o :ήc@sdZddlZddlmZddlmZmZee j fddZ ddZ d d Z Gd d d eZdidfd dZdiddfddZdiddfddZdifddZdS)z; :author: Stefan van der Walt, 2008 :license: modified BSD N)_supported_float_typecheck_nDcCs(t||k}t|||||<dSN)npabssign)xvalmaskr A/tmp/pip-target-vg8gfxp4/lib/python/skimage/filters/lpi_filter.py _min_limit srcCs>t|jt|dd}|tddt||D}|S)z5Return an array of oshape from the centre of x. rcss"|] \}}t|||VqdSrslice).0snr r r s z_centre..)rarrayshapetuplezip)r oshapestartoutr r r _centresrcCs,tj||jd}||tdd|jD<|S)zPad the data to the given shape with zeros. Parameters ---------- data : 2-d ndarray Input data shape : (2,) tuple )dtypecss|]}td|VqdS)rNrrrr r r r%sz_pad..)rzerosrrr)datarrr r r _pads r"c@s(eZdZdZddZddZddZdS) LPIFilter2Dz6Linear Position-Invariant Filter (2-dimensional) cKs&t|std||_||_d|_dS)a Parameters ---------- impulse_response : callable `f(r, c, **filter_params)` Function that yields the impulse response. ``r`` and ``c`` are 1-dimensional vectors that represent row and column positions, in other words coordinates are (r[0],c[0]),(r[0],c[1]) etc. `**filter_params` are passed through. In other words, ``impulse_response`` would be called like this: >>> def impulse_response(r, c, **filter_params): ... pass >>> >>> r = [0,0,0,1,1,1,2,2,2] >>> c = [0,1,2,0,1,2,0,1,2] >>> filter_params = {'kw1': 1, 'kw2': 2, 'kw3': 3} >>> impulse_response(r, c, **filter_params) Examples -------- Gaussian filter: Use a 1-D gaussian in each direction without normalization coefficients. >>> def filt_func(r, c, sigma = 1): ... return np.exp(-np.hypot(r, c)/sigma) >>> filter = LPIFilter2D(filt_func) z$Impulse response must be a callable.N)callable ValueErrorimpulse_response filter_params_cache)selfr&r'r r r __init__.s  zLPIFilter2D.__init__c Cs,t|j}||ddk7}t|jdd}t|j}|j|dd}|jdus2t|jj|krtjdd|D}t |D]\}}|||dd 8}q@| dd j }|j|dd}|j |dddf|dddffi|j  |}t||}t|} | |_n|j} t||}t|} | | fS) zECalculate filter and data FFT in preparation for filtering. rrrF)copyNcSsg|] }tdt|qS)r)rfloatrr r r `sz(LPIFilter2D._prepare..g@)rrrrrastyper(anymgrid enumeratereshapeTr&r'r"fftfftn) r)r!dshaper float_dtypecoordskcoordfFGr r r _prepareTs.  $    zLPIFilter2D._preparecCs>t|dd||\}}t||}tt||j}|S)zqApply the filter to the given data. Parameters ---------- data : (M,N) ndarray rr!)rr?r5ifftnrrrr)r)r!r=r>rr r r __call__vs zLPIFilter2D.__call__N)__name__ __module__ __qualname____doc__r*r?rAr r r r r#)s & "r#cCs,t|dd|durt|fi|}||S)aApply the given filter to data. Parameters ---------- data : (M,N) ndarray Input data. impulse_response : callable `f(r, c, **filter_params)` Impulse response of the filter. See LPIFilter2D.__init__. filter_params : dict Additional keyword parameters to the impulse_response function. Other Parameters ---------------- predefined_filter : LPIFilter2D If you need to apply the same filter multiple times over different images, construct the LPIFilter2D and specify it here. Examples -------- Gaussian filter: >>> def filt_func(r, c): ... return np.exp(-np.hypot(r, c)/1) >>> >>> from skimage import data >>> filtered = forward(data.coins(), filt_func) rr!N)rr#)r!r&r'predefined_filterr r r forwards rGc Cst|dd|durt|fi|}n|}||\}}t|t|jjjdd|}t ||k}t |||||<t t t t |||jS)a1Apply the filter in reverse to the given data. Parameters ---------- data : (M,N) ndarray Input data. impulse_response : callable `f(r, c, **filter_params)` Impulse response of the filter. See LPIFilter2D.__init__. filter_params : dict Additional keyword parameters to the impulse_response function. max_gain : float Limit the filter gain. Often, the filter contains zeros, which would cause the inverse filter to have infinite gain. High gain causes amplification of artefacts, so a conservative limit is recommended. Other Parameters ---------------- predefined_filter : LPIFilter2D If you need to apply the same filter multiple times over different images, construct the LPIFilter2D and specify it here. rr!Nr r)rr#r?rrfinforealrepsrrrr5 ifftshiftr@r) r!r&r'max_gainrFfiltr=r>r r r r inverses "rOg?c Cst|ddt|tst|dd|durt|fi|}n|}||\}}t|t|jj j dt |d}d||||}t t t t |||jS)aMinimum Mean Square Error (Wiener) inverse filter. Parameters ---------- data : (M,N) ndarray Input data. K : float or (M,N) ndarray Ratio between power spectrum of noise and undegraded image. impulse_response : callable `f(r, c, **filter_params)` Impulse response of the filter. See LPIFilter2D.__init__. filter_params : dict Additional keyword parameters to the impulse_response function. Other Parameters ---------------- predefined_filter : LPIFilter2D If you need to apply the same filter multiple times over different images, construct the LPIFilter2D and specify it here. rr!KNrHr)r isinstancer,r#r?rrrIrJrrKrrr5rLr@r) r!r&r'rPrFrNr=r> H_mag_sqrr r r wieners   "rScCstr)NotImplementedError)r!lamr&r'r r r constrained_least_squaressrV)rEnumpyr scipy.fftr5 _shared.utilsrrrIr,rKrrr"objectr#rGrOrSrVr r r r s(  \ % ( *