B \*W!@s`ddlmZddlmZmZmZddlZddlm Z ddl Z ddl m ZddlmZddlmZdd lmZd d gZd Ze jdddgdddgdddgge jd Ze jde jd Ze jde jd Zddddddddddddddddd d!d"d#d$d%d&d'd(d d)d*d+d,d-d.d/ ZeeZ d0d1Z!d2d3Z"Gd4d5d5e#Z$d>d7d Z%d?d9d Z&d:d;Z'd.wrapper)r )r3r6r4)r3r5_cachedIs r7cstfdd}|S)Ncs(|jdkrtdj|f||S)Nz,Property %s is not implemented for 3D images)_ndimNotImplementedErrorr/)selfargskwargs)methodr4r5func2dXs  zonly2d..func2d)r )r>r?r4)r>r5only2dWsr@c@seZdZdZddZeddZddZdd Zd d Z ed d Z eddZ ddZ e ddZddZddZddZddZeddZeddZed d!Zed"d#Zed$d%Zd&d'Zd(d)Zd*d+Zd,d-Zd.d/Zd0d1Zd2d3Zed4d5Zed6d7Z e d8d9Z!ed:d;Z"e dd?Z$d@dAZ%dBdCZ&dDdEZ'edFdGZ(edHdIZ)e dJdKZ*edLdMZ+dNdOZ,dPdQZ-dRdSZ.dTS)U_RegionPropertieszoPlease refer to `skimage.measure.regionprops` for more information on the available region properties. cCs|dk r|j|jkstd||_||_||_||_||_i|_|j|_ |jdksZ|dkrhd|_ d|_ n4|dkr~d|_ d|_ n|dkrd|_ d|_ ntddS)Nz2Label and intensity image must have thesame shape.r8ZrcFZxyTzYIncorrect value for regionprops coordinates: %s. 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Parameters ---------- label_image : (N, M) ndarray Labeled input image. Labels with value 0 are ignored. .. versionchanged:: 0.14.2 Previously, ``label_image`` was processed by ``numpy.squeeze`` and so any number of singleton dimensions was allowed. This resulted in inconsistent handling of images with singleton dimensions. To recover the old behaviour, use ``regionprops(np.squeeze(label_image), ...)``. intensity_image : (N, M) ndarray, optional Intensity (i.e., input) image with same size as labeled image. Default is None. cache : bool, optional Determine whether to cache calculated properties. The computation is much faster for cached properties, whereas the memory consumption increases. coordinates : 'rc' or 'xy', optional Coordinate conventions for 2D images. (Only 'rc' coordinates are supported for 3D images.) Returns ------- properties : list of RegionProperties Each item describes one labeled region, and can be accessed using the attributes listed below. Notes ----- The following properties can be accessed as attributes or keys: **area** : int Number of pixels of region. **bbox** : tuple Bounding box ``(min_row, min_col, max_row, max_col)``. Pixels belonging to the bounding box are in the half-open interval ``[min_row; max_row)`` and ``[min_col; max_col)``. **bbox_area** : int Number of pixels of bounding box. **centroid** : array Centroid coordinate tuple ``(row, col)``. **convex_area** : int Number of pixels of convex hull image. **convex_image** : (H, J) ndarray Binary convex hull image which has the same size as bounding box. **coords** : (N, 2) ndarray Coordinate list ``(row, col)`` of the region. **eccentricity** : float Eccentricity of the ellipse that has the same second-moments as the region. The eccentricity is the ratio of the focal distance (distance between focal points) over the major axis length. The value is in the interval [0, 1). When it is 0, the ellipse becomes a circle. **equivalent_diameter** : float The diameter of a circle with the same area as the region. **euler_number** : int Euler characteristic of region. Computed as number of objects (= 1) subtracted by number of holes (8-connectivity). **extent** : float Ratio of pixels in the region to pixels in the total bounding box. Computed as ``area / (rows * cols)`` **filled_area** : int Number of pixels of filled region. **filled_image** : (H, J) ndarray Binary region image with filled holes which has the same size as bounding box. **image** : (H, J) ndarray Sliced binary region image which has the same size as bounding box. **inertia_tensor** : (2, 2) ndarray Inertia tensor of the region for the rotation around its mass. **inertia_tensor_eigvals** : tuple The two eigen values of the inertia tensor in decreasing order. **intensity_image** : ndarray Image inside region bounding box. **label** : int The label in the labeled input image. **local_centroid** : array Centroid coordinate tuple ``(row, col)``, relative to region bounding box. **major_axis_length** : float The length of the major axis of the ellipse that has the same normalized second central moments as the region. **max_intensity** : float Value with the greatest intensity in the region. **mean_intensity** : float Value with the mean intensity in the region. **min_intensity** : float Value with the least intensity in the region. **minor_axis_length** : float The length of the minor axis of the ellipse that has the same normalized second central moments as the region. **moments** : (3, 