本文整理汇总了Python中skimage.morphology方法的典型用法代码示例。如果您正苦于以下问题:Python skimage.morphology方法的具体用法?Python skimage.morphology怎么用?Python skimage.morphology使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类skimage
的用法示例。
在下文中一共展示了skimage.morphology方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: dilation
# 需要导入模块: import skimage [as 别名]
# 或者: from skimage import morphology [as 别名]
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
## Sequence
示例2: test_different_size_px_arguments
# 需要导入模块: import skimage [as 别名]
# 或者: from skimage import morphology [as 别名]
def test_different_size_px_arguments(self):
# different sizes in px
param1 = iap.FromLowerResolution(iap.Binomial(0.5), size_px=2)
param2 = iap.FromLowerResolution(iap.Binomial(0.5), size_px=16)
seen_components = [0, 0]
seen_pixels = [0, 0]
for _ in sm.xrange(100):
samples1 = param1.draw_samples((16, 16, 1))
samples2 = param2.draw_samples((16, 16, 1))
_, num1 = skimage.morphology.label(samples1, connectivity=1,
background=0, return_num=True)
_, num2 = skimage.morphology.label(samples2, connectivity=1,
background=0, return_num=True)
seen_components[0] += num1
seen_components[1] += num2
seen_pixels[0] += np.sum(samples1 == 1)
seen_pixels[1] += np.sum(samples2 == 1)
assert seen_components[0] < seen_components[1]
assert (
seen_pixels[0] / seen_components[0]
> seen_pixels[1] / seen_components[1]
)
示例3: test_different_size_px_arguments_with_tuple
# 需要导入模块: import skimage [as 别名]
# 或者: from skimage import morphology [as 别名]
def test_different_size_px_arguments_with_tuple(self):
# different sizes in px, one given as tuple (a, b)
param1 = iap.FromLowerResolution(iap.Binomial(0.5), size_px=2)
param2 = iap.FromLowerResolution(iap.Binomial(0.5), size_px=(2, 16))
seen_components = [0, 0]
seen_pixels = [0, 0]
for _ in sm.xrange(400):
samples1 = param1.draw_samples((16, 16, 1))
samples2 = param2.draw_samples((16, 16, 1))
_, num1 = skimage.morphology.label(samples1, connectivity=1,
background=0, return_num=True)
_, num2 = skimage.morphology.label(samples2, connectivity=1,
background=0, return_num=True)
seen_components[0] += num1
seen_components[1] += num2
seen_pixels[0] += np.sum(samples1 == 1)
seen_pixels[1] += np.sum(samples2 == 1)
assert seen_components[0] < seen_components[1]
assert (
seen_pixels[0] / seen_components[0]
> seen_pixels[1] / seen_components[1]
)
示例4: test_min_size
# 需要导入模块: import skimage [as 别名]
# 或者: from skimage import morphology [as 别名]
def test_min_size(self):
# min_size
param1 = iap.FromLowerResolution(iap.Binomial(0.5), size_px=2)
param2 = iap.FromLowerResolution(iap.Binomial(0.5), size_px=1,
min_size=16)
seen_components = [0, 0]
seen_pixels = [0, 0]
for _ in sm.xrange(100):
samples1 = param1.draw_samples((16, 16, 1))
samples2 = param2.draw_samples((16, 16, 1))
_, num1 = skimage.morphology.label(samples1, connectivity=1,
background=0, return_num=True)
_, num2 = skimage.morphology.label(samples2, connectivity=1,
background=0, return_num=True)
seen_components[0] += num1
seen_components[1] += num2
seen_pixels[0] += np.sum(samples1 == 1)
seen_pixels[1] += np.sum(samples2 == 1)
assert seen_components[0] < seen_components[1]
assert (
seen_pixels[0] / seen_components[0]
> seen_pixels[1] / seen_components[1]
)
示例5: test_size_percent
# 需要导入模块: import skimage [as 别名]
# 或者: from skimage import morphology [as 别名]
def test_size_percent(self):
# different sizes in percent
param1 = iap.FromLowerResolution(iap.Binomial(0.5), size_percent=0.01)
param2 = iap.FromLowerResolution(iap.Binomial(0.5), size_percent=0.8)
seen_components = [0, 0]
seen_pixels = [0, 0]
for _ in sm.xrange(100):
samples1 = param1.draw_samples((16, 16, 1))
samples2 = param2.draw_samples((16, 16, 1))
_, num1 = skimage.morphology.label(samples1, connectivity=1,
background=0, return_num=True)
_, num2 = skimage.morphology.label(samples2, connectivity=1,
background=0, return_num=True)
seen_components[0] += num1
seen_components[1] += num2
seen_pixels[0] += np.sum(samples1 == 1)
seen_pixels[1] += np.sum(samples2 == 1)
assert seen_components[0] < seen_components[1]
assert (
seen_pixels[0] / seen_components[0]
> seen_pixels[1] / seen_components[1]
)
示例6: binary_dilation
# 需要导入模块: import skimage [as 别名]
# 或者: from skimage import morphology [as 别名]
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(image, selem=mask)
return x
示例7: test_different_size_px_argument_with_stochastic_parameters
# 需要导入模块: import skimage [as 别名]
# 或者: from skimage import morphology [as 别名]
def test_different_size_px_argument_with_stochastic_parameters(self):
# different sizes in px, given as StochasticParameter
param1 = iap.FromLowerResolution(iap.Binomial(0.5),
size_px=iap.Deterministic(1))
param2 = iap.FromLowerResolution(iap.Binomial(0.5),
size_px=iap.Choice([8, 16]))
seen_components = [0, 0]
