# RGB Encoding & Mono & MobilenetSSD This example shows how to configure the depthai video encoder in h.265 format to encode the RGB camera input at Full-HD resolution at 30FPS, and transfers the encoded video over XLINK to the host, saving it to disk as a video file. At the same time, a MobileNetv2SSD network is ran on the frames from right grayscale camera Pressing Ctrl+C will stop the recording and then convert it using ffmpeg into an mp4 to make it playable. Note that ffmpeg will need to be installed and runnable for the conversion to mp4 to succeed. Be careful, this example saves encoded video to your host storage. So if you leave it running, you could fill up your storage on your host. It's a combination of [RGB Encoding](https://docs.luxonis.com/software/depthai/examples/rgb_encoding.md) and [Mono & MobilenetSSD](https://docs.luxonis.com/software/depthai/examples/mono_depth_mobilenetssd.md). ### Similar samples: * [RGB Encoding](https://docs.luxonis.com/software/depthai/examples/rgb_encoding.md) * [RGB & Mono Encoding](https://docs.luxonis.com/software/depthai/examples/rgb_mono_encoding.md) * [Encoding Max Limit](https://docs.luxonis.com/software/depthai/examples/encoding_max_limit.md) * [RGB Encoding & MobilenetSSD](https://docs.luxonis.com/software/depthai/examples/rgb_encoding_mobilenet.md) * [RGB Encoding & Mono with MobilenetSSD & Depth](https://docs.luxonis.com/software/depthai/examples/rgb_encoding_mono_mobilenet_depth.md) ## Demo ## Setup Please run the [install script](https://github.com/luxonis/depthai-python/blob/main/examples/install_requirements.py) to download all required dependencies. Please note that this script must be ran from git context, so you have to download the [depthai-python](https://github.com/luxonis/depthai-python) repository first and then run the script ```bash git clone https://github.com/luxonis/depthai-python.git cd depthai-python/examples python3 install_requirements.py ``` For additional information, please follow the [installation guide](https://docs.luxonis.com/software/depthai/manual-install.md). ## Source code #### Python ```python #!/usr/bin/env python3 from pathlib import Path import sys import cv2 import depthai as dai import numpy as np # Get argument first nnPath = str((Path(__file__).parent / Path('../models/mobilenet-ssd_openvino_2021.4_6shave.blob')).resolve().absolute()) if len(sys.argv) > 1: nnPath = sys.argv[1] if not Path(nnPath).exists(): import sys raise FileNotFoundError(f'Required file/s not found, please run "{sys.executable} install_requirements.py"') # MobilenetSSD label texts labelMap = ["background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"] # Create pipeline pipeline = dai.Pipeline() # Define sources and outputs camRgb = pipeline.create(dai.node.ColorCamera) monoRight = pipeline.create(dai.node.MonoCamera) videoEncoder = pipeline.create(dai.node.VideoEncoder) nn = pipeline.create(dai.node.MobileNetDetectionNetwork) manip = pipeline.create(dai.node.ImageManip) videoOut = pipeline.create(dai.node.XLinkOut) xoutRight = pipeline.create(dai.node.XLinkOut) manipOut = pipeline.create(dai.node.XLinkOut) nnOut = pipeline.create(dai.node.XLinkOut) videoOut.setStreamName('h265') xoutRight.setStreamName("right") manipOut.setStreamName("manip") nnOut.setStreamName("nn") # Properties camRgb.setBoardSocket(dai.CameraBoardSocket.CAM_A) camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P) monoRight.setCamera("right") monoRight.setResolution(dai.MonoCameraProperties.SensorResolution.THE_720_P) videoEncoder.setDefaultProfilePreset(30, dai.VideoEncoderProperties.Profile.H265_MAIN) nn.setConfidenceThreshold(0.5) nn.setBlobPath(nnPath) nn.setNumInferenceThreads(2) nn.input.setBlocking(False) # The