# 空间检测网络

此示例创建一个流水线，使用RGB和立体深度流执行YOLOv6-Nano空间目标检测，在彩色化深度帧和RGB帧上可视化带有边界框和空间坐标的结果，并使用自定义可视化节点。

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/spatial_detection.pipeline.json

```json
{"pipeline": {"connections": [{"node1Id": 0, "node1Output": "0", "node1OutputGroup": "dynamicOutputs", "node2Id": 5, "node2Input": "in", "node2InputGroup": ""}, {"node1Id": 1, "node1Output": "0", "node1OutputGroup": "dynamicOutputs", "node2Id": 3, "node2Input": "left", "node2InputGroup": ""}, {"node1Id": 2, "node1Output": "0", "node1OutputGroup": "dynamicOutputs", "node2Id": 3, "node2Input": "right", "node2InputGroup": ""}, {"node1Id": 3, "node1Output": "depth", "node1OutputGroup": "", "node2Id": 13, "node2Input": "in", "node2InputGroup": ""}, {"node1Id": 3, "node1Output": "depth", "node1OutputGroup": "", "node2Id": 4, "node2Input": "inputDepth", "node2InputGroup": ""}, {"node1Id": 6, "node1Output": "out", "node1OutputGroup": "", "node2Id": 4, "node2Input": "inputDetections", "node2InputGroup": ""}, {"node1Id": 5, "node1Output": "passthrough", "node1OutputGroup": "", "node2Id": 4, "node2Input": "inputImg", "node2InputGroup": ""}, {"node1Id": 5, "node1Output": "passthrough", "node1OutputGroup": "", "node2Id": 11, "node2Input": "in", "node2InputGroup": ""}, {"node1Id": 5, "node1Output": "passthrough", "node1OutputGroup": "", "node2Id": 6, "node2Input": "imageIn", "node2InputGroup": ""}, {"node1Id": 5, "node1Output": "passthrough", "node1OutputGroup": "", "node2Id": 3, "node2Input": "inputAlignTo", "node2InputGroup": ""}, {"node1Id": 5, "node1Output": "out", "node1OutputGroup": "", "node2Id": 6, "node2Input": "in", "node2InputGroup": ""}, {"node1Id": 4, "node1Output": "out", "node1OutputGroup": "", "node2Id": 9, "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": [[11, {"alias": "", "id": 11, "ioInfo": [[["", "in"], {"blocking": true, "group": "", "id": 43, "name": "in", "queueSize": 3, "type": 3, "waitForMessage": false}]], "logLevel": 3, "name": "XLinkOut", "parentId": -1, "properties": {"maxFpsLimit": -1.0, "metadataOnly": false, "streamName": "__x_5__passthrough"}}], [9, {"alias": "", "id": 9, "ioInfo": [[["", "in"], {"blocking": true, "group": "", "id": 42, "name": "in", "queueSize": 3, "type": 3, "waitForMessage": false}]], "logLevel": 3, "name": "XLinkOut", "parentId": -1, "properties": {"maxFpsLimit": -1.0, "metadataOnly": false, "streamName": "__x_4__out"}}], [6, {"alias": "detectionParser", "id": 6, "ioInfo": [[["", "out"], {"blocking": false, "group": "", "id": 41, "name": "out", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "imageIn"], {"blocking": false, "group": "", "id": 40, "name": "imageIn", "queueSize": 1, "type": 3, "waitForMessage": true}], [["", "in"], {"blocking": true, "group": "", "id": 39, "name": "in", "queueSize": 1, "type": 3, "waitForMessage": true}]], "logLevel": 3, "name": "DetectionParser", "parentId": 4, "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"}}}], [5, {"alias": "neuralNetwork", "id": 5, "ioInfo": [[["", "passthrough"], {"blocking": false, "group": "", "id": 38, "name": "passthrough", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "out"], {"blocking": false, "group": "", "id": 37, "name": "out", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "in"], {"blocking": false, "group": "", "id": 36, "name": "in", "queueSize": 3, "type": 3, "waitForMessage": true}]], "logLevel": 3, "name": "NeuralNetwork", "parentId": 4, "properties": {"backend": "", "backendProperties": {}, "blobSize": 8689834, "blobUri": "asset:__blob", "modelSource": 0, "modelUri": "", "numFrames": 8, "numNCEPerThread": 0, "numShavesPerThread": 0, "numThreads": 0}}], [4, {"alias": "", "id": 4, "ioInfo": [[["", "passthroughDepth"], {"blocking": false, "group": "", "id": 34, "name": "passthroughDepth", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "boundingBoxMapping"], {"blocking": false, "group": "", "id": 33, "name": "boundingBoxMapping", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "spatialLocationCalculatorOutput"], {"blocking": false, "group": "", "id": 35, "name": "spatialLocationCalculatorOutput", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "out"], {"blocking": false, "group": "", "id": 