ó  ‰\c@sŞdZddlZddlmZddlmZddlmZddl m Z m Z m Z deed „Zd d „Zd d „Zd„Zddddd„ZdS(s) Methods to characterize image textures. i˙˙˙˙Ni(t assert_nD(t img_as_float(tgray2rgbi(t _glcm_loopt_local_binary_patternt_multiblock_lbpc Cst|dƒt|ddƒt|ddƒtj|ƒ}|jƒ}tj|jtjƒrotdƒ‚n|jtjtj fkrĽ|dkrĽtdƒ‚ntj|jtj ƒrátj |dkƒrátdƒ‚n|dkröd }n||krtd ƒ‚ntj|d tj ƒ}tj|d tj ƒ}tj||t|ƒt|ƒfd tjd d ƒ}t|||||ƒ|r˛tj|dƒ}||}n|r|jtj ƒ}tjtj|ddƒ} d| | dk<|| :}n|S(sË Calculate the grey-level co-occurrence matrix. A grey level co-occurrence matrix is a histogram of co-occurring greyscale values at a given offset over an image. Parameters ---------- image : array_like Integer typed input image. Only positive valued images are supported. If type is other than uint8, the argument `levels` needs to be set. distances : array_like List of pixel pair distance offsets. angles : array_like List of pixel pair angles in radians. levels : int, optional The input image should contain integers in [0, `levels`-1], where levels indicate the number of grey-levels counted (typically 256 for an 8-bit image). This argument is required for 16-bit images or higher and is typically the maximum of the image. As the output matrix is at least `levels` x `levels`, it might be preferable to use binning of the input image rather than large values for `levels`. symmetric : bool, optional If True, the output matrix `P[:, :, d, theta]` is symmetric. This is accomplished by ignoring the order of value pairs, so both (i, j) and (j, i) are accumulated when (i, j) is encountered for a given offset. The default is False. normed : bool, optional If True, normalize each matrix `P[:, :, d, theta]` by dividing by the total number of accumulated co-occurrences for the given offset. The elements of the resulting matrix sum to 1. The default is False. Returns ------- P : 4-D ndarray The grey-level co-occurrence histogram. The value `P[i,j,d,theta]` is the number of times that grey-level `j` occurs at a distance `d` and at an angle `theta` from grey-level `i`. If `normed` is `False`, the output is of type uint32, otherwise it is float64. The dimensions are: levels x levels x number of distances x number of angles. References ---------- .. [1] The GLCM Tutorial Home Page, http://www.fp.ucalgary.ca/mhallbey/tutorial.htm .. [2] Pattern Recognition Engineering, Morton Nadler & Eric P. Smith .. [3] Wikipedia, http://en.wikipedia.org/wiki/Co-occurrence_matrix Examples -------- Compute 2 GLCMs: One for a 1-pixel offset to the right, and one for a 1-pixel offset upwards. >>> image = np.array([[0, 0, 1, 1], ... [0, 0, 1, 1], ... [0, 2, 2, 2], ... [2, 2, 3, 3]], dtype=np.uint8) >>> result = greycomatrix(image, [1], [0, np.pi/4, np.pi/2, 3*np.pi/4], ... levels=4) >>> result[:, :, 0, 0] array([[2, 2, 1, 0], [0, 2, 0, 0], [0, 0, 3, 1], [0, 0, 0, 1]], dtype=uint32) >>> result[:, :, 0, 1] array([[1, 1, 3, 0], [0, 1, 1, 0], [0, 0, 0, 2], [0, 0, 0, 0]], dtype=uint32) >>> result[:, :, 0, 2] array([[3, 0, 2, 0], [0, 2, 2, 0], [0, 0, 1, 2], [0, 0, 0, 0]], dtype=uint32) >>> result[:, :, 0, 3] array([[2, 0, 0, 0], [1, 1, 2, 0], [0, 0, 2, 1], [0, 0, 0, 0]], dtype=uint32) iit distancestangless^Float images are not supported by greycomatrix. Convert the image to an unsigned integer type.s{The levels argument is required for data types other than uint8. The resulting matrix will be at least levels ** 2 in size.is)Negative-valued images are not supported.isUThe maximum grayscale value in the image should be smaller than the number of levels.tdtypetordertCitaxesN(iiii(ii(Rtnptascontiguousarraytmaxt issubdtypeRtfloatingt ValueErrortuint8tint8tNonet signedintegertanytfloat64tzerostlentuint32Rt transposetastypetapply_over_axestsum( timageRRtlevelst symmetrictnormedt image_maxtPtPtt glcm_sums((s6lib/python2.7/site-packages/skimage/feature/texture.pyt greycomatrixs:W  '-   $  tcontrastcCsmt|ddƒ|j\}}}}||ks7t‚|dksIt‚|dks[t‚tjd|…d|…f\}}|dkr||d}nf|dkrżtj||ƒ}nD|dkrädd||d}n|dkróntd |ƒ‚|d krDtjtj|dd dƒd} tj | ƒ} n%|d krvtjtj|dd dƒd} nó|d krtj ||fdtj ƒ} tj t |ƒƒj|dddfƒ}tj t |ƒƒjd|ddfƒ}|tjtj||d dƒd} |tjtj||d dƒd} tj tjtj|| dd