我们从Python开源项目中，提取了以下6个代码示例，用于说明如何使用skimage.feature.peak_local_max()。
def circles_per_radius(hough_radii, hough_res, number_circles_per_radius=16): """ Rates found circles by intensity of peaks inside the Hough matrix. Chooses the best circles for each radius Returns: Selected circle centers with their quality and radius """ centers = [] accums = [] radii = [] # for each radius and hough peak image # zip([32,33,34,35,36],[(32, hough_peaks_img),(33, hough_peaks_img),(34, hough_peaks_img), ... ]) for radius, h in zip(hough_radii, hough_res): # iterieren durch circles (h) # sort peaks, which represent the quality of circles by intensity peaks = peak_local_max(h, num_peaks=number_circles_per_radius) centers.extend(peaks) accums.extend(h[peaks[:, 0], peaks[:, 1]]) # iterate through every (y,x) in peaks and get corresponding color value from h, which represents quality value of circle (?) # so acuums represents quality radii.extend([radius] * number_circles_per_radius) # return (centers, accums, radii)
def get_echos(dat_arr,min_distance=10): """ getting local maxima and minima from 1D data -> e.g. speckle echos strategy: using peak_local_max (from skimage) with min_distance parameter to find well defined local maxima using np.argmin to find absolute minima between relative maxima returns [max_ind,min_ind] -> lists of indices corresponding to local maxima/minima by LW 10/23/2018 """ from skimage.feature import peak_local_max max_ind=peak_local_max(dat_arr, min_distance) # !!! careful, skimage function reverses the order (wtf?) min_ind=[] for i in range(len(max_ind[:-1])): min_ind.append(max_ind[i+1][0]+np.argmin(dat_arr[max_ind[i+1][0]:max_ind[i][0]])) #unfortunately, skimage function fu$$s up the format: max_ind is an array of a list of lists...fix this: mmax_ind=[] for l in max_ind: mmax_ind.append(l[0]) #return [mmax_ind,min_ind] return [list(reversed(mmax_ind)),list(reversed(min_ind))]
def findMaximaOnFG(self, param): self.defineFG(param) #self.smooth_corr() self.coorExtract = [0, 0] xmin, ymin = self.coorExtract img=self.candi img [self.FG ==0] =0 im = img_as_float(img) image_max = ndimage.maximum_filter(im, size=10, mode='constant') coordinates = peak_local_max(im, min_distance=10) tep=np.zeros(self.candi.shape) for i,ide in enumerate(coordinates): tep[ide[0],ide[1]] = self.candi[ide[0],ide[1]] lbl = ndimage.label(tep)[0] centerc = np.round(ndimage.measurements.center_of_mass(tep, lbl, range(1,np.max(lbl)+1))) if centerc.size > 0: self.centersX = centerc[:,0].astype(int) self.centersY = centerc[:,1].astype(int) self.nComponents = len(self.centersX)
def find_candidates(greyscale_image, neighborhood_size=20, candidate_threshold=.5): corner_likelihood = compute_inital_corner_likelihood(greyscale_image) # TODO: the absolute threshold should be statistically determined based on actual checkerboard images candidates = peak_local_max(corner_likelihood, neighborhood_size, corner_likelihood.max() * candidate_threshold) return candidates
def process(self, im): (width, height, _) = im.image.shape img_adapted = im.prep(self.transform) if width > self.max_resized or height > self.max_resized: scale_height = self.max_resized / height scale_width = self.max_resized / width scale = min(scale_height, scale_width) img_adapted = resize(img_adapted, (int(width * scale), int(height * scale))) edges = canny(img_adapted, sigma=self.sigma) # Detect two radii # Calculate image diameter shape = im.image.shape diam = math.sqrt(shape[0] ** 2 + shape[1] ** 2) radii = np.arange(diam / 3, diam * 0.8, 2) hough_res = hough_circle(edges, radii) accums = [] for radius, h in zip(radii, hough_res): # For each radius, extract two circles peaks = peak_local_max(h, num_peaks=1, min_distance=1) if len(peaks) > 0: accums.extend(h[peaks[:, 0], peaks[:, 1]]) if len(accums) == 0: # TODO: fix, should not happen return [0] idx = np.argmax(accums) return [accums[idx]]
def makeSegmentation(d, s, nbins = 256, verbose = True, sigma = 0.75, min_distance = 1): fn = os.path.join(datadir, 'data_stage%d_%s.npy' % (s,features[0])); dists = np.load(fn); fn = os.path.join(datadir, 'data_stage%d_%s.npy' % (s,features[1])); rots = np.load(fn); ddata = np.vstack([np.log(dists[:,d]), (rots[:,d])]).T #gmmdata = np.vstack([dists[:,j], (rots[:,j])]).T #ddata.shape nanids = np.logical_or(np.any(np.isnan(ddata), axis=1), np.any(np.isinf(ddata), axis=1)); ddata = ddata[~nanids,:]; #ddata.shape imgbin = None; img2 = smooth(ddata, nbins = [nbins, nbins], sigma = (sigma,sigma)) #img = smooth(ddata, nbins = [nbins, nbins], sigma = (1,1)) local_maxi = peak_local_max(img2, indices=False, min_distance = min_distance) imgm2 = img2.copy(); imgm2[local_maxi] = 3 * imgm2.max(); if verbose: imgbin = smooth(ddata, nbins = [nbins, nbins], sigma = None) plt.figure(220); plt.clf() plt.subplot(2,2,1) plt.imshow(imgbin) plt.subplot(2,2,2) plt.imshow(img2) plt.subplot(2,2,3); plt.imshow(imgm2, cmap=plt.cm.jet, interpolation='nearest') markers = ndi.label(local_maxi)[0] labels = watershed(-img2, markers, mask = None); print "max labels: %d" % labels.max() if verbose: fig, axes = plt.subplots(ncols=3, sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'}) ax0, ax1, ax2 = axes ax0.imshow(img2, cmap=plt.cm.jet, interpolation='nearest') ax0.set_title('PDF') labels[imgbin==0] = 0; labels[0,0] = -1; ax1.imshow(labels, cmap=plt.cm.rainbow, interpolation='nearest') ax1.set_title('Segmentation on Data') #labelsws[0,0] = -1; #ax2.imshow(labelsws, cmap=plt.cm.rainbow, interpolation='nearest') #ax1.set_title('Segmentation Full') return labels; #classification for a specific work based on the segmentation above
