, "waitForMessage": true } ] ], "name": "XLinkOut", "properties": { "maxFpsLimit": -1.0, "metadataOnly": false, "streamName": "out" } } ] ] } } ``` ### 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。