skimage2.feature.draw_haar_like_feature#

skimage2.feature.draw_haar_like_feature(image, r, c, width, height, feature_coord, color_positive_block=(1.0, 0.0, 0.0), color_negative_block=(0.0, 1.0, 0.0), alpha=0.5, max_n_features=None, rng=None)[source]#

Visualization of Haar-like features.

Parameters:

imagendarray of shape (M, N)

The region of an integral image for which the features need to be computed.

rint

Row-coordinate of top left corner of the detection window.

cint

Column-coordinate of top left corner of the detection window.

widthint

Width of the detection window.

heightint

Height of the detection window.

feature_coordndarray of list of tuples or None, optional

The array of coordinates to be extracted. This is useful when you want to recompute only a subset of features. In this case feature_type needs to be an array containing the type of each feature, as returned by haar_like_feature_coord(). By default, all coordinates are computed.

color_positive_blocktuple of 3 floats

Floats specifying the color for the positive block. Corresponding values define (R, G, B) values. Default value is red (1, 0, 0).

color_negative_blocktuple of 3 floats

Floats specifying the color for the negative block Corresponding values define (R, G, B) values. Default value is blue (0, 1, 0).

alphafloat

Value in the range [0, 1] that specifies opacity of visualization. 1 - fully transparent, 0 - opaque.

max_n_featuresint, default=None

The maximum number of features to be returned. By default, all features are returned.

rng{numpy.random.Generator, int}, optional

Pseudo-random number generator. By default, a PCG64 generator is used (see numpy.random.default_rng()). If rng is an int, it is used to seed the generator.

The rng is used when generating a set of features smaller than the total number of available features.

Returns:

featuresndarray of shape (M, N): An image in which the different features will be added.

Examples

>>> import numpy as np
>>> from _skimage2.feature import haar_like_feature_coord
>>> from _skimage2.feature import draw_haar_like_feature
>>> feature_coord, _ = haar_like_feature_coord(2, 2, 'type-4')
>>> image = draw_haar_like_feature(np.zeros((2, 2)),
...                                0, 0, 2, 2,
...                                feature_coord,
...                                max_n_features=1)
>>> image
array([[[0. , 0.5, 0. ],
        [0.5, 0. , 0. ]],

       [[0.5, 0. , 0. ],
        [0. , 0.5, 0. ]]])