o :ήc"> @sddlmZddlZddlmZddlmZddl m Z ddl m Z ddZ dd d Zd d Zd dZddZe ddddddddejddejejf ddZdddejfddZdS))warnN)measure) remove_arg)ensure_spacingc Cspt|}||}t| }t||}t|r t|}nd}t||||d}t||kr6|d|}|S)z8 Return the highest intensity peak coordinates. N)spacingp_normmax_out)npnonzeroargsort transposeisfiniteintrlen) imagemask num_peaks min_distancercoord intensities idx_maxsortr r;/tmp/pip-target-vg8gfxp4/lib/python/skimage/feature/peak.py_get_high_intensity_peaks s      rcCs|jdks |jdkr||kStj||dd}||k}|dur#t|nt||}|rCd|dd<|durCt|t|}d||<|||kM}|S)zJ Return the mask containing all peak candidates above thresholds. constant) footprintmodeNFT)sizendimaximum_filterr all logical_xorbinary_opening)rr thresholdr image_maxoutimage_is_trivial isolated_pxrrr_get_peak_mask"s   r*cCs^t|D](\}}|dkr qd|tdf|td|f<d|tdf|t| df<q|S)z#Set label border values to 0. rN) enumerateslice)label border_widthiwidthrrr_exclude_border;s "r1cCs2|dur|n|}|durt|||}|S)zSReturn the threshold value according to an absolute and a relative value. N)minmax)r threshold_abs threshold_relr%rrr_get_thresholdHsr6cCst|tr|r |ndf|j}|St|tr&|dkrtd|f|j}|St|trPt||jkr6td|D]}t|tsCtd|dkrKtdq8|}|Std)zIReturn border_width values relative to a min_distance if requested. rz+`exclude_border` cannot be a negative valuezP`exclude_border` should have the same length as the dimensionality of the image.zD`exclude_border`, when expressed as a tuple, must only contain ints.z,`exclude_border` can not be a negative valueze`exclude_border` must be bool, int, or tuple with the same length as the dimensionality of the image.) isinstanceboolndimr ValueErrortupler TypeError)rrexclude_borderr.excluderrr_get_excluded_border_widthUs8     r?indicesz0.20)changed_versionrTc Cs |dus |jdkr|dkrtdtddt|||} t|||} |dur6d|d} tj| f|jtd}nt |}|durSt ||| }t || }t ||||| }nt |j tdd| }t|jtjrmt|jj}nt|jj}g}tt|D]H\}}|durq}|||dk}||}||t|<t ||| |}t ||| || }t|D]\}}|dd|f|j7<q||q}|rt|}ntjd td}t||krtj|td}d |t |j!<t ||||| }|r|Stj|td}d |t |j!<|S) a#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 both `threshold_abs` and `threshold_rel` are provided, the maximum of the two is chosen as the minimum intensity threshold of peaks. .. versionchanged:: 0.18 Prior to version 0.18, peaks of the same height within a radius of `min_distance` were all returned, but this could cause unexpected behaviour. From 0.18 onwards, an arbitrary peak within the region is returned. See issue gh-2592. Parameters ---------- image : ndarray Input image. min_distance : int, optional The minimal allowed distance separating peaks. To find the maximum number of peaks, use `min_distance=1`. threshold_abs : float or None, optional Minimum intensity of peaks. By default, the absolute threshold is the minimum intensity of the image. threshold_rel : float or None, optional Minimum intensity of peaks, calculated as ``max(image) * threshold_rel``. exclude_border : int, tuple of ints, or bool, optional If positive integer, `exclude_border` excludes peaks from within `exclude_border`-pixels of the border of the image. If tuple of non-negative ints, the length of the tuple must match the input array's dimensionality. Each element of the tuple will exclude peaks from within `exclude_border`-pixels of the border of the image along that dimension. If True, takes the `min_distance` parameter as value. If zero or False, peaks are identified regardless of their distance from the border. indices : bool, optional If True, the output will be an array representing peak coordinates. The coordinates are sorted according to peaks values (Larger first). If False, the output will be a boolean array shaped as `image.shape` with peaks present at True elements. ``indices`` is deprecated and will be removed in version 0.20. Default behavior will be to always return peak coordinates. You can obtain a mask as shown in the example below. 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`. 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. p_norm : float Which Minkowski p-norm to use. Should be in the range [1, inf]. A finite large p may cause a ValueError if overflow can occur. ``inf`` corresponds to the Chebyshev distance and 2 to the Euclidean distance. 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. Internally, 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. See also -------- skimage.feature.corner_peaks 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, 2], [3, 4]]) >>> peak_local_max(img1, min_distance=2) array([[3, 2]]) >>> img2 = np.zeros((20, 20, 20)) >>> img2[10, 10, 10] = 1 >>> img2[15, 15, 15] = 1 >>> peak_idx = peak_local_max(img2, exclude_border=0) >>> peak_idx array([[10, 10, 10], [15, 15, 15]]) >>> peak_mask = np.zeros_like(img2, dtype=bool) >>> peak_mask[tuple(peak_idx.T)] = True >>> np.argwhere(peak_mask) array([[10, 10, 10], [15, 15, 15]]) NrzmWhen min_distance < 1, peak_local_max acts as finding image > max(threshold_abs, threshold_rel * max(image)).r) stackleveldtypesafe)casting)rrT)"rrRuntimeWarningr?r6r onesr9r8asarrayr*r1rastyper issubdtyperDfloatingfinfor2iinfor+r find_objectscopy logical_notstartappendvstackemptyr zeros_liker;T)rrr4r5r=r@rrlabelsnum_peaks_per_labelrr.r%rr coordinates_labelsbg_vallabels_peak_coord label_idxroi label_mask img_objectidxsr'rrrpeak_local_maxwsr|         rdcCs4|}|j\}}|durdt|}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\}}|D]e\}}| ||f}||kr||}||}t |dk||k}||}||}|dk}|||||<|||7<||k}|||||<|||8<d| ||f<||||||qt |}t |}t |}|t|krt|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. 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