NN model expects BGR input. By default ImageManip output type would be same as input (gray in this case) manip.initialConfig.setFrameType(dai.ImgFrame.Type.BGR888p) manip.initialConfig.setResize(300, 300) # Linking camRgb.video.link(videoEncoder.input) videoEncoder.bitstream.link(videoOut.input) monoRight.out.link(manip.inputImage) manip.out.link(nn.input) monoRight.out.link(xoutRight.input) manip.out.link(manipOut.input) nn.out.link(nnOut.input) # Connect to device and start pipeline with dai.Device(pipeline) as device: # Queues queue_size = 8 qRight = device.getOutputQueue("right", queue_size) qManip = device.getOutputQueue("manip", queue_size) qDet = device.getOutputQueue("nn", queue_size) qRgbEnc = device.getOutputQueue('h265', maxSize=30, blocking=True) frame = None frameManip = None detections = [] offsetX = (monoRight.getResolutionWidth() - monoRight.getResolutionHeight()) // 2 color = (255, 0, 0) croppedFrame = np.zeros((monoRight.getResolutionHeight(), monoRight.getResolutionHeight())) def frameNorm(frame, bbox): normVals = np.full(len(bbox), frame.shape[0]) normVals[::2] = frame.shape[1] return (np.clip(np.array(bbox), 0, 1) * normVals).astype(int) videoFile = open('video.h265', 'wb') cv2.namedWindow("right", cv2.WINDOW_NORMAL) cv2.namedWindow("manip", cv2.WINDOW_NORMAL) while True: inRight = qRight.tryGet() inManip = qManip.tryGet() inDet = qDet.tryGet() while qRgbEnc.has(): qRgbEnc.get().getData().tofile(videoFile) if inRight is not None: frame = inRight.getCvFrame() if inManip is not None: frameManip = inManip.getCvFrame() if inDet is not None: detections = inDet.detections if frame is not None: for detection in detections: bbox = frameNorm(croppedFrame, (detection.xmin, detection.ymin, detection.xmax, detection.ymax)) bbox[::2] += offsetX cv2.putText(frame, labelMap[detection.label], (bbox[0] + 10, bbox[1] + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, color) cv2.putText(frame, f"{int(detection.confidence * 100)}%", (bbox[0] + 10, bbox[1] + 40), cv2.FONT_HERSHEY_TRIPLEX, 0.5, color) cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color, 2) # Show the frame cv2.imshow("right", frame) if frameManip is not None: for detection in detections: bbox = frameNorm(frameManip, (detection.xmin, detection.ymin, detection.xmax, detection.ymax)) cv2.putText(frameManip, labelMap[detection.label], (bbox[0] + 10, bbox[1] + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, color) cv2.putText(frameManip, f"{int(detection.confidence * 100)}%", (bbox[0] + 10, bbox[1] + 40), cv2.FONT_HERSHEY_TRIPLEX, 0.5, color) cv2.rectangle(frameManip, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color, 2) # Show the frame cv2.imshow("manip", frameManip) if cv2.waitKey(1) == ord('q'): break print("To view the encoded data, convert the stream file (.h265) into a video file (.mp4) using a command below:") print("ffmpeg -framerate 30 -i video.h265 -c copy video.mp4") ``` #### C++ ```cpp #include // Includes common necessary includes for development using depthai library #include "depthai/depthai.hpp" // MobilenetSSD label texts static const std::vector labelMap = {"background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"}; int main(int argc, char** argv) { using namespace std; // Default blob path provided by Hunter private data download // Applicable for easier example usage only std::string nnPath(BLOB_PATH); // If path to blob specified, use that if(argc > 1) { nnPath = std::string(argv[1]); } // Print which blob we are using printf("Using blob at path: %s\n", nnPath.c_str()); // Create pipeline dai::Pipeline pipeline; // Define sources and outputs auto camRgb = pipeline.create(); auto