32, "name": "out", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "inputDetections"], {"blocking": true, "group": "", "id": 31, "name": "inputDetections", "queueSize": 1, "type": 3, "waitForMessage": true}], [["", "inputImg"], {"blocking": true, "group": "", "id": 30, "name": "inputImg", "queueSize": 2, "type": 3, "waitForMessage": true}], [["", "inputDepth"], {"blocking": false, "group": "", "id": 29, "name": "inputDepth", "queueSize": 4, "type": 3, "waitForMessage": true}]], "logLevel": 3, "name": "SpatialDetectionNetwork", "parentId": -1, "properties": {"calculationAlgorithm": 4, "depthThresholds": {"lowerThreshold": 100, "upperThreshold": 5000}, "detectedBBScaleFactor": 0.5}}], [3, {"alias": "", "id": 3, "ioInfo": [[["", "confidenceMap"], {"blocking": false, "group": "", "id": 28, "name": "confidenceMap", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "debugDispCostDump"], {"blocking": false, "group": "", "id": 27, "name": "debugDispCostDump", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "debugExtDispLrCheckIt2"], {"blocking": false, "group": "", "id": 26, "name": "debugExtDispLrCheckIt2", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "debugDispLrCheckIt2"], {"blocking": false, "group": "", "id": 24, "name": "debugDispLrCheckIt2", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "debugExtDispLrCheckIt1"], {"blocking": false, "group": "", "id": 25, "name": "debugExtDispLrCheckIt1", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "debugDispLrCheckIt1"], {"blocking": false, "group": "", "id": 23, "name": "debugDispLrCheckIt1", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "outConfig"], {"blocking": false, "group": "", "id": 22, "name": "outConfig", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "rectifiedRight"], {"blocking": false, "group": "", "id": 21, "name": "rectifiedRight", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "rectifiedLeft"], {"blocking": false, "group": "", "id": 20, "name": "rectifiedLeft", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "depth"], {"blocking": false, "group": "", "id": 16, "name": "depth", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "right"], {"blocking": true, "group": "", "id": 15, "name": "right", "queueSize": 3, "type": 3, "waitForMessage": false}], [["", "left"], {"blocking": true, "group": "", "id": 14, "name": "left", "queueSize": 3, "type": 3, "waitForMessage": false}], [["", "syncedRight"], {"blocking": false, "group": "", "id": 19, "name": "syncedRight", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "syncedLeft"], {"blocking": false, "group": "", "id": 18, "name": "syncedLeft", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "inputAlignTo"], {"blocking": false, "group": "", "id": 13, "name": "inputAlignTo", "queueSize": 1, "type": 3, "waitForMessage": true}], [["", "disparity"], {"blocking": false, "group": "", "id": 17, "name": "disparity", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "inputConfig"], {"blocking": true, "group": "", "id": 12, "name": "inputConfig", "queueSize": 3, "type": 3, "waitForMessage": false}]], "logLevel": 3, "name": "StereoDepth", "parentId": -1, "properties": {"alphaScaling": null, "baseline": null, "depthAlignCamera": -1, "depthAlignmentUseSpecTranslation": null, "disparityToDepthUseSpecTranslation": null, "enableFrameSync": true, "enableRectification": true, "enableRuntimeStereoModeSwitch": false, "focalLength": null, "focalLengthFromCalibration": true, "height": null, "initialConfig": {"algorithmControl": {"centerAlignmentShiftFactor": null, "customDepthUnitMultiplier": 1000.0, "depthAlign": 1, "depthUnit": 2, "disparityShift": 0, "enableExtended": true, "enableLeftRightCheck": true, "enableSubpixel": true, "enableSwLeftRightCheck": false, "leftRightCheckThreshold": 10, "numInvalidateEdgePixels": 0, "subpixelFractionalBits": 5}, "censusTransform": {"enableMeanMode": true, "kernelMask": 0, "kernelSize": -1, "noiseThresholdOffset": 1, "noiseThresholdScale": 1, "threshold": 0}, "confidenceMetrics": {"flatnessConfidenceThreshold": 2, "flatnessConfidenceWeight": 8, "flatnessOverride": false, "motionVectorConfidenceThreshold": 