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Fast computation of rectangular histogram of oriented gradients (R-HOG) features using integral histogram
This repository contains an implementation of the rectangular histogram of oriented gradients feature descriptor (R-HOG) using integral histograms. The integral histogram representation allows to quickly compute HOG features in subregions of an image in constant time. This is particularly useful if the features in an image must be computed repeatedly, e.g., in a sliding window manner.
For a complete summary of differences between HOGpp and existing implementations, refer to the feature matrix.
In Python:
from hogpp import IntegralHOGDescriptor
desc = IntegralHOGDescriptor()
# Load image
image = # ...
# Precompute the gradient histograms. This needs to be done only once for each image.
desc.compute(image)
# Extract the feature descriptor of a region of interest. The method can be
# called multiple times for different subregions of the above image. Note the
# use of matrix indexing along each axis opposed to Cartesian coordinates.
roi = (0, 0, 128, 64) # top left (row, column) size (height, width)
X = desc(roi)
HOGpp is provided under the Apache License 2.0.
FAQs
Fast computation of rectangular histogram of oriented gradients (R-HOG) features using integral histogram
We found that hogpp demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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Security Fundamentals
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