本文整理匯總了Python中skimage.morphology.binary_dilation方法的典型用法代碼示例。如果您正苦於以下問題：Python morphology.binary_dilation方法的具體用法？Python morphology.binary_dilation怎麽用？Python morphology.binary_dilation使用的例子？那麽恭喜您, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在模塊skimage.morphology的用法示例。

在下文中一共展示了morphology.binary_dilation方法的24個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於我們的係統推薦出更棒的Python代碼示例。

示例1: _expand_raster

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# 需要導入模塊: from skimage import morphology [as 別名]

# 或者: from skimage.morphology import binary_dilation [as 別名]

def _expand_raster(raster, distance = (4,2)):

try:

from skimage import draw, morphology

except:

raise ImportError("""

The fill function requires the module "scikit-image"

to operate. Please retry after installing scikit-image:

$ pip install --upgrade scikit-image """)

if distance[0] <= 0.5 and distance[1] <= 0.5: return raster

num_pixels = np.array(np.ceil(distance), dtype = int)

neighborhood = np.zeros((num_pixels[1]*2+1, num_pixels[0]*2+1), dtype=np.bool)

rr, cc = draw.ellipse(num_pixels[1], num_pixels[0], distance[1]+0.5, distance[0]+0.5)

neighborhood[rr, cc] = 1

return morphology.binary_dilation(image = raster, selem=neighborhood)

開發者ID:amccaugh，項目名稱:phidl，代碼行數:19，

示例2: get_connected_component_shape

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# 需要導入模塊: from skimage import morphology [as 別名]

# 或者: from skimage.morphology import binary_dilation [as 別名]

def get_connected_component_shape(layer, event):

cords = np.round(layer.coordinates).astype(int)

val = layer.get_value()

if val is None:

return

if val != 0:

data = layer.data

binary = data == val

if 'Shift' in event.modifiers:

binary_new = binary_erosion(binary)

data[binary] = 0

data[binary_new] = val

else:

binary_new = binary_dilation(binary)

data[binary_new] = val

size = np.sum(binary_new)

layer.data = data

msg = f'clicked at {cords} on blob {val} which is now {size} pixels'

else:

msg = f'clicked at {cords} on background which is ignored'

layer.status = msg

print(msg)

開發者ID:napari，項目名稱:napari，代碼行數:24，

示例3: compute_binary_mask_sprengel

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# 需要導入模塊: from skimage import morphology [as 別名]

# 或者: from skimage.morphology import binary_dilation [as 別名]

def compute_binary_mask_sprengel(spectrogram, threshold):

""" Computes a binary mask for the spectrogram

# Arguments

spectrogram : a numpy array representation of a spectrogram (2-dim)

threshold : a threshold for times larger than the median

# Returns

binary_mask : the binary mask

"""

# normalize to [0, 1)

norm_spectrogram = normalize(spectrogram)

# median clipping

binary_image = median_clipping(norm_spectrogram, threshold)

# erosion

binary_image = morphology.binary_erosion(binary_image, selem=np.ones((4, 4)))

# dilation

binary_image = morphology.binary_dilation(binary_image, selem=np.ones((4, 4)))

# extract mask

mask = np.array([np.max(col) for col in binary_image.T])

mask = smooth_mask(mask)

return mask

開發者ID:johnmartinsson，項目名稱:bird-species-classification，代碼行數:27，

示例4: getEdgeEnhancedWeightMap

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# 需要導入模塊: from skimage import morphology [as 別名]

# 或者: from skimage.morphology import binary_dilation [as 別名]

def getEdgeEnhancedWeightMap(label, label_ids =[0,1,2,3], scale=1, edgescale=1, assign_equal_wt=False):

shape = (0,)+label.shape[1:]

weight_map = np.empty(shape, dtype='uint8')

if assign_equal_wt:

return np.ones_like(label)

for i in range(label.shape[0]):

#Estimate weight maps:

weights = np.ones(len(label_ids))

slice_map = np.ones(label[i,:,:].shape)

for _id in label_ids:

class_frequency = np.sum(label[i,:,:] == label_ids[_id])

if class_frequency:

weights[label_ids.index(_id)] = scale*label[i,:,:].size/class_frequency

slice_map[np.where(label[i,:,:]==label_ids.index(_id))] = weights[label_ids.index(_id)]

edge = np.float32(morph.binary_dilation(

canny(np.float32(label[i,:,:]==label_ids.index(_id)),sigma=1), selem=selem))

edge_frequency = np.sum(np.sum(edge==1.0))

if edge_frequency:

slice_map[np.where(edge==1.0)] += edgescale*label[i,:,:].size/edge_frequency

# print (weights)

# utils.imshow(edge, cmap='gray')

# utils.imshow(weight_map, cmap='gray')

weight_map = np.append(weight_map, np.expand_dims(slice_map, axis=0), axis=0)

return np.float32(weight_map)

