3 dd@shdZddlZddljZddlZddlZddl m Z m Z ddl m Z mZmZmZmZmZmZmZddlmZe jdZGdd d eZGd d d eZGd d d eZGdddeZGdddeZGdddeZGdddeZGdddeZ Gddde Z!GdddeZ"GdddeZ#GdddeZ$Gd d!d!eZ%Gd"d#d#eZ&Gd$d%d%eZ'dS)&z~ Image assessment algorithms. Typical overlap and error computation measures to evaluate results from other processing units. N)configlogging)SimpleInterface BaseInterfacetraits TraitedSpecFileInputMultiPathBaseInterfaceInputSpec isdefined)NipyBaseInterfaceznipype.interfacec @sLeZdZeddddZeddddZejddddd d dd Zedd d Z dS)DistanceInputSpecTz+Has to have the same dimensions as volume2.)exists mandatorydescz+Has to have the same dimensions as volume1.eucl_mineucl_cog eucl_mean eucl_wmeaneucl_maxa""eucl_min": Euclidean distance between two closest points "eucl_cog": mean Euclidian distance between the Center of Gravity of volume1 and CoGs of volume2 "eucl_mean": mean Euclidian minimum distance of all volume2 voxels to volume1 "eucl_wmean": mean Euclidian minimum distance of all volume2 voxels to volume1 weighted by their values "eucl_max": maximum over minimum Euclidian distances of all volume2 voxels to volume1 (also known as the Hausdorff distance))r usedefaultz(calculate overlap only within this mask.)rrN) __name__ __module__ __qualname__r volume1volume2rEnummethod mask_volumer r ;/tmp/pip-build-7vycvbft/nipype/nipype/algorithms/metrics.pyrs   rc@s2eZdZejZejddZejddZe Z dS)DistanceOutputSpec)shapeN)r#)r#) rrrrFloatdistanceArraypoint1point2r histogramr r r r!r":s  r"c@s^eZdZdZeZeZdZddZ ddZ ddZ d d Z dd d Z ddZddZddZdS)Distancez(Calculates distance between two volumes.zhist.pdfcCs*ddlm}||}tj|tj|}|S)Nr)binary_erosion)Zscipy.ndimage.morphologyr,np logical_andZ logical_not)selfdatar,ZerodedZborderr r r! _find_borderIs zDistance._find_bordercCstt|jdkr*|dddddddf}tjtj|}tj|tj|jdf}tj||}|ddddfS)Nrr#)lenr$r-vstackZnonzeroonesdot)r/r0affineindicesZ coordinatesr r r!_get_coordinatesPs  zDistance._get_coordinatescCsddlm}m}tj|jjt}|j|}tj|jjt}|j|}|j ||j } |j ||j } || j | j } tj tj | | j\} } || j | ddf| j | ddf| j | ddf| j | ddffS)Nr)cdist euclidean)scipy.spatial.distancer;r<r- asanyarraydataobjastypeboolr1r:r8TZ unravel_indexZargminr$)r/nii1nii2r;r< origdata1border1 origdata2border2set1_coordinatesset2_coordinates dist_matrixr(r)r r r! _eucl_minXs  $zDistance._eucl_mincCs6ddlm}ddlm}m}tj|j}tj|dktj |@}tj ||j dd}tj |tj dgf}tj |j|ddddf}tj|j} tj| dktj | @} || \} } tjd| f} x2t| D]&} tj || | | d| dd| f<qWtj |j| ddddf}||j|j}tj|S)Nr)r;)center_of_masslabelr3r#r2)r=r;Zscipy.ndimage.measurementsrMrNr-r>r?rintisnanarrayreshaper5r7r8r6rangerBmean)r/rCrDr;rMrNrEZcog_tZ cog_t_coorrGZ labeled_dataZn_labelsZcogsiZ cogs_coorrKr r r! _eucl_cogms     &zDistance._eucl_cogFcCsddlm}tj|jjt}|j|}tj|jjt}|j||j }|j||j } ||j | j } tj | dd} ddl } | j tjddddlj} | j| j| dddd | j|j| j| j|rtj| |j|jd Stj| SdS) Nr)r;)axisZ executionZmatplotlib_backend2r3Zgreen)ZnormedZ facecolor)weights)r=r;r-r>r?r@rAr1r:r8rBamin matplotlibZusergetZmatplotlib.pyplotZpyplotfigurehistZsavefig_hist_filenameZclfcloseaverageflatrU)r/rCrDweightedr;rErFrGrIrJrKZmin_dist_matrixr\Zpltr r r! _eucl_means&    zDistance._eucl_meanc Cs4ddlm}tj|j}tj|dktj|@}tj|j}tj|dktj|@}t|jj rtjt j |jj j}tj|dktj|@}tj ||}tj ||}|j dks|j dkrtjS|j|}|j|}|j||j} |j||j} || j| j} tjtj| ddtj| ddf} tj | S)Nr)r;)rXr3)r=r;r-r>r?rPrQr inputsrnbloadr.maxnanr1r:r8rBZ concatenater[) r/rCrDr;rErGmaskdatarFrHrIrJZ distancesZminsr r r! _eucl_maxs&        "zDistance._eucl_maxcCstj|jjdd}tj|jjdd}|jjdkrJ|j||\|_|_|_ nr|jjdkrf|j |||_nV|jjdkr|j |||_n:|jjdkr|j ||dd|_n|jjd kr|j |||_|S) NF)ZmmaprrrrT)rdr) rgrhrfrrrrL _distance_point1_point2rWrerl)r/runtimerCrDr r r!