3) ndarray Spatial moments up to 3rd order:: m_ji = sum{ array(x, y) * x^j * y^i } where the sum is over the `x`, `y` coordinates of the region. **moments_central** : (3, 3) ndarray Central moments (translation invariant) up to 3rd order:: mu_ji = sum{ array(x, y) * (x - x_c)^j * (y - y_c)^i } where the sum is over the `x`, `y` coordinates of the region, and `x_c` and `y_c` are the coordinates of the region's centroid. **moments_hu** : tuple Hu moments (translation, scale and rotation invariant). **moments_normalized** : (3, 3) ndarray Normalized moments (translation and scale invariant) up to 3rd order:: nu_ji = mu_ji / m_00^[(i+j)/2 + 1] where `m_00` is the zeroth spatial moment. **orientation** : float In 'rc' coordinates, angle between the 0th axis (rows) and the major axis of the ellipse that has the same second moments as the region, ranging from `-pi/2` to `pi/2` counter-clockwise. In `xy` coordinates, as above but the angle is now measured from the "x" or horizontal axis. **perimeter** : float Perimeter of object which approximates the contour as a line through the centers of border pixels using a 4-connectivity. **solidity** : float Ratio of pixels in the region to pixels of the convex hull image. **weighted_centroid** : array Centroid coordinate tuple ``(row, col)`` weighted with intensity image. **weighted_local_centroid** : array Centroid coordinate tuple ``(row, col)``, relative to region bounding box, weighted with intensity image. **weighted_moments** : (3, 3) ndarray Spatial moments of intensity image up to 3rd order:: wm_ji = sum{ array(x, y) * x^j * y^i } where the sum is over the `x`, `y` coordinates of the region. **weighted_moments_central** : (3, 3) ndarray Central moments (translation invariant) of intensity image up to 3rd order:: wmu_ji = sum{ array(x, y) * (x - x_c)^j * (y - y_c)^i } where the sum is over the `x`, `y` coordinates of the region, and `x_c` and `y_c` are the coordinates of the region's weighted centroid. **weighted_moments_hu** : tuple Hu moments (translation, scale and rotation invariant) of intensity image. **weighted_moments_normalized** : (3, 3) ndarray Normalized moments (translation and scale invariant) of intensity image up to 3rd order:: wnu_ji = wmu_ji / wm_00^[(i+j)/2 + 1] where ``wm_00`` is the zeroth spatial moment (intensity-weighted area). Each region also supports iteration, so that you can do:: for prop in region: print(prop, region[prop]) See Also -------- label References ---------- .. [1] Wilhelm Burger, Mark Burge. Principles of Digital Image Processing: Core Algorithms. Springer-Verlag, London, 2009. .. [2] B. Jähne. Digital Image Processing. Springer-Verlag, Berlin-Heidelberg, 6. edition, 2005. .. [3] T. H. Reiss. Recognizing Planar Objects Using Invariant Image Features, from Lecture notes in computer science, p. 676. Springer, Berlin, 1993. .. [4] http://en.wikipedia.org/wiki/Image_moment Examples -------- >>> from skimage import data, util >>> from skimage.measure import label >>> img = util.img_as_ubyte(data.coins()) > 110 >>> label_img = label(img, connectivity=img.ndim) >>> props = regionprops(label_img) >>> # centroid of first labeled object >>> props[0].centroid (22.729879860483141, 81.912285234465827) >>> # centroid of first labeled object >>> props[0]['centroid'] (22.729879860483141, 81.912285234465827) )r8rz"Only 2-D and 3-D images supported.z$Label image must be of integer type.Nr)rM) rG TypeErrorrOZ issubdtyperZintegerreZ find_objects enumeraterAappend) rKrLr2rMZregionsZobjectsrSZslr rr4r4r5r isF    r`c Cs|dkrt}nt}|tj}tj||dd}||}tjdtjd}d|ddd d d d g<t d |ddg<dt d d |ddg<tj |t dd dgd dd gdd dggddd}tj | dd}t||}|S)aGCalculate total perimeter of all objects in binary image. Parameters ---------- image : array Binary image. neighbourhood : 4 or 8, optional Neighborhood connectivity for border pixel determination. Returns ------- perimeter : float Total perimeter of all objects in binary image. References ---------- .. [1] K. Benkrid, D. Crookes. Design and FPGA Implementation of a Perimeter Estimator. The Queen's University of Belfast. http://www.cs.qub.ac.uk/~d.crookes/webpubs/papers/perimeter.doc r`r)Z border_value2)rrr8!  Zconstant)modeZcval)Z minlength)STREL_4STREL_8rirOrsreZbinary_erosionZzerosrjrZconvolvearrayZbincountZraveldot) rZ neighbourhoodZstrelZ eroded_imageZ border_imageZperimeter_weightsZperimeter_imageZperimeter_histogramZtotal_perimeterr4r4r5r Ds"    csBddl}ddltj}|jd||jd}tfdd|D}|S)Nrz)\*\*(\w+)\*\* \:.*? 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