1, "motionVectorConfidenceWeight": 4, "occlusionConfidenceWeight": 20}, "costAggregation": {"divisionFactor": 1, "horizontalPenaltyCostP1": 250, "horizontalPenaltyCostP2": 500, "p1Config": {"defaultValue": 11, "edgeThreshold": 15, "edgeValue": 10, "enableAdaptive": true, "smoothThreshold": 5, "smoothValue": 22}, "p2Config": {"defaultValue": 33, "edgeValue": 22, "enableAdaptive": true, "smoothValue": 63}, "verticalPenaltyCostP1": 250, "verticalPenaltyCostP2": 500}, "costMatching": {"confidenceThreshold": 55, "disparityWidth": 1, "enableCompanding": false, "enableSwConfidenceThresholding": false, "invalidDisparityValue": 0, "linearEquationParameters": {"alpha": 0, "beta": 2, "threshold": 127}}, "filtersBackend": 2, "postProcessing": {"adaptiveMedianFilter": {"confidenceThreshold": 200, "enable": true}, "bilateralSigmaValue": 0, "brightnessFilter": {"maxBrightness": 256, "minBrightness": 0}, "decimationFilter": {"decimationFactor": 1, "decimationMode": 0}, "filteringOrder": [3, 1, 2, 4, 5], "holeFilling": {"enable": true, "fillConfidenceThreshold": 200, "highConfidenceThreshold": 210, "invalidateDisparities": true, "minValidDisparity": 1}, "median": 0, "spatialFilter": {"alpha": 0.5, "delta": 0, "enable": false, "holeFillingRadius": 2, "numIterations": 1}, "speckleFilter": {"differenceThreshold": 2, "enable": false, "speckleRange": 50}, "temporalFilter": {"alpha": 0.4000000059604645, "delta": 0, "enable": false, "persistencyMode": 3}, "thresholdFilter": {"maxRange": 65535, "minRange": 0}}}, "mesh": {"meshLeftUri": "", "meshRightUri": "", "meshSize": null, "stepHeight": 16, "stepWidth": 16}, "numFramesPool": 3, "numPostProcessingMemorySlices": -1, "numPostProcessingShaves": -1, "outHeight": 400, "outKeepAspectRatio": true, "outWidth": 640, "rectificationUseSpecTranslation": null, "rectifyEdgeFillColor": 0, "useHomographyRectification": null, "width": null}}], [2, {"alias": "", "id": 2, "ioInfo": [[["dynamicOutputs", "0"], {"blocking": false, "group": "dynamicOutputs", "id": 11, "name": "0", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "raw"], {"blocking": false, "group": "", "id": 10, "name": "raw", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "mockIsp"], {"blocking": true, "group": "", "id": 9, "name": "mockIsp", "queueSize": 8, "type": 3, "waitForMessage": false}], [["", "inputControl"], {"blocking": true, "group": "", "id": 8, "name": "inputControl", "queueSize": 3, "type": 3, "waitForMessage": false}]], "logLevel": 3, "name": "Camera", "parentId": -1, "properties": {"boardSocket": 2, "cameraName": "", "fps": -1.0, "imageOrientation": -1, "initialControl": {"aeLockMode": false, "aeMaxExposureTimeUs": 5, "aeRegion": {"height": 0, "priority": 1213374846, "width": 0, "x": 96, "y": 0}, "afRegion": {"height": 33369, "priority": 23712, "width": 58768, "x": 23717, "y": 0}, "antiBandingMode": 0, "autoFocusMode": 3, "awbLockMode": false, "awbMode": 0, "brightness": 0, "captureIntent": 0, "chromaDenoise": 104, "cmdMask": 0, "contrast": 119, "controlMode": 0, "effectMode": 0, "enableHdr": false, "expCompensation": 0, "expManual": {"exposureTimeUs": 0, "frameDurationUs": 0, "sensitivityIso": 576}, "frameSyncMode": 0, "lensPosAutoInfinity": 0, "lensPosAutoMacro": 0, "lensPosition": 0, "lensPositionRaw": 0.0, "lowPowerNumFramesBurst": 129, "lowPowerNumFramesDiscard": 2, "lumaDenoise": 116, "miscControls": [], "saturation": 105, "sceneMode": 0, "sharpness": 100, "strobeConfig": {"activeLevel": 0, "enable": 0, "gpioNumber": 0}, "strobeTimings": {"durationUs": 23712, "exposureBeginOffsetUs": 0, "exposureEndOffsetUs": -2108037792}, "wbColorTemp": 13410}, "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": [640, 400]}}, "type": null}], "resolutionHeight": -1, "resolutionWidth": -1}}], [1, {"alias": "", "id": 1, "ioInfo": [[["dynamicOutputs", "0"], {"blocking": false, "group": "dynamicOutputs", "id": 7, "name": "0", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "raw"], {"blocking": false, "group": "", "id": 6, "name": "raw", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "mockIsp"], {"blocking": true, "group": "", "id": 5, "name": "mockIsp", "queueSize": 