_run_interfaces     zDistance._run_interfacecCsZ|jj}|j|d<|jjdkr8|j|d<|j|d<n|jjdkrVtjj |j |d<|S) Nr&rr(r)rrr*)rr) _outputsr]rmrfrrnroospathabspathr`)r/outputsr r r! _list_outputss      zDistance._list_outputsN)F)rrr__doc__r input_specr" output_specr`r1r:rLrWrerlrqrwr r r r!r+As r+c@szeZdZeddddZeddddZedddZejddddd Z ed dd Z ej d d ddddZ ej ddddddZ dS)OverlapInputSpecTz+Has to have the same dimensions as volume2.)rrrz+Has to have the same dimensions as volume1.z(calculate overlap only within this mask.)rrFzconsider zeros as a label)rrrzdiff.nii)rnonevolume squared_volz'none': no class-overlap weighting is performed. 'volume': computed class-overlaps are weighted by class volume 'squared_vol': computed class-overlaps are weighted by the squared volume of the class)rrZvoxelmmzunits for volumes)rrrN)rrrr rrrrZBool bg_overlapout_filer weighting vol_unitsr r r r!r{s     r{c@seZdZejddZejddZejejddZejejddZ ejddZ ejejddZ ejej ddZ ed d d Zd S) OverlapOutputSpeczaveraged jaccard index)rzaveraged dice indexzthe Jaccard index (JI) per ROIzthe Dice index (DI) per ROIzaveraged volume differencezvolume differences of ROIszdetected labelsTzerror map of differences)rrN)rrrrr%jaccarddiceListroi_jiroi_divolume_difference roi_voldiffZIntlabelsr diff_filer r r r!rs   rc@s0eZdZdZeZeZddZddZ ddZ dS) Overlapa1 Calculates Dice and Jaccard's overlap measures between two ROI maps. The interface is backwards compatible with the former version in which only binary files were accepted. The averaged values of overlap indices can be weighted. Volumes now can be reported in :math:`mm^3`, although they are given in voxels to keep backwards compatibility. Example ------- >>> overlap = Overlap() >>> overlap.inputs.volume1 = 'cont1.nii' >>> overlap.inputs.volume2 = 'cont2.nii' >>> res = overlap.run() # doctest: +SKIP cCsHddlm}m}||d}tj|p,tj|s2dSd|||j|jS)Nr)rrr3)r=rrr-anyrc)r/Z booldata1Z booldata2rrrmethodsr r r!_bool_vec_dissimilaritys  zOverlap._bool_vec_dissimilaritycCstj|jj}tj|jj}d}|jjdkrDtj|jj dd}tj |j }d|tj |dktj |<t|j}|jtj|}tj |j jtj|}d|tj |dktj |<t|jjrtj tj|jjj }tj |dktj |}d||<d||<g} g} g} tj||dkjdj} |jjrF| jddx`| D]X} | j|j|| k|| kdd| j|t||| k| j|t||| kqLWtggd}tj| |d<d |d|dd|d <tjt| ftjd }|jj d kr&|tj| }|jj d kr&|d}|tj!|}tj"|j#}d|||dk<tj$tj%||j&|j|jj'| |_(||_)tj| tj| tj| |_*t+tj!||d d|_,t+tj!||dd|_-tj!||j*|_.|S)Ng?rr#rr3r)r)rrg@r)dtyper|r~rrO)/rgrhrfrrrr-prodheaderZ get_zoomsr>r? logical_orrQintrir@Zmin_scalar_typer runiquerStolistrinsertappendrr4dictrRr6float32rsumzerosr$save Nifti1Imager8r_labels _ove_rois _vol_roisround_dice_jaccard_volume)r/rprCrDZscaleZdata1Zmax1Zdata2rkresZvolumes1Zvolumes2rlresultsrZZ both_datar r r!rq's^            zOverlap._run_interfacecCs~|jj}|j|d<|j|d<|j|d<|j|d<|jdj|d<|jdj|d<|jj|d<t j j |j j |d<|S) Nrrrrrrrr)rrr]rrrrrrrrsrtrurfr)r/rvr r r!rwhs     zOverlap._list_outputsN) rrrrxr{ryrrzrrqrwr r r r!rs Arc@sbeZdZeedddddZeedddddZedddZej dd d dd d Z ed dddZ dS)FuzzyOverlapInputSpecT)rz8Reference image. Requires the same dimensions as in_tst.)rrz3Test image. Requires the same dimensions as in_ref.z"calculate overlap only within mask)rrr|r}r~z'none': no class-overlap weighting is performed. 