Easy to use Python camera interface for NVIDIA Jetson

Related tags

Deep Learningjetcam
Overview

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

Comments
  • Camera works, Jetcam does not

    Camera works, Jetcam does not

    I am trying to get a Raspberry Pi v2 camera module working on a Jetson Xavier NX with Jetpack 4.4 installed.

    (Specifically, I want to use Jetcam because one of your other projects, https://github.com/NVIDIA-AI-IOT/trt_pose uses Jetcam in its live demo.)

    I know my camera is connected properly and working because if I run:

    gst-launch-1.0 nvarguscamerasrc ! nvoverlaysink
    

    ... I get a video image on screen immediately, no problem.

    However, running even the most basic example (csi_camera notebook), I always get errors:

    ---------------------------------------------------------------------------
    RuntimeError                              Traceback (most recent call last)
    /usr/local/lib/python3.6/dist-packages/jetcam-0.0.0-py3.6.egg/jetcam/csi_camera.py in __init__(self, *args, **kwargs)
         23             if not re:
    ---> 24                 raise RuntimeError('Could not read image from camera.')
         25         except:
    
    RuntimeError: Could not read image from camera.
    
    During handling of the above exception, another exception occurred:
    
    RuntimeError                              Traceback (most recent call last)
    <ipython-input-2-4d23bcae2fae> in <module>
          1 from jetcam.csi_camera import CSICamera
          2 
    ----> 3 camera = CSICamera(width=224, height=224, capture_width=1980, capture_height=1080, capture_fps=30)
    
    /usr/local/lib/python3.6/dist-packages/jetcam-0.0.0-py3.6.egg/jetcam/csi_camera.py in __init__(self, *args, **kwargs)
         25         except:
         26             raise RuntimeError(
    ---> 27                 'Could not initialize camera.  Please see error trace.')
         28 
         29         atexit.register(self.cap.release)
    
    RuntimeError: Could not initialize camera.  Please see error trace
    

    I've even tried the fix (hack?) suggested in https://github.com/NVIDIA-AI-IOT/jetcam/issues/12 but this makes no difference.

    Any advice on what to look for or what the issue could be?

    opened by anselanza 3
  • remove duplicate comma

    remove duplicate comma

    This duplicate comma causes an error on Jetpack 4.3 (OpenCV 4). error opening bin: could not parse caps "video/x-raw, , format=(string)BGR" Fix #17

    opened by borongyuan 3
  • camera can not initial

    camera can not initial

    Python 3.6.9 (default, Oct 8 2020, 12:12:24) [GCC 8.4.0] on linux Type "help", "copyright", "credits" or "license" for more information.

    from jetcam.csi_camera import CSICamera camera = CSICamera(width=224, height=224, capture_width=1080, capture_height=720, capture_fps=30) Traceback (most recent call last): File "/usr/local/lib/python3.6/dist-packages/jetcam-0.0.0-py3.6.egg/jetcam/csi_camera.py", line 24, in init RuntimeError: Could not read image from camera.

    During handling of the above exception, another exception occurred:

    Traceback (most recent call last): File "", line 1, in File "/usr/local/lib/python3.6/dist-packages/jetcam-0.0.0-py3.6.egg/jetcam/csi_camera.py", line 27, in init RuntimeError: Could not initialize camera. Please see error trace.

    opened by wangnan31415926 1
  • cv2.cpython-36m-aarch64-linux-gnu.so: undefined symbol

    cv2.cpython-36m-aarch64-linux-gnu.so: undefined symbol

    [email protected]:/usr/lib$ python3
    Python 3.6.8 (default, Jan 14 2019, 11:02:34)
    [GCC 8.0.1 20180414 (experimental) [trunk revision 259383]] on linux
    Type "help", "copyright", "credits" or "license" for more information.
    >>> from jetcam.usb_camera import USBCamera
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
      File "/usr/local/lib/python3.6/dist-packages/jetcam-0.0.0-py3.6.egg/jetcam/usb_camera.py", line 3, in <module>
    ImportError: /usr/local/lib/python3.6/dist-packages/cv2.cpython-36m-aarch64-linux-gnu.so: undefined symbol: _ZTIN2cv3dnn14dnn4_v201809175LayerE
    >>>
    
    
    