開發者ID:mahendrakhened，項目名稱:Automated-Cardiac-Segmentation-and-Disease-Diagnosis，代碼行數:26，

示例5: test_apply

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# 需要導入模塊: from skimage import morphology [as 別名]

# 或者: from skimage.morphology import binary_dilation [as 別名]

def test_apply():

input_mask_collection = binary_mask_collection_2d()

output_mask_collection = input_mask_collection._apply(binary_dilation)

assert input_mask_collection._pixel_ticks == output_mask_collection._pixel_ticks

assert input_mask_collection._physical_ticks == output_mask_collection._physical_ticks

assert input_mask_collection._log == output_mask_collection._log

assert len(input_mask_collection) == len(output_mask_collection)

region_0, region_1 = output_mask_collection.masks()

assert region_0.name == '0'

assert region_1.name == '1'

temp = np.ones((2, 6), dtype=np.bool)

assert np.array_equal(region_0, temp)

temp = np.ones((3, 4), dtype=np.bool)

temp[0, 0] = 0

assert np.array_equal(region_1, temp)

assert np.array_equal(region_0[Axes.Y.value], [0, 1])

assert np.array_equal(region_0[Axes.X.value], [0, 1, 2, 3, 4, 5])

assert np.array_equal(region_1[Axes.Y.value], [2, 3, 4])

assert np.array_equal(region_1[Axes.X.value], [2, 3, 4, 5])

開發者ID:spacetx，項目名稱:starfish，代碼行數:27，

示例6: get_image_area_to_sample

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# 需要導入模塊: from skimage import morphology [as 別名]

# 或者: from skimage.morphology import binary_dilation [as 別名]

def get_image_area_to_sample(img):

"""

calculate set g_c, which has two properties

1) They represent background pixels

2) They are within a certain distance to the object

:param img: Image that represents the object instance

"""

#TODO: In the paper 'Deep Interactive Object Selection', they calculate g_c first based on the original object instead

# of the dilated one.

# Dilate the object by d_margin pixels to extend the object boundary

img_area = np.copy(img)

img_area = morphology.binary_dilation(img_area, morphology.diamond(D_MARGIN)).astype(np.uint8)

g_c = np.logical_not(img_area).astype(int)

g_c[np.where(distance_transform_edt(g_c) > D)] = 0

return g_c

開發者ID:JonathonLuiten，項目名稱:PReMVOS，代碼行數:21，

示例7: basin

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# 需要導入模塊: from skimage import morphology [as 別名]

# 或者: from skimage.morphology import binary_dilation [as 別名]

def basin(label_mask, wall):

h,w = np.shape(label_mask)

y, x = np.mgrid[0:h, 0:w]

struct = generate_binary_structure(2,2)

shifty, shiftx = np.mgrid[0:3, 0:3]

shifty = (shifty-1).flatten()

shiftx = (shiftx-1).flatten()

for i in range(4):

obdr = label_mask^binary_dilation(label_mask, struct)

ibdr = label_mask^binary_erosion(label_mask, struct)

yob, xob = y[obdr], x[obdr]

ynb, xnb = yob.reshape(-1,1)+shifty, xob.reshape(-1,1)+shiftx

wallnb = np.min(map_coords(wall, (ynb, xnb))*(map_coords(ibdr, (ynb, xnb))==1)+\

5*(map_coords(ibdr, (ynb, xnb))!=1),1)

keep = (wall[yob,xob]>wallnb)&(wallnb<=4)

label_mask[yob[keep], xob[keep]]=True

if np.sum(keep)==0:

break

return label_mask

開發者ID:jacobkie，項目名稱:2018DSB，代碼行數:23，

示例8: binary_dilation

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# 需要導入模塊: from skimage import morphology [as 別名]

# 或者: from skimage.morphology import binary_dilation [as 別名]

def binary_dilation(x, radius=3):

""" Return fast binary morphological dilation of an image.

see `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

開發者ID:zjuela，項目名稱:LapSRN-tensorflow，代碼行數:15，

示例9: outline_polygons

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# 需要導入模塊: from skimage import morphology [as 別名]

# 或者: from skimage.morphology import binary_dilation [as 別名]

def outline_polygons(self, width=EDGE_WIDTH, color=LABEL_EDGE):

from skimage.morphology import binary_dilation, disk

im = np.asarray(self.image).copy()

outset = binary_dilation(im == LABEL_POSITIVE, disk(width / 2))

inset = binary_dilation(im != LABEL_POSITIVE, disk(width - width / 2))

boundary = outset & inset

im[boundary] = color

self.image = Image.fromarray(im)

self.artist = ImageDraw.Draw(self.image)

