hog

hog

skimage.feature.hog(image, orientations=9, pixels_per_cell=(8, 8), cells_per_block=(3, 3), visualise=False, transform_sqrt=False, feature_vector=True, normalise=None) [source]

Extract Histogram of Oriented Gradients (HOG) for a given image.

Compute a Histogram of Oriented Gradients (HOG) by

1. (optional) global image normalisation
2. computing the gradient image in x and y
3. computing gradient histograms
4. normalising across blocks
5. flattening into a feature vector

Parameters:

image : (M, N) ndarray Input image (greyscale). orientations : int Number of orientation bins. pixels_per_cell : 2 tuple (int, int) Size (in pixels) of a cell. cells_per_block : 2 tuple (int,int) Number of cells in each block. visualise : bool, optional Also return an image of the HOG. transform_sqrt : bool, optional Apply power law compression to normalise the image before processing. DO NOT use this if the image contains negative values. Also see notes section below. feature_vector : bool, optional Return the data as a feature vector by calling .ravel() on the result just before returning. normalise : bool, deprecated The parameter is deprecated. Use transform_sqrt for power law compression. normalise has been deprecated.

Returns:

newarr : ndarray HOG for the image as a 1D (flattened) array. hog_image : ndarray (if visualise=True) A visualisation of the HOG image.

Notes

Power law compression, also known as Gamma correction, is used to reduce the effects of shadowing and illumination variations. The compression makes the dark regions lighter. When the kwarg transform_sqrt is set to True, the function computes the square root of each color channel and then applies the hog algorithm to the image.

References

• http://en.wikipedia.org/wiki/Histogram_of_oriented_gradients
• Dalal, N and Triggs, B, Histograms of Oriented Gradients for Human Detection, IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2005 San Diego, CA, USA