seen_pixels = [0, 0]
for _ in sm.xrange(100):
samples1 = param1.draw_samples((16, 16, 1))
samples2 = param2.draw_samples((16, 16, 1))
_, num1 = skimage.morphology.label(samples1, connectivity=1,
background=0, return_num=True)
_, num2 = skimage.morphology.label(samples2, connectivity=1,
background=0, return_num=True)
seen_components[0] += num1
seen_components[1] += num2
seen_pixels[0] += np.sum(samples1 == 1)
seen_pixels[1] += np.sum(samples2 == 1)
assert seen_components[0] < seen_components[1]
assert (
seen_pixels[0] / seen_components[0]
> seen_pixels[1] / seen_components[1]
)
示例8: overlay
# 需要导入模块: import skimage [as 别名]
# 或者: from skimage import morphology [as 别名]
def overlay(self, img, imbin, contour=False):
colim = color.gray2rgb(img)
colorvalue = (0, 100, 200)
if contour:
se = morphology.diamond(2)
ero = morphology.erosion(imbin, se)
grad = imbin - ero
colim[grad > 0] = colorvalue
else:
colim[imbin>0] = colorvalue
return colim
示例9: morpho_rec
# 需要导入模块: import skimage [as 别名]
# 或者: from skimage import morphology [as 别名]
def morpho_rec(self, img, size=10):
# internal gradient of the cells:
se = morphology.diamond(size)
ero = morphology.erosion(img, se)
rec = morphology.reconstruction(ero, img, method='dilation').astype(np.dtype('uint8'))
return rec
示例10: morpho_rec2
# 需要导入模块: import skimage [as 别名]
# 或者: from skimage import morphology [as 别名]
def morpho_rec2(self, img, size=10):
# internal gradient of the cells:
se = morphology.diamond(size)
dil = morphology.dilation(img, se)
rec = morphology.reconstruction(dil, img, method='erosion').astype(np.dtype('uint8'))
return rec
示例11: test_morpho2
# 需要导入模块: import skimage [as 别名]
# 或者: from skimage import morphology [as 别名]
def test_morpho2(self, bigsize=20.0, smallsize=3.0, threshold=5.0):
img = self.read_H_image()
pref = self.morpho_rec(img, 10)
filename = os.path.join(test_out_folder, 'morpho_00_rec_%s.png' % self.image_name)
skimage.io.imsave(filename, pref)
res = self.difference_of_gaussian(pref, bigsize, smallsize)
filename = os.path.join(test_out_folder, 'morpho_01_diff_%s_%i_%i.png' % (self.image_name, int(bigsize), int(smallsize)))
skimage.io.imsave(filename, res)
#res = self.morpho_rec2(diff, 15)
#filename = os.path.join(test_out_folder, 'morpho_02_rec_%s.png' % self.image_name)
#skimage.io.imsave(filename, res)
res[res>threshold] = 255
filename = os.path.join(test_out_folder, 'morpho_03_res_%s_%i.png' % (self.image_name, threshold))
skimage.io.imsave(filename, res)
se = morphology.diamond(3)
ero = morphology.erosion(res, se)
filename = os.path.join(test_out_folder, 'morpho_03_ero_%s_%i.png' % (self.image_name, threshold))
skimage.io.imsave(filename, ero)
res[ero>0] = 0
overlay_img = self.overlay(img, res)
filename = os.path.join(test_out_folder, 'morpho_04_overlay_%s_%i.png' % (self.image_name, int(threshold)))
skimage.io.imsave(filename, overlay_img)
return
示例12: get_rough_detection
# 需要导入模块: import skimage [as 别名]
# 或者: from skimage import morphology [as 别名]
def get_rough_detection(self, img, bigsize=40.0, smallsize=4.0, thresh = 0):
diff = self.difference_of_gaussian(-img, bigsize, smallsize)
diff[diff>thresh] = 1
se = morphology.square(4)
ero = morphology.erosion(diff, se)
labimage = label(ero)
#rec = morphology.reconstruction(ero, img, method='dilation').astype(np.dtype('uint8'))
# connectivity=1 corresponds to 4-connectivity.
morphology.remove_small_objects(labimage, min_size=600, connectivity=1, in_place=True)
#res = np.zeros(img.shape)
ero[labimage==0] = 0
ero = 1 - ero
labimage = label(ero)
morphology.remove_small_objects(labimage, min_size=400, connectivity=1, in_place=True)
ero[labimage==0] = 0
res = 1 - ero
res[res>0] = 255
#temp = 255 - temp
#temp = morphology.remove_small_objects(temp, min_size=400, connectivity=1, in_place=True)
#res = 255 - temp
return res
示例13: prefilter_new
# 需要导入模块: import skimage [as 别名]
# 或者: from skimage import morphology [as 别名]
def prefilter_new(self, img, rec_size=20, se_size=3):
img_cc = ccore.numpy_to_image(img, copy=True)
im1 = ccore.diameter_open(img_cc, rec_size, 8)
im2 = ccore.diameter_close(im1, int(rec_size / 2), 8)
#im1 = self.morpho_rec(img, rec_size)
#im2 = self.morpho_rec2(im1, int(rec_size / 2))
se = morphology.disk(se_size)
im3 = morphology.closing(im2.toArray(), se)
return im3
示例14: h_minima
# 需要导入模块: import skimage [as 别名]
# 或者: from skimage import morphology [as 别名]
def h_minima(self, img, h):
img_shift = img.copy()
img_shift[img_shift >= 255 - h] = 255-h
img_shift = img_shift + h
rec = morphology.reconstruction(img_shift, img, method='erosion').astype(np.dtype('uint8'))
diff = rec - img
return diff
示例15: get_segmented_lungs
# 需要导入模块: import skimage [as 别名]
# 或者: from skimage import morphology [as 别名]
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