0-Ph ddlZddlZddlmZddlmZddlmZddlmZddl m Z ddl Z ddl mZddlmZd d lmZd d lmZd d lmZd d lmZmZmZmZgdZiddddddddddddddddddddd d!d"d#d$d%d&d%d'd(d)d*d+d,id-d,d.d/d0d/d1d2d3d2d4d5d6d7d8d9d:d;dd=d?d@dAdBdCdBdDdEdFdEdGdHidIdHdJdKdLdKdMdNdOdNdPdQdRdSdTdSdUdVdWdXdYdZd[d\d]d^d_d`dadbdcdddedddfdfdgdgdhdhdididjdjdk Zidededed/edEedSede dedBedfedhed!e!dle!d#ed%ed(e d*eid,ed7e!de!d2e!d=e!d9ed;edHedKedNedQed@e dVeded5edXedieeeee eeee!edm Z"dne"#DZ$e%e&Z'doZ(dpZ)dqZ*drZ+dsZ,dtZ-GdudvZ.e.Z/ddyZ0 ddzdxddd{d|Z1 ddddd}d~Z2dZ3dZ4 e4dS)Nwraps)atan2)pi)sqrt)warn)ndimage)pdist)_moments) find_contours)marching_cubes) euler_number perimeterperimeter_crofton_normalize_spacing) regionpropsrrrAreaarea BoundingBoxbboxBoundingBoxArea area_bbox bbox_areaCentralMomentsmoments_centralCentroidcentroid ConvexArea area_convex convex_area ConvexImage image_convex convex_image Coordinatescoords Eccentricity eccentricity EquivDiameterequivalent_diameter_areaequivalent_diameter EulerNumberrExtentextent FeretDiameterferet_diameter_maxFeretDiameterMax FilledArea area_filled filled_area FilledImage image_filled filled_image HuMoments moments_huImageimage InertiaTensorinertia_tensorInertiaTensorEigvalsinertia_tensor_eigvalsIntensityImageimage_intensityintensity_imageLabellabel LocalCentroidcentroid_locallocal_centroidMajorAxisLengthaxis_major_lengthmajor_axis_length MaxIntensity intensity_max max_intensity MeanIntensityintensity_meanmean_intensity MinIntensity intensity_min min_intensity std_intensity intensity_stdMinorAxisLengthaxis_minor_lengthminor_axis_lengthMomentsmomentsNormalizedMomentsmoments_normalized Orientation orientation PerimeterrCroftonPerimeterrSlicesliceSoliditysolidityWeightedCentralMomentsmoments_weighted_centralweighted_moments_centralcentroid_weightedmoments_weighted_hucentroid_weighted_localmoments_weightedmoments_weighted_normalized) WeightedCentroidweighted_centroidWeightedHuMomentsweighted_moments_huWeightedLocalCentroidweighted_local_centroidWeightedMomentsweighted_momentsWeightedNormalizedMomentsweighted_moments_normalized coords_scaled) rfrirl num_pixelsr^rrrbrdc.g|]\}}|tk|S)object).0coldtypes \/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/skimage/measure/_regionprops.py rs!NNN*#uevoo#ooo) rArLrOrRrUrkrfrhrjrirlctj|}t|j}|j|t|jz}|S)zInfer the number of required arguments for a function Parameters ---------- func : callable The function that is being inspected. Returns ------- n_args : int The number of required arguments of func. )inspectgetfullargspeclenargsdefaults)funcargspecn_argss r_infer_number_of_required_argsrsF$T**G   F##g&''' Mrc tjd|zt}tj|dd}tjd|zt}tj|dd}||g}tj |rt dkr fd }n}t||\}}tj|rtj|s4tj |j tj |j krtj |j j } n tj } | S) aInfer the dtype of a region property calculated by func. If a region property function always returns the same shape and type of output regardless of input size, then the dtype is the dtype of the returned array. Otherwise, the property has object dtype. Parameters ---------- func : callable Function to be tested. The signature should be array[bool] -> Any if intensity is False, or *(array[bool], array[float]) -> Any otherwise. intensity : bool Whether the regionprop is calculated on an intensity image. ndim : int The number of dimensions for which to check func. Returns ------- dtype : NumPy data type The data type of the returned property. r r~)rr F)constant_values))r rrcL||jSN)randomshape)maskrrngs r_funcz&_infer_regionprop_dtype.._funcs#4cjj4455 5r)nponesboolpadr default_rngrmapisscalararrayrr~typeobject_) r intensityndimmask_1mask_2 propmasksrprops1props2r~rs ` @r_infer_regionprop_dtypersE,WTD[ - - -F VFFE : : :F WTD[ - - -F VFFE : : :F I )   ! !C3D99Q>> 6 6 6 6 6 6 6 **NFF F K   8F   !RXf%5%5%; ; ;  &+  Lrc<tfd}|S)Ncx|j}j}|js |S||vr|||<||Sr)_cache__name__ _cache_active)objcachepropfs rwrapperz_cached..wrappersO z  1S66M u  !C&&E$KT{rr)rrs` r_cachedrs3 1XX    X  Nrc<tfd}|S)Ncb|jdkrtdjd|g|Ri|S)Nrz Property z! is not implemented for 3D images)_ndimNotImplementedErrorr)selfrkwargsmethods rfunc2dzonly2d..func2dsR :>>%NFONNN vd,T,,,V,,,rr)rrs` ronly2drs3 6]]----]- Mrcg}tdddD]Otfdt|D}|t d|zP|S)a>Compute ellipsoid