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hogpp

Fast computation of rectangular histogram of oriented gradients (R-HOG) features using integral histogram

https://github.com/sergiud/hogpp

Science Score: 44.0%

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    Low similarity (14.7%) to scientific vocabulary

Keywords

computer-vision feature-extraction histogram-of-oriented-gradients hog-features hog-features-extraction image-processing integral-histogram machine-learning
Last synced: 6 months ago · JSON representation ·

Repository

Fast computation of rectangular histogram of oriented gradients (R-HOG) features using integral histogram

Basic Info
Statistics
  • Stars: 12
  • Watchers: 1
  • Forks: 1
  • Open Issues: 0
  • Releases: 4
Topics
computer-vision feature-extraction histogram-of-oriented-gradients hog-features hog-features-extraction image-processing integral-histogram machine-learning
Created over 4 years ago · Last pushed 6 months ago
Metadata Files
Readme License Citation

README.md

HOGpp

Linux macOS Windows codecov Documentation Status PyPI - Version

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.

Features

  • C++ templated implementation
  • Python support for 32, 64, and 80 bit floating point precision
  • Unrestricted input size (e.g., OpenCV as of version 4.5.5 requires the input to be a multiple of the block size)
  • Support for arbitrary integer (8 bit to 64 bit, both signed and unsigned) and floating point input (e.g., OpenCV requires 8-bit unsigned integer input)
  • Masking support (i.e., spatial exclusion of gradient magnitudes from contributing to features)

For a complete summary of differences between HOGpp and existing implementations, refer to the feature matrix.

Getting Started

In Python:

```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) ```

License

HOGpp is provided under the Apache License 2.0.

Owner

  • Name: Sergiu Deitsch
  • Login: sergiud
  • Kind: user

Citation (CITATION.cff) 

cff-version: 1.2.0
message: If you use HOGpp in your work, please reference it as below.
title: HOGpp
abstract: Fast computation of rectangular histogram of oriented gradients (R-HOG) features using integral histogram
authors:
- family-names: Deitsch
  given-names: Sergiu
  orcid: https://orcid.org/0000-0001-8865-8066
version: 0.1.0
date-released: 2021-08-14
license: Apache-2.0
repository-code: https://github.com/sergiud/hogpp

GitHub Events

Total
  • Release event: 4
  • Watch event: 3
  • Delete event: 32
  • Issue comment event: 14
  • Push event: 183
  • Pull request event: 22
  • Create event: 39
Last Year
  • Release event: 4
  • Watch event: 3
  • Delete event: 32
  • Issue comment event: 14
  • Push event: 183
  • Pull request event: 22
  • Create event: 39

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 911 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 6
  • Total maintainers: 1
pypi.org: hogpp

Fast computation of rectangular histogram of oriented gradients (R-HOG) features using integral histogram

  • Versions: 6
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 911 Last month
Rankings
Dependent packages count: 10.3%
Average: 34.1%
Dependent repos count: 58.0%
Maintainers (1)
sergiud
Last synced: 6 months ago

Dependencies

requirements_dev.txt pypi
  • numpy * development
  • pybind11 >=2.6.2 development
  • pytest * development
  • pytest-xdist * development
.github/workflows/linux.yml actions
  • actions/cache v3 composite
  • actions/checkout v3 composite
  • actions/setup-python v4 composite
  • codecov/codecov-action v3 composite
.github/workflows/macos.yml actions
  • actions/cache v3 composite
  • actions/checkout v3 composite
  • actions/setup-python v4 composite
  • ashutoshvarma/setup-ninja master composite
  • codecov/codecov-action v3 composite
.github/workflows/windows.yml actions
  • actions/cache v3 composite
  • actions/checkout v3 composite
  • actions/setup-python v4 composite
  • codecov/codecov-action v3 composite
  • msys2/setup-msys2 v2 composite
requirements_lint.txt pypi
  • autopep8 >=2.0.2
  • flake8 >=6.0.0
  • isort >=5.12.0