The iTeach_package is a toolkit designed for running object detection using the DH-YOLO model, specifically for identifying doors and handles in images. This package provides easy-to-use command-line tools for performing inference with a pre-trained DH-YOLO model.
Pretrained model checkpoints can be downloaded from this link.
To install the package, use pip:
pip install iteach_toolkitBelow is an example of how to use the package for running inference on an image.
import os
from PIL import Image as PILImg
from iteach_toolkit.DHYOLO import DHYOLODetector
# Set up paths
os.system("wget https://huggingface.co/spaces/IRVLUTD/DH-YOLO/resolve/main/test_imgs/jpad-irvl-test.jpg")
image_path = "./jpad-irvl-test.jpg"
model_path = "/path/to/yolov5_model.pt"
# Initialize the DHYOLODetector class
dhyolo = DHYOLODetector(model_path)
# Perform prediction on the image
orig_image, detections = dhyolo.predict(image_path, conf_thres=0.7, iou_thres=0.7, max_det=1000)
# Plot the bounding boxes on the original image
orig_image, image_with_bboxes = dhyolo.plot_bboxes(attach_watermark=True)
# Convert the image (with bounding boxes) from a NumPy array to a PIL Image for display.
pil_img_with_bboxes = PILImg.fromarray(image_with_bboxes)
# Plot the image
pil_img_with_bboxes.show()This project is licensed under the MIT License.
Please cite iTeach if it helps your work or research 🙌:
@misc{padalunkal2024iteach,
title={iTeach: Interactive Teaching for Robot Perception using Mixed Reality},
author={Jishnu Jaykumar P and Cole Salvato and Vinaya Bomnale and Jikai Wang and Yu Xiang},
year={2024},
eprint={2410.09072},
archivePrefix={arXiv},
primaryClass={cs.RO}
}