B \ E@sxdZddlZddlmZddlmZddlmZddl m Z m Z m Z dd d Z dd dZdddZddZdddZdS)z) Methods to characterize image textures. N) assert_nD) img_as_float)gray2rgb) _glcm_loop_local_binary_pattern_multiblock_lbpFc CsXt|dt|ddt|ddt|}|}t|jtjrLtd|jtjtj fkrn|dkrntdt|jtj rt |dkrtd |dkrd }||krtd tj|tj d }tj|tj d }tj ||t|t|ftjd d}t||||||rt|d}||}|rT|tj }tjtj|dd} d| | dk<|| }|S)a 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) rr distancesanglesz^Float images are not supported by greycomatrix. Convert the image to an unsigned integer type.Nz{The levels argument is required for data types other than uint8. The resulting matrix will be at least levels ** 2 in size.rz)Negative-valued images are not supported.zUThe maximum grayscale value in the image should be smaller than the number of levels.)dtypeC)r order)rrr)rr)axes)rnpascontiguousarraymaxZ issubdtyper Zfloating ValueErrorZuint8Zint8Z signedintegeranyfloat64zeroslenZuint32rZ transposeZastypeapply_over_axessum) imager r ZlevelsZ symmetricZnormedZ image_maxPZPtZ glcm_sumsr6lib/python3.7/site-packages/skimage/feature/texture.py greycomatrixs:W        r contrastcCst|dd|j\}}}}||ks&t|dks2t|dks>ttjd|d|f\}}|dkrn||d}nL|dkrt||}n4|dkrdd||d}n|d krn td ||d krtjtj|dd d d} t | } n|dkrtjtj|dd d d} np|dkrLtj ||ftj d} t t ||dddf}t t |d|ddf}|tjtj||d d d} |tjtj||d d d} t tjtj|| dd d d} t tjtj|| dd d d}tjtj|| | d d d}| dk}d||dk<d| |<|dk}||| |||| |<n6|dkr|||ddf}tjtj||d d d} | S)aCalculate texture properties of a GLCM. Compute a feature of a grey level co-occurrence matrix to serve as a compact summary of the matrix. The properties are computed as follows: - 'contrast': :math:`\sum_{i,j=0}^{levels-1} P_{i,j}(i-j)^2` - 'dissimilarity': :math:`\sum_{i,j=0}^{levels-1}P_{i,j}|i-j|` - 'homogeneity': :math:`\sum_{i,j=0}^{levels-1}\frac{P_{i,j}}{1+(i-j)^2}` - 'ASM': :math:`\sum_{i,j=0}^{levels-1} P_{i,j}^2` - 'energy': :math:`\sqrt{ASM}` - 'correlation': .. math:: \sum_{i,j=0}^{levels-1} P_{i,j}\left[\frac{(i-\mu_i) \ (j-\mu_j)}{\sqrt{(\sigma_i^2)(\sigma_j^2)}}\right] Parameters ---------- P : ndarray Input array. `P` is the grey-level co-occurrence histogram for which to compute the specified property. 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. prop : {'contrast', 'dissimilarity', 'homogeneity', 'energy', 'correlation', 'ASM'}, optional The property of the GLCM to compute. The default is 'contrast'. Returns ------- results : 2-D ndarray 2-dimensional array. `results[d, a]` is the property 'prop' for the d'th distance and the a'th angle. References ---------- .. [1] The GLCM Tutorial Home Page, http://www.fp.ucalgary.ca/mhallbey/tutorial.htm Examples -------- Compute the contrast for GLCMs with distances [1, 2] and angles [0 degrees, 90 degrees] >>> 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 ]]) rrr!r dissimilarity homogeneityg?)ASMenergy correlationz%s is an invalid propertyr&)rr)r)rrr%r')r rgV瞯@}|r d|| ||||||f||}|| ||||||f<qd|| ||||||f||}|| ||||||f<qW| S)aMulti-block local binary pattern visualization. Blocks with higher sums are colored with alpha-blended white rectangles, whereas blocks with lower sums are colored alpha-blended cyan. Colors and the `alpha` parameter can be changed. Parameters ---------- image : ndarray of float or uint Image on which to visualize the pattern. 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 9 equal rectangles that will be used to compute a feature. height : int Height of one of 9 equal rectangles that will be used to compute a feature. lbp_code : int The descriptor of feature to visualize. If not provided, the descriptor with 0 value will be used. color_greater_block : tuple of 3 floats Floats specifying the color for the block that has greater intensity value. They should be in the range [0, 1]. Corresponding values define (R, G, B) values. Default value is white (1, 1, 1). color_greater_block : tuple of 3 floats Floats specifying the color for the block that has greater intensity value. They should be in the range [0, 1]. Corresponding values define (R, G, B) values. Default value is cyan (0, 0.69, 0.96). alpha : float Value in the range [0, 1] that specifies opacity of visualization. 1 - fully transparent, 0 - opaque. Returns ------- output : ndarray of float Image with MB-LBP visualization. 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))rF)rFr)rFr)rr)rr)rr)rrF)rrFNrr) rZasarrayrcopyrr(rrr+ enumerate)rr=r>r?r@rAZcolor_greater_blockZcolor_less_blockZalphar;Zneighbour_rect_offsetsZcentral_rect_rZcentral_rect_cZ element_numoffsetZoffset_rZoffset_cZcurr_rZcurr_cZhas_greater_valueZ new_valuerrrdraw_multiblock_lbprs6:     rK)NFF)r!)r0)rrCrDrE)__doc__ZnumpyrZ _shared.utilsrutilrZcolorrZ_texturerrr r r/r<rBrKrrrrs     p ;/