axis lengths from inertia tensor eigenvalues. Parameters --------- inertia_tensor_eigvals : sequence of float A sequence of 3 floating point eigenvalues, sorted in descending order. Returns ------- axis_lengths : list of float The ellipsoid axis lengths sorted in descending order. Notes ----- Let a >= b >= c be the ellipsoid semi-axes and s1 >= s2 >= s3 be the inertia tensor eigenvalues. The inertia tensor eigenvalues are given for a solid ellipsoid in [1]_. s1 = 1 / 5 * (a**2 + b**2) s2 = 1 / 5 * (a**2 + c**2) s3 = 1 / 5 * (b**2 + c**2) Rearranging to solve for a, b, c in terms of s1, s2, s3 gives a = math.sqrt(5 / 2 * ( s1 + s2 - s3)) b = math.sqrt(5 / 2 * ( s1 - s2 + s3)) c = math.sqrt(5 / 2 * (-s1 + s2 + s3)) We can then simply replace sqrt(5/2) by sqrt(10) to get the full axes lengths rather than the semi-axes lengths. References ---------- .. [1] https://en.wikipedia.org/wiki/List_of_moments_of_inertia#List_of_3D_inertia_tensors rc38K|]\}}|kr|dzn|VdS)rNrz)r|ivaxs r z6_inertia_eigvals_to_axes_lengths_3D..+s7WWTQ!r''BqWWWWWWr )rangesum enumerateappendr)r? axis_lengthswrs @r#_inertia_eigvals_to_axes_lengths_3DrsxFLAr2** WWWWY?U5V5VWWW W WDaLL)))) rc&eZdZdZdddddZdZfdZeedZ eedZ ed Z ed Z ed Z eed Zeed ZedZedZeedZedZedZedZedZedZeedZeedZeedZeedZeedZdZedZedZ edZ!edZ"ed Z#ed!Z$ed"Z%eed#Z&eed$Z'eed%Z(eed&Z)eed'Z*eed(Z+eed)Z,ed*Z-ed+Z.ed,Z/eed-Z0eed.Z1eed/Z2eed0Z3d1Z4d2Z5d3Z6xZ7S)4RegionPropertieszoPlease refer to `skimage.measure.regionprops` for more information on the available region properties. Nextra_propertiesspacingoffsetc6|M|j} |jd| |jkr|j| | dzfvstd|j|jk} nd} ||_|!t j|jft }t j||_||_ ||_ ||_ ||_ ||_ i|_|j|_| |_t#t%|j|_|t j|jd}t+||j|_t j|j|_i|_|D|D].} | j} t7|| rd| d} t9| /d|D|_dSdS) Nr zLLabel and intensity image shapes must match, except for channel (last) axis.Fr?zExtra property 'zR' is shadowed by existing property and will be inaccessible. Consider renaming it.ci|] }|j| Srzr)r|rs r z-RegionProperties.__init__..ns%W%W%WddmT%W%W%Wr)rr ValueErrorrDrzerosintr_offset_slicerb _label_image_intensity_imagerrr _multichanneltupler _spatial_axesfullr_spacingprod _pixel_area_extra_propertiesrhasattrr)rrbrD label_imagerB cache_activerrrr multichannelrnamemsgs r__init__zRegionProperties.__init__5s  &#D%ete, 0AAA#(T4!8,<<< 7',/DDLL L >X{/1===Fx''   ' /)  % )"5#4#455 ?gdj#..G*7DJ?? 74=11!#  '(  }4&&(4((( III%W%WFV%W%W%WD " " " ( 'rc:|dkr|Sj|tvrtd|d|jvrƉj|t }|dkrxj_jrBfdtjj dD}tj |dSj jStd||d krj Std |d |d |tvra||krIj't|tvrtd|dtt|Std t!d|d )N __setstate__z Attribute 'z<' unavailable when `intensity_image` has not been specified.rcLg|] }jjd|f!S.)r;rA)r|rrrs rrz0RegionProperties.__getattr__..sC--- !!DT-A#q&-IJJ---rraxisz&intensity image required to calculate r zECustom regionprop function's number of arguments must be 1 or 2, but z takes z arguments.'z' object has no attribute ')__getattribute__r_require_intensity_imageAttributeErrorrrrrrArrstackr;PROPSlowergetattrr)rattrrmultichannel_listrs` @r __getattr__zRegionProperties.__getattr__ps+ > ! ! ((.. .  (T5M-M-M +d+++  4) ) ))$/D3D99F{{(4)F-----%*4+?+Eb+I%J%J---) "x(9CCCC#tDJ0DEEE(GGG1tDJ'''$G&*GG39GGGU]]tzz||t33%-$K#;;;$/$/// 4t-- - !ST$ZZ!S!SD!S!S!STT Trc|tvr/tt||dSt||dSr)rsuper __setattr__)rrvalue __class__s rrzRegionProperties.__setattr__sP 5== GG  d U 3 3 3 3 3 GG  e , , , , ,rc4tj|jSr)rrr;rs rrxzRegionProperties.num_pixelssvdj!!!rcDtj|j|jzSr)rrr;rrs rrzRegionProperties.areasvdj!!D$444rctfdtjDfdtjDzS)z Returns ------- A tuple of the bounding box's start coordinates for each dimension, followed by the end coordinates for each dimension c4g|]}j|jSrzrbstartr|rrs rrz)RegionProperties.bbox..s" < < .s"===atz!}!===r)rrrrs`rrzRegionProperties.bboxs^ < < < <% *;*; < < <====5+<+<=== >   rc*|jj|jzSr)r;sizerrs rrzRegionProperties.area_bboxsz!111rcRt|jdSNrr)rrwmeanrs rrzRegionProperties.centroids$T',,!,44555rcDtj|j|jzSr)rrr#rrs rr zRegionProperties.area_convexsvd'((4+;;;rc.ddlm}||jS)Nr)convex_hull_image)morphology.convex_hullrr;)rrs rr#zRegionProperties.image_convexs) ?>>>>>  ,,,rctjj}tjfdt jD}||zjzjzS)Nc4g|]}j|jSrzr r s rrz2RegionProperties.coords_scaled.."!Q!Q!Q!