# RGB深度对齐 > 你也可以使用 > [ImageAlign](https://docs.luxonis.com/software/depthai-components/nodes/image_align.md) > 节点（ > [示例](https://github.com/luxonis/depthai-python/blob/main/examples/ImageAlign/depth_align.py) > ）进行深度对齐，但我们建议使用StereoDepth的对齐以获得更好的性能。 本示例展示了RGB深度对齐的用法。由于OAK-D有一个彩色摄像头和一对立体摄像头，你可以将深度图对齐到彩色帧上，从而获得RGB深度。 在此示例中，RGB和深度并非完美同步。要实现同步，你需要添加[软件同步](https://docs.luxonis.com/hardware/platform/deploy/frame-sync.md)，该功能已添加到[同步演示](https://docs.luxonis.com/software/depthai/examples/depth_video_sync.md)中，其中RGB和深度帧的延迟在毫秒以下。 默认情况下，深度图会被缩放以匹配我们要对齐的摄像头传感器的分辨率。换句话说，如果深度对齐到1080P彩色传感器，StereoDepth也会将深度放大到1080P。 可以通过配置[StereoDepth](https://docs.luxonis.com/software/depthai-components/nodes/stereo_depth.md)的stereo.setOutputSize(width, height)来避免深度缩放。 要将深度与更高分辨率的彩色流（例如1200万像素）对齐，你需要限制深度图的分辨率。可以通过stereo.setOutputSize(w,h)来实现。代码[示例在此](https://gist.github.com/Erol444/25f374fa18efa7939ec9bb848b39249a)。 ## 演示 ## 安装 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 #!/usr/bin/env python3 import cv2 import numpy as np import depthai as dai import argparse # Weights to use when blending depth/rgb image (should equal 1.0) rgbWeight = 0.4 depthWeight = 0.6 parser = argparse.ArgumentParser() parser.add_argument('-alpha', type=float, default=None, help="Alpha scaling parameter to increase float. [0,1] valid interval.") args = parser.parse_args() alpha = args.alpha def updateBlendWeights(percent_rgb): """ Update the rgb and depth weights used to blend depth/rgb image @param[in] percent_rgb The rgb weight expressed as a percentage (0..100) """ global depthWeight global rgbWeight rgbWeight = float(percent_rgb)/100.0 depthWeight = 1.0 - rgbWeight fps = 30 # The disparity is computed at this resolution, then upscaled to RGB resolution monoResolution = dai.MonoCameraProperties.SensorResolution.THE_720_P # Create pipeline pipeline = dai.Pipeline() device = dai.Device() queueNames = [] # Define sources and outputs camRgb = pipeline.create(dai.node.Camera) left = pipeline.create(dai.node.MonoCamera) right = pipeline.create(dai.node.MonoCamera) stereo = pipeline.create(dai.node.StereoDepth) rgbOut = pipeline.create(dai.node.XLinkOut) disparityOut = pipeline.create(dai.node.XLinkOut) rgbOut.setStreamName("rgb") queueNames.append("rgb") disparityOut.setStreamName("disp") queueNames.append("disp") #Properties rgbCamSocket = dai.CameraBoardSocket.CAM_A camRgb.setBoardSocket(rgbCamSocket) camRgb.setSize(1280, 720) camRgb.setFps(fps) # For now, RGB needs fixed focus to properly align with depth. # This value was used during calibration try: calibData = device.readCalibration2() lensPosition = calibData.getLensPosition(rgbCamSocket) if lensPosition: camRgb.initialControl.setManualFocus(lensPosition) except: raise left.setResolution(monoResolution) left.setCamera("left") left.setFps(fps) right.setResolution(monoResolution) right.setCamera("right") right.setFps(fps) stereo.setDefaultProfilePreset(dai.node.StereoDepth.PresetMode.HIGH_DENSITY) # LR-check is required for depth alignment stereo.setLeftRightCheck(True) stereo.setDepthAlign(rgbCamSocket) # Linking camRgb.video.link(rgbOut.input) left.out.link(stereo.left) right.out.link(stereo.right) stereo.disparity.link(disparityOut.input) camRgb.setMeshSource(dai.CameraProperties.WarpMeshSource.CALIBRATION) if alpha is not None: camRgb.setCalibrationAlpha(alpha) stereo.setAlphaScaling(alpha) # Connect to device and start pipeline with device: device.startPipeline(pipeline) frameRgb = None frameDisp = None # Configure windows; trackbar adjusts blending ratio of rgb/depth rgbWindowName = "rgb" depthWindowName = "depth" blendedWindowName = "rgb-depth" cv2.namedWindow(rgbWindowName) cv2.namedWindow(depthWindowName) cv2.namedWindow(blendedWindowName) cv2.createTrackbar('RGB Weight %', blendedWindowName, int(rgbWeight*100), 100, updateBlendWeights) while True: latestPacket = {} latestPacket["rgb"] = None latestPacket["disp"] = None queueEvents = device.getQueueEvents(("rgb", "disp")) for queueName in queueEvents: packets = device.getOutputQueue(queueName).tryGetAll() if len(packets) > 0: latestPacket[queueName] = packets[-1] if