B \, @sxddlZddlmZddlmZddlmZddl m Z ddZ dddd d ej ddej f d d Z dddej fd d ZdS)N)relabel_sequential)measure) rank_ordercCs^t|}t|d|krF||}t|}t||| d}n t|}|dddS)z8 Return the highest intensity peak coordinates. rN)npZnonzerolenargsortZ transposeZ column_stack)imagemask num_peaksZcoordZ intensities idx_maxsortr3lib/python3.7/site-packages/skimage/feature/peak.py_get_high_intensity_peakss   rTc  Csdt|tkr|r|nd}tj|tjd} |dk rt|} tt| dkrv|dk} dt|| d|j || <|tj }t|} x>| | dkD].} || k}| t ||||||d| |dd 7} qWt || |}|dkr|St |j}d| |<| St||jdkr,|dkr(tdtjS| S|dk rHtj||d d }nd |d}tj||d d }||k} |rxjt| jD]\}| d|} |dk r|j|nd |}d| d|d <| | d d<| d|} q~Wg}|dkr|}|||dk r||||r0| |t|kM} t || |}|dkrJ|St |j}d| |<| SdS) aFind 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]]) r)dtypeNrF) min_distance threshold_abs threshold_relexclude_borderindicesr footprintlabelsT)rrconstant)rmoder)sizer)typeboolrZ zeros_likeuniqueanyZdiffrZastyperZint32peak_local_maxrtupleTallZflatemptyintndiZmaximum_filterrangendimZswapaxesshapeminappendmax)r rrrrrr rrZnum_peaks_per_labeloutZ label_valuesr labelZmaskimZ coordinatesZ nd_indicesZ image_maxriremoveZ thresholdsrrrr!sn\              "      r!cCs<|}|j\}}|dkr(dt|}d|d}d|d} tj||dddd} tj| | dddd} || k} || 9}||k} t| } t| | }t |dd d ddd }tj d d |Dt d}g}g}g}tj | |d| |df\}}x|D]\}}| ||f}||kr||}||}t |dk||k}||}||}|dk}|||||<|||7<||k}|||||<|||8<d| ||f<||||||qWt |}t |}t |}|t|kr2t|ddd d|}||}||}||}|||fS)a{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. Ng?rrrr)rZaxisrZcvalcSs|jS)N)Z max_intensity)xrrrsz"_prominent_peaks..)keyrcSsg|]}t|jqSr)rroundZcentroid).0prrr sz$_prominent_peaks..)r)copyr*rr-r'Zmaximum_filter1drr/Z regionpropssortedZarrayr&ZmgridZ logical_andr,rr )r Z min_xdistanceZ min_ydistanceZ thresholdr ZimgZrowsZcolsZ ycoords_sizeZ xcoords_sizeZimg_maxr Zimg_tZ label_imgZpropsZcoordsZ img_peaksZ ycoords_peaksZ xcoords_peaksZ ycoords_extZ xcoords_extZ ycoords_idxZ xcoords_idxZaccumZ ycoords_nhZ xcoords_nhZ ycoords_inZ xcoords_lowZ xcoords_highr rrr_prominent_peakssb                 r;)ZnumpyrZ scipy.ndimageZndimager'Z segmentationrrfiltersrrinfr!r;rrrrs    +