fasthog 0.1.7

Reasonably fast implementation of HOG descriptor calculation

FastHOG

Reasonably fast implementation of Histogram of Oriented Gradients. Designed to be roughly equivalent to skimage.feature.hog. While slightly less flexible (it only supports float64 single-channel images), it is significantly faster, so ideal in workloads where HOG calculation is a bottleneck, as was in the https://github.com/flatironinstitute/ManifoldEM project that inspired this repository.

Installation

pip install fasthog

Usage

Example taken from https://scikit-image.org/docs/stable/auto_examples/features_detection/plot_hog.html

from fasthog import hog
from skimage import data
from skimage.color import rgb2gray

image = rgb2gray(data.astronaut())

fd, hog_image = hog(
    image,
    n_bins=8,
    pixels_per_cell=(16, 16),
    cells_per_block=(1, 1),
)

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4), sharex=True, sharey=True)

ax1.axis('off')
ax1.imshow(image, cmap=plt.cm.gray)
ax1.set_title('Input image')

# Rescale histogram for better display
hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 10))

ax2.axis('off')
ax2.imshow(hog_image_rescaled, cmap=plt.cm.gray)
ax2.set_title('Histogram of Oriented Gradients')
plt.show()