img_to_graph#

sklearn.feature_extraction.image.img_to_graph(img,*,mask=None,return_as=<class'scipy.sparse._coo.coo_matrix'>,dtype=None)[source]#

Graph of the pixel-to-pixel gradient connections.

Edges are weighted with the gradient values.

Read more in theUser Guide.

Parameters:

imgarray-like of shape (height, width) or (height, width, channel)

2D or 3D image.

maskndarray of shape (height, width) or (height, width, channel), dtype=bool, default=None

An optional mask of the image, to consider only part of thepixels.

return_asnp.ndarray or a sparse matrix class, default=sparse.coo_matrix

The class to use to build the returned adjacency matrix.

dtypedtype, default=None

The data of the returned sparse matrix. By default it is thedtype of img.

Returns:

graphndarray or a sparse matrix class

The computed adjacency matrix.

Examples

>>>importnumpyasnp>>>fromsklearn.feature_extraction.imageimportimg_to_graph>>>img=np.array([[0,0],[0,1]])>>>img_to_graph(img,return_as=np.ndarray)array([[0, 0, 0, 0],       [0, 0, 0, 1],       [0, 0, 0, 1],       [0, 1, 1, 1]])