$*Q-"5!Q!Q!Qr)rargwherer;rrrrrrindices object_offsets` rrwzRegionProperties.coords_scaleds[+dj))!Q!Q!Q!QuTZ?P?P!Q!Q!QRR '4=84<GGrctjj}tjfdt jD}||zjzS)Nc4g|]}j|jSrzr r s rrz+RegionProperties.coords..rr)rrr;rrrrrs` rr&zRegionProperties.coordssT+dj))!Q!Q!Q!QuTZ?P?P!Q!Q!QRR w&55rcP|j\}}|dkrdStd||z z S)Nrr )r?r)rl1l2s rr(zRegionProperties.eccentricitys4,B 771ARK   rcLd|jz|jztz d|jz zS)Nrr )rrPIrs rr*z)RegionProperties.equivalent_diameter_areas%DJ*R/Q^DDrcf|jdvrtdt|j|jS)Nrz4Euler number is implemented for 2D or 3D images only)rrrr;rs rrzRegionProperties.euler_numbers: :V # #%I DJ 333rc |j|jz Sr)rrrs rr.zRegionProperties.extentsy4>))rcPtj|jddd}|jdkr%tjt |dd}n!|jdkrt |d \}}}}t||jzd }ttj |S) Nrconstantr)moder?high)fully_connectedr')level sqeuclidean) rrr#rvstackr rr rrmax)ridentity_convex_hull coordinates_ distancess rr0z#RegionProperties.feret_diameter_maxs!v  qz1    :??)2CPPPKKZ1__#12Fc#R#R#R KAq+ 5}EE BF9%%&&&rcDtj|j|jzSr)rrr6rrs rr3zRegionProperties.area_filledsvd'((4+;;;rcntjd|jz}tj|j|S)Nr')rrrndibinary_fill_holesr;)r structures rr6zRegionProperties.image_filled s/GD4:-.. $TZ;;;rc8|j|j|jkSr)rrbrDrs rr;zRegionProperties.images , ::rcR|j}tj|j||jS)Nr)rr r=r;rrmus rr=zRegionProperties.inertia_tensors( !&tz2t}MMMMrcBtj|j|jS)N)T)r r?r;r=rs rr?z'RegionProperties.inertia_tensor_eigvalss.tzT=PQQQQrc|jtd|js|jnt j|j|j}|j|j|zS)NzNo intensity image specified.)rrrr;r expand_dimsrrb)rr;s rrAz RegionProperties.image_intensity"s^  ( !@AA A% 8DJJ DJ77  $TZ0588rcN|jtjdS)NFcopy)rAastyperfloat64rs r_image_intensity_doublez(RegionProperties._image_intensity_double.s #**2:E*BBBrcjdjzfdtjt fdt jDS)Nrc6d|zdzdjdz |z zzSNrMrr rrrs r _get_elementz5RegionProperties.centroid_local.._get_element6'$;% Q0E(FF Frc3@K|]}|z VdSrrzr|rMM0rRs rrz2RegionProperties.centroid_local..:7KK!LL&&'",KKKKKKr)rZrrasarrayrrrrVrWrRs`@@@rrFzRegionProperties.centroid_local1s L tdj ! G G G G Gz KKKKKKtz9J9JKKK K K   rc|j|j}tj|dtjdSNrrFrG)rAr;rr2rIrJrvalss rrLzRegionProperties.intensity_max=;#DJ/vd###**2:E*BBBrcNtj|j|jdSr)rrrAr;rs rrOzRegionProperties.intensity_meanBs!wt+DJ7a@@@@rc|j|j}tj|dtjdSr\)rAr;rminrIrJr]s rrRzRegionProperties.intensity_minFr_rcR|j|j}tj|dSr)rAr;rstdr]s rrUzRegionProperties.intensity_stdKs&#DJ/vd####rc|jdkr|jd}dt|zS|jdkr1|j}td|d|dz|dz zStd)Nrrr'rr z-axis_major_length only available in 2D and 3Drr?rr)rr!evs rrIz"RegionProperties.axis_major_lengthPst :??,Q/BtBxx<  Z1__,Bbebembe3455 5LMM Mrc|jdkr|jd}dt|zS|jdkr2|j}td|d |dz|dzzStd) Nrrrfr'rrr z-axis_minor_length only available in 2D and 3Drg)rr"rhs rrWz"RegionProperties.axis_minor_length\sv :??,R0BtBxx<  Z1__,Br!ufr!unr!u4566 6LMM Mrctj|jtjd|j}|SNr'r?)r rZr;rIruint8r)rrVs rrZzRegionProperties.momentshs4  TZ..rx88!T] S S Srctj|jtj|jd|j}|S)Nr'orderr)r rr;rIrrlrFrr@s rrz RegionProperties.moments_centralnsE % J  bh ' '  M      rctd|jDrtdtj|jS)Nc3"K|] }|dkV dS)rNrz)r|ss rrz.RegionProperties.moments_hu..|s&//AqCx//////r+`moments_hu` supports spacing = (1, 1) only)anyrrr r9r\rs rr9zRegionProperties.moments_huysI ///// / / U%&STT T"4#:;;;rcDtj|jd|jSrk)r r\rrrs rr\z#RegionProperties.moments_normalizeds**  !T]    rc|jj\}}}}||z dkr|dkr tdz St dz Sdtd|z||z zS)Nrg@r,)r=flatr$r)rabcs rr^zRegionProperties.orientations_(- 1a q5A::1uuCxsSy rAvq1u--- -rcttj|jdkrt dt |jd|jdzSNr z,`perimeter` supports isotropic spacings onlyrfr)rruniquerrrr;rs rrzRegionProperties.perimetersP ry'' ( (A - -%&TUU UQ''$-*:::rcttj|jdkrt dt |jd|jdzSr})rrr~rrrr;rs rrz"RegionProperties.perimeter_croftonsP ry'' ( (A - -%&TUU U Q//$-2BBBrc |j|jz Sr)rr rs rrdzRegionProperties.soliditysy4+++rct|j}tdt||j|jDS)Nc38K|]\}}}||j|zzVdSr)r )r|idxslcspcs rrz5RegionProperties.centroid_weighted..sG  S# #)c/ !      r)rjrziprbr)rctrs rrhz"RegionProperties.centroid_weightedsH*  !$S$*dm!D!D      rcjdjzfdtjt fdt jDS)NrMc6d|zdzdjdz |z zzSrOrPrQs rrRz>RegionProperties.centroid_weighted_local.._get_elementrSrc3@K|]}|z VdSrrzrUs rrz;RegionProperties.centroid_weighted_local..rXr)rkrrrYrrrZs`@@@rrjz(RegionProperties.centroid_weighted_locals  ! tdj ! G G G G Gz KKKKKKtz9J9JKKK K K   rcjr.sE$U36]!T]SSSrrrr'rn) rKrrrrrr rZr)rrZr;s` @rrkz!RegionProperties.moments_weighteds,,..   