18 - RGB Encoding with 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.

Demo

Setup

Please run the following command to install the required dependencies

 python3 -m pip install -U pip
 python3 -m pip install opencv-python
 python3 -m pip install -U --force-reinstall depthai

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_2021.2_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"')

pipeline = dai.Pipeline()

cam = pipeline.createColorCamera()
cam.setBoardSocket(dai.CameraBoardSocket.RGB)
cam.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)
cam.setPreviewSize(300, 300)
cam.setInterleaved(False)

videoEncoder = pipeline.createVideoEncoder()
videoEncoder.setDefaultProfilePreset(1920, 1080, 30, dai.VideoEncoderProperties.Profile.H265_MAIN)
cam.video.link(videoEncoder.input)

nn = pipeline.createMobileNetDetectionNetwork()
nn.setConfidenceThreshold(0.5)
nn.setBlobPath(nnPath)
nn.setNumInferenceThreads(2)
nn.input.setBlocking(False)
cam.preview.link(nn.input)

videoOut = pipeline.createXLinkOut()
videoOut.setStreamName('h265')
videoEncoder.bitstream.link(videoOut.input)

xoutRgb = pipeline.createXLinkOut()
xoutRgb.setStreamName("rgb")
cam.preview.link(xoutRgb.input)

nnOut = pipeline.createXLinkOut()
nnOut.setStreamName("nn")
nn.out.link(nnOut.input)

# 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"]

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

    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):
        for detection in detections:
            bbox = frameNorm(frame, (detection.xmin, detection.ymin, detection.xmax, detection.ymax))
            cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (255, 0, 0), 2)
            cv2.putText(frame, labelMap[detection.label], (bbox[0] + 10, bbox[1] + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
            cv2.putText(frame, f"{int(detection.confidence * 100)}%", (bbox[0] + 10, bbox[1] + 40), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
        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")

Got questions?

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