8, "type": 3, "waitForMessage": false}], [["", "inputControl"], {"blocking": true, "group": "", "id": 4, "name": "inputControl", "queueSize": 3, "type": 3, "waitForMessage": false}]], "logLevel": 3, "name": "Camera", "parentId": -1, "properties": {"boardSocket": 1, "cameraName": "", "fps": -1.0, "imageOrientation": -1, "initialControl": {"aeLockMode": false, "aeMaxExposureTimeUs": 537120116, "aeRegion": {"height": 28526, "priority": 1818324338, "width": 43520, "x": 17610, "y": 49227}, "afRegion": {"height": 26999, "priority": 3395843172, "width": 42434, "x": 31337, "y": 25701}, "antiBandingMode": 224, "autoFocusMode": 3, "awbLockMode": false, "awbMode": 68, "brightness": 120, "captureIntent": 0, "chromaDenoise": 67, "cmdMask": 0, "contrast": 70, "controlMode": 161, "effectMode": 120, "enableHdr": false, "expCompensation": 97, "expManual": {"exposureTimeUs": 1684372073, "frameDurationUs": 1952999273, "sensitivityIso": 1701357250}, "frameSyncMode": 202, "lensPosAutoInfinity": 114, "lensPosAutoMacro": 109, "lensPosition": 0, "lensPositionRaw": 0.0, "lowPowerNumFramesBurst": 166, "lowPowerNumFramesDiscard": 109, "lumaDenoise": 202, "miscControls": [], "saturation": 112, "sceneMode": 161, "sharpness": 115, "strobeConfig": {"activeLevel": 0, "enable": 0, "gpioNumber": 0}, "strobeTimings": {"durationUs": 1751607653, "exposureBeginOffsetUs": 51833, "exposureEndOffsetUs": 1755709440}, "wbColorTemp": 39577}, "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": [640, 400]}}, "type": null}], "resolutionHeight": -1, "resolutionWidth": -1}}], [13, {"alias": "", "id": 13, "ioInfo": [[["", "in"], {"blocking": true, "group": "", "id": 44, "name": "in", "queueSize": 3, "type": 3, "waitForMessage": false}]], "logLevel": 3, "name": "XLinkOut", "parentId": -1, "properties": {"maxFpsLimit": -1.0, "metadataOnly": false, "streamName": "__x_3__depth"}}], [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": 2186963712, "width": 0, "x": 4163, "y": 1}, "afRegion": {"height": 0, "priority": 0, "width": 0, "x": 23712, "y": 0}, "antiBandingMode": 90, "autoFocusMode": 3, "awbLockMode": false, "awbMode": 72, "brightness": -105, "captureIntent": 130, "chromaDenoise": 0, "cmdMask": 0, "contrast": -33, "controlMode": 160, "effectMode": 92, "enableHdr": false, "expCompensation": 39, "expManual": {"exposureTimeUs": 23712, "frameDurationUs": 23712, "sensitivityIso": 2186963820}, "frameSyncMode": 0, "lensPosAutoInfinity": 128, "lensPosAutoMacro": 107, "lensPosition": 0, "lensPositionRaw": 0.0, "lowPowerNumFramesBurst": 0, "lowPowerNumFramesDiscard": 0, "lumaDenoise": 0, "miscControls": [], "saturation": -34, "sceneMode": 107, "sharpness": 0, "strobeConfig": {"activeLevel": 168, "enable": 0, "gpioNumber": 113}, "strobeTimings": {"durationUs": 16, "exposureBeginOffsetUs": 23712, "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": {"index": 0, "value": 30.0}}, "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 argparse
from pathlib import Path
import cv2
import depthai as dai
import numpy as np

NEURAL_FPS = 8
STEREO_DEFAULT_FPS = 20

parser = argparse.ArgumentParser()
parser.add_argument(
    "--depthSource", type=str, default="stereo", choices=["stereo", "neural"]
)
args = parser.parse_args()
# For better results on OAK4, use a segmentation model like "luxonis/yolov8-instance-segmentation-large:coco-640x480"
# for depth estimation over the objects mask instead of the full bounding box.
modelDescription = dai.NNModelDescription("yolov6-nano")
size = (640, 400)

if args.depthSource == "stereo":
    fps = STEREO_DEFAULT_FPS
else:
    fps = NEURAL_FPS

class SpatialVisualizer(dai.node.HostNode):
    def __init__(self):
        dai.node.HostNode.__init__(self)
        self.sendProcessingToPipeline(True)
    def build(self, depth:dai.Node.Output, detections: dai.Node.Output, rgb: dai.Node.Output):
        self.link_args(depth, detections, rgb) # Must match the inputs to the process method

    def process(self, depthPreview, detections, rgbPreview):
        depthPreview = depthPreview.getCvFrame()
        rgbPreview = rgbPreview.getCvFrame()
        depthFrameColor = self.processDepthFrame(depthPreview)
        self.displayResults(rgbPreview, depthFrameColor, detections.detections)

    def processDepthFrame(self, depthFrame):
        depthDownscaled = depthFrame[::4]
        if np.all(depthDownscaled == 0):
            minDepth = 0
        else:
            minDepth = np.percentile(depthDownscaled[depthDownscaled != 0], 1)
        maxDepth = np.percentile(depthDownscaled, 99)
        depthFrameColor = np.interp(depthFrame, (minDepth, maxDepth), (0, 255)).astype(np.uint8)
        return cv2.applyColorMap(depthFrameColor, cv2.COLORMAP_HOT)

    def displayResults(self, rgbFrame, depthFrameColor, detections):
        height, width, _ = rgbFrame.shape
        for detection in detections:
            self.drawBoundingBoxes(depthFrameColor, detection)
            self.drawDetections(rgbFrame, detection, width, height)

        cv2.imshow("Depth frame", depthFrameColor)
        cv2.imshow("Color frame", rgbFrame)
        if cv2.waitKey(1) == ord('q'):
            self.stopPipeline()

    def drawBoundingBoxes(self, depthFrameColor, detection):
        roiData = detection.boundingBoxMapping
        roi = roiData.roi
        roi = roi.denormalize(depthFrameColor.shape[1], depthFrameColor.shape[0])
        topLeft = roi.topLeft()
        bottomRight = roi.bottomRight()
        cv2.rectangle(depthFrameColor, (int(topLeft.x), int(topLeft.y)), (int(bottomRight.x), int(bottomRight.y)), (255, 255, 255), 1)

    def drawDetections(self, frame, detection, frameWidth, frameHeight):
        x1 = int(detection.xmin * frameWidth)
        x2 = int(detection.xmax * frameWidth)
        y1 = int(detection.ymin * frameHeight)
        y2 = int(detection.ymax * frameHeight)
        label = detection.labelName
        color = (255, 255, 255)
        cv2.putText(frame, str(label), (x1 + 10, y1 + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, color)
        cv2.putText(frame, "{:.2f}".format(detection.confidence * 100), (x1 + 10, y1 + 35), cv2.FONT_HERSHEY_TRIPLEX, 0.5, color)
        cv2.putText(frame, f"X: {int(detection.spatialCoordinates.x)} mm", (x1 + 10, y1 + 50), cv2.FONT_HERSHEY_TRIPLEX, 0.5, color)
        cv2.putText(frame, f"Y: {int(detection.spatialCoordinates.y)} mm", (x1 + 10, y1 + 65), cv2.FONT_HERSHEY_TRIPLEX, 0.5, color)
        cv2.putText(frame, f"Z: {int(detection.spatialCoordinates.z)} mm", (x1 + 10, y1 + 80), cv2.FONT_HERSHEY_TRIPLEX, 0.5, color)
        cv2.rectangle(frame, (x1, y1), (x2, y2), color, 1)

# Creates the pipeline and a default device implicitly
with dai.Pipeline() as p:
    # Define sources and outputs
    platform = p.getDefaultDevice().getPlatform()

    camRgb = p.create(dai.node.Camera).build(dai.CameraBoardSocket.CAM_A, sensorFps=fps)
    monoLeft = p.create(dai.node.Camera).build(dai.CameraBoardSocket.CAM_B, sensorFps=fps)
    monoRight = p.create(dai.node.Camera).build(dai.CameraBoardSocket.CAM_C, sensorFps=fps)
    if args.depthSource == "stereo":
        depthSource = p.create(dai.node.StereoDepth)
        depthSource.setExtendedDisparity(True)
        monoLeft.requestOutput(size).link(depthSource.left)
        monoRight.requestOutput(size).link(depthSource.right)
    elif args.depthSource == "neural":
        depthSource = p.create(dai.node.NeuralDepth).build(
            monoLeft.requestFullResolutionOutput(),
            monoRight.requestFullResolutionOutput(),
            dai.DeviceModelZoo.NEURAL_DEPTH_LARGE,
        )
    else:
        raise ValueError(f"Invalid depth source: {args.depthSource}")

    spatialDetectionNetwork = p.create(dai.node.SpatialDetectionNetwork).build(
        camRgb, depthSource, modelDescription
    )
    visualizer = p.create(SpatialVisualizer)

    spatialDetectionNetwork.spatialLocationCalculator.initialConfig.setSegmentationPassthrough(False)
    spatialDetectionNetwork.input.setBlocking(False)
    spatialDetectionNetwork.setDepthLowerThreshold(100)
    spatialDetectionNetwork.setDepthUpperThreshold(5000)

    visualizer.build(
        spatialDetectionNetwork.passthroughDepth,
        spatialDetectionNetwork.out,
        spatialDetectionNetwork.passthrough,
    )

    print("Starting pipeline with depth source: ", args.depthSource)

    p.run()
```

