easy-local-features 0.3.5

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easy-local-features-baselines

Just some scripts to make things easier for the local features baselines.

Installation

I suggest using a conda environment to install the requirements. You can create one using the following command.

conda create -n elf python=3.9 # the python version is not so critical, but I used 3.9.
conda activate elf

Now we can insall the requirements using pip.

pip install -r requirements.txt
pip install .

How to use

from easy_local_features.feature.baseline_deal import DEAL_baseline
# from easy_local_features.feature.baseline_dalf import DALF_baseline
# from easy_local_features.feature.baseline_aliked import ALIKED_baseline
# from easy_local_features.feature.baseline_disk import DISK_baseline
# from easy_local_features.feature.baseline_r2d2 import R2D2_baseline
# from easy_local_features.feature.baseline_superpoint import SuperPoint_baseline
# from easy_local_features.matching.baseline_superglue import SuperGlue_baseline

# Load an image
img = cv2.imread("assets/notredame.png")

# Initialize the extractor
extractor = DEAL_baseline()

# Return keypoints and descriptors just like OpenCV
keypoints, descriptors = extractor.detectAndCompute(img)

TODO

• Add a setup.py to make it a pip package
• Make a general maching class
  • The idea is to have a class that can match images using any local feature extractor and any matching method.
• Fix requirements to install automatically with the package (maybe)
• Add a script to download some datasets
• Add a download script for the pretrained models
• Add more baselines :)
  • DEAL
    • Add LICENSE file
  • R2D2
  • DISK
  • SuperPoin
  • SuperGlue
  • DALF
    • Add LICENSE file
  • ASLFeat
  • LoFTR
    • Add LICENSE file
  • DKM
  • ALIKED
    • NOT WORKING ON MAC M1
    • Add code
    • Add LICENSE file
    • Test on MAC M1 with CPU
    • Test on Linux with CPU
    • Test with CUDA