iTeach Toolkit Package 🛠️

Overview 🔍

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.

Model Checkpoints 📥

Pretrained model checkpoints can be downloaded from this link.

Installation ⚙️

To install the package, use pip:

pip install iteach_toolkit

Usage 🖥️

Below 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()

License 📜

This project is licensed under the MIT License.

BibTex 📚

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}
}