monoRight = pipeline.create(); auto videoEncoder = pipeline.create(); auto nn = pipeline.create(); auto manip = pipeline.create(); auto videoOut = pipeline.create(); auto xoutRight = pipeline.create(); auto manipOut = pipeline.create(); auto nnOut = pipeline.create(); videoOut->setStreamName("h265"); xoutRight->setStreamName("right"); manipOut->setStreamName("manip"); nnOut->setStreamName("nn"); // Properties camRgb->setBoardSocket(dai::CameraBoardSocket::CAM_A); camRgb->setResolution(dai::ColorCameraProperties::SensorResolution::THE_1080_P); monoRight->setCamera("right"); monoRight->setResolution(dai::MonoCameraProperties::SensorResolution::THE_720_P); videoEncoder->setDefaultProfilePreset(30, dai::VideoEncoderProperties::Profile::H265_MAIN); nn->setConfidenceThreshold(0.5); nn->setBlobPath(nnPath); nn->setNumInferenceThreads(2); nn->input.setBlocking(false); // The NN model expects BGR input. By default ImageManip output type would be same as input (gray in this case) manip->initialConfig.setFrameType(dai::ImgFrame::Type::BGR888p); manip->initialConfig.setResize(300, 300); // Linking camRgb->video.link(videoEncoder->input); videoEncoder->bitstream.link(videoOut->input); monoRight->out.link(manip->inputImage); manip->out.link(nn->input); monoRight->out.link(xoutRight->input); manip->out.link(manipOut->input); nn->out.link(nnOut->input); // Connect to device and start pipeline dai::Device device(pipeline); // Queues int queueSize = 8; auto qRight = device.getOutputQueue("right", queueSize); auto qManip = device.getOutputQueue("manip", queueSize); auto qDet = device.getOutputQueue("nn", queueSize); auto qRgbEnc = device.getOutputQueue("h265", 30, true); cv::Mat frame; cv::Mat frameManip; std::vector detections; int offsetX = (monoRight->getResolutionWidth() - monoRight->getResolutionHeight()) / 2; auto color = cv::Scalar(255, 0, 0); auto videoFile = std::ofstream("video.h265", std::ios::binary); cv::namedWindow("right", cv::WINDOW_NORMAL); cv::namedWindow("manip", cv::WINDOW_NORMAL); while(true) { auto inRight = qRight->tryGet(); auto inManip = qManip->tryGet(); auto inDet = qDet->tryGet(); auto out1 = qRgbEnc->get(); videoFile.write((char*)out1->getData().data(), out1->getData().size()); if(inRight) { frame = inRight->getCvFrame(); } if(inManip) { frameManip = inManip->getCvFrame(); } if(inDet) { detections = inDet->detections; } if(!frame.empty()) { for(auto& detection : detections) { int x1 = detection.xmin * monoRight->getResolutionHeight() + offsetX; int y1 = detection.ymin * monoRight->getResolutionHeight(); int x2 = detection.xmax * monoRight->getResolutionHeight() + offsetX; int y2 = detection.ymax * monoRight->getResolutionHeight(); uint32_t labelIndex = detection.label; std::string labelStr = to_string(labelIndex); if(labelIndex < labelMap.size()) { labelStr = labelMap[labelIndex]; } cv::putText(frame, labelStr, cv::Point(x1 + 10, y1 + 20), cv::FONT_HERSHEY_TRIPLEX, 0.5, color); std::stringstream confStr; confStr << std::fixed << std::setprecision(2) << detection.confidence * 100; cv::putText(frame, confStr.str(), cv::Point(x1 + 10, y1 + 40), cv::FONT_HERSHEY_TRIPLEX, 0.5, color); cv::rectangle(frame, cv::Rect(cv::Point(x1, y1), cv::Point(x2, y2)), color, cv::FONT_HERSHEY_SIMPLEX); } // Show the frame cv::imshow("right", frame); } if(!frameManip.empty()) { for(auto& detection : detections) { int x1 = detection.xmin * frameManip.cols; int y1 = detection.ymin * frameManip.rows; int x2 = detection.xmax * frameManip.cols; int y2 = detection.ymax * frameManip.rows; uint32_t labelIndex = detection.label; std::string labelStr = to_string(labelIndex); if(labelIndex < labelMap.size()) { labelStr = labelMap[labelIndex]; } cv::putText(frameManip, labelStr, cv::Point(x1 + 10, y1 + 20), cv::FONT_HERSHEY_TRIPLEX, 0.5, color); std::stringstream confStr; confStr << std::fixed << std::setprecision(2) << detection.confidence * 100; cv::putText(frameManip, confStr.str(), cv::Point(x1 + 10, y1 + 40), cv::FONT_HERSHEY_TRIPLEX, 0.5, color); cv::rectangle(frameManip, cv::Rect(cv::Point(x1, y1), cv::Point(x2, y2)), color, cv::FONT_HERSHEY_SIMPLEX); } // Show the frame cv::imshow("manip", frameManip); } int key = cv::waitKey(1); if(key == 'q' || key == 'Q') { break; } } cout << "To view the encoded data, convert the stream file (.h265) into a video file (.mp4), using a command below:" << endl; cout << "ffmpeg -framerate 30 -i video.h265 -c copy video.mp4" << endl; return 0; } ``` ## Pipeline ### examples/rgb_encoding_mono_mobilenet.pipeline.json ```json { "pipeline": { "connections": [ { "node1Id": 0, "node1Output": "video", "node1OutputGroup": "", "node2Id": 2, "node2Input": "in", "node2InputGroup": "" }, { "node1Id": 2, "node1Output": "bitstream", "node1OutputGroup": "", "node2Id": 5, "node2Input": "in", "node2InputGroup": "" }, { "node1Id": 1, "node1Output": "out", "node1OutputGroup": "", "node2Id": 4, "node2Input": 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"queueSize": 8, "type": 3, "waitForMessage": true } ], [ [ "", "out" ], { "blocking": false, "group": "", "id": 21, "name": "out", "queueSize": 8, "type": 0, "waitForMessage": false } ] ], "name": "ImageManip", "properties": { "initialConfig": { "cropConfig": { "cropRatio": 1.0, "cropRect": { "xmax": 0.0, "xmin": 0.0, "ymax": 0.0, "ymin": 0.0 }, "cropRotatedRect": { "angle": 0.0, "center": { "x": 0.0, "y": 0.0 }, "size": { "height": 0.0, "width": 0.0 } }, "enableCenterCropRectangle": false, "enableRotatedRect": false, "normalizedCoords": true, "widthHeightAspectRatio": 1.0 }, "enableCrop": false, "enableFormat": true, "enableResize": true, "formatConfig": { "colormap": 0, "colormapMax": 255, "colormapMin": 0, "flipHorizontal": false, "flipVertical": false, "type": 8 }, "interpolation": -1, "resizeConfig": { "bgBlue": 0, "bgGreen": 0, "bgRed": 0, "enableRotation": false, "enableWarp4pt": false, "enableWarpMatrix": false, "height": 300, "keepAspectRatio": true, "lockAspectRatioFill": false, "normalizedCoords": true, "rotationAngleDeg": 0.0, "warpBorderReplicate": false, "warpFourPoints": [], "warpMatrix3x3": [], "width": 300 }, "reusePreviousImage": false, "skipCurrentImage": false }, "meshHeight": 0, "meshUri": "", "meshWidth": 0, "numFramesPool": 4, "outputFrameSize": 1048576 } } ], [ 5, { "id": 5, "ioInfo": [ [ [ "", "in" ], { "blocking": true, "group": "", "id": 22, "name": "in", "queueSize": 8, "type": 3, "waitForMessage": true } ] ], "name": "XLinkOut", "properties": { "maxFpsLimit": -1.0, "metadataOnly": false, "streamName": "h265" } } ], [ 6, { "id": 6, "ioInfo": [ [ [ "", "in" ], { "blocking": true, "group": "", "id": 23, "name": "in", "queueSize": 8, "type": 3, "waitForMessage": true } ] ], "name": "XLinkOut", "properties": { "maxFpsLimit": -1.0, "metadataOnly": false, "streamName": "right" } } ], [ 7, { "id": 7, "ioInfo": [ [ [ "", "in" ], { "blocking": true, "group": "", "id": 24, "name": "in", "queueSize": 8, "type": 3, "waitForMessage": true } ] ], "name": "XLinkOut", "properties": { "maxFpsLimit": -1.0, "metadataOnly": false, "streamName": "manip" } } ], [ 8, { "id": 8, "ioInfo": [ [ [ "", "in" ], { "blocking": true, "group": "", "id": 25, "name": "in", "queueSize": 8, "type": 3, "waitForMessage": true } ] ], "name": "XLinkOut", "properties": { "maxFpsLimit": -1.0, "metadataOnly": false, "streamName": "nn" } } ] ] } } ``` ### Need assistance? 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