Nh"5;r?33 GG&uAt}MMMGrcjjr?fdtjdD}t j|d}ntjdj }|S)Ncjg|]/}tjd|fd|fdj0S).r')centerror)r rr)r|rrr;rs rrz=RegionProperties.moments_weighted_central..sX(#q&M#c1f+Q rrrr'rn) rjrKrrrrrr rr)r moments_listrZrr;s` @@rrfz)RegionProperties.moments_weighted_centrals*,,..   u{2// L h|"555GG.s!T]Grcntj|jtjddgkst d|j|jrA|jjd}tj fdt|DdStj S)Nr rsrcHg|]}tjd|fSr)r r9)r|rnus rrz8RegionProperties.moments_weighted_hu..s,KKKQ$RQZ00KKKrr) rrrallrrlrrrrrr r9)r nchannelsrs @rriz$RegionProperties.moments_weighted_hus''28QF+;+;;@@BB U%&STT T  -   +-3B7I8KKKK% :J:JKKK  &r** *rcjjrBjjd}t jfdt |DdStjdj S)NrcXg|]&}tjd|fdj'Sr)r r\r)r|rrArs rrz@RegionProperties.moments_weighted_normalized..sN/36 !T]rrr'rn) rfrrrrrrr r\r)rrrAs` @rrlz,RegionProperties.moments_weighted_normalizeds *   S-3B7I8#9--   .rDMRRR Rrct}|jt}||}t t |Sr) PROP_VALSrr differenceitersorted)rpropsunavailable_propss r__iter__zRegionProperties.__iter__s>  ( 8 $$%677EF5MM"""rcbt||d}||St|t|Sr)rr)rkeyrs r __getitem__zRegionProperties.__getitem__s2c4((  L4s,, ,rc t|tsdStD]S} tjt ||dt ||dB#t$rYdSwxYwdS)NFT) isinstancerrrtesting assert_equalrAssertionError)rotherrs r__eq__zRegionProperties.__eq__ s%!122 5  C  ''D#t,,geS$.G.G"   uuu ts>A!! A0/A0)8r __module__ __qualname____doc__rrrpropertyrrxrrrrr r#rwr&rr(r*rr.r0r3r6r;r=r?rArKrFrLrOrRrUrIrWrZrr9r\r^rrrdrhrjrkrfrirlrrr __classcell__)rs@rrr0s9X9X9X9X9Xv3U3U3Uj-----  "" WX" 55 WX5   X  22X266X6 << WX< -- WX- HHXH 66X6  !! VX! EEXE44X4**X* ' 'X '<<X< << WX< ;; WX; NN WXN RR WXR 99 WX9CCC   X  CCXCAAXACCXC$$X$ N NX N N NX N  WX  WX << VX<     WX  .. VX. ;; VX;  CC VXC ,,X,  X    X      WX   WX"  + + VX + SS WXS ###---       rrrDr-c i}t|}|D]6}|d}|}t||}t||}|tvrt|} n0|j|} t | |jdu|jj } tj |s|tvs| tj urQtj|| } t|D]} || || | <tj| ||<g} g}tjtj|D]q}| |t+t,|f|z|t|dkr|n|drt|}tj||f| }t|D]D}tj|||}t1|D]\} }||||| f<Et1| D]\} }|dd| f||<8|S)a4Convert image region properties list into a column dictionary. Parameters ---------- regions : (K,) list List of RegionProperties objects as returned by :func:`regionprops`. properties : tuple or list of str, optional Properties that will be included in the resulting dictionary For a list of available properties, please see :func:`regionprops`. Users should remember to add "label" to keep track of region identities. separator : str, optional For non-scalar properties not listed in OBJECT_COLUMNS, each element will appear in its own column, with the index of that element separated from the property name by this separator. For example, the inertia tensor of a 2D region will appear in four columns: ``inertia_tensor-0-0``, ``inertia_tensor-0-1``, ``inertia_tensor-1-0``, and ``inertia_tensor-1-1`` (where the separator is ``-``). Object columns are those that cannot be split in this way because the number of columns would change depending on the object. For example, ``image`` and ``coords``. Returns ------- out_dict : dict Dictionary mapping property names to an array of values of that property, one value per region. This dictionary can be used as input to pandas ``DataFrame`` to map property names to columns in the frame and regions to rows. Notes ----- Each column contains either a scalar property, an object property, or an element in a multidimensional array. Properties with scalar values for each region, such as "eccentricity", will appear as a float or int array with that property name as key. Multidimensional properties *of fixed size* for a given image dimension, such as "centroid" (every centroid will have three elements in a 3D image, no matter the region size), will be split into that many columns, with the name {property_name}{separator}{element_num} (for 1D properties), {property_name}{separator}{elem_num0}{separator}{elem_num1} (for 2D properties), and so on. For multidimensional properties that don't have a fixed size, such as "image" (the image of a region varies in size depending on the region size), an object array will be used, with the corresponding property name as the key. Examples -------- >>> from skimage import data, util, measure >>> image = data.coins() >>> label_image = measure.label(image > 110, connectivity=image.ndim) >>> proplist = regionprops(label_image, image) >>> props = _props_to_dict(proplist, properties=['label', 'inertia_tensor', ... 