📃 IEEE Xplore 📃
A Demo with detection of a single red spline object from complex task environment and partial blocking scenario
Main dependencies:
The program runs on a Dell XPS-15 7590 (2019) Laptop with Ubuntu 22.04 LTS, 32 GB RAM and Nvidia GeForce GTX 1650. For envrionment management miniconda was used, for more details about environment please refer the file 'requriements.txt'. The run file is wirtten based on Intel Realsense Camera interface, before running the program, please install the Intel Realsense dependency from here. Some key packages need to be installed please see below.
python (3.8)
pytorch (1.4.0)
opencv
pillow
scikit-image
scipy
shapely
Installation:
pip install -e .
Download the weights and place them inside a weights folder.
Import as a standard python package with
from fastdlo.core import Pipeline
Then initialize the class
p = Pipeline(checkpoint_siam=checkpoint_siam, checkpoint_seg=checkpoint_seg, img_w=IMG_W, img_h=IMG_H, colorRange=colorRange, is_interpolation=activate_interpolation)
where the p is Pipeline instance, checkpoint_* are the ckpt file paths (include file name), img_w is frame width, img_h is frame height, colorRange is HSV limitation that should be detected, in form of [((H_lower, S_lower, V_lower),(H_upper, S_upper, V_upper)),..], and is_interpolation is the boolean variable that indicates whether the interpolation functionality should be activated.
The inference can be obtained with
img_out, dlo_mask_pointSet, dlo_path = p.run(source_img=color_image, mask_th=77)
where the img_out is the output masked image, dlo_mask_pointSet is the points array of mask in the image pixel unit [x,y], dlo_path is the points array of DLO detection path in the image pixel unit [x,y]
A example run file is prepared with name run.py under the main folder, the DLO merging & Interpolation functionality is implemented in core.py, color filtering object is added in a seperate file colorFilter.py.
DeepLabV3+ implementation based on https://github.com/VainF/DeepLabV3Plus-Pytorch
The origin source code from https://github.com/lar-unibo/fastdlo