USB Camera YOLOv5x Inference

This is a real-time Ultralytics YOLOv5x inference sample based on hbm_runtime. It reads frames from a USB camera for object detection and visualizes the results in fullscreen. The sample code is located in /app/pydev_demo/09_usb_camera_sample/.

This is a real-time Ultralytics YOLOv5x inference sample based on hbm_runtime. It reads frames from a USB camera for object detection and visualizes the results in fullscreen. The sample code is located in /app/pydev_demo/usb_camera_sample/.

Features

• Model loading

  Load the specified .hbm model file through hbm_runtime and extract model name, input/output shapes, quantization information, and more.

• Camera capture

  Automatically scan devices under /dev/video*, open the first available USB camera, and configure MJPEG encoding, 1080p resolution, and 30 FPS.

• Image preprocessing

  Resize BGR images to the model input resolution (letterbox or plain scaling) and convert them to NV12 format.

• Inference execution

  Submit input tensors through the run() method to perform model forward inference on the BPU.

• Postprocessing

  Includes quantized output decoding, candidate box filtering (by score threshold), NMS deduplication, and coordinate mapping back to the original image size.

• Visualization

  Draw detection boxes with class labels and confidence scores on the image, display them fullscreen in a window, and support real-time processing and exit control.

Model Description

See the Ultralytics YOLOv5x object detection sample section.

Environment Dependencies

• Ensure the dependencies in pydev are installed

  pip install -r ../requirements.txt

  pip install -r ../requirements.txt --break-system-packages

Directory Structure

.
├── usb_camera_yolov5x.py       # Main program
└── README.md                   # Usage instructions

Parameter Description

Parameter	Description	Default Value
--model-path	BPU quantized model path (.hbm)	/opt/hobot/model/s100/basic/yolov5x_672x672_nv12.hbm
--priority	Inference priority (0~255, 255 is highest)	0
--bpu-cores	BPU core index list (for example, 0 1)	[0]
--label-file	Class label file path	/app/res/labels/coco_classes.names
--nms-thres	IoU threshold for Non-Maximum Suppression (NMS)	0.45
--score-thres	Detection confidence threshold	0.25

Parameter	Description	Default Value
--model-path	BPU quantized model path (.hbm)	/opt/hobot/model/s600/basic/yolov5x_672x672_nv12.hbm
--priority	Inference priority (0~255, 255 is highest)	0
--bpu-cores	BPU core index list (for example, 0 1)	[0]
--label-file	Class label file path	/app/res/labels/coco_classes.names
--nms-thres	IoU threshold for Non-Maximum Suppression (NMS)	0.45
--score-thres	Detection confidence threshold	0.25

Quick Start

Note: This program must run in a desktop environment.

• Run the model

  • Use default parameters

    python usb_camera_yolov5x.py

  • Run with specified parameters

    python usb_camera_yolov5x.py \
    --model-path /opt/hobot/model/s100/basic/yolov5x_672x672_nv12.hbm \
    --priority 0 \
    --bpu-cores 0 \
    --label-file /app/res/labels/coco_classes.names \
    --nms-thres 0.45 \
    --score-thres 0.25

    python usb_camera_yolov5x.py \
    --model-path /opt/hobot/model/s600/basic/yolov5x_672x672_nv12.hbm \
    --priority 0 \
    --bpu-cores 0 \
    --label-file /app/res/labels/coco_classes.names \
    --nms-thres 0.45 \
    --score-thres 0.25

• Exit

  Move the mouse over the display window and press q to quit.

• View results

  After successful execution, object detection results are displayed on screen in real time.

Notes

• This program must run in a desktop environment.

• If the specified model path does not exist, try searching under /opt/hobot/model/s100/basic/.

• If the specified model path does not exist, try searching under /opt/hobot/model/s600/basic/.

License

Copyright (C) 2025, XiangshunZhao D-Robotics.

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as
published by the Free Software Foundation, either version 3 of the
License, or (at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License
along with this program.  If not, see <https://www.gnu.org/licenses/>.