https://github.com/opendronemap/pypopsift
Python module for CUDA accelerated SIFT on GPUs
https://github.com/opendronemap/pypopsift
cuda gpu popsift python sift
Last synced: 5 days ago
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Python module for CUDA accelerated SIFT on GPUs
- Host: GitHub
- URL: https://github.com/opendronemap/pypopsift
- Owner: OpenDroneMap
- License: mpl-2.0
- Created: 2020-07-15T14:59:39.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2025-06-20T16:12:26.000Z (10 days ago)
- Last Synced: 2025-06-21T22:26:27.970Z (8 days ago)
- Topics: cuda, gpu, popsift, python, sift
- Language: C++
- Homepage:
- Size: 20.5 KB
- Stars: 44
- Watchers: 2
- Forks: 15
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# pypopsift
CUDA accelerated SIFT in Python
This library is a wrapper around [PopSift](https://github.com/alicevision/popsift) to compute SIFT keypoints and descriptors on the GPU using CUDA. It's written to be a drop-in replacement for existing OpenCV functions such as `cv2.FeatureDetector_create('SIFT')` and ` cv2.DescriptorExtractor_create('SIFT')`.
## Building
Requirements:
* CUDA Toolkit >= 7
* CMake >= 3.14
* A C++11 capable compiler (g++ 5.4.0 works fine)
```
# git clone --recurse-submodules https://github.com/uav4geo/pypopsift
# cd pypopsift && mkdir build && cd build
# make -j8
```
To install the Python package:
```
# cd pypopsift
# pip install .
```
## Usage
```
import cv2
import numpy as np
from pypopsift import popsift
filename = "/path/to/image.JPG"
config = {
'sift_peak_threshold': 0.1,
'sift_edge_threshold': 10.0,
'feature_min_frames': 8000,
'feature_use_adaptive_suppression': False,
'feature_process_size': 2048
}
def resized_image(image, config):
"""Resize image to feature_process_size."""
max_size = config['feature_process_size']
h, w, _ = image.shape
size = max(w, h)
if 0 < max_size < size:
dsize = w * max_size // size, h * max_size // size
return cv2.resize(image, dsize=dsize, interpolation=cv2.INTER_AREA)
else:
return image
flags = cv2.IMREAD_COLOR
image = cv2.imread(filename, flags)
if image is None:
raise IOError("Unable to load image {}".format(filename))
if len(image.shape) == 3:
image[:, :, :3] = image[:, :, [2, 1, 0]]
assert len(image.shape) == 3
image = resized_image(image, config)
image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
points, desc = popsift(image.astype(np.uint8), # values between 0, 1
peak_threshold=config['sift_peak_threshold'],
edge_threshold=config['sift_edge_threshold'],
target_num_features=config['feature_min_frames'])
print(points.shape)
print(points)
print(desc.shape)
print(desc)
```
## License
Mozilla Public License 2.0
## Acknowledgements
```
@inproceedings{Griwodz2018Popsift,
author = {Griwodz, Carsten and Calvet, Lilian and Halvorsen, P{\aa}l},
title = {Popsift: A Faithful SIFT Implementation for Real-time Applications},
booktitle = {Proceedings of the 9th {ACM} Multimedia Systems Conference},
series = {MMSys '18},
year = {2018},
isbn = {978-1-4503-5192-8},
location = {Amsterdam, Netherlands},
pages = {415--420},
numpages = {6},
doi = {10.1145/3204949.3208136},
acmid = {3208136},
publisher = {ACM},
address = {New York, NY, USA},
}
```