o :ήc1@sndZddlmZddlZddlmZddlmZddl m Z   ddd Z dd d Z ddZ dddZdS)a7 Implementation of the masked normalized cross-correlation. Based on the following publication: D. Padfield. Masked object registration in the Fourier domain. IEEE Transactions on Image Processing (2012) and the author's original MATLAB implementation, available on this website: http://www.dirkpadfield.com/ )partialN next_fast_len)_supported_float_type333333?c Cs|dur|j|jkrtd|t}||f||ffD]\}}|j|jkr)tdqt||||tt|jd|d}tj t || kdd}tj |dd} | t |jd} t |jt |j} | | d S) aSMasked image translation registration by masked normalized cross-correlation. Parameters ---------- reference_image : ndarray Reference image. moving_image : ndarray Image to register. Must be same dimensionality as ``reference_image``, but not necessarily the same size. reference_mask : ndarray Boolean mask for ``reference_image``. The mask should evaluate to ``True`` (or 1) on valid pixels. ``reference_mask`` should have the same shape as ``reference_image``. moving_mask : ndarray or None, optional Boolean mask for ``moving_image``. The mask should evaluate to ``True`` (or 1) on valid pixels. ``moving_mask`` should have the same shape as ``moving_image``. If ``None``, ``reference_mask`` will be used. overlap_ratio : float, optional Minimum allowed overlap ratio between images. The correlation for translations corresponding with an overlap ratio lower than this threshold will be ignored. A lower `overlap_ratio` leads to smaller maximum translation, while a higher `overlap_ratio` leads to greater robustness against spurious matches due to small overlap between masked images. Returns ------- shifts : ndarray Shift vector (in pixels) required to register ``moving_image`` with ``reference_image``. Axis ordering is consistent with numpy (e.g. Z, Y, X) References ---------- .. [1] Dirk Padfield. Masked Object Registration in the Fourier Domain. IEEE Transactions on Image Processing, vol. 21(5), pp. 2706-2718 (2012). :DOI:`10.1109/TIP.2011.2181402` .. [2] D. Padfield. "Masked FFT registration". In Proc. Computer Vision and Pattern Recognition, pp. 2918-2925 (2010). :DOI:`10.1109/CVPR.2010.5540032` NzHInput images have different shapes, moving_mask must be explicitely set.z3Image sizes must match their respective mask sizes.full)axesmode overlap_ratio)axisrr)shape ValueErrorastypeboolcross_correlate_maskedtuplerangendimnpstacknonzeromaxmeanarray) reference_image moving_imagereference_mask moving_maskr immaskxcorrmaximacentershifts size_mismatchr'[/tmp/pip-target-vg8gfxp4/lib/python/skimage/registration/_masked_phase_cross_correlation.py_masked_phase_cross_correlations6.      r)rc'sv|dvr td|dt|}t|}t|j|jg} | jdkr'td|| }tj|td} || }tj|td} t | j } t t |j } | t |D]}|j||j|krftd|dqRt|j|D]}|j||j|d |<qnttd d D}tfd d |D}ttj||d }ttj||d fdd}d|t| <d|t| <t||d}t| |d}||}||}|| | }||| }|||}t||dd<t|| |dd<|||}|||}|||}||||8}|t|}|||}|t||8}t|d|dd<|t|}|||} | t||8} t| d| dd<t|| }!||}|!|}!||}|dkr|tt|j|d}"|"|!}!|"|}|"|}d| tjt|!|dd}#|!|#k}$tj|!| d}%||$|!|$|%|$<tj|%dd |%d|tj||dd}&d|%||&k<|%S)u Masked normalized cross-correlation between arrays. Parameters ---------- arr1 : ndarray First array. arr2 : ndarray Seconds array. The dimensions of `arr2` along axes that are not transformed should be equal to that of `arr1`. m1 : ndarray Mask of `arr1`. The mask should evaluate to `True` (or 1) on valid pixels. `m1` should have the same shape as `arr1`. m2 : ndarray Mask of `arr2`. The mask should evaluate to `True` (or 1) on valid pixels. `m2` should have the same shape as `arr2`. mode : {'full', 'same'}, optional 'full': This returns the convolution at each point of overlap. At the end-points of the convolution, the signals do not overlap completely, and boundary effects may be seen. 'same': The output is the same size as `arr1`, centered with respect to the `‘full’` output. Boundary effects are less prominent. axes : tuple of ints, optional Axes along which to compute the cross-correlation. overlap_ratio : float, optional Minimum allowed overlap ratio between images. The correlation for translations corresponding with an overlap ratio lower than this threshold will be ignored. A lower `overlap_ratio` leads to smaller maximum translation, while a higher `overlap_ratio` leads to greater robustness against spurious matches due to small overlap between masked images. Returns ------- out : ndarray Masked normalized cross-correlation. Raises ------ ValueError : if correlation `mode` is not valid, or array dimensions along non-transformation axes are not equal. References ---------- .. [1] Dirk Padfield. Masked Object Registration in the Fourier Domain. IEEE Transactions on Image Processing, vol. 21(5), pp. 2706-2718 (2012). :DOI:`10.1109/TIP.2011.2181402` .. [2] D. Padfield. "Masked FFT registration". In Proc. Computer Vision and Pattern Recognition, pp. 2918-2925 (2010). :DOI:`10.1109/CVPR.2010.5540032` >rsamezCorrelation mode 'z' is not valid.cz+complex-valued arr1, arr2 are not supported)dtypezVArray shapes along non-transformation axes should be equal, but dimensions along axis z are not.r cSsg|] }tdt|qS)r)sliceint).0szr'r'r( sz*cross_correlate_masked..csg|]}t|qSr'r)r2ax) final_shaper'r(r4s)sr cs |jSN)real)x)_ifftr'r(iffts z$cross_correlate_masked..ifftg)r Nr-)newshaper g@@T)r keepdimsr,)a_mina_maxout) rrasarrayrr/kindrrrfinfoepssetrrrlistrr fftmodulefftnifftn logical_not_fliproundfmaxsquaresqrt _centeredrabs zeros_likeclip)'arr1arr2m1m2r r r fixed_imager float_dtype fixed_maskrrEall_axesr final_slice fast_shapefftr<rotated_moving_imagerotated_moving_mask fixed_fftrotated_moving_fftfixed_mask_fftrotated_moving_mask_fftnumber_overlap_masked_pxmasked_correlated_fixed_fft$masked_correlated_rotated_moving_fft numeratorfixed_squared_fft fixed_denomrotated_moving_squared_fft moving_denomdenom _centeringtolnonzero_indicesrAnumber_px_thresholdr')r;r6r(rcs7                     rcCslt|}t|j}tddg|j}|D]}||||d}|||}t||||<q|t|S)zZ Return the center `newshape` portion of `arr`, leaving axes not in `axes` untouched. Nr)rrBrrr0rr)arrr=r currshapeslicesr5startindendindr'r'r(rQs    rQcCsX|durtdddg|j}ntdddg|j}|D] }tddd||<q|t|S)z~ Reverse array over many axes. Generalization of arr[::-1] for many dimensions. If `axes` is `None`, flip along all axes. Nr,)r0rr)rsr reverser r'r'r(rL%s  rL)Nr)rr*rr8)__doc__ functoolsrnumpyr scipy.fftr_rHr _shared.utilsrr)rrQrLr'r'r'r(s     O 3