`skimage.feature.haar:draw_haar_like_feature`

source: /dev/scikit-image/src/skimage/feature/haar.py :235

Signature

   def     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     )

Summary

Visualization of Haar-like features.

Parameters

image : ndarray of shape (M, N)

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

r : int

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

c : int

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

width : int

Width of the detection window.

height : int

Height of the detection window.

feature_coord : ndarray 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_block : tuple 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_block : tuple 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).

alpha : float

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

max_n_features : int, 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

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

Examples

import numpy as np
from skimage.feature import haar_like_feature_coord
from skimage.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

✓

Aliases

skimage.feature.draw_haar_like_feature