| Line 62: | Line 62: | ||
Modify the following code section inside '''async def detection_loop''' method to allow capturing with the camera: |
Modify the following code section inside '''async def detection_loop''' method to allow capturing with the camera: |
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<syntaxhighlight lang="python"> |
<syntaxhighlight lang="python"> |
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# Delete or comment the three lines of code below |
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# camera = cv2.VideoCapture(CAMERA_DEVICE) |
# camera = cv2.VideoCapture(CAMERA_DEVICE) |
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# camera.set(cv2.CAP_PROP_FRAME_WIDTH, width) |
# camera.set(cv2.CAP_PROP_FRAME_WIDTH, width) |
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# camera.set(cv2.CAP_PROP_FRAME_HEIGHT, height) |
# camera.set(cv2.CAP_PROP_FRAME_HEIGHT, height) |
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# Add the following lines to allow capturing with nvarguscamerasrc |
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gst_pipeline = ( |
gst_pipeline = ( |
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f"nvarguscamerasrc ! " |
f"nvarguscamerasrc ! " |
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Revision as of 18:19, 26 November 2025
How to run NVIDIA NanoOWL tutorial
Keywords: NVIDIA Jetson, NanoOWL, object detection
Description
This wiki pages shows how to run the tree prediction with a live camera tutorial from NVIDIA NanoOWL. NanoOWL is a project that optimizes OWL-ViT to run real-time on NVIDIA Jetson Orin Platforms with NVIDIA TensorRT. NanoOWL also introduces a new "tree detection" pipeline that combines OWL-ViT and CLIP to enable nested detection and classification of anything, at any level, simply by providing text.
Set up
- One of the following Jetson:
- Jetson Orin NX (16 GB)
- Jetson Orin Nano (8 GB)
- Jetson AGX Orin (32 GB or 64 GB)
- Running one of the following JetPack versions:
- Jetpack 5 (L4T r35.x)
- Jetpack 6 (L4T r36.x)
Tutorial
1. Clone and setup jetson-containers
git clone https://github.com/dusty-nv/jetson-containers
bash jetson-containers/install.sh
2. Run the following to pull or build a compatible container image.
jetson-containers run --workdir /opt/nanoowl $(autotag nanoowl)
After running above command you should be in workdir (/opt/nanoowl) inside the container.
3. Verify you have a camera connected:
ls /dev/video*
4. Install missing module inside container:
pip install aiohttp
Note: If this takes too long or fails, try the following command:
pip install aiohttp --index-url https://pypi.org/simple --prefer-binary
5. Run the tree_demo.py example.
cd examples/tree_demo
python3 tree_demo.py --camera 0 --resolution 640x480 ../../data/owl_image_encoder_patch32.engine
Adaptations
- In case you are using a camera that does not support Gstreamer element v4l2src, for example a camera with CSI connector, you may encounter the following error:
[ WARN:0@15.685] global cap_gstreamer.cpp:2829 handleMessage OpenCV | GStreamer warning: Embedded video playback halted; module v4l2src0 reported: Internal data stream error.
Modify the following code section inside async def detection_loop method to allow capturing with the camera:
# Delete or comment the three lines of code below
# camera = cv2.VideoCapture(CAMERA_DEVICE)
# camera.set(cv2.CAP_PROP_FRAME_WIDTH, width)
# camera.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
# Add the following lines to allow capturing with nvarguscamerasrc
gst_pipeline = (
f"nvarguscamerasrc ! "
f"video/x-raw(memory:NVMM), width={width}, height={height}, format=NV12, framerate=30/1 ! "
f"nvvidconv ! video/x-raw, format=BGRx ! videoconvert ! "
f"appsink"
)
camera = cv2.VideoCapture(gst_pipeline, cv2.CAP_GSTREAMER)
camera.set(cv2.CAP_PROP_BUFFERSIZE, 1)