'inertia_tensor_eigvals']) >>> props # doctest: +ELLIPSIS +SKIP {'label': array([ 1, 2, ...]), ... 'inertia_tensor-0-0': array([ 4.012...e+03, 8.51..., ...]), ... ..., 'inertia_tensor_eigvals-1': array([ 2.67...e+02, 2.83..., ...])} The resulting dictionary can be directly passed to pandas, if installed, to obtain a clean DataFrame: >>> import pandas as pd # doctest: +SKIP >>> data = pd.DataFrame(props) # doctest: +SKIP >>> data.head() # doctest: +SKIP label inertia_tensor-0-0 ... inertia_tensor_eigvals-1 0 1 4012.909888 ... 267.065503 1 2 8.514739 ... 2.834806 2 3 0.666667 ... 0.000000 3 4 0.000000 ... 0.000000 4 5 0.222222 ... 0.111111 rN)rrrr )rrgetr COL_DTYPESrrrr;rrrOBJECT_COLUMNSremptyrrHndindexrrjoinrstrrYr)regions properties separatoroutnrr orig_proprpr~r column_bufferrmodified_propslocsind n_columns column_datakloc modified_props r_props_to_dictr!sqb C G A.7.7 AJ yyt$$ Q   :  t$EE&t,D+,D8W\E ;r?? 7dn448K8KHQe444M1XX 4 4#*1:d#3 a  W]33C NN NDz"(2,,// = =%%innSylS>P5Q5Q&R&RSSS 3s88a<???K1XX 0 0Z 4 011'oo00FAs(*3K1%%0%.n$=$= 7 7 =%0A%6M"" 7 JrT)rrrrct|||||}|t|d|Dz}t|dkr|j}t jd|zt }d|d|z<|0t j|j|j|dz|j}t|||||}t||| } d | DSt||| S) aCompute image properties and return them as a pandas-compatible table. The table is a dictionary mapping column names to value arrays. See Notes section below for details. .. versionadded:: 0.16 Parameters ---------- label_image : (M, N[, P]) ndarray Labeled input image. Labels with value 0 are ignored. intensity_image : (M, N[, P][, C]) ndarray, optional Intensity (i.e., input) image with same size as labeled image, plus optionally an extra dimension for multichannel data. The channel dimension, if present, must be the last axis. Default is None. .. versionchanged:: 0.18.0 The ability to provide an extra dimension for channels was added. properties : tuple or list of str, optional Properties that will be included in the resulting dictionary For a list of available properties, please see :func:`regionprops`. Users should remember to add "label" to keep track of region identities. cache : bool, optional Determine whether to cache calculated properties. The computation is much faster for cached properties, whereas the memory consumption increases. separator : str, optional For non-scalar properties not listed in OBJECT_COLUMNS, each element will appear in its own column, with the index of that element separated from the property name by this separator. For example, the inertia tensor of a 2D region will appear in four columns: ``inertia_tensor-0-0``, ``inertia_tensor-0-1``, ``inertia_tensor-1-0``, and ``inertia_tensor-1-1`` (where the separator is ``-``). Object columns are those that cannot be split in this way because the number of columns would change depending on the object. For example, ``image`` and ``coords``. extra_properties : Iterable of callables Add extra property computation functions that are not included with skimage. The name of the property is derived from the function name, the dtype is inferred by calling the function on a small sample. If the name of an extra property clashes with the name of an existing property the extra property will not be visible and a UserWarning is issued. A property computation function must take a region mask as its first argument. If the property requires an intensity image, it must accept the intensity image as the second argument. spacing: tuple of float, shape (ndim,) The pixel spacing along each axis of the image. Returns ------- out_dict : dict Dictionary mapping property names to an array of values of that property, one value per region. This dictionary can be used as input to pandas ``DataFrame`` to map property names to columns in the frame and regions to rows. If the image has no regions, the arrays will have length 0, but the correct type. Notes ----- Each column contains either a scalar property, an object property, or an element in a multidimensional array. Properties with scalar values for each region, such as "eccentricity", will appear as a float or int array with