    My HW is jetson nano and SW env is

    [email protected]:/usr/lib$ jetson-release
     - NVIDIA Jetson NANO/TX1
       * Jetpack 4.2 [L4T 32.1.0]
       * CUDA GPU architecture 5.3
     - Libraries:
       * CUDA 10.0.166
       * cuDNN 7.3.1.28-1+cuda10.0
       * TensorRT 5.0.6.3-1+cuda10.0
       * Visionworks 1.6.0.500n
       * OpenCV 4.0.1 compiled CUDA: YES
     - Jetson Performance: active
    [email protected]:/usr/lib$
    
    
    opened by hgnan 0
  • Install failure

    Install failure

    I runsudo python3 setup.py install

    I get the following:

    /usr/local/lib/python3.8/dist-packages/setuptools/command/install.py:34: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
      warnings.warn(
    /usr/local/lib/python3.8/dist-packages/setuptools/command/easy_install.py:144: EasyInstallDeprecationWarning: easy_install command is deprecated. Use build and pip and other standards-based tools.
      warnings.warn(
    /usr/local/lib/python3.8/dist-packages/pkg_resources/__init__.py:123: PkgResourcesDeprecationWarning: 0.1.36ubuntu1 is an invalid version and will not be supported in a future release
      warnings.warn(
    /usr/local/lib/python3.8/dist-packages/pkg_resources/__init__.py:123: PkgResourcesDeprecationWarning: 0.23ubuntu1 is an invalid version and will not be supported in a future release
      warnings.warn(
    running bdist_egg
    running egg_info
    writing jetcam.egg-info/PKG-INFO
    writing dependency_links to jetcam.egg-info/dependency_links.txt
    writing top-level names to jetcam.egg-info/top_level.txt
    reading manifest file 'jetcam.egg-info/SOURCES.txt'
    adding license file 'LICENSE.md'
    writing manifest file 'jetcam.egg-info/SOURCES.txt'
    installing library code to build/bdist.linux-aarch64/egg
    running install_lib
    running build_py
    creating build/bdist.linux-aarch64/egg
    creating build/bdist.linux-aarch64/egg/jetcam
    copying build/lib/jetcam/csi_camera.py -> build/bdist.linux-aarch64/egg/jetcam
    copying build/lib/jetcam/__init__.py -> build/bdist.linux-aarch64/egg/jetcam
    copying build/lib/jetcam/usb_camera.py -> build/bdist.linux-aarch64/egg/jetcam
    copying build/lib/jetcam/camera.py -> build/bdist.linux-aarch64/egg/jetcam
    copying build/lib/jetcam/utils.py -> build/bdist.linux-aarch64/egg/jetcam
    byte-compiling build/bdist.linux-aarch64/egg/jetcam/csi_camera.py to csi_camera.cpython-38.pyc
    byte-compiling build/bdist.linux-aarch64/egg/jetcam/__init__.py to __init__.cpython-38.pyc
    byte-compiling build/bdist.linux-aarch64/egg/jetcam/usb_camera.py to usb_camera.cpython-38.pyc
    byte-compiling build/bdist.linux-aarch64/egg/jetcam/camera.py to camera.cpython-38.pyc
    byte-compiling build/bdist.linux-aarch64/egg/jetcam/utils.py to utils.cpython-38.pyc
    creating build/bdist.linux-aarch64/egg/EGG-INFO
    copying jetcam.egg-info/PKG-INFO -> build/bdist.linux-aarch64/egg/EGG-INFO
    copying jetcam.egg-info/SOURCES.txt -> build/bdist.linux-aarch64/egg/EGG-INFO
    copying jetcam.egg-info/dependency_links.txt -> build/bdist.linux-aarch64/egg/EGG-INFO
    copying jetcam.egg-info/top_level.txt -> build/bdist.linux-aarch64/egg/EGG-INFO
    zip_safe flag not set; analyzing archive contents...
    creating 'dist/jetcam-0.0.0-py3.8.egg' and adding 'build/bdist.linux-aarch64/egg' to it
    removing 'build/bdist.linux-aarch64/egg' (and everything under it)
    Processing jetcam-0.0.0-py3.8.egg
    Removing /usr/lib/python3.8/site-packages/jetcam-0.0.0-py3.8.egg
    Copying jetcam-0.0.0-py3.8.egg to /usr/lib/python3.8/site-packages
    jetcam 0.0.0 is already the active version in easy-install.pth
    
    Installed /usr/lib/python3.8/site-packages/jetcam-0.0.0-py3.8.egg
    Processing dependencies for jetcam==0.0.0
    Finished processing dependencies for jetcam==0.0.0
    

    import jetcam returns ModuleNotFoundError: No module named 'jetcam'

    What am I doing wrong?

    opened by master0v 1
  • Jetbot Camera Not Working- RuntimeError: Could not initialize camera.  Please see error trace.

