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   @   sš   d  d l  Z d  d l j Z d d l m Z d d l m Z d d l	 m
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 d „  Z d d d e e e j d d e j d „	 Z d d d e j d	 „ Z d S(
   iÿÿÿÿNi   (   t   relabel_sequential(   t   measure(   t
   rank_orderc         C   sy   t  j | ƒ } t | d ƒ | k rY |  | } t  j | ƒ } t  j | ƒ | | } n t  j | ƒ } | d d d … S(   s8   
    Return the highest intensity peak coordinates.
    i    Niÿÿÿÿ(   t   npt   nonzerot   lent   argsortt	   transposet   column_stack(   t   imaget   maskt	   num_peakst   coordt   intensitiest   idx_maxsort(    (    s3   lib/python2.7/site-packages/skimage/feature/peak.pyt   _get_high_intensity_peaks   s    
i   c
         C   sY  t  | ƒ t k r' | r | n d } n  t j |  d t j ƒ}
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 ƒ | | <n  | j	 t j ƒ } t j | ƒ } xh | | d k D]V } | | k } |
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 Sn  | d k	 rét j |  d
 | d d ƒ} n) d | d } t j |  d | d d ƒ} |  | k } | r£x| t | j ƒ D]h } | j d | ƒ } | d k	 re| j | n d | } t | | d *| | d )| j d | ƒ } q4Wn  g  } | d k rÄ|  j ƒ  } n  | j | ƒ | d k	 r÷| j | |  j ƒ  ƒ n  | r| |  t | ƒ k M} n  t |  | | ƒ } | t k r8| St | j ƒ } t |
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 Sd S(   s  Find peaks in an image as coordinate list or boolean mask.

    Peaks are the local maxima in a region of `2 * min_distance + 1`
    (i.e. peaks are separated by at least `min_distance`).

    If peaks are flat (i.e. multiple adjacent pixels have identical
    intensities), the coordinates of all such pixels are returned.

    If both `threshold_abs` and `threshold_rel` are provided, the maximum
    of the two is chosen as the minimum intensity threshold of peaks.

    Parameters
    ----------
    image : ndarray
        Input image.
    min_distance : int, optional
        Minimum number of pixels separating peaks in a region of `2 *
        min_distance + 1` (i.e. peaks are separated by at least
        `min_distance`).
        To find the maximum number of peaks, use `min_distance=1`.
    threshold_abs : float, optional
        Minimum intensity of peaks. By default, the absolute threshold is
        the minimum intensity of the image.
    threshold_rel : float, optional
        Minimum intensity of peaks, calculated as `max(image) * threshold_rel`.
    exclude_border : int, optional
        If nonzero, `exclude_border` excludes peaks from
        within `exclude_border`-pixels of the border of the image.
    indices : bool, optional
        If True, the output will be an array representing peak
        coordinates.  If False, the output will be a boolean array shaped as
        `image.shape` with peaks present at True elements.
    num_peaks : int, optional
        Maximum number of peaks. When the number of peaks exceeds `num_peaks`,
        return `num_peaks` peaks based on highest peak intensity.
    footprint : ndarray of bools, optional
        If provided, `footprint == 1` represents the local region within which
        to search for peaks at every point in `image`.  Overrides
        `min_distance` (also for `exclude_border`).
    labels : ndarray of ints, optional
        If provided, each unique region `labels == value` represents a unique
        region to search for peaks. Zero is reserved for background.
    num_peaks_per_label : int, optional
        Maximum number of peaks for each label.

    Returns
    -------
    output : ndarray or ndarray of bools

        * If `indices = True`  : (row, column, ...) coordinates of peaks.
        * If `indices = False` : Boolean array shaped like `image`, with peaks
          represented by True values.

    Notes
    -----
    The peak local maximum function returns the coordinates of local peaks
    (maxima) in an image. A maximum filter is used for finding local maxima.
    This operation dilates the original image. After comparison of the dilated
    and original image, this function returns the coordinates or a mask of the
    peaks where the dilated image equals the original image.

