9iy2SSKJr SSKJr SSKrSSKJr SSKJr SSK J r SS K J r SS K Jr S rS rSS jrSSjrSSjrg))chain)addN)haar_like_feature_coord_wrapperhaar_like_feature_wrapper)gray2rgb) rectangle) img_as_float)ztype-2-xztype-2-yztype-3-xztype-3-yztype-4cUc[nU$[U[5(aU/nOUnUH#nU[;dM[SUS[S35e U$)z?Transform feature type to an iterable and check that it exists.z'The given feature type is unknown. Got z instead of one of .) FEATURE_TYPE isinstancestr ValueError) feature_type feature_type_feat_ts T/var/www/html/land-doc-ocr/venv/lib/python3.13/site-packages/skimage/feature/haar.py_validate_feature_typersp$   lC ( ()NM(M#F\) =fXF&q*$ c [U5n[UVs/sHn[XU5PM sn6upV[R"U5[R "U54$s snf)aCompute the coordinates of Haar-like features. Parameters ---------- width : int Width of the detection window. height : int Height of the detection window. feature_type : str or list of str or None, optional The type of feature to consider: - 'type-2-x': 2 rectangles varying along the x axis; - 'type-2-y': 2 rectangles varying along the y axis; - 'type-3-x': 3 rectangles varying along the x axis; - 'type-3-y': 3 rectangles varying along the y axis; - 'type-4': 4 rectangles varying along x and y axis. By default all features are extracted. Returns ------- feature_coord : (n_features, n_rectangles, 2, 2), ndarray of list of tuple coord Coordinates of the rectangles for each feature. feature_type : (n_features,), ndarray of str The corresponding type for each feature. Examples -------- >>> import numpy as np >>> from skimage.transform import integral_image >>> from skimage.feature import haar_like_feature_coord >>> feat_coord, feat_type = haar_like_feature_coord(2, 2, 'type-4') >>> feat_coord # doctest: +SKIP array([ list([[(0, 0), (0, 0)], [(0, 1), (0, 1)], [(1, 1), (1, 1)], [(1, 0), (1, 0)]])], dtype=object) >>> feat_type array(['type-4'], dtype=object) )rziprnp concatenatehstack)widthheightrrr feat_coord feat_types rhaar_like_feature_coordr"!sdR+<8M( ' ,E6 B' J >>* %ryy'; ;;  sAc^^^^^^TcM[U5n[R"[[R "UUUUUU4SjU5555$TR SUR S:wa [S5e[Vs/sHoU:HPM n n[[U [5V Vs/sHLup[R"U 5(dM"[R"U 5[TTTTTUTU 54PMN snn 6up[R"U 5n [R"U 5n U R5X'U $s snfs snn f)aCompute the Haar-like features for a region of interest (ROI) of an integral image. Haar-like features have been successfully used for image classification and object detection [1]_. It has been used for real-time face detection algorithm proposed in [2]_. Parameters ---------- int_image : (M, N) ndarray 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_type : str or list of str or None, optional 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 `feature_coord`, it should correspond to the feature type of each associated coordinate feature. 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 :func:`haar_like_feature_coord`. By default, all coordinates are computed. Returns ------- haar_features : (n_features,) ndarray of int or float 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 `int_image`: `int` when the data type of `int_image` is `uint` or `int` and `float` when the data type of `int_image` is `float`. Notes ----- 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 :ref:`sphx_glr_auto_examples_applications_plot_haar_extraction_selection_classification.py` for more insights. Examples -------- >>> 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, -5, -1, -2, -3, -4, -5, -1, -2, -3, -4, -5, -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. >>> from skimage.feature import haar_like_feature_coord >>> 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, 0, 0, 0, -1, -3, -5, -2, -4, -1, -3, -5, -2, -4, -2, -4, -2, -4, -2, -1, -3, -2, -1, -1, -1, -1, -1]) References ---------- .. [1] https://en.wikipedia.org/wiki/Haar-like_feature .. [2] Oren, M., Papageorgiou, C., Sinha, P., Osuna, E., & Poggio, T. (1997, June). Pedestrian detection using wavelet templates. In Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on (pp. 193-199). IEEE. http://tinyurl.com/y6ulxfta :DOI:`10.1109/CVPR.1997.609319` .. [3] 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` c 3F># UHn[TTTTTUT5v M g7fNr).0rc feature_coordr int_imagerrs r $haar_like_feature..s5$#0.!1a #0s!rz?Inconsistent size between feature coordinatesand feature types.)rrrlistr from_iterableshaperrr count_nonzero flatnonzerorrcopy) r)r*r'rrrr(rr mask_featuremaskhaar_feature_idx haar_features ````` ` rhaar_like_featurer7VsRZ.|< yy ##$$#0 $      q !\%7%7%: :T >JJ\6.\ J),%( l$C %DLD##D)NN4(-!1a d@S %D  * &>>*:;~~l3 )5):):)< &%K s E 2!E .E c [RRU 5n [R"U[RS9n[R"U[RS9nU cUn OU R XYSS9n [R "U5n [UR5S:a [U5n [U 5n U Hn [U 5HupUunn[[[UX/55n[[[UX/55n[UU5unnUS-S-S:XaSU- U UU4-X--nOSU- U UU4-X--nUU UU4'M M U $)aC Visualization of Haar-like features. Parameters ---------- image : (M, N) ndarray 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 :func:`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 :func:`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 : (M, N), ndarray 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 array([[[0. , 0.5, 0. ], [0.5, 0. , 0. ]], [[0.5, 0. , 0. ], [0. , 0.5, 0. ]]]) )dtypeF)sizereplacerr r)rrandom default_rngasarrayfloat64choicer2lenr/r r enumeratetuplemaprr )imager*r'rrr(color_positive_blockcolor_negative_blockalphamax_n_featuresrngfeature_coord_outputcoordidx_rectrect coord_start coord_endrrcc new_values rdraw_haar_like_featurerVsTZ ))   $C::&:"**M::&:"**M&MPUV WWU^F 5;;!% & !F'.NH%) "KCqf =>Kc#y1&9:I{I6FBA"q(Y&R.85;WW Y&R.85;WW &F2r6N/  Mrr%)NN))?rX)rXrWrXg?NN) itertoolsroperatorrnumpyr_haarrrcolorr drawr utilr rrr"r7rVrrrasR2,I $2