haar_like_feature(int_image, r, c, width, height, feature_type=None, feature_coord=None)
Haar-like features have been successfully used for image classification and object detection . It has been used for real-time face detection algorithm proposed in .
When extracting those features in parallel, be aware that the choice of the backend (i.e. multiprocessing vs threading) will have an impact on the performance. The rule of thumb is as follows: use multiprocessing when extracting features for all possible ROI in an image; use threading when extracting the feature at specific location for a limited number of ROIs. Refer to the example sphx_glr_auto_examples_applications_plot_haar_extraction_selection_classification.py
for more insights.
Integral image for which the features need to be computed.
Row-coordinate of top left corner of the detection window.
Column-coordinate of top left corner of the detection window.
Width of the detection window.
Height of the detection window.
The type of feature to consider:
'type-2-x': 2 rectangles varying along the x axis;
'type-2-y': 2 rectangles varying along the y axis;
'type-3-x': 3 rectangles varying along the x axis;
'type-3-y': 3 rectangles varying along the y axis;
'type-4': 4 rectangles varying along x and y axis.
By default all features are extracted.
If using with :None:None:`feature_coord`, it should correspond to the feature type of each associated coordinate feature.
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.
Resulting Haar-like features. Each value is equal to the subtraction of sums of the positive and negative rectangles. The data type depends of the data type of :None:None:`int_image`: :None:None:`int` when the data type of :None:None:`int_image` is uint
or :None:None:`int` and :None:None:`float` when the data type of :None:None:`int_image` is :None:None:`float`.
Compute the Haar-like features for a region of interest (ROI) of an integral image.
>>> import numpy as np
... from skimage.transform import integral_image
... from skimage.feature import haar_like_feature
... img = np.ones((5, 5), dtype=np.uint8)
... img_ii = integral_image(img)
... feature = haar_like_feature(img_ii, 0, 0, 5, 5, 'type-3-x')
... feature array([-1, -2, -3, -4, -1, -2, -3, -4, -1, -2, -3, -4, -1, -2, -3, -4, -1, -2, -3, -4, -1, -2, -3, -4, -1, -2, -3, -1, -2, -3, -1, -2, -3, -1, -2, -1, -2, -1, -2, -1, -1, -1])
You can compute the feature for some pre-computed coordinates.
This example is valid syntax, but we were not able to check execution>>> from skimage.feature import haar_like_feature_coordSee :
... feature_coord, feature_type = zip(
... *[haar_like_feature_coord(5, 5, feat_t)
... for feat_t in ('type-2-x', 'type-3-x')])
... # only select one feature over two
... feature_coord = np.concatenate([x[::2] for x in feature_coord])
... feature_type = np.concatenate([x[::2] for x in feature_type])
... feature = haar_like_feature(img_ii, 0, 0, 5, 5,
... feature_type=feature_type,
... feature_coord=feature_coord)
... feature array([ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -3, -1, -3, -1, -3, -1, -3, -1, -3, -1, -3, -1, -3, -2, -1, -3, -2, -2, -2, -1])
The following pages refer to to this document either explicitly or contain code examples using this.
skimage.feature.haar.haar_like_feature
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