#### C++

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

#include "depthai/depthai.hpp"

constexpr float NEURAL_FPS = 8.0f;
constexpr float STEREO_DEFAULT_FPS = 20.0f;

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

void signalHandler(int) {
    quitEvent = true;
}

// Custom host node for spatial visualization
class SpatialVisualizer : public dai::NodeCRTP<dai::node::HostNode, SpatialVisualizer> {
   public:
    Input& depthInput = inputs["depth"];
    Input& detectionsInput = inputs["detections"];
    Input& rgbInput = inputs["rgb"];

    std::vector<std::string> labelMap;

    std::shared_ptr<SpatialVisualizer> build(Output& depth, Output& detections, Output& rgb) {
        depth.link(depthInput);
        detections.link(detectionsInput);
        rgb.link(rgbInput);
        sendProcessingToPipeline(true);
        return std::static_pointer_cast<SpatialVisualizer>(this->shared_from_this());
    }

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

        auto depthFrame = in->get<dai::ImgFrame>("depth");
        auto detections = in->get<dai::SpatialImgDetections>("detections");
        auto rgbFrame = in->get<dai::ImgFrame>("rgb");

        cv::Mat depthCv = depthFrame->getCvFrame();
        cv::Mat rgbCv = rgbFrame->getCvFrame();
        cv::Mat depthFrameColor = processDepthFrame(depthCv);
        displayResults(rgbCv, depthFrameColor, detections->detections);

        return nullptr;
    }

   private:
    cv::Mat processDepthFrame(const cv::Mat& depthFrame) {
        // Downscale depth frame
        cv::Mat depthDownscaled;
        cv::resize(depthFrame, depthDownscaled, cv::Size(), 0.25, 0.25);

        // Find min and max depth values
        double minDepth = 0, maxDepth = 0;
        cv::Mat mask = (depthDownscaled != 0);
        if(cv::countNonZero(mask) > 0) {
            cv::minMaxLoc(depthDownscaled, &minDepth, &maxDepth, nullptr, nullptr, mask);
        }