that property name as key. Multidimensional properties *of fixed size* for a given image dimension, such as "centroid" (every centroid will have three elements in a 3D image, no matter the region size), will be split into that many columns, with the name {property_name}{separator}{element_num} (for 1D properties), {property_name}{separator}{elem_num0}{separator}{elem_num1} (for 2D properties), and so on. For multidimensional properties that don't have a fixed size, such as "image" (the image of a region varies in size depending on the region size), an object array will be used, with the corresponding property name as the key. Examples -------- >>> from skimage import data, util, measure >>> image = data.coins() >>> label_image = measure.label(image > 110, connectivity=image.ndim) >>> props = measure.regionprops_table(label_image, image, ... properties=['label', 'inertia_tensor', ... 'inertia_tensor_eigvals']) >>> props # doctest: +ELLIPSIS +SKIP {'label': array([ 1, 2, ...]), ... 'inertia_tensor-0-0': array([ 4.012...e+03, 8.51..., ...]), ... ..., 'inertia_tensor_eigvals-1': array([ 2.67...e+02, 2.83..., ...])} The resulting dictionary can be directly passed to pandas, if installed, to obtain a clean DataFrame: >>> import pandas as pd # doctest: +SKIP >>> data = pd.DataFrame(props) # doctest: +SKIP >>> data.head() # doctest: +SKIP label inertia_tensor-0-0 ... inertia_tensor_eigvals-1 0 1 4012.909888 ... 267.065503 1 2 8.514739 ... 2.834806 2 3 0.666667 ... 0.000000 3 4 0.000000 ... 0.000000 4 5 0.222222 ... 0.111111 [5 rows x 7 columns] If we want to measure a feature that does not come as a built-in property, we can define custom functions and pass them as ``extra_properties``. For example, we can create a custom function that measures the intensity quartiles in a region: >>> from skimage import data, util, measure >>> import numpy as np >>> def quartiles(regionmask, intensity): ... return np.percentile(intensity[regionmask], q=(25, 50, 75)) >>> >>> image = data.coins() >>> label_image = measure.label(image > 110, connectivity=image.ndim) >>> props = measure.regionprops_table(label_image, intensity_image=image, ... properties=('label',), ... extra_properties=(quartiles,)) >>> import pandas as pd # doctest: +SKIP >>> pd.DataFrame(props).head() # doctest: +SKIP label quartiles-0 quartiles-1 quartiles-2 0 1 117.00 123.0 130.0 1 2 111.25 112.0 114.0 2 3 111.00 111.0 111.0 3 4 111.00 111.5 112.5 4 5 112.50 113.0 114.0 )rBrrrNcg|] }|j Srzr)r|rs rrz%regionprops_table..=s(T(T(T4(T(T(Trrr9rr r)rrc*i|]\}}||ddS)Nrrz)r|rrs rrz%regionprops_table..Ps&333TQ1RaR5333r) rlistrrrrrrr~ritems) rrBrrrrrrrout_ds rregionprops_tablers6^') G#*%%(T(TCS(T(T(TT  7||qhtd{#666 #$ D4K  & h!O$9$%%$@@%+O +-    w:SSS33U[[]]3333 'jI N N NNrrc |jdvrtdtj|jtjs=tj|jt rtdtd|"tj|jft}nBtj |}|jdks|j |jkrtd|d g}tj |}t|D]9\} } | | dz} t| | |||||| } || :|S) u*Measure properties of labeled image regions. Parameters ---------- label_image : (M, N[, P]) ndarray Labeled input image. Labels with value 0 are ignored. .. versionchanged:: 0.14.1 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 : (M, N[, P][, C]) ndarray, optional Intensity (i.e., input) image with same size as labeled image, plus optionally an extra dimension for multichannel data. Currently, this extra channel dimension, if present, must be the last axis. Default is None. .. versionchanged:: 0.18.0 The ability to provide an extra dimension for channels was added. cache : bool, optional Determine whether to cache calculated properties. The computation is much faster for cached properties, whereas the memory consumption increases. extra_properties : Iterable of callables Add extra property computation functions that are not included with skimage. The name of the property is derived from the function name, the dtype is inferred by calling the function on a small sample. If the name of an extra property clashes with the name of an existing property the extra property will not be visible and a UserWarning is issued. A property computation function must take a region mask as its first argument. If the property requires an intensity image, it must accept the intensity image as the second argument. spacing: tuple of float, shape (ndim,) The pixel spacing along each axis of the image. offset : array-like of int, shape `(label_image.ndim,)`, optional Coordinates of the origin ("top-left" corner) of the label image. Normally this is ([0, ]0, 0), but it might be different if one wants to obtain regionprops of subvolumes within a larger volume. 