我们从Python开源项目中，提取了以下13个代码示例，用于说明如何使用skimage.morphology.watershed()。
def segmentation(_image, typeOfFruit): #_denoisedImage = rank.median(_image, disk(2)); #_elevationMap = sobel(_denoisedImage); #print(_image); _elevationMap = sobel(_image); #print(_image); _marker = np.zeros_like(_image); if (typeOfFruit == 'Counting'): _marker[_image < 1998] = 1; _marker[_image > 61541] = 2; elif (typeOfFruit == 'Temperature'): #print("Printing Image"); #print(_image < 10); _marker[_image < 30] = 1; #print(_marker); _marker[_image > 150] = 2; #_marker = rank.gradient(_denoisedImage, disk(5)) < 10; #_marker = ndi.label(_marker)[0]; #_elevationMap = rank.gradient(_denoisedImage, disk(2)); _segmentation = watershed(_elevationMap, _marker); #print(_segmentation); return _segmentation;
def run(self, ips, snap, img, para = None): imgs = ips.imgs gradient = np.zeros(imgs.shape, dtype=np.float32) gradient += ndimg.sobel(imgs, axis=0, output=np.float32)**2 gradient += ndimg.sobel(imgs, axis=1, output=np.float32)**2 gradient += ndimg.sobel(imgs, axis=2, output=np.float32)**2 gradient **= 0.5 msk = np.zeros(imgs.shape, dtype=np.uint8) msk[imgs>para['thr2']] = 1 msk[imgs<para['thr1']] = 2 #rst = watershed(gradient, msk) rst = watershed(gradient, msk.astype(np.uint16)) imgs[:] = (rst==1)*255 ips.lut = self.buflut
def staffline_surroundings_mask(staffline_cropobject): """Find the parts of the staffline's bounding box which lie above or below the actual staffline. These areas will be very small for straight stafflines, but might be considerable when staffline curvature grows. """ # We segment both masks into "above staffline" and "below staffline" # areas. elevation = staffline_cropobject.mask * 255 # Blur, to plug small holes somewhat: elevation = gaussian(elevation, sigma=1.0) # Prepare the segmentation markers: 1 is ABOVE, 2 is BELOW markers = numpy.zeros(staffline_cropobject.mask.shape) markers[0, :] = 1 markers[-1, :] = 2 markers[staffline_cropobject.mask != 0] = 0 seg = watershed(elevation, markers) bmask = numpy.ones(seg.shape) bmask[seg != 2] = 0 tmask = numpy.ones(seg.shape) tmask[seg != 1] = 0 return bmask, tmask
def staffline_surroundings_mask(staffline_cropobject): """Find the parts of the staffline's bounding box which lie above or below the actual staffline. These areas will be very small for straight stafflines, but might be considerable when staffline curvature grows. """ # We segment both masks into "above staffline" and "below staffline" # areas. elevation = staffline_cropobject.mask * 255 # Blur, to plug small holes somewhat: elevation = gaussian(elevation, sigma=1.0) # Prepare the segmentation markers: 1 is ABOVE, 2 is BELOW markers = numpy.zeros(staffline_cropobject.mask.shape) markers[0, :] = 1 markers[-1, :] = 2 markers[staffline_cropobject.mask != 0] = 0 seg = watershed(elevation, markers) bmask = numpy.ones(seg.shape) bmask[seg != 2] = 0 tmask = numpy.ones(seg.shape) tmask[seg != 1] = 0 return bmask, tmask ##############################################################################
def seperate_lungs(image): #Creation of the markers as shown above: marker_internal, marker_external, marker_watershed = generate_markers(image) #Creation of the Sobel-Gradient sobel_filtered_dx = ndimage.sobel(image, 1) sobel_filtered_dy = ndimage.sobel(image, 0) sobel_gradient = np.hypot(sobel_filtered_dx, sobel_filtered_dy) sobel_gradient *= 255.0 / np.max(sobel_gradient) #Watershed algorithm watershed = morphology.watershed(sobel_gradient, marker_watershed) #Reducing the image created by the Watershed algorithm to its outline outline = ndimage.morphological_gradient(watershed, size=(3,3)) outline = outline.astype(bool) #Performing Black-Tophat Morphology for reinclusion #Creation of the disk-kernel and increasing its size a bit blackhat_struct = [[0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0]] blackhat_struct = ndimage.iterate_structure(blackhat_struct, 8) #Perform the Black-Hat outline += ndimage.black_tophat(outline, structure=blackhat_struct) #Use the internal marker and the Outline that was just created to generate the lungfilter lungfilter = np.bitwise_or(marker_internal, outline) #Close holes in the lungfilter #fill_holes is not used here, since in some slices the heart would be reincluded by accident ##structure = np.ones((BINARY_CLOSING_SIZE,BINARY_CLOSING_SIZE)) # 5 is not enough, 7 is structure = morphology.disk(BINARY_CLOSING_SIZE) # better , 5 seems sufficient, we use 7 for safety/just in case lungfilter = ndimage.morphology.binary_closing(lungfilter, structure=structure, iterations=3) #, iterations=3) # was structure=np.ones((5,5)) ### NOTE if no iterattions, i.e. default 1 we get holes within lungs for the disk(5) and perhaps more #Apply the lungfilter (note the filtered areas being assigned -2000 HU) segmented = np.where(lungfilter == 1, image, -2000*np.ones((512, 512))) ### was -2000 return segmented, lungfilter, outline, watershed, sobel_gradient, marker_internal, marker_external, marker_watershed