        // Normalize depth frame
        cv::Mat depthFrameColor;
        depthFrame.convertTo(depthFrameColor, CV_8UC1, 255.0 / (maxDepth - minDepth), -minDepth * 255.0 / (maxDepth - minDepth));

        // Apply color map
        cv::Mat colorized;
        cv::applyColorMap(depthFrameColor, colorized, cv::COLORMAP_HOT);
        return colorized;
    }

    void displayResults(cv::Mat& rgbFrame, cv::Mat& depthFrameColor, const std::vector<dai::SpatialImgDetection>& detections) {
        int height = rgbFrame.rows;
        int width = rgbFrame.cols;

        for(const auto& detection : detections) {
            drawBoundingBoxes(depthFrameColor, detection);
            drawDetections(rgbFrame, detection, width, height);
        }

        cv::imshow("depth", depthFrameColor);
        cv::imshow("rgb", rgbFrame);

        if(cv::waitKey(1) == 'q') {
            stopPipeline();
        }
    }

    void drawBoundingBoxes(cv::Mat& depthFrameColor, const dai::SpatialImgDetection& detection) {
        auto roi = detection.boundingBoxMapping.roi;
        roi = roi.denormalize(depthFrameColor.cols, depthFrameColor.rows);
        auto topLeft = roi.topLeft();
        auto bottomRight = roi.bottomRight();
        cv::rectangle(depthFrameColor,
                      cv::Point(static_cast<int>(topLeft.x), static_cast<int>(topLeft.y)),
                      cv::Point(static_cast<int>(bottomRight.x), static_cast<int>(bottomRight.y)),
                      cv::Scalar(255, 255, 255),
                      1);
    }

    void drawDetections(cv::Mat& frame, const dai::SpatialImgDetection& detection, int frameWidth, int frameHeight) {
        int x1 = static_cast<int>(detection.xmin * frameWidth);
        int x2 = static_cast<int>(detection.xmax * frameWidth);
        int y1 = static_cast<int>(detection.ymin * frameHeight);
        int y2 = static_cast<int>(detection.ymax * frameHeight);

        std::string label;
        try {
            label = labelMap[detection.label];
        } catch(...) {
            label = std::to_string(detection.label);
        }

        cv::Scalar color(255, 255, 255);
        cv::putText(frame, label, cv::Point(x1 + 10, y1 + 20), cv::FONT_HERSHEY_TRIPLEX, 0.5, color);
        cv::putText(frame, std::to_string(detection.confidence * 100), cv::Point(x1 + 10, y1 + 35), cv::FONT_HERSHEY_TRIPLEX, 0.5, color);
        cv::putText(frame,
                    "X: " + std::to_string(static_cast<int>(detection.spatialCoordinates.x)) + " mm",
                    cv::Point(x1 + 10, y1 + 50),
                    cv::FONT_HERSHEY_TRIPLEX,
                    0.5,
                    color);
        cv::putText(frame,
                    "Y: " + std::to_string(static_cast<int>(detection.spatialCoordinates.y)) + " mm",
                    cv::Point(x1 + 10, y1 + 65),
                    cv::FONT_HERSHEY_TRIPLEX,
                    0.5,
                    color);
        cv::putText(frame,
                    "Z: " + std::to_string(static_cast<int>(detection.spatialCoordinates.z)) + " mm",
                    cv::Point(x1 + 10, y1 + 80),
                    cv::FONT_HERSHEY_TRIPLEX,
                    0.5,
                    color);
        cv::rectangle(frame, cv::Point(x1, y1), cv::Point(x2, y2), color, 1);
    }
};

int main(int argc, char** argv) {
    signal(SIGTERM, signalHandler);
    signal(SIGINT, signalHandler);

    // Initialize argument parser
    argparse::ArgumentParser program("spatial_detection", "1.0.0");
    program.add_description("Spatial detection network example with configurable depth source");
    program.add_argument("--depthSource").default_value(std::string("stereo")).help("Depth source: stereo, neural, tof");

    // Parse arguments
    try {
        program.parse_args(argc, argv);
    } catch(const std::runtime_error& err) {
        std::cerr << err.what() << '\n';
        std::cerr << program;
        return EXIT_FAILURE;
    }