dƒdƒ} tj tjtj|| dd dƒdƒ}tjtj|| | d dƒd}| dk}t||dk>> image = np.array([[0, 0, 1, 1], ... [0, 0, 1, 1], ... [0, 2, 2, 2], ... [2, 2, 3, 3]], dtype=np.uint8) >>> g = greycomatrix(image, [1, 2], [0, np.pi/2], levels=4, ... normed=True, symmetric=True) >>> contrast = greycoprops(g, 'contrast') >>> contrast array([[ 0.58333333, 1. ], [ 1.25 , 2.75 ]]) iR$iR(it dissimilarityt homogeneitygđ?tASMtenergyt correlations%s is an invalid propertyR iRgV瞯Ň<(R+R,R-(ii(ii(ii(ii(ii(ii(ii(ii(ii(ii(ii(ii(ii(ii(R(R)R*(ii(ii(RtshapetAssertionErrorR togridtabsRRRtsqrtRRtarraytrangetreshapetTruetFalse(R$tpropt num_levelt num_level2tnum_distt num_angletItJtweightstasmtresultstdiff_itdiff_jtstd_itstd_jtcovtmask_0tmask_1((s6lib/python2.7/site-packages/skimage/feature/texture.pyt greycoprops™sR9%     # & **''      ! &tdefaultcCst|dƒitdƒd6tdƒd6tdƒd6tdƒd 6td ƒd 6}tj|d tjƒ}t|||||jƒƒ}|S( sćGray scale and rotation invariant LBP (Local Binary Patterns). LBP is an invariant descriptor that can be used for texture classification. Parameters ---------- image : (N, M) array Graylevel image. P : int Number of circularly symmetric neighbour set points (quantization of the angular space). R : float Radius of circle (spatial resolution of the operator). method : {'default', 'ror', 'uniform', 'var'} Method to determine the pattern. * 'default': original local binary pattern which is gray scale but not rotation invariant. * 'ror': extension of default implementation which is gray scale and rotation invariant. * 'uniform': improved rotation invariance with uniform patterns and finer quantization of the angular space which is gray scale and rotation invariant. * 'nri_uniform': non rotation-invariant uniform patterns variant which is only gray scale invariant [2]_. * 'var': rotation invariant variance measures of the contrast of local image texture which is rotation but not gray scale invariant. Returns ------- output : (N, M) array LBP image. References ---------- .. [1] Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. Timo Ojala, Matti Pietikainen, Topi Maenpaa. http://www.ee.oulu.fi/research/mvmp/mvg/files/pdf/pdf_94.pdf, 2002. .. [2] Face recognition with local binary patterns. Timo Ahonen, Abdenour Hadid, Matti Pietikainen, http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.214.6851, 2004. itDRJtRtrortUtuniformtNt nri_uniformtVtvarR(RtordR R tdoubleRtlower(RR$RLtmethodtmethodstoutput((s6lib/python2.7/site-packages/skimage/feature/texture.pytlocal_binary_pattern s-     cCs4tj|dtjƒ}t|||||ƒ}|S(s›Multi-block local binary pattern (MB-LBP). The features are calculated similarly to local binary patterns (LBPs), (See :py:meth:`local_binary_pattern`) except that summed blocks are used instead of individual pixel values. MB-LBP is an extension of LBP that can be computed on multiple scales in constant time using the integral image. Nine equally-sized rectangles are used to compute a feature. For each rectangle, the sum of the pixel intensities is computed. Comparisons of these sums to that of the central rectangle determine the feature, similarly to LBP. Parameters ---------- int_image : (N, M) array Integral image. r : int Row-coordinate of top left corner of a rectangle containing feature. c : int Column-coordinate of top left corner of a rectangle containing feature. width : int Width of one of the 9 equal rectangles that will be used to compute a feature. height : int Height of one of the 9 equal rectangles that will be used to compute a feature. Returns ------- output : int 8-bit MB-LBP feature descriptor. References ---------- .. [1] Face Detection Based on Multi-Block LBP Representation. Lun Zhang, Rufeng Chu, Shiming Xiang, Shengcai Liao, Stan Z. Li http://www.cbsr.ia.ac.cn/users/scliao/papers/Zhang-ICB07-MBLBP.pdf R(R R tfloat32R(t int_imagetrtctwidththeighttlbp_code((s6lib/python2.7/site-packages/skimage/feature/texture.pytmultiblock_lbpDs)igŽGázć?g¸…ëQ¸î?gŕ?c  Csôtj|dtjƒ}tj|dtjƒ}tj|ƒ} t|jƒdkrct|ƒ} nt| ƒ} dd d d d d ddf} tj| ƒ} | dd…dfc|9<| dd…dfc|9<||} ||} xt | ƒD]÷\} }|\}}| |}| |}|dd| >@}|r”d|| |||…|||…f||}|| |||…|||…fs  Š p ; /