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** : float Area of the region i.e. number of pixels of the region scaled by pixel-area. **area_bbox** : float Area of the bounding box i.e. number of pixels of bounding box scaled by pixel-area. **area_convex** : float Area of the convex hull image, which is the smallest convex polygon that encloses the region. **area_filled** : float Area of the region with all the holes filled in. **axis_major_length** : float The length of the major axis of the ellipse that has the same normalized second central moments as the region. **axis_minor_length** : float The length of the minor axis of the ellipse that has the same normalized second central moments as the 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)``. **centroid** : array Centroid coordinate tuple ``(row, col)``. **centroid_local** : array Centroid coordinate tuple ``(row, col)``, relative to region bounding box. **centroid_weighted** : array Centroid coordinate tuple ``(row, col)`` weighted with intensity image. **centroid_weighted_local** : array Centroid coordinate tuple ``(row, col)``, relative to region bounding box, weighted with intensity image. **coords_scaled** : (K, 2) ndarray Coordinate list ``(row, col)`` of the region scaled by ``spacing``. **coords** : (K, 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_area** : float The diameter of a circle with the same area as the region. **euler_number** : int Euler characteristic of the set of non-zero pixels. Computed as number of connected components subtracted by number of holes (input.ndim connectivity). In 3D, number of connected components plus number of holes subtracted by number of tunnels. **extent** : float Ratio of pixels in the region to pixels in the total bounding box. Computed as ``area / (rows * cols)`` **feret_diameter_max** : float Maximum Feret's diameter computed as the longest distance between points around a region's convex hull contour as determined by ``find_contours``. [5]_ **image** : (H, J) ndarray Sliced binary region image which has the same size as bounding box. **image_convex** : (H, J) ndarray Binary convex hull image which has the same size as bounding box. **image_filled** : (H, J) ndarray Binary region image with filled holes which has the same size as bounding box. **image_intensity** : ndarray Image inside region bounding box. **inertia_tensor** : ndarray Inertia tensor of the region for the rotation around its mass. **inertia_tensor_eigvals** : tuple The eigenvalues of the inertia tensor in decreasing order. **intensity_max** : float Value with the greatest intensity in the region. **intensity_mean** : float Value with the mean intensity in the region. **intensity_min** : float Value with the least intensity in the region. **intensity_std** : float Standard deviation of the intensity in the region. **label** : int The label in the labeled input image. **moments** : (3, 3) ndarray Spatial moments up to 3rd order:: m_ij = sum{ array(row, col) * row^i * col^j } where the sum is over the `row`, `col` coordinates of the region. **moments_central** : (3, 3) ndarray Central moments (translation invariant) up to 3rd order:: mu_ij = sum{ array(row, col) * (row - row_c)^i * (col - col_c)^j } where the sum is over the `row`, `col` coordinates of the region, and `row_c` and `col_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_ij = mu_ij / m_00^[(i+j)/2 + 1] where `m_00` is the zeroth spatial moment. **moments_weighted** : (3, 3) ndarray Spatial moments of intensity image up to 3rd order:: wm_ij = sum{ array(row, col) * row^i * col^j } where the sum is over the `row`, `col` coordinates of the