# -*- coding: utf-8 -*-
# deringers.py

# Copyright (c) 2013, Elettra - Sincrotrone Trieste S.C.p.A.
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from numpy import real, float32, uint16, ComplexWarning, iinfo, finfo
from numpy import exp, arange, floor, kron, ones
from numpy import zeros, mean, median, var, vstack, copy
from numpy.lib import pad
from numpy.fft import fft, fftshift, ifft, ifftshift, fft2, ifft2
from scipy.signal import filtfilt, butter, medfilt
from pywt import wavedec2, waverec2
from warnings import simplefilter

"""De-striping filters better suited for CT ring artifacts compensation.

:Organization:
  Elettra - Sincrotrone Trieste S.C.p.A.

:Version: 2013.05.01

Requirements
------------
* `Python 2.7 <http://www.python.org>`
* `Numpy 1.7.1 <http://www.numpy.org>`
* `Scipy 1.7 <http://www.scipy.org>`

Notes
-----
The API is not stable yet and might change in the future.

References
----------
[1] F. Brun, A. Accardo, G. Kourousias, D. Dreossi, R. Pugliese. Effective implementation 
of ring artifacts removal filters for synchrotron radiation microtomographic images. Proc.
of the 8th International Symposium on Image and Signal Processing (ISPA), pp. 672-676, Sept. 4-6, 
Trieste (Italy), 2013.

"""

def rivers_filter(im, n=11):
    """Process a sinogram image with the Rivers de-striping algorithm.

    Parameters
    ----------
    im : array_like
        Image data as numpy array.
    n : int
        Size of the median low-pass filtering.
       
    Example (using tiffile.py)
    --------------------------
    >>> im = imread('original.tif')
    >>> im = rivers_filter(im, 13)    
    >>> imsave('filtered.tif', im) 

    References
    ----------
    M. Rivers, http://cars9.uchicago.edu/software/epics/tomoRecon.html.

    """
    # Copy image:
    im_f = copy(im.astype(float32))

    # Compute mean of each column:
    col = im_f.mean(axis=0)

    # Perform low pass filtering:  
    flt_col = medfilt(col, n)
    
    # Apply compensation on each row:
    for i in range(0, im_f.shape[0]):
        im_f[i,] = im_f[i,] - (col - flt_col)

    # Return image according to input type:
    if (im.dtype == 'uint16'):
        
        # Check extrema for uint16 images:
        im_f [ im_f < iinfo(uint16).min ] = iinfo(uint16).min
        im_f [ im_f > iinfo(uint16).max ] = iinfo(uint16).max

        # Return image:
        return im_f.astype(uint16)
    else:
        return im_f

def boinhaibel_filter(im, n=11):
    """Process a sinogram image with the Boin and Haibel de-striping algorithm.

    Parameters
    ----------
    im : array_like
        Image data as numpy array.
    n : int
        Size of the median low-pass filtering.
       
    Example (using tiffile.py)
    --------------------------
    >>> im = imread('original.tif')
    >>> im = boinhaibel_filter(im, 11)    
    >>> imsave('filtered.tif', im) 

    References
    ----------
    M. Boin and A. Haibel, Compensation of ring artefacts in synchrotron 
    tomographic images, Optics Express 14(25):12071-12075, 2006.

    """
    # Copy image:
    im_f = copy(im.astype(float32))

    # Compute sum of each column (avoid further division by zero):
    col = im_f.sum(axis=0) + finfo(float32).eps

    # Perform low pass filtering:
    flt_col = medfilt(col, n)

    # Apply compensation on each row:
    for i in range(0, im_f.shape[0]):
        im_f[i,] = im_f[i,] * (flt_col/col)

    # Return image according to input type:
    if (im.dtype == 'uint16'):
        
        # Check extrema for uint16 images:
        im_f [ im_f < iinfo(uint16).min ] = iinfo(uint16).min
        im_f [ im_f > iinfo(uint16).max ] = iinfo(uint16).max

        # Return image:
        return im_f.astype(uint16)
    else:
        return im_f

def raven_filter(im, n, u0):
    """Process a sinogram image with the Raven de-striping algorithm.

    Parameters
    ----------
    im : array_like
        Image data as numpy array.
    n : int
        Order of the Butterworth low-pass filtering.
    u0 : float
        Cutoff frequency of the Butterworth low-pass filtering.
           
    Example (using tiffile.py)
    --------------------------
    >>> im = imread('original.tif')
    >>> im = raven_filter(im, 0.01)    
    >>> imsave('filtered.tif', im) 

    References
    ----------
    C. Raven, Numerical removal of ring artifacts in microtomography,
    Review of Scientific Instruments 69(8):2978-2980, 1998.

    """
    # Replicate padding of input image: 
    padsize = int(im.shape[1]/2)
    im_f = pad(im.astype(float32), ( (0,0), (padsize,padsize) ), 'edge')

    # Compute FT:
    im_f = fft2(im_f) 

    # Apply 1-D low-pass Butterworth filtering at zero frequency:
    b, a = butter(n, u0)
    im_f[0,:] = filtfilt(b, a, im_f[0,:])

    # Compute inverse FFT of the filtered data:
    im_f = real(ifft2( im_f ))

    # Return image according to input type:
    if (im.dtype == 'uint16'):
        
        # Check extrema for uint16 images:
        im_f [ im_f < iinfo(uint16).min ] = iinfo(uint16).min
        im_f [ im_f > iinfo(uint16).max ] = iinfo(uint16).max

        # Return image:
        return im_f.astype(uint16)
    else:
        return im_f

def munchetal_filter(im, wlevel, sigma, wname='db15'):
    """Process a sinogram image with the Munch et al. de-striping algorithm.

    Parameters
    ----------
    im : array_like
        Image data as numpy array.
    wname : {'haar', 'db1'-'db20', 'sym2'-'sym20', 'coif1'-'coif5', 'dmey'}
        The wavelet transform to use.
    wlevel : int
        Levels of the wavelet decomposition.
    sigma : float
        Cutoff frequency of the Butterworth low-pass filtering.
       
    Example (using tiffile.py)
    --------------------------
    >>> im = imread('original.tif')
    >>> im = munchetal_filter(im, 'db15', 4, 1.0)    
    >>> imsave('filtered.tif', im) 

    References
    ----------
    B. Munch, P. Trtik, F. Marone, M. Stampanoni, Stripe and ring artifact removal with
    combined wavelet-Fourier filtering, Optics Express 17(10):8567-8591, 2009.

    """  
    # Disable a warning:
    simplefilter("ignore", ComplexWarning)

    # Wavelet decomposition:
    coeffs = wavedec2(im.astype(float32), wname, level=wlevel)
    coeffsFlt = [coeffs[0]] 

    # FFT transform of horizontal frequency bands:
    for i in range(1, wlevel + 1):  

        # FFT:
        fcV = fftshift(fft(coeffs[i][1], axis=0))  
        my, mx = fcV.shape
        
        # Damping of vertical stripes:
        damp = 1-exp(-(arange(-floor(my/2.),-floor(my/2.)+my)**2)/(2*(sigma**2)))      
        dampprime = kron(ones((1,mx)), damp.reshape((damp.shape[0],1)))
        fcV = fcV * dampprime    

        # Inverse FFT:
        fcVflt  = ifft(ifftshift(fcV), axis=0)
        cVHDtup = (coeffs[i][0], fcVflt, coeffs[i][2])             
        coeffsFlt.append(cVHDtup)

    # Get wavelet reconstruction:
    im_f = real(waverec2(coeffsFlt, wname))

    # Return image according to input type:
    if (im.dtype == 'uint16'):
        
        # Check extrema for uint16 images:
        im_f [ im_f < iinfo(uint16).min ] = iinfo(uint16).min
        im_f [ im_f > iinfo(uint16).max ] = iinfo(uint16).max

        # Return filtered image (an additional row and/or column might be present):
        return im_f[0:im.shape[0],0:im.shape[1]].astype(uint16)
    else:
        return im_f[0:im.shape[0],0:im.shape[1]]
    
def sijberspostnov_filter(im, winsize, thresh):
    """Process a sinogram image with the Sijbers and Postnov de-striping algorithm.

    Parameters
    ----------
    im : array_like
        Image data as numpy array.    
    winsize : int
        Size of the local floating window used to look for homogeneity.
    thresh : float
        Image rows (within the floating window) having variance below 
        this tresh will be corrected.
       
    Example (using tiffile.py)
    --------------------------
    >>> im = imread('original.tif')
    >>> im = sijberspostnov_filter(im, 51, 0.001)    
    >>> imsave('filtered.tif', im) 

    References
    ----------
    J. Sijbers and A. Postnov, Reduction of ring artifacts in high resolution
    micro-CT reconstructions, Physics in Medicine and Biology 49(14):247-253, 2004.

    """  

    # Initializations:
    im_f = copy(im).astype(float32)
    dimx = im.shape[1]
    dimy = im.shape[0]

    # Normalize thresh parameter:
    #thresh = thresh*65536.0
        
    glob_art = zeros(dimx)
    prevsize = 0

    # Within a sliding window:
    for i in range(0, dimx-winsize):
            
        ct = 0
        matrix = zeros(winsize)

        # For each line of the current window:
        for j in range(0, dimy):
        
            # Compute the variance within current sliding window:
            v = im_f[j, i:(i+winsize) ]            
            curr_var = var(v)
        
            # If variance is below threshold:
            if (curr_var < thresh): 
                # Add current line with mean subtracted to a temporary matrix:           
                v = v - mean(v)   
                matrix = vstack([matrix,v])    
                ct = ct + 1
            
        # Determine local artifact correction vector:
        if (ct > 1):
            ct = ct - 1
            matrix = matrix[1:ct,:]
            loc_art = median(matrix, axis=0)
        else:
            if (ct == 1):
                loc_art = matrix[1,:]
            else:
                loc_art = zeros(winsize)
           
        # Determine global artifact correction vector:
        for k in range(0, winsize):
            if ( matrix.shape[0] > prevsize ):
                glob_art[k+i] = loc_art[k]             
    
        prevsize = matrix.shape[0]    

    # Correct each line of the input image:
    for i in range(0, im.shape[0]):
        im_f[i,:] = im_f[i,:] - glob_art

     # Return image according to input type:
    if (im.dtype == 'uint16'):
        
        # Check extrema for uint16 images:
        im_f [ im_f < iinfo(uint16).min ] = iinfo(uint16).min
        im_f [ im_f > iinfo(uint16).max ] = iinfo(uint16).max

        # Return image:
        return im_f.astype(uint16)
    else:
        return im_f