JetCam

JetCam is an easy to use Python camera interface for NVIDIA Jetson.

• Works with various USB and CSI cameras using Jetson's Accelerated GStreamer Plugins

• Easily read images as numpy arrays with image = camera.read()

• Set the camera to running = True to attach callbacks to new frames

JetCam makes it easy to prototype AI projects in Python, especially within the Jupyter Lab programming environment installed in JetCard.

If you find an issue, please let us know!

Setup

git clone https://github.com/NVIDIA-AI-IOT/jetcam
cd jetcam
sudo python3 setup.py install

JetCam is tested against a system configured with the JetCard setup. Different system configurations may require additional steps.

Usage

Below we show some usage examples. You can find more in the notebooks.

Create CSI camera

Call CSICamera to use a compatible CSI camera. capture_width, capture_height, and capture_fps will control the capture shape and rate that images are aquired. width and height control the final output shape of the image as returned by the read function.

from jetcam.csi_camera import CSICamera

camera = CSICamera(width=224, height=224, capture_width=1080, capture_height=720, capture_fps=30)

Create USB camera

Call USBCamera to use a compatbile USB camera. The same parameters as CSICamera apply, along with a parameter capture_device that indicates the device index. You can check the device index by calling ls /dev/video*.

from jetcam.usb_camera import USBCamera

camera = USBCamera(capture_device=1)

Read

Call read() to read the latest image as a numpy.ndarray of data type np.uint8 and shape (224, 224, 3). The color format is BGR8.

image = camera.read()

The read function also updates the camera's internal value attribute.

camera.read()
image = camera.value

Callback

You can also set the camera to running = True, which will spawn a thread that acquires images from the camera. These will update the camera's value attribute automatically. You can attach a callback to the value using the traitlets library. This will call the callback with the new camera value as well as the old camera value

camera.running = True

def callback(change):
    new_image = change['new']
    # do some processing...

camera.observe(callback, names='value')

Cameras

CSI Cameras

These cameras work with the CSICamera class. Try them out by following the example notebook.

Model	Infared	FOV	Resolution	Cost
Raspberry Pi Camera V2		62.2	3280x2464	$25
Raspberry Pi Camera V2 (NOIR)	x	62.2	3280x2464	$31
Arducam IMX219 CS lens mount			3280x2464	$65
Arducam IMX219 M12 lens mount			3280x2464	$60
LI-IMX219-MIPI-FF-NANO			3280x2464	$29
WaveShare IMX219-77		77	3280x2464	$19
WaveShare IMX219-77IR	x	77	3280x2464	$21
WaveShare IMX219-120		120	3280x2464	$20
WaveShare IMX219-160		160	3280x2464	$23
WaveShare IMX219-160IR	x	160	3280x2464	$25
WaveShare IMX219-200		200	3280x2464	$27

USB Cameras

These cameras work with the USBCamera class. Try them out by following the example notebook.

Model	Infared	FOV	Resolution	Cost
Logitech C270		60	1280x720	$18

See also

• JetBot - An educational AI robot based on NVIDIA Jetson Nano

• JetRacer - An educational AI racecar using NVIDIA Jetson Nano

• JetCard - An SD card image for web programming AI projects with NVIDIA Jetson Nano

• torch2trt - An easy to use PyTorch to TensorRT converter