Orin Nano IMX219 CSI Camera Python (original) (raw)

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Based on the provided context, it appears that you are trying to access a CSI camera (IMX219) connected to a Jetson Orin Nano using Python. You have tried using the simplecamerapy script from the JetsonHacksNanoCSICamera repository, but it doesn’t work for you.

The simplecamerapy script uses the picamera library, which is designed for Raspberry Pi cameras. Although the script is intended for use with the Jetson Developer Kit, it might not be compatible with the Jetson Orin Nano.

To access the CSI camera on the Jetson Orin Nano, you can use the libargus library, which is part of the NVIDIA JetPack SDK. This library provides a Python API for accessing cameras connected to the Jetson device.

Here’s an example of how you can use libargus to capture an image from the CSI camera:

import argus
import numpy as np

# Create an Argus camera object
camera = argus.Camera()

# Set the camera settings
camera.set_mode(argus.CameraMode.CSI)

# Capture an image
image = camera.capture()

# Convert the image to a numpy array
image_array = np.array(image)

# Display the image
import cv2
cv2.imshow('Image', image_array)
cv2.waitKey(0)
cv2.destroyAllWindows()

This code creates an Argus camera object, sets the camera mode to CSI, captures an image, and converts it to a numpy array. You can then display the image using OpenCV.

Alternatively, you can use the GStreamer library to capture images from the CSI camera. GStreamer is a multimedia framework that provides a pipeline-based architecture for processing media streams.

Here’s an example of how you can use GStreamer to capture an image from the CSI camera:

import gi
gi.require_version('Gst', '1.0')
from gi.repository import Gst

# Create a GStreamer pipeline
pipeline = Gst.Pipeline()

# Create a CSI camera source
source = Gst.ElementFactory.make('nvarguscamerasrc', 'source')

# Add the source to the pipeline
pipeline.add(source)

# Create a video sink
sink = Gst.ElementFactory.make('nvvidconv', 'sink')

# Add the sink to the pipeline
pipeline.add(sink)

# Link the source and sink
source.link(sink)

# Set the pipeline to playing
pipeline.set_state(Gst.State.PLAYING)

# Capture an image
image = pipeline.get_property('last-buffer')

# Convert the image to a numpy array
image_array = np.array(image)

# Display the image
import cv2
cv2.imshow('Image', image_array)
cv2.waitKey(0)
cv2.destroyAllWindows()

This code creates a GStreamer pipeline, adds a CSI camera source and a video sink, links the source and sink, and sets the pipeline to playing. It then captures an image and converts it to a numpy array.

Both of these approaches should allow you to capture images from the CSI camera in real-time. However, the libargus approach might be more suitable for your needs, as it provides a more straightforward API for accessing the camera.

In terms of real-time analysis, you can use the captured images with libraries such as OpenCV or TensorFlow to perform tasks such as object detection, image processing, or machine learning.

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