Simple implementation for testing opencv with gstreamer for nvidia jetson.

test_cameras_opencv_gs.py

import jetson.inference as inference
import jetson.utils as utils

import cv2
import time

# Load the net with jetson inference
net1 = inference.detectNet("ssd-mobilenet-v2", threshold=0.5)

# Define video sources
video_source1 = "/dev/video0"
video_source2 = "/dev/video1"

# Define gs camera pipeline
camSet1 = f'v4l2src device={video_source1} ! video/x-raw, width=640, height=480, framerate=30/1 ! videoconvert !  appsink'
camSet2 = f'v4l2src device={video_source2} ! video/x-raw, width=640, height=480, framerate=30/1 ! videoconvert !  appsink'

# Open camera with opencv + gs
camera1 = cv2.VideoCapture(camSet1)
camera2 = cv2.VideoCapture(camSet2)

while True:
    # CAMERA ONE


    # start fps counter
    start_time1 = time.time()

    # Read camera
    _, img1 = camera1.read()
    # Convert opencv image to cuda image
    img1_cuda = utils.cudaFromNumpy(img1)
    # Perform detection
    detections1 = net1.Detect(img1_cuda)

    # calculate fps
    fps1 = round(1.0 / (time.time() - start_time1), 2)
    cv2.putText(img1, f'fps: {fps1}', (5, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 1)
    
    # Display image
    cv2.imshow('myCam1', img1)



    # CAMERA TWO


    # start fps counter
    start_time2 = time.time()
    # Read camera
    _, img2 = camera2.read()
    # Convert opencv image to cuda image
    img2_cuda = utils.cudaFromNumpy(img2)
    # Perform detection
    detections2 = net1.Detect(img2_cuda)

    # calculate fps
    fps2 = round(1.0 / (time.time() - start_time2), 2)
    cv2.putText(img2, f'fps: {fps2}', (5, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 1)
    
    # Display image
    cv2.imshow('myCam2', img2)

    # Exit if pressed q on one window
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release resources
camera1.release()
camera2.release()
cv2.destroyAllWindows()

Will check this out. Thanks for sharing