Pre-release
This release is a pre-release and may not be stable for production use.
Efficient Adaptive Non-Maximal Suppression (ANMS)
This project provides a Cython implementation of the Efficient Adaptive Non-Maximal Suppression (ANMS) algorithms, designed to achieve a homogeneous spatial distribution of keypoints. This implementation is based on the paper "Efficient adaptive non-maximal suppression algorithms for homogeneous spatial keypoint distribution" and offers improved packaging and installation via pip.
Installation
You can install the adaptivenms package directly using pip:
pip install adaptivenms
If you have cloned the repository, you can also install it from the project directory:
pip install .
Example
The following example demonstrates how to use the square_covering_adaptive_nms function to select a target number of keypoints with a homogeneous distribution.
import numpy as np
from adaptivenms import square_covering_adaptive_nms
# Randomly generate sample keypoint data
n_kpts = 10000
width, height = 960, 720
kpts_x = np.random.randint(width, size=(n_kpts,))
kpts_y = np.random.randint(height, size=(n_kpts,))
kpts = np.stack([kpts_x, kpts_y], axis=-1)
responses = np.random.random(n_kpts)
# Apply square_covering_adaptive_nms
kpts_idxs = square_covering_adaptive_nms(
kpts, # 2-dimensional array of shape [N, 2] representing keypoint coordinates (x, y)
responses, # 1-dimensional array of shape [N,] representing keypoint responses (e.g., cornerness scores)
width=width, # Width of the image
height=height, # Height of the image
target_num_kpts=2048, # Desired number of keypoints to retain
indices_only=True, # If True, returns only the indices of selected keypoints; otherwise, returns the selected keypoints themselves
up_tol=10, # Tolerance for allowing slightly more selected keypoints than target_num_kpts
max_num_iter=30, # Maximum number of binary search iterations to find the optimal suppression radius
)
print(f"Original number of keypoints: {n_kpts}")
print(f"Number of selected keypoints: {len(kpts_idxs)}")
References
- Paper: Efficient adaptive non-maximal suppression algorithms for homogeneous spatial keypoint distribution
- Original C++ Implementation: GitHub - BAILOOL/ANMS-Codes
- Original Cython Implementation (Forked from): GitHub - viniavskyi-ostap/adaptive-nms
1 maintainer
Metadata
1 maintainer
Release files for adaptive-nms 1.0.0a2
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Release files / adaptive_nms-1.0.0a2-cp38-cp38-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl
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| Size | 607.6 kB |
| Tags | CPython 3.8 Linux glibc 2.24+ ARM64 Linux glibc 2.28+ ARM64 |
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