    Examples
    --------
    >>> img1 = np.zeros((7, 7))
    >>> img1[3, 4] = 1
    >>> img1[3, 2] = 1.5
    >>> img1
    array([[ 0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ],
           [ 0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ],
           [ 0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ],
           [ 0. ,  0. ,  1.5,  0. ,  1. ,  0. ,  0. ],
           [ 0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ],
           [ 0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ],
           [ 0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ]])

    >>> peak_local_max(img1, min_distance=1)
    array([[3, 4],
           [3, 2]])

    >>> peak_local_max(img1, min_distance=2)
    array([[3, 2]])

    >>> img2 = np.zeros((20, 20, 20))
    >>> img2[10, 10, 10] = 1
    >>> peak_local_max(img2, exclude_border=0)
    array([[10, 10, 10]])

    i    t   dtypei   t   min_distancet   threshold_abst   threshold_relt   exclude_bordert   indicesR   t	   footprintt   labelsi   t   modet   constantt   sizeN(   i    i   (   t   typet   boolR   t
   zeros_liket   Nonet   uniquet   anyt   diffR   t   astypeR   t   int32t   peak_local_maxt   FalseR   t   Truet   tuplet   Tt   allt   flatt   emptyt   intt   ndit   maximum_filtert   ranget   ndimt   swapaxest   shapet   mint   appendt   max(   R	   R   R   R   R   R   R   R   R   t   num_peaks_per_labelt   outt   label_valuesR
   t   labelt   maskimt   coordinatest
   nd_indicest	   image_maxR   t   it   removet
   thresholds(    (    s3   lib/python2.7/site-packages/skimage/feature/peak.pyR$      sn    \+


c      
   C   s  |  j  ƒ  } | j \ } } | d k r= d t j | ƒ } n  d | d } d | d }	 t j | d | d d d d d	 d ƒ}
 t j |
 d |	 d d d d d	 d ƒ}
 | |
 k } | | 9} | | k } t j | ƒ } t j	 | |
 ƒ } t
 | d
 d „  ƒd d d … } t j g  | D] } t j | j ƒ ^ qd t ƒ} g  } g  } g  } t j | | d … | | d … f \ } } x| D]\ } } |
 | | f } | | k rˆ| | } | | } t j | d k | | k  ƒ } | | } | | } | d k  } | | | | | <| | c | 7<| | k } | | | | | <| | c | 8<d |
 | | f <| j | ƒ | j | ƒ | j | ƒ qˆqˆWt j | ƒ } t j | ƒ } t j | ƒ } | t | ƒ k  rt j | ƒ d d d … |  } | | } | | } | | } n  | | | f S(   s{  Return peaks with non-maximum suppression.

    Identifies most prominent features separated by certain distances.
    Non-maximum suppression with different sizes is applied separately
    in the first and second dimension of the image to identify peaks.

    Parameters
    ----------
    image : (M, N) ndarray
        Input image.
    min_xdistance : int
        Minimum distance separating features in the x dimension.
    min_ydistance : int
        Minimum distance separating features in the y dimension.
    threshold : float
        Minimum intensity of peaks. Default is `0.5 * max(image)`.
    num_peaks : int
        Maximum number of peaks. When the number of peaks exceeds `num_peaks`,
        return `num_peaks` coordinates based on peak intensity.

    Returns
    -------
    intensity, xcoords, ycoords : tuple of array
        Peak intensity values, x and y indices.
    g      à?i   i   R   t   axisi    R   R   t   cvalt   keyc         S   s   |  j  S(   N(   t   max_intensity(   t   x(    (    s3   lib/python2.7/site-packages/skimage/feature/peak.pyt   <lambda>÷   s    NiÿÿÿÿR   (   t   copyR2   R   R   R5   R-   t   maximum_filter1dR   R9   t   regionpropst   sortedt   arrayt   roundt   centroidR,   t   mgridt   logical_andR4   R   R   (   R	   t   min_xdistancet   min_ydistancet	   thresholdR   t   imgt   rowst   colst   ycoords_sizet   xcoords_sizet   img_maxR
   t   img_tt	   label_imgt   propst   pt   coordst	   img_peakst   ycoords_peakst   xcoords_peakst   ycoords_extt   xcoords_extt   ycoords_idxt   xcoords_idxt   accumt
   ycoords_nht
   xcoords_nht
   ycoords_int   xcoords_lowt   xcoords_highR   (    (    s3   lib/python2.7/site-packages/skimage/feature/peak.pyt   _prominent_peaksÆ   sb    
"4


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 

(   t   numpyR   t   scipy.ndimaget   ndimageR-   t   segmentationR    t    R   t   filtersR   R   R   R&   t   infR$   Rk   (    (    (    s3   lib/python2.7/site-packages/skimage/feature/peak.pyt   <module>   s   		ª