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

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# HOGpp

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This repository implements the rectangular histogram of oriented gradients
feature descriptor (R-HOG) using integral histograms. The integral histogram
representation allows computing HOG features for any subregion in constant time
regardless of its size. This is particularly useful when extracting features
from many overlapping regions, e.g., in sliding-window detection.

## 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](https://github.com/opencv/opencv/blob/5f249a3e67bfe3627e184bf5535da64daeaeb1c8/modules/objdetect/src/hog.cpp#L95-L96))
* 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](https://hogpp.readthedocs.io/en/stable/index.html#comparison-to-existing-libraries).

## 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](http://www.apache.org/licenses/LICENSE-2.0).

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