In this section, we are going to visualize different types of Haar-like feature descriptors, of which there are five different types. The value of the descriptor is equal to the difference between the sum of intensity values in the blue and the red one.

The next code block shows how to use the scikit-image feature module's haar_like_feature_coord() and draw_haar_like_feature() functions to visualize different types of Haar feature descriptors:

from skimage.feature import haar_like_feature_coord
from skimage.feature import draw_haar_like_feature
images = [np.zeros((2, 2)), np.zeros((2, 2)), np.zeros((3, 3)),
np.zeros((3, 3)), np.zeros((2, 2))]
feature_types = ['type-2-x', 'type-2-y', 'type-3-x', 'type-3-y', 'type-4']
fig, axes = pylab.subplots(3, 2, figsize=(5,7))
for axes, img, feat_t in zip(np.ravel(axes), images, feature_types):
    coordinates, _ = haar_like_feature_coord(img.shape[0], img.shape[1], feat_t)
    haar_feature = draw_haar_like_feature(img, 0, 0, img.shape[0],img.shape[1], coordinates, max_n_features=1, random_state=0, color_positive_block=(1.0, 0.0, 0.0), color_negative_block=(0.0, 0.0, 1.0), alpha=0.8)
    axes.imshow(haar_feature), axes.set_title(feat_t), axes.set_axis_off()
#fig.suptitle('Different Haar-like feature descriptors')
pylab.axis('off'), pylab.tight_layout(), pylab.show()

The following screenshot shows the output of the preceding code block: