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()。
def removeSmallObject(filledImg, minSize = 20000): biImgRsize = filledImg.shape[0] * 0.1 biImgCsize = filledImg.shape[1] * 0.1 biImg = filledImg[biImgRsize:-biImgRsize, biImgCsize:-biImgCsize] cleanedImg = morphology.remove_small_objects(biImg, minSize) return cleanedImg
def __init__(self, data, morphology=None): """ This function will set up the underlying data structure. If no morphology is provided, the morphology array will be generated as `numpy.max(data,axis=-1)`, i.e. a maximum projection over data along the time axis. :param data: :param morphology: This can either be a 2D numpy array with the same shape as the video, or None. """ self.postprocessor = self.no_postprocessor # call the property setter which will initialize the mean data and threshold value self.data = data if morphology is None: self.morphology = numpy.max(data, axis=-1) else: self.morphology = morphology # todo: the active frame is merely a utility to synchronize widgets. maybe it should go to the gui... self.active_frame = 0
def erosion(x, radius=3): """ Return greyscale morphological erosion of an image, see `skimage.morphology.erosion <http://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morphology.erosion>`_. Parameters ----------- x : 2D array image. radius : int for the radius of mask. """ from skimage.morphology import disk, dilation, erosion mask = disk(radius) x = erosion(x, selem=mask) return x ## Object Detection
def morphology_changed(self): """This signal will be triggered when the morphology image changed.""" return Event()
def morphology(self): """ An image which describes the static morphology. A good choice is to use the maximum projection over the non normalized data. :getter: obtain the morphology image. :setter: set the morphology image, will trigger the :py:attr:`samuroi.SamuROIData.morphology_changed` event. :type: 2D numpy array with same image shape as data. """ return self.__morphology
def morphology(self, morphology): if (morphology.shape != self.data.shape[0:2]): raise Exception("Invalid morphology shape.") self.__morphology = morphology # choose some appropriate new threshold value self.threshold = numpy.percentile(self.morphology.flatten(), q=90) self.morphology_changed()
def threshold(self, t): self.__threshold = t self.threshold_changed() elevation_map = skimage.filters.sobel(self.morphology) markers = numpy.zeros_like(self.morphology) markers[self.morphology < self.threshold] = 1 markers[self.morphology > self.threshold * 1.1] = 2 segmentation = skimage.morphology.watershed(elevation_map, markers) self.overlay = segmentation == 2
def binary_dilation(x, radius=3): """ Return fast binary morphological dilation of an image. see `skimage.morphology.binary_dilation <http://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morphology.binary_dilation>`_. Parameters ----------- x : 2D array image. radius : int for the radius of mask. """ from skimage.morphology import disk, binary_dilation mask = disk(radius) x = binary_dilation(x, selem=mask) return x
def dilation(x, radius=3): """ Return greyscale morphological dilation of an image, see `skimage.morphology.dilation <http://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morphology.dilation>`_. Parameters ----------- x : 2D array image. radius : int for the radius of mask. """ from skimage.morphology import disk, dilation mask = disk(radius) x = dilation(x, selem=mask) return x
def binary_erosion(x, radius=3): """ Return binary morphological erosion of an image, see `skimage.morphology.binary_erosion <http://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morphology.binary_erosion>`_. Parameters ----------- x : 2D array image. radius : int for the radius of mask. """ from skimage.morphology import disk, dilation, binary_erosion mask = disk(radius) x = binary_erosion(x, selem=mask) return x
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