    // Get arguments
    std::string depthSourceArg = program.get<std::string>("--depthSource");

    // Validate depth source argument
    if(depthSourceArg != "stereo" && depthSourceArg != "neural" && depthSourceArg != "tof") {
        std::cerr << "Invalid depth source: " << depthSourceArg << '\n';
        std::cerr << "Valid options are: stereo, neural, tof" << '\n';
        return EXIT_FAILURE;
    }

    try {
        float fps = STEREO_DEFAULT_FPS;
        if(depthSourceArg == "neural") {
            fps = NEURAL_FPS;
        }

        // Create pipeline
        dai::Pipeline pipeline;

        const std::pair<int, int> size = {640, 400};

        // Define sources and outputs
        auto camRgb = pipeline.create<dai::node::Camera>();
        camRgb->build(dai::CameraBoardSocket::CAM_A, std::nullopt, fps);

        auto platform = pipeline.getDefaultDevice()->getPlatform();

        // Create depth source based on argument
        dai::node::DepthSource depthSource;

        if(depthSourceArg == "stereo") {
            auto monoLeft = pipeline.create<dai::node::Camera>();
            auto monoRight = pipeline.create<dai::node::Camera>();
            auto stereo = pipeline.create<dai::node::StereoDepth>();

            monoLeft->build(dai::CameraBoardSocket::CAM_B, std::nullopt, fps);
            monoRight->build(dai::CameraBoardSocket::CAM_C, std::nullopt, fps);

            stereo->setExtendedDisparity(true);
            monoLeft->requestOutput(size, std::nullopt, dai::ImgResizeMode::CROP)->link(stereo->left);
            monoRight->requestOutput(size, std::nullopt, dai::ImgResizeMode::CROP)->link(stereo->right);

            depthSource = stereo;
        } else if(depthSourceArg == "neural") {
            auto monoLeft = pipeline.create<dai::node::Camera>();
            auto monoRight = pipeline.create<dai::node::Camera>();

            monoLeft->build(dai::CameraBoardSocket::CAM_B, std::nullopt, fps);
            monoRight->build(dai::CameraBoardSocket::CAM_C, std::nullopt, fps);

            auto neuralDepth = pipeline.create<dai::node::NeuralDepth>();
            neuralDepth->build(*monoLeft->requestFullResolutionOutput(), *monoRight->requestFullResolutionOutput(), dai::DeviceModelZoo::NEURAL_DEPTH_LARGE);

            depthSource = neuralDepth;
        } else if(depthSourceArg == "tof") {
            auto tof = pipeline.create<dai::node::ToF>();
            depthSource = tof;
        }

        // Create spatial detection network using the unified build method with DepthSource variant
        auto spatialDetectionNetwork = pipeline.create<dai::node::SpatialDetectionNetwork>();
        auto visualizer = pipeline.create<SpatialVisualizer>();

        // Configure spatial detection network
        spatialDetectionNetwork->input.setBlocking(false);
        spatialDetectionNetwork->setBoundingBoxScaleFactor(0.5f);
        spatialDetectionNetwork->setDepthLowerThreshold(100);
        spatialDetectionNetwork->setDepthUpperThreshold(5000);

        // Set up model and build with DepthSource variant
        dai::NNModelDescription modelDesc;
        // For better results on OAK4, use a segmentation model like "luxonis/yolov8-instance-segmentation-large:coco-640x480"
        // for depth estimation over the objects mask instead of the full bounding box.
        modelDesc.model = "yolov6-nano";
        spatialDetectionNetwork->build(camRgb, depthSource, modelDesc);

        // Set label map
        visualizer->labelMap = spatialDetectionNetwork->getClasses().value();
        spatialDetectionNetwork->spatialLocationCalculator->initialConfig->setSegmentationPassthrough(false);

        // Linking
        visualizer->build(spatialDetectionNetwork->passthroughDepth, spatialDetectionNetwork->out, spatialDetectionNetwork->passthrough);

        std::cout << "Pipeline starting with depth source: " << depthSourceArg << '\n';

        // Start pipeline
        pipeline.run();

    } catch(const std::exception& e) {
        std::cerr << "Error: " << e.what() << '\n';
        return EXIT_FAILURE;
    }

    return EXIT_SUCCESS;
}
```

### 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。