    Jetbot Camera Not Working- RuntimeError: Could not initialize camera. Please see error trace.

    Hello, For some reason I can't get my camera to work again. For context, I tried to use a custom dataset from roboflow but then my kernel kept dying after installing roboflow. I reconfigured the right numpy and edited my .bashrc as I saw in NVIDIA's forum. But now the camera wont initialize. I know it works because it used to work before. I also am able to save a short video with it and able to call it in the terminal. But whenever I try to run a cell in Jupyter that requires the camera, it fails. I've tried restarting the camera too. But no luck :( Any help would be appreciated!

    opened by niiita 1
  • Camera ON LED continues to be on unless I restart the OS.

    Camera ON LED continues to be on unless I restart the OS.

    Hi,

    How can I close the camera after camera.unobserve(update_image, names='value') ? The camera ON LED continues to be on unless I restart the OS. I am using Logitech C270 USB camera. Is there a command to close the camera?

    opened by jam244 0
  • jetcam thread race - read thread and processing thread

    jetcam thread race - read thread and processing thread

    with camera.running = True, jetcam spawns a thread which reads into camera.value

    Now let's say we do, new_image = change['new'] and do some processing. I guess Python does shallow copying and only assigns a reference to the original image array in the new_image variable. So, effectively, new_image and camera.value are pointing to the same memory region. Lets say my processing-thread takes a very long time. In the mean time, camera.value is updated by jetcam-thread. This can cause a thread race. Is that right?

    opened by PhilipsKoshy 0
  • Cannot query video position: status=0, value=-1, duration=-1

    Cannot query video position: status=0, value=-1, duration=-1

    i tried camera = USBCamera(width=224, height=224, capture_width=640, capture_height=480, capture_device=0) the reply is [ WARN:0] global /home/nvidia/host/build_opencv/nv_opencv/modules/videoio/src/cap_gstreamer.cpp (933) open OpenCV | GStreamer warning: Cannot query video position: status=0, value=-1, duration=-1

    opened by Chenhait 0
  • AttributeError: 'directional_link' object has no attribute 'link'

    AttributeError: 'directional_link' object has no attribute 'link'

    The beginning steps is OK. But when 'camera_link.link()' fail to execute and I got an error: `--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) in ----> 1 camera_link.link()

    AttributeError: 'directional_link' object has no attribute 'link'`

    Don't know what is the reason.

    opened by watershade 0
Releases(v0.0.0)
Owner
NVIDIA AI IOT
NVIDIA AI IOT
An implementation of Video Frame Interpolation via Adaptive Separable Convolution using PyTorch

This work has now been superseded by: https://github.com/sniklaus/revisiting-sepconv sepconv-slomo This is a reference implementation of Video Frame I

Simon Niklaus 984 Dec 16, 2022
Synthetic structured data generators

Join us on What is Synthetic Data? Synthetic data is artificially generated data that is not collected from real world events. It replicates the stati

YData 850 Jan 07, 2023
Attendance Monitoring with Face Recognition using Python

Attendance Monitoring with Face Recognition using Python A python GUI integrated attendance system using face recognition to take attendance. In this

Vaibhav Rajput 2 Jun 21, 2022
基于DouZero定制AI实战欢乐斗地主

DouZero_For_Happy_DouDiZhu: 将DouZero用于欢乐斗地主实战 本项目基于DouZero 环境配置请移步项目DouZero 模型默认为WP,更换模型请修改start.py中的模型路径 运行main.py即可 SL (baselines/sl/): 基于人类数据进行深度学习

1.5k Jan 08, 2023
PyTorch implementation of PSPNet

PSPNet with PyTorch Unofficial implementation of "Pyramid Scene Parsing Network" (https://arxiv.org/abs/1612.01105). This repository is just for caffe

Kazuto Nakashima 52 Nov 16, 2022
Fast and exact ILP-based solvers for the Minimum Flow Decomposition (MFD) problem, and variants of it.

