menpo.feature.double_igo(pixels, *, double_angles=True, verbose=False)

Extracts Image Gradient Orientation (IGO) features from the input image. The output image has N * C number of channels, where N is the number of channels of the original image and C = 2 or C = 4 depending on whether double angles are used.

  • pixels (Image or subclass or (C, X, Y, ..., Z) ndarray) – Either the image object itself or an array with the pixels. The first dimension is interpreted as channels. This means an N-dimensional image is represented by an N+1 dimensional array.

  • double_angles (bool, optional) –

    Assume that phi represents the gradient orientations.

    If this flag is False, the features image is the concatenation of cos(phi) and sin(phi), thus 2 channels.

    If True, the features image is the concatenation of cos(phi), sin(phi), cos(2 * phi), sin(2 * phi), thus 4 channels.

  • verbose (bool, optional) – Flag to print IGO related information.


igo (Image or subclass or (X, Y, ..., Z, C) ndarray) – The IGO features image. It has the same type and shape as the input pixels. The output number of channels depends on the double_angles flag.


ValueError – Image has to be 2D in order to extract IGOs.



G. Tzimiropoulos, S. Zafeiriou and M. Pantic, “Subspace learning from image gradient orientations”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, num. 12, p. 2454–2466, 2012.