def seperate_lungs_3d(image): #Creation of the markers as shown above: marker_internal, marker_external, marker_watershed = generate_markers_3d(image) #Creation of the Sobel-Gradient sobel_filtered_dx = ndimage.sobel(image, axis=2) sobel_filtered_dy = ndimage.sobel(image, axis=1) sobel_gradient = np.hypot(sobel_filtered_dx, sobel_filtered_dy) sobel_gradient *= 255.0 / np.max(sobel_gradient) #Watershed algorithm watershed = morphology.watershed(sobel_gradient, marker_watershed) #Reducing the image created by the Watershed algorithm to its outline outline = ndimage.morphological_gradient(watershed, size=(1,3,3)) outline = outline.astype(bool) #Performing Black-Tophat Morphology for reinclusion #Creation of the disk-kernel and increasing its size a bit blackhat_struct = [[0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0]] blackhat_struct = ndimage.iterate_structure(blackhat_struct, 8) blackhat_struct = blackhat_struct[np.newaxis,:,:] #Perform the Black-Hat outline += ndimage.black_tophat(outline, structure=blackhat_struct) # very long time #Use the internal marker and the Outline that was just created to generate the lungfilter lungfilter = np.bitwise_or(marker_internal, outline) #Close holes in the lungfilter #fill_holes is not used here, since in some slices the heart would be reincluded by accident ##structure = np.ones((BINARY_CLOSING_SIZE,BINARY_CLOSING_SIZE)) # 5 is not enough, 7 is structure = morphology.disk(BINARY_CLOSING_SIZE) # better , 5 seems sufficient, we use 7 for safety/just in case structure = structure[np.newaxis,:,:] lungfilter = ndimage.morphology.binary_closing(lungfilter, structure=structure, iterations=3) #, iterations=3) # was structure=np.ones((5,5)) ### NOTE if no iterattions, i.e. default 1 we get holes within lungs for the disk(5) and perhaps more #Apply the lungfilter (note the filtered areas being assigned -2000 HU) segmented = np.where(lungfilter == 1, image, -2000*np.ones(marker_internal.shape)) return segmented, lungfilter, outline, watershed, sobel_gradient, marker_internal, marker_external, marker_watershed
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
def get_segmented_lungs(image): #Creation of the markers as shown above: marker_internal, marker_external, marker_watershed = generate_markers(image) #Creation of the Sobel-Gradient sobel_filtered_dx = ndimage.sobel(image, 1) sobel_filtered_dy = ndimage.sobel(image, 0) sobel_gradient = np.hypot(sobel_filtered_dx, sobel_filtered_dy) sobel_gradient *= 255.0 / np.max(sobel_gradient) #Watershed algorithm watershed = morphology.watershed(sobel_gradient, marker_watershed) #Reducing the image created by the Watershed algorithm to its outline outline = ndimage.morphological_gradient(watershed, size=(3,3)) outline = outline.astype(bool) #Performing Black-Tophat Morphology for reinclusion #Creation of the disk-kernel and increasing its size a bit blackhat_struct = [[0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0]] #blackhat_struct = ndimage.iterate_structure(blackhat_struct, 8) blackhat_struct = ndimage.iterate_structure(blackhat_struct, 14) # <- retains more of the area, 12 works well. Changed to 14, 12 still excluded some parts. #Perform the Black-Hat outline += ndimage.black_tophat(outline, structure=blackhat_struct) #Use the internal marker and the Outline that was just created to generate the lungfilter lungfilter = np.bitwise_or(marker_internal, outline) #Close holes in the lungfilter #fill_holes is not used here, since in some slices the heart would be reincluded by accident lungfilter = ndimage.morphology.binary_closing(lungfilter, structure=np.ones((5,5)), iterations=3) #Apply the lungfilter (note the filtered areas being assigned threshold_min HU) segmented = np.where(lungfilter == 1, image, threshold_min*np.ones(image.shape)) #return segmented, lungfilter, outline, watershed, sobel_gradient, marker_internal, marker_external, marker_watershed return segmented
