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"""
============================
Haar-like feature descriptor
============================
Haar-like features are simple digital image features that were introduced in a
real-time face detector [1]_. These features can be efficiently computed on any
scale in constant time, using an integral image [1]_. After that, a small
number of critical features is selected from this large set of potential
features (e.g., using AdaBoost learning algorithm as in [1]_). The following
example will show the mechanism to build this family of descriptors.
References
----------
.. [1] Viola, Paul, and Michael J. Jones. "Robust real-time face
detection." International journal of computer vision 57.2
(2004): 137-154.
https://www.merl.com/publications/docs/TR2004-043.pdf
:DOI:`10.1109/CVPR.2001.990517`
"""
import numpy as np
import matplotlib.pyplot as plt
from skimage.feature import haar_like_feature_coord
from skimage.feature import draw_haar_like_feature
###############################################################################
# Different types of Haar-like feature descriptors
###############################################################################
# The Haar-like feature descriptors come into 5 different types as illustrated
# in the figure below. The value of the descriptor is equal to the difference
# between the sum of intensity values in the green and the red one.
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, axs = plt.subplots(3, 2)
for ax, img, feat_t in zip(np.ravel(axs), images, feature_types):
coord, _ = 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], coord, max_n_features=1, rng=0
)
ax.imshow(haar_feature)
ax.set_title(feat_t)
ax.set_xticks([])
ax.set_yticks([])
fig.suptitle('The different Haar-like feature descriptors')
plt.axis('off')
plt.show()
###############################################################################
# The value of the descriptor is equal to the difference between the sum of the
# intensity values in the green rectangle and the red one. The red area is
# subtracted to the sum of the pixel intensities of the green In practice, the
# Haar-like features will be placed in all possible location of an image and a
# feature value will be computed for each of these locations.
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