peak_indices = peak.peak_local_max(trivial, min_distance=1, indices=True)asserttype(peak_indices)isnp.ndarrayassertpeak_indices.size ==0peaks = peak.peak_local_max(trivial, min_distance=1, indices=False)assert(peaks.astype(np.bool) == trivial).all() 开发者ID:TheArindham,项目名称:scikit-...
peak_local_max(image, min_distance=1, threshold_abs=None, threshold_rel=None, exclude_border=True, indices=True, num_peaks=inf, footprint=None, labels=None, num_peaks_per_label=inf, p_norm=inf) 在圖像中查找峰值作為坐標列表或布爾掩碼。 峰值是 2 * min_distance + 1 區域中的局部最大值...
Currently, the method peak_local_max returns sorted indices (w.r.t. the intensities) if the number of indices is > num_peaks. I do understand that it is nowhere stated that peak_local_max does that, it is merely a side-effect, but I found it quite convenient. Different behaviour arise...
So if indices is going away and already generates a deprecation warning, rather than provide examples of using indices=False (which, by the way, two of the examples DO do), we should actually provide examples of NOT using the deprecated argument. OK, so I should have thought of this -- ...
distance_transform_edt(mask) local_maxi = peak_local_max(distance, indices=False, footprint=kernel, labels=mask) markers = measure.label(local_maxi) labels_ws = watershed(-distance, markers, mask=mask) if labels_ws.max() < 2: return [mask], labels_ws res_masks = [] for idx in ...
local_maxi =peak_local_max(distance, indices=False, footprint=kernel, labels=mask) markers = measure.label(local_maxi) markers[~mask] =-1labels_rw = random_walker(mask, markers)iflabels_rw.max() <2:return[mask.astype(np.uint8)], labels_rw ...
In fact, ndi.find_objects returns None for skipped label indices 😉. 👍 1 Sorry, something went wrong. grlee77 closed this as completed in #4760 Nov 7, 2020 alexdesiqueira mentioned this issue Nov 10, 2020 2020's calendar of community management #4486 Closed Copy link ...
indices((80, 80)) x1, y1, x2, y2 = 28, 28, 44, 52 r1, r2 = 16, 20 mask_circle1 = (x - x1)**2 + (y - y1)**2 < r1**2 mask_circle2 = (x - x2)**2 + (y - y2)**2 < r2**2 image = np.logical_or(mask_circle1, mask_circle2) # Now we want to ...
cucim.skimage.feature.peak_local_max(image, min_distance=1, threshold_abs=None, threshold_rel=None, exclude_border=True, indices=True, num_peaks=inf, footprint=None, labels=None, num_peaks_per_label=inf, p_norm=inf) 在圖像中查找峰值作為坐標列表或布爾掩碼。
Description I am trying to extract local maxima from a distance transform (made using openCV). When I run: coordinates = peak_local_max(distance_xform, min_distance=100, num_peaks=12) The following values are in coordinates: [[2775 2647]...