RGB Encoding & 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. In the same time, a MobileNetv2SSD network is ran on the frames from the same RGB camera that is used for encoding

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 and RGB & MobilenetSSD.

Similiar samples:

Demo

Setup

Please run the install script to download all required dependencies. Please note that this script must be ran from git context, so you have to download the depthai-python repository first and then run the script

git clone https://github.com/luxonis/depthai-python.git
cd depthai-python/examples
python3 install_requirements.py

For additional information, please follow installation guide

This example also requires MobilenetSDD blob (mobilenet-ssd_openvino_2021.2_6shave.blob file) to work - you can download it from here

Source code

Also available on GitHub

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#!/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.createColorCamera()
videoEncoder = pipeline.createVideoEncoder()
nn = pipeline.createMobileNetDetectionNetwork()

xoutRgb = pipeline.createXLinkOut()
videoOut = pipeline.createXLinkOut()
nnOut = pipeline.createXLinkOut()

xoutRgb.setStreamName("rgb")
videoOut.setStreamName("h265")
nnOut.setStreamName("nn")

# Properties
camRgb.setBoardSocket(dai.CameraBoardSocket.RGB)
camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)
camRgb.setPreviewSize(300, 300)
camRgb.setInterleaved(False)

videoEncoder.setDefaultProfilePreset(1920, 1080, 30, dai.VideoEncoderProperties.Profile.H265_MAIN)

nn.setConfidenceThreshold(0.5)
nn.setBlobPath(nnPath)
nn.setNumInferenceThreads(2)
nn.input.setBlocking(False)

# Linking
camRgb.video.link(videoEncoder.input)
camRgb.preview.link(xoutRgb.input)
camRgb.preview.link(nn.input)
videoEncoder.bitstream.link(videoOut.input)
nn.out.link(nnOut.input)

# Connect to device and start pipeline
with dai.Device(pipeline) as device, open('video.h265', 'wb') as videoFile:

    # Queues
    queue_size = 8
    qRgb = device.getOutputQueue("rgb", queue_size)
    qDet = device.getOutputQueue("nn", queue_size)
    qRgbEnc = device.getOutputQueue('h265', maxSize=30, blocking=True)

    frame = None
    detections = []

    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)

    def displayFrame(name, frame):
        color = (255, 0, 0)
        for detection in detections:
            bbox = frameNorm(frame, (detection.xmin, detection.ymin, detection.xmax, detection.ymax))
            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(name, frame)

    while True:
        inRgb = qRgb.tryGet()
        inDet = qDet.tryGet()

        while qRgbEnc.has():
            qRgbEnc.get().getData().tofile(videoFile)

        if inRgb is not None:
            frame = inRgb.getCvFrame()

        if inDet is not None:
            detections = inDet.detections

        if frame is not None:
            displayFrame("rgb", frame)

        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")

Also available on GitHub

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#include <iostream>

// Inludes common necessary includes for development using depthai library
#include "depthai/depthai.hpp"

// MobilenetSSD label texts
static const std::vector<std::string> 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<dai::node::ColorCamera>();
    auto videoEncoder = pipeline.create<dai::node::VideoEncoder>();
    auto nn = pipeline.create<dai::node::MobileNetDetectionNetwork>();

    auto xoutRgb = pipeline.create<dai::node::XLinkOut>();
    auto videoOut = pipeline.create<dai::node::XLinkOut>();
    auto nnOut = pipeline.create<dai::node::XLinkOut>();

    xoutRgb->setStreamName("rgb");
    videoOut->setStreamName("h265");
    nnOut->setStreamName("nn");

    // Properties
    camRgb->setBoardSocket(dai::CameraBoardSocket::RGB);
    camRgb->setResolution(dai::ColorCameraProperties::SensorResolution::THE_1080_P);
    camRgb->setPreviewSize(300, 300);
    camRgb->setInterleaved(false);

    videoEncoder->setDefaultProfilePreset(1920, 1080, 30, dai::VideoEncoderProperties::Profile::H265_MAIN);

    nn->setConfidenceThreshold(0.5);
    nn->setBlobPath(nnPath);
    nn->setNumInferenceThreads(2);
    nn->input.setBlocking(false);

    // Linking
    camRgb->video.link(videoEncoder->input);
    camRgb->preview.link(xoutRgb->input);
    camRgb->preview.link(nn->input);
    videoEncoder->bitstream.link(videoOut->input);
    nn->out.link(nnOut->input);

    // Connect to device and start pipeline
    dai::Device device(pipeline);

    // Queues
    int queueSize = 8;
    auto qRgb = device.getOutputQueue("rgb", queueSize);
    auto qDet = device.getOutputQueue("nn", queueSize);
    auto qRgbEnc = device.getOutputQueue("h265", 30, true);

    cv::Mat frame;
    std::vector<dai::ImgDetection> detections;

    // Add bounding boxes and text to the frame and show it to the user
    auto displayFrame = [](std::string name, cv::Mat frame, std::vector<dai::ImgDetection>& detections) {
        auto color = cv::Scalar(255, 0, 0);
        // nn data, being the bounding box locations, are in <0..1> range - they need to be normalized with frame width/height
        for(auto& detection : detections) {
            int x1 = detection.xmin * frame.cols;
            int y1 = detection.ymin * frame.rows;
            int x2 = detection.xmax * frame.cols;
            int y2 = detection.ymax * frame.rows;

            int 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(name, frame);
    };

    auto videoFile = std::ofstream("video.h264", std::ios::binary);

    while(true) {
        auto inRgb = qRgb->tryGet<dai::ImgFrame>();
        auto inDet = qDet->tryGet<dai::ImgDetections>();

        auto out = qRgbEnc->get<dai::ImgFrame>();
        videoFile.write((char*)out->getData().data(), out->getData().size());

        if(inRgb) {
            frame = inRgb->getCvFrame();
        }

        if(inDet) {
            detections = inDet->detections;
        }

        if(!frame.empty()) {
            displayFrame("rgb", frame, detections);
        }

        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.h264 -c copy video.mp4" << endl;
    return 0;
}

Got questions?

We’re always happy to help with code or other questions you might have.