69 lines
2.7 KiB
Python
69 lines
2.7 KiB
Python
"""
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============================
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Haar-like feature descriptor
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============================
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Haar-like features are simple digital image features that were introduced in a
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real-time face detector [1]_. These features can be efficiently computed on any
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scale in constant time, using an integral image [1]_. After that, a small
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number of critical features is selected from this large set of potential
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features (e.g., using AdaBoost learning algorithm as in [1]_). The following
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example will show the mechanism to build this family of descriptors.
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References
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----------
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.. [1] Viola, Paul, and Michael J. Jones. "Robust real-time face
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detection." International journal of computer vision 57.2
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(2004): 137-154.
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https://www.merl.com/publications/docs/TR2004-043.pdf
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:DOI:`10.1109/CVPR.2001.990517`
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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from skimage.feature import haar_like_feature_coord
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from skimage.feature import draw_haar_like_feature
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###############################################################################
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# Different types of Haar-like feature descriptors
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###############################################################################
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# The Haar-like feature descriptors come into 5 different types as illustrated
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# in the figure below. The value of the descriptor is equal to the difference
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# between the sum of intensity values in the green and the red one.
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images = [np.zeros((2, 2)), np.zeros((2, 2)),
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np.zeros((3, 3)), np.zeros((3, 3)),
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np.zeros((2, 2))]
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feature_types = ['type-2-x', 'type-2-y',
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'type-3-x', 'type-3-y',
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'type-4']
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fig, axs = plt.subplots(3, 2)
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for ax, img, feat_t in zip(np.ravel(axs), images, feature_types):
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coord, _ = haar_like_feature_coord(img.shape[0], img.shape[1], feat_t)
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haar_feature = draw_haar_like_feature(img, 0, 0,
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img.shape[0],
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img.shape[1],
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coord,
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max_n_features=1,
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random_state=0)
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ax.imshow(haar_feature)
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ax.set_title(feat_t)
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ax.set_xticks([])
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ax.set_yticks([])
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fig.suptitle('The different Haar-like feature descriptors')
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plt.axis('off')
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plt.show()
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###############################################################################
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# The value of the descriptor is equal to the difference between the sum of the
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# intensity values in the green rectangle and the red one. The red area is
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# subtracted to the sum of the pixel intensities of the green In practice, the
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# Haar-like features will be placed in all possible location of an image and a
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# feature value will be computed for each of these locations.
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