region. **moments_weighted_central** : (3, 3) ndarray Central moments (translation invariant) of intensity image up to 3rd order:: wmu_ij = sum{ array(row, col) * (row - row_c)^i * (col - col_c)^j } where the sum is over the `row`, `col` coordinates of the region, and `row_c` and `col_c` are the coordinates of the region's weighted centroid. **moments_weighted_hu** : tuple Hu moments (translation, scale and rotation invariant) of intensity image. **moments_weighted_normalized** : (3, 3) ndarray Normalized moments (translation and scale invariant) of intensity image up to 3rd order:: wnu_ij = wmu_ij / wm_00^[(i+j)/2 + 1] where ``wm_00`` is the zeroth spatial moment (intensity-weighted area). **num_pixels** : int Number of foreground pixels. **orientation** : float 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. **perimeter** : float Perimeter of object which approximates the contour as a line through the centers of border pixels using a 4-connectivity. **perimeter_crofton** : float Perimeter of object approximated by the Crofton formula in 4 directions. **slice** : tuple of slices A slice to extract the object from the source image. **solidity** : float Ratio of pixels in the region to pixels of the convex hull image. 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] https://en.wikipedia.org/wiki/Image_moment .. [5] W. Pabst, E. Gregorová. Characterization of particles and particle systems, pp. 27-28. ICT Prague, 2007. https://old.vscht.cz/sil/keramika/Characterization_of_particles/CPPS%20_English%20version_.pdf Examples -------- >>> from skimage import data, util >>> from skimage.measure import label, regionprops >>> 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 (22.72987986048314, 81.91228523446583) >>> # centroid of first labeled object >>> props[0]['centroid'] (22.72987986048314, 81.91228523446583) Add custom measurements by passing functions as ``extra_properties`` >>> from skimage import data, util >>> from skimage.measure import label, regionprops >>> import numpy as np >>> img = util.img_as_ubyte(data.coins()) > 110 >>> label_img = label(img, connectivity=img.ndim) >>> def pixelcount(regionmask): ... return np.sum(regionmask) >>> props = regionprops(label_img, extra_properties=(pixelcount,)) >>> props[0].pixelcount 7741 >>> props[1]['pixelcount'] 42 r&z"Only 2-D and 3-D images supported.zNon-integer image types are ambiguous: use skimage.measure.label to label the connected components of label_image, or label_image.astype(np.uint8) to interpret the True values as a single label.z+Non-integer label_image types are ambiguousNrr zIOffset should be an array-like of integers of shape (label_image.ndim,); z was provided.)rrr)r TypeErrorr issubdtyper~integerrrrrYrrr: find_objectsrrr) rrBrrrr offset_arrrobjectsrslrDrs rrrUs||v%%<=== =*BJ 7 7 K =*D 1 1 K5 IJJ J ~X{/1=== Z'' ?a  :?k6F#F#F****  G{++G7##2 : A     -     u Nrcddl}ddltjpd}d}tjdkrd}||||j}fd|D}|S)Nrz+\*\*(\w+)\*\* \:.*?\n(.*?)(?=\n [\*\S]+))r' z'\*\*(\w+)\*\* \:.*?\n(.*?)(?=\n[\*\S]+))flagsci|]>}|d|d?S)r r)groupdedent)r|mtextwraps rrz_parse_docs..s;IIIA HOOAGGAJJ77IIIr)rerrrsys version_infofinditerDOTALL)rdoc arg_regexmatchesprop_docrs @r _parse_docsrsq IIIOOO   #C>I 7""> kk)S k::GIIIIIIIH Orct}dttDD]"}||tt|_#dS)Nc<g|]}|d|S)r5) startswith)r|members rrz,_install_properties_docs..s, W W W@Q@QRU@V@V Wf W W Wr)rdirrrr)rps r_install_properties_docsrsU}}H W W3'7#8#8 W W W;;/7{ !$$,,;;r)rr)Nr)NT)5rr functoolsrmathrrr$rwarningsrnumpyrscipyr r:scipy.spatial.distancer rr _find_contoursr _marching_cubes_lewinerr_regionprops_utilsrrrr__all__rfloatrr{rrrsetvaluesrrrrrrrr_RegionPropertiesrrrrrrzrrrs  (((((())))))333333 L K KC  FC 6C {C  C  ' C   C -C =C >C NC 8C NC /C 5C  >!C " h#C &)'C C (,)C *-+C ,=-C .>/C 0N1C 23C 4 W5C 6%7C 849C :';C <(=C > W?C @%AC B&CC D*EC F,GC HOIC C C J_KC L%MC N&OC POQC R_SC T_UC V*WC X,YC Zy[C \-]C ^=_C `aC b+cC h WiC j kC n8oC p :qC C r,,.068)*!>#@EC C C J, E,,5,5 ,  ,  , C,,e,,u, f,V,E,, C!," e#,,$%%,& V',(F),*F+,,v-,.e/,0e1,2U3,4e5,6U7,8U9,: S;,<u=,>u?,@%A,B%C,DE,,F!& #( W,,, \ON (8(8(:(:NNN C     (...b"   '''Tjjjjjjjj\%BBBBN lO  lOlOlOlOlOb m   mmmmm`   ;;;r