MFD-ILP Fast and exact ILP-based solvers for the Minimum Flow Decomposition (MFD) problem, and variants of it. The solvers are implemented using Pytho

Algorithmic Bioinformatics Group @ University of Helsinki 4 Oct 23, 2022
Machine learning framework for both deep learning and traditional algorithms

NeoML is an end-to-end machine learning framework that allows you to build, train, and deploy ML models. This framework is used by ABBYY engineers for

NeoML 704 Dec 27, 2022
Official repo for the work titled "SharinGAN: Combining Synthetic and Real Data for Unsupervised GeometryEstimation"

SharinGAN Official repo for the work titled "SharinGAN: Combining Synthetic and Real Data for Unsupervised GeometryEstimation" The official project we

Koutilya PNVR 23 Oct 19, 2022
Direct Multi-view Multi-person 3D Human Pose Estimation

Implementation of NeurIPS-2021 paper: Direct Multi-view Multi-person 3D Human Pose Estimation [paper] [video-YouTube, video-Bilibili] [slides] This is

Sea AI Lab 251 Dec 30, 2022
Source codes for Improved Few-Shot Visual Classification (CVPR 2020), Enhancing Few-Shot Image Classification with Unlabelled Examples

Source codes for Improved Few-Shot Visual Classification (CVPR 2020), Enhancing Few-Shot Image Classification with Unlabelled Examples (WACV 2022) and Beyond Simple Meta-Learning: Multi-Purpose Model

PLAI Group at UBC 42 Dec 06, 2022
Forecasting directional movements of stock prices for intraday trading using LSTM and random forest

Forecasting directional movements of stock-prices for intraday trading using LSTM and random-forest https://arxiv.org/abs/2004.10178 Pushpendu Ghosh,

Pushpendu Ghosh 270 Dec 24, 2022
TorchFlare is a simple, beginner-friendly, and easy-to-use PyTorch Framework train your models effortlessly.

TorchFlare TorchFlare is a simple, beginner-friendly and an easy-to-use PyTorch Framework train your models without much effort. It provides an almost

Atharva Phatak 85 Dec 26, 2022
Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis

Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis Website | ICCV paper | arXiv | Twitter This repository contains the official i

Ajay Jain 73 Dec 27, 2022
Planar Prior Assisted PatchMatch Multi-View Stereo

ACMP [News] The code for ACMH is released!!! [News] The code for ACMM is released!!! About This repository contains the code for the paper Planar Prio

Qingshan Xu 127 Dec 31, 2022
An SMPC companion library for Syft

SyMPC A library that extends PySyft with SMPC support SyMPC /ˈsɪmpəθi/ is a library which extends PySyft ≥0.3 with SMPC support. It allows computing o

Arturo Marquez Flores 0 Oct 13, 2021
Code for “ACE-HGNN: Adaptive Curvature ExplorationHyperbolic Graph Neural Network”

ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network This repository is the implementation of ACE-HGNN in PyTorch. Environment pyt

9 Nov 28, 2022
Make Watson Assistant send messages to your Discord Server

Make Watson Assistant send messages to your Discord Server Prerequisites Sign up for an IBM Cloud account. Fill in the required information and press

1 Jan 10, 2022
PlenOctrees: NeRF-SH Training & Conversion

PlenOctrees Official Repo: NeRF-SH training and conversion This repository contains code to train NeRF-SH and to extract the PlenOctree, constituting

Alex Yu 323 Dec 29, 2022
Codes and scripts for "Explainable Semantic Space by Grounding Languageto Vision with Cross-Modal Contrastive Learning"

Visually Grounded Bert Language Model This repository is the official implementation of Explainable Semantic Space by Grounding Language to Vision wit

17 Dec 17, 2022
Facilitating Database Tuning with Hyper-ParameterOptimization: A Comprehensive Experimental Evaluation

A Comprehensive Experimental Evaluation for Database Configuration Tuning This is the source code to the paper "Facilitating Database Tuning with Hype

DAIR Lab 9 Oct 29, 2022