latestPacket["rgb"] is not None: frameRgb = latestPacket["rgb"].getCvFrame() cv2.imshow(rgbWindowName, frameRgb) if latestPacket["disp"] is not None: frameDisp = latestPacket["disp"].getFrame() maxDisparity = stereo.initialConfig.getMaxDisparity() # Optional, extend range 0..95 -> 0..255, for a better visualisation if 1: frameDisp = (frameDisp * 255. / maxDisparity).astype(np.uint8) # Optional, apply false colorization if 1: frameDisp = cv2.applyColorMap(frameDisp, cv2.COLORMAP_HOT) frameDisp = np.ascontiguousarray(frameDisp) cv2.imshow(depthWindowName, frameDisp) # Blend when both received if frameRgb is not None and frameDisp is not None: # Need to have both frames in BGR format before blending if len(frameDisp.shape) < 3: frameDisp = cv2.cvtColor(frameDisp, cv2.COLOR_GRAY2BGR) blended = cv2.addWeighted(frameRgb, rgbWeight, frameDisp, depthWeight, 0) cv2.imshow(blendedWindowName, blended) frameRgb = None frameDisp = None if cv2.waitKey(1) == ord('q'): break ``` #### C++ ```cpp #include #include #include "utility.hpp" // Includes common necessary includes for development using depthai library #include "depthai/depthai.hpp" // Optional. If set (true), the ColorCamera is downscaled from 1080p to 720p. // Otherwise (false), the aligned depth is automatically upscaled to 1080p static std::atomic downscaleColor{true}; static constexpr int fps = 30; // The disparity is computed at this resolution, then upscaled to RGB resolution static constexpr auto monoRes = dai::MonoCameraProperties::SensorResolution::THE_720_P; static float rgbWeight = 0.4f; static float depthWeight = 0.6f; static void updateBlendWeights(int percentRgb, void* ctx) { rgbWeight = float(percentRgb) / 100.f; depthWeight = 1.f - rgbWeight; } int main() { using namespace std; // Create pipeline dai::Pipeline pipeline; dai::Device device; std::vector queueNames; // Define sources and outputs auto camRgb = pipeline.create(); auto left = pipeline.create(); auto right = pipeline.create(); auto stereo = pipeline.create(); auto rgbOut = pipeline.create(); auto depthOut = pipeline.create(); rgbOut->setStreamName("rgb"); queueNames.push_back("rgb"); depthOut->setStreamName("depth"); queueNames.push_back("depth"); // Properties camRgb->setBoardSocket(dai::CameraBoardSocket::CAM_A); camRgb->setResolution(dai::ColorCameraProperties::SensorResolution::THE_1080_P); camRgb->setFps(fps); if(downscaleColor) camRgb->setIspScale(2, 3); // For now, RGB needs fixed focus to properly align with depth. // This value was used during calibration try { auto calibData = device.readCalibration2(); auto lensPosition = calibData.getLensPosition(dai::CameraBoardSocket::CAM_A); if(lensPosition) { camRgb->initialControl.setManualFocus(lensPosition); } } catch(const std::exception& ex) { std::cout << ex.what() << std::endl; return 1; } left->setResolution(monoRes); left->setCamera("left"); left->setFps(fps); right->setResolution(monoRes); right->setCamera("right"); right->setFps(fps); stereo->setDefaultProfilePreset(dai::node::StereoDepth::PresetMode::HIGH_DENSITY); // LR-check is required for depth alignment stereo->setLeftRightCheck(true); stereo->setDepthAlign(dai::CameraBoardSocket::CAM_A); // Linking camRgb->isp.link(rgbOut->input); left->out.link(stereo->left); right->out.link(stereo->right); stereo->disparity.link(depthOut->input); // Connect to device and start pipeline device.startPipeline(pipeline); // Sets queues size and behavior for(const auto& name : queueNames) { device.getOutputQueue(name, 4, false); } std::unordered_map frame; auto rgbWindowName = "rgb"; auto depthWindowName = "depth"; auto blendedWindowName = "rgb-depth"; cv::namedWindow(rgbWindowName); cv::namedWindow(depthWindowName); cv::namedWindow(blendedWindowName); int defaultValue = (int)(rgbWeight * 100); cv::createTrackbar("RGB Weight %", blendedWindowName, &defaultValue, 100, updateBlendWeights); while(true) { std::unordered_map> latestPacket; auto queueEvents = device.getQueueEvents(queueNames); for(const auto& name : queueEvents) { auto packets = device.getOutputQueue(name)->tryGetAll(); auto count = packets.size(); if(count > 0) { latestPacket[name] = packets[count - 1]; } } for(const auto& name : queueNames) { if(latestPacket.find(name) != latestPacket.end()) { if(name == depthWindowName) { frame[name] = latestPacket[name]->getFrame(); auto maxDisparity = stereo->initialConfig.getMaxDisparity(); // Optional, extend range 0..95 -> 0..255, for a better visualisation if(1) frame[name].convertTo(frame[name], CV_8UC1, 255. / maxDisparity); // Optional, apply false colorization if(1) cv::applyColorMap(frame[name], frame[name], cv::COLORMAP_HOT); } else { frame[name] = latestPacket[name]->getCvFrame(); } cv::imshow(name, frame[name]); } } // Blend when both received if(frame.find(rgbWindowName) != frame.end() && frame.find(depthWindowName) != frame.end()) { // Need to have both frames in BGR format before blending if(frame[depthWindowName].channels() < 3) { cv::cvtColor(frame[depthWindowName], frame[depthWindowName], cv::COLOR_GRAY2BGR); } cv::Mat blended; cv::addWeighted(frame[rgbWindowName], rgbWeight, frame[depthWindowName], depthWeight, 0, blended); cv::imshow(blendedWindowName, blended); frame.clear(); } int key = cv::waitKey(1); if(key == 'q' || key == 'Q') { return 0; } } return 0; } ``` ## Pipeline ### examples/rgb_depth_aligned.pipeline.json ```json { "pipeline": { "connections": [ { "node1Id": 0, "node1Output": "video", "node1OutputGroup": "", "node2Id": 4, "node2Input": "in", "node2InputGroup": "" }, { "node1Id": 2, "node1Output": "out", "node1OutputGroup": "", "node2Id": 3, "node2Input": "right", "node2InputGroup": "" }, { "node1Id": 1, "node1Output": "out", "node1OutputGroup": "", "node2Id": 3, "node2Input": "left", "node2InputGroup": "" }, { "node1Id": 3, "node1Output": "disparity", "node1OutputGroup": "", "node2Id": 5, "node2Input": "in", "node2InputGroup": "" } ], "globalProperties": { "calibData": null, "cameraTuningBlobSize": null, "cameraTuningBlobUri": "", "leonCssFrequencyHz": 700000000.0, "leonMssFrequencyHz": 700000000.0, "pipelineName": null, "pipelineVersion": 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} } ], [ 1, { "id": 1, "ioInfo": [ [ [ "", "inputControl" ], { "blocking": true, "group": "", "id": 9, "name": "inputControl", "queueSize": 8, "type": 3, "waitForMessage": false } ], [ [ "", "out" ], { "blocking": false, "group": "", "id": 10, "name": "out", "queueSize": 8, "type": 0, "waitForMessage": false } ], [ [ "", "raw" ], { "blocking": false, "group": "", "id": 11, "name": "raw", "queueSize": 8, "type": 0, "waitForMessage": false } ], [ [ "", "frameEvent" ], { "blocking": false, "group": "", "id": 12, "name": "frameEvent", "queueSize": 8, "type": 0, "waitForMessage": false } ] ], "name": "MonoCamera", "properties": { "boardSocket": -1, "cameraName": "left", "fps": 30.0, "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, 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"invalidDisparityValue": 0, "linearEquationParameters": { "alpha": 0, "beta": 2, "threshold": 127 } }, "postProcessing": { "bilateralSigmaValue": 0, "brightnessFilter": { "maxBrightness": 256, "minBrightness": 0 }, "decimationFilter": { "decimationFactor": 1, "decimationMode": 0 }, "median": 5, "spatialFilter": { "alpha": 0.5, "delta": 0, "enable": false, "holeFillingRadius": 2, "numIterations": 1 }, "speckleFilter": { "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": null, "outKeepAspectRatio": true, "outWidth": null, "rectificationUseSpecTranslation": null, "rectifyEdgeFillColor": 0, "useHomographyRectification": null, "width": null } } ], [ 4, { "id": 4, "ioInfo": [ [ [ "", "in" ], { "blocking": true, "group": "", "id": 33, "name": "in", "queueSize": 8, "type": 3, "waitForMessage": true } ] ], "name": "XLinkOut", "properties": { "maxFpsLimit": -1.0, "metadataOnly": false, "streamName": "rgb" } } ], [ 5, { "id": 5, "ioInfo": [ [ [ "", "in" ], { "blocking": true, "group": "", "id": 34, "name": "in", "queueSize": 8, "type": 3, "waitForMessage": true } ] ], "name": "XLinkOut", "properties": { "maxFpsLimit": -1.0, "metadataOnly": false, "streamName": "disp" } } ] ] } } ``` ### 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。