'volume': computed class-overlaps are weighted by class volume 'squared_vol': computed class-overlaps are weighted by the squared volume of the class)rrzdiff.niiz-alternative name for resulting difference-map)rrN) rrrr r in_refin_tstin_maskrrrrr r r r!rvs& rc@sHeZdZejddZejddZejejddZejejddZ dS)FuzzyOverlapOutputSpecz*Fuzzy Jaccard Index (fJI), all the classes)rz'Fuzzy Dice Index (fDI), all the classesz0Array containing the fJIs of each computed classz0Array containing the fDIs of each computed classN) rrrrr%rrr class_fji class_fdir r r r!rs   rc@s eZdZdZeZeZddZdS) FuzzyOverlapaCalculates various overlap measures between two maps, using the fuzzy definition proposed in: Crum et al., Generalized Overlap Measures for Evaluation and Validation in Medical Image Analysis, IEEE Trans. Med. Ima. 25(11),pp 1451-1461, Nov. 2006. in_ref and in_tst are lists of 2/3D images, each element on the list containing one volume fraction map of a class in a fuzzy partition of the domain. Example ------- >>> overlap = FuzzyOverlap() >>> overlap.inputs.in_ref = [ 'ref_class0.nii', 'ref_class1.nii' ] >>> overlap.inputs.in_tst = [ 'tst_class0.nii', 'tst_class1.nii' ] >>> overlap.inputs.weighting = 'volume' >>> res = overlap.run() # doctest: +SKIP c Csdtj|jjj}tj|jjj}|j|jks@td|j|jf|jd}tj |t d}t |jj rtj tj|jj jdk}tj|dtjf|d}|j|jkst||}||}tj|dkrtjdtj|}tj|dkrtjdtj|}tj|d kr"tjd ||j}tj|d krHtjd ||j}tjtj||jd|f}tjtj||jd|f}|jdd |jdd }tj |td} |jjd krtj||dkdd jd|f} d | } |jjdkr| d} | tj| } d||d } t| j||jd<t| j| |jd<dd|D|jd<dd| D|jd<|S)Nz3Size of "in_tst" %s must match that of "in_ref" %s.r3)rr.gzFNegative values encountered in "in_ref" input, taking absolute values.zFNegative values encountered in "in_tst" input, taking absolute values.g?z@Values greater than 1.0 found in "in_ref" input, scaling values.z@Values greater than 1.0 found in "in_tst" input, scaling values.)rXr|r~rg@rrcSsg|] }t|qSr )float).0vr r r! sz/FuzzyOverlap._run_interface..rcSsg|] }t|qSr )r)rrr r r!rsrrOrOrOrOrO) rgZ concat_imagesrfrr?rr$ RuntimeErrorr-Z ones_likerAr rr>rhrepeatZnewaxisAssertionErrorrifloggerwarningabsriZ atleast_2dZminimumrSmaximumrrrr7Z_results) r/rpZrefdataZtstdataZncompmaskZ numeratorsZ denominatorsZjaccardsrZZvolumesZdicesr r r!rqs\        zFuzzyOverlap._run_interfaceN) rrrrxrryrrzrqr r r r!rsrc@sReZdZeddddZeddddZedddZejddd ddd Z ed d Z d S)ErrorMapInputSpecTz8Reference image. Requires the same dimensions as in_tst.)rrrz3Test image. Requires the same dimensions as in_ref.z(calculate overlap only within this mask.)rr sqeuclideanr<z0error map metric (as implemented in scipy cdist))rrrzName for the output file)rN) rrrr rrrrrmetricout_mapr r r r!rs  rc@s$eZdZedddZejddZdS)ErrorMapOutputSpecTzresulting error map)rrz'Average distance between volume 1 and 2)rN)rrrr rrr%r&r r r r!rs rc@s,eZdZdZeZeZdZddZ ddZ dS)ErrorMapaCalculates the error (distance) map between two input volumes. Example ------- >>> errormap = ErrorMap() >>> errormap.inputs.in_ref = 'cont1.nii' >>> errormap.inputs.in_tst = 'cont2.nii' >>> res = errormap.run() # doctest: +SKIP cCsJtj|jj}tj|j}tjtj|jjj}|j|jks@t d}|j }|jdkrl|j d}|j dd}t |jj rtj tj|jj j}||j krtdt|j t|fn tj|d}|jd}tj|dk} |jd|| jtj} |jd|| jtj} | | } |jjdkrL| d} |dkr@tj| dd} n tj| } n|jjdkrjtjj| dd} tj|tjd }| || <tj| |_|j|}|jj}|jtjd |d <|j|t |jj st!j"t!j#|jj\}}|d krt!j"|\}}||}t!j$|d ||_%n |jj |_%tj&|jtj|j'|j(|j%|S)Nr3r2z`Mask should match volume shape, mask is %s and volumes are %s)r$rr)rXr<)rZ data_typez.gzZ_errmaprOrOrOrOrO))rgrhrfrr-Zsqueezer?rndimrr$r rr>rlistr6rSwherer@rrrZlinalgZnormZ zeros_likerbrmrcopyZset_data_dtypeZset_data_shaperopsplitextbasenameru _out_filerr8 to_filename)r/rpZnii_refZref_dataZtst_datacompsZmapshapeZmskZ mskvectorZmsk_idxsZ refvectorZ tstvectorZ diffvectorZ errvectorZ errvectorexpZerrmaphdrfnameextZext2r r r!rq/s^                 zErrorMap._run_interfacecCs$|jj}|j|d<|j|d<|S)Nrr&)rzr]rrm)r/rvr r r!rwxs   zErrorMap._list_outputsN) rrrrxrryrrzrrqrwr r r r!rs  Irc@sfeZdZeddddZeddddZedddZedddZej ej dddd d d ej d dd Z dS)SimilarityInputSpecTz 3D/4D volume)rrrz 3D volume)rrcccrZcrl1miZnmiZslrastr or callable Cost-function for assessing image similarity. If a string, one of 'cc': correlation coefficient, 'cr': correlation ratio, 'crl1': L1-norm based correlation ratio, 'mi': mutual information, 'nmi': normalized mutual information, 'slr': supervised log-likelihood ratio. If a callable, it should take a two-dimensional array representing the image joint histogram as an input and return a float.)rrN) rrrr rrmask1mask2rZEitherrCallablerr r r r!rs  rc@seZdZejejddZdS)SimilarityOutputSpecz1Similarity between volume 1 and 2, frame by frame)rN)rrrrrr% similarityr r r r!rsrc@s(eZdZdZeZeZddZddZ dS) SimilarityaCalculates similarity between two 3D or 4D volumes. Both volumes have to be in the same coordinate system, same space within that coordinate system and with the same voxel dimensions. .. note:: This interface is an extension of :py:class:`nipype.interfaces.nipy.utils.Similarity` to support 4D files. Requires :py:mod:`nipy` Example ------- >>> from nipype.algorithms.metrics import Similarity >>> similarity = Similarity() >>> similarity.inputs.volume1 = 'rc1s1.nii' >>> similarity.inputs.volume2 = 'rc1s2.nii' >>> similarity.inputs.mask1 = 'mask.nii' >>> similarity.inputs.mask2 = 'mask.nii' >>> similarity.inputs.metric = 'cr' >>> res = similarity.run() # doctest: +SKIP cCs6ddlm}ddlm}tj|jj}tj|jj}t |j }|dksN|dkrZ|g}|g}|dkrvtj |}tj |}|dks|dkrt d|t |jjrtjtj|jjjdk} nd} t |jjrtjtj|jjjdk} nd} g|_x@t||D]2\} } || | |jj| | d } |jj| j|qW|S) Nr)HistogramRegistration)Affiner#rr2z2Image dimensions not supported (detected %dD file)r3)Zfrom_imgZto_imgrZ from_maskZto_mask)Z3nipy.algorithms.registration.histogram_registrationrZ#nipy.algorithms.registration.affinerrgrhrfrrr4r$Z four_to_threerr rr-r>r?r _similarityziprreval)r/rprrZvol1_niiZvol2_niiZdimsZvols1Zvols2rrZts1Zts2Zhistregr r r!rqs<        zSimilarity._run_interfacecCs|jj}|j|d<|S)Nr)rrr]r)r/rvr r r!rws  zSimilarity._list_outputsN) rrrrxrryrrzrqrwr r r r!rs /r)(rxrsos.pathrtrZnibabelrgnumpyr-rrrZinterfaces.baserrrrr r r r Zinterfaces.nipy.baser getLoggerrrr"r+r{rrrrrrrrrrrr r r r!s0 (   n! b`