And:
#lets define where to copy images
out_img1 = cudaAllocMapped(width=1920, height=1080, format=‘rgb8’) #lets allocate memory for output image stream 1
out_img2 = cudaAllocMapped(width=1920, height=1080, format=‘rgb8’) #lets allocate memory for output image stream 2
out_img3 = cudaAllocMapped(width=1920, height=1080, format=‘rgb8’) #lets allocate memory for output image stream 3
out_img4 = cudaAllocMapped(width=1920, height=1080, format=‘rgb8’) #lets allocate memory for output image stream 4
and:
#lets load camera 1 and define focuser
camera1 = jetson_utils.videoSource(“csi://0”, argv=[“–input-width=1920”, “–input-height=1080”, “–input-rate=30”]) # select camera 1 - Capture a frame and return the cudaImage
focuser1 = Focuser(30)
#lets load camera 2 and define focuser
camera2 = jetson_utils.videoSource(“csi://1”, argv=[“–input-width=1920”, “–input-height=1080”, “–input-rate=30”]) # select camera 4 - Capture a frame and return the cudaImage
focuser2 = Focuser(32)
#lets load camera 3 and define focuser
camera3 = jetson_utils.videoSource(“csi://2”, argv=[“–input-width=1920”, “–input-height=1080”, “–input-rate=30”]) # select camera 2 - Capture a frame and return the cudaImage
focuser3 = Focuser(34)
#lets load camera 4 and define focuser
camera4 = jetson_utils.videoSource(“csi://3”, argv=[“–input-width=1920”, “–input-height=1080”, “–input-rate=30”]) # select camera 3 - Capture a frame and return the cudaImage
focuser4 = Focuser(35)
and:
net1 = jetson_inference.detectNet(argv=[‘–model=/home/visioline/install/jetson-inference-devit/python/training/detection/ssd/models/jw512/ssd-mobilenet-v2.onnx’, ‘–labels=/home/visioline/install/jetson-inference-devit/python/training/detection/ssd/models/jw512/labels.txt’, ‘–input-blob=input_0’, ‘–output-cvg=scores’, ‘–output-bbox=boxes’, ‘–confidence=0.3’, ‘–input-width=1920’, ‘–input-height=1080’, ‘–input-rate=30’, ‘–tracking=True’, ‘–tracker=KLT’, ‘–tracker-min-frames=1’, ‘–tracker-lost-frames=5’, ‘–tracker-overlap=0.5’, ‘–clustering=0.5’, ‘–batch_size=2’])
net2 = jetson_inference.detectNet(argv=[‘–model=/home/visioline/install/jetson-inference-devit/python/training/detection/ssd/models/jw512/ssd-mobilenet-v2.onnx’, ‘–labels=/home/visioline/install/jetson-inference-devit/python/training/detection/ssd/models/jw512/labels.txt’, ‘–input-blob=input_0’, ‘–output-cvg=scores’, ‘–output-bbox=boxes’, ‘–confidence=0.3’, ‘–input-width=1920’, ‘–input-height=1080’, ‘–input-rate=30’, ‘–tracking=True’, ‘–tracker=KLT’, ‘–tracker-min-frames=1’, ‘–tracker-lost-frames=5’, ‘–tracker-overlap=0.5’, ‘–clustering=0.5’, ‘–batch_size=2’])
net3 = jetson_inference.detectNet(argv=[‘–model=/home/visioline/install/jetson-inference-devit/python/training/detection/ssd/models/jw512/ssd-mobilenet-v2.onnx’, ‘–labels=/home/visioline/install/jetson-inference-devit/python/training/detection/ssd/models/jw512/labels.txt’, ‘–input-blob=input_0’, ‘–output-cvg=scores’, ‘–output-bbox=boxes’, ‘–confidence=0.3’, ‘–input-width=1920’, ‘–input-height=1080’, ‘–input-rate=30’, ‘–tracking=True’, ‘–tracker=KLT’, ‘–tracker-min-frames=1’, ‘–tracker-lost-frames=5’, ‘–tracker-overlap=0.5’, ‘–clustering=0.5’, ‘–batch_size=2’])
net4 = jetson_inference.detectNet(argv=[‘–model=/home/visioline/install/jetson-inference-devit/python/training/detection/ssd/models/jw512/ssd-mobilenet-v2.onnx’, ‘–labels=/home/visioline/install/jetson-inference-devit/python/training/detection/ssd/models/jw512/labels.txt’, ‘–input-blob=input_0’, ‘–output-cvg=scores’, ‘–output-bbox=boxes’, ‘–confidence=0.3’, ‘–input-width=1920’, ‘–input-height=1080’, ‘–input-rate=30’, ‘–tracking=True’, ‘–tracker=KLT’, ‘–tracker-min-frames=1’, ‘–tracker-lost-frames=5’, ‘–tracker-overlap=0.5’, ‘–clustering=0.5’, ‘–batch_size=2’])
maybe this information is needed…