開發者ID:jfemiani，項目名稱:facade-segmentation，代碼行數:11，

示例10: compute_binary_mask_lasseck

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# 需要導入模塊: from skimage import morphology [as 別名]

# 或者: from skimage.morphology import binary_dilation [as 別名]

def compute_binary_mask_lasseck(spectrogram, threshold):

# normalize to [0, 1)

norm_spectrogram = normalize(spectrogram)

# median clipping

binary_image = median_clipping(norm_spectrogram, threshold)

# closing binary image (dilation followed by erosion)

binary_image = morphology.binary_closing(binary_image, selem=np.ones((4, 4)))

# dialate binary image

binary_image = morphology.binary_dilation(binary_image, selem=np.ones((4, 4)))

# apply median filter

binary_image = filters.median(binary_image, selem=np.ones((2, 2)))

# remove small objects

binary_image = morphology.remove_small_objects(binary_image, min_size=32, connectivity=1)

mask = np.array([np.max(col) for col in binary_image.T])

mask = smooth_mask(mask)

return mask

# TODO: This method needs some real testing

開發者ID:johnmartinsson，項目名稱:bird-species-classification，代碼行數:28，

示例11: smooth_mask

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# 需要導入模塊: from skimage import morphology [as 別名]

# 或者: from skimage.morphology import binary_dilation [as 別名]

def smooth_mask(mask):

""" Smooths a binary mask using 4x4 dilation

# Arguments

mask : the binary mask

# Returns

mask : a smoother binary mask

"""

n_hood = np.ones(4)

mask = morphology.binary_dilation(mask, n_hood)

mask = morphology.binary_dilation(mask, n_hood)

# type casting is a bitch

return mask

開發者ID:johnmartinsson，項目名稱:bird-species-classification，代碼行數:15，

示例12: sprengel_binary_mask_from_wave_file

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# 需要導入模塊: from skimage import morphology [as 別名]

# 或者: from skimage.morphology import binary_dilation [as 別名]

def sprengel_binary_mask_from_wave_file(filepath):

fs, x = utils.read_wave_file(filepath)

Sxx = sp.wave_to_amplitude_spectrogram(x, fs)

Sxx_log = sp.wave_to_log_amplitude_spectrogram(x, fs)

# plot spectrogram

fig = plt.figure(1)

subplot_image(Sxx_log, 411, "Spectrogram")

Sxx = pp.normalize(Sxx)

binary_image = pp.median_clipping(Sxx, 3.0)

subplot_image(binary_image + 0, 412, "Median Clipping")

binary_image = morphology.binary_erosion(binary_image, selem=np.ones((4, 4)))

subplot_image(binary_image + 0, 413, "Erosion")

binary_image = morphology.binary_dilation(binary_image, selem=np.ones((4, 4)))

subplot_image(binary_image + 0, 414, "Dilation")

mask = np.array([np.max(col) for col in binary_image.T])

mask = morphology.binary_dilation(mask, np.ones(4))

mask = morphology.binary_dilation(mask, np.ones(4))

# plot_vector(mask, "Mask")

fig.set_size_inches(10, 12)

plt.tight_layout()

fig.savefig(utils.get_basename_without_ext(filepath) + "_binary_mask.png", dpi=100)

開發者ID:johnmartinsson，項目名稱:bird-species-classification，代碼行數:33，

示例13: create_mask

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# 需要導入模塊: from skimage import morphology [as 別名]

# 或者: from skimage.morphology import binary_dilation [as 別名]

def create_mask(aseg_data, dnum, enum):

from skimage.morphology import binary_dilation, binary_erosion

from skimage.measure import label

print ("Creating dilated mask ...")

# treat lateral orbital frontal and parsorbitalis special to avoid capturing too much of eye nerve

lat_orb_front_mask = np.logical_or(aseg_data == 2012, aseg_data == 1012)

parsorbitalis_mask = np.logical_or(aseg_data == 2019, aseg_data == 1019)

frontal_mask = np.logical_or(lat_orb_front_mask, parsorbitalis_mask)

print("Frontal region special treatment: ", format(np.sum(frontal_mask)))

# reduce to binary

datab = (aseg_data > 0)

datab[frontal_mask] = 0

# dilate and erode

for x in range(dnum):

datab = binary_dilation(datab, np.ones((3, 3, 3)))

for x in range(enum):

datab = binary_erosion(datab, np.ones((3, 3, 3)))

# extract largest component

labels = label(datab)

assert (labels.max() != 0) # assume at least 1 real connected component

print(" Found {} connected component(s)!".format(labels.max()))

if labels.max() > 1:

print(" Selecting largest component!")

datab = (labels == np.argmax(np.bincount(labels.flat)[1:]) + 1)

# add frontal regions back to mask

datab[frontal_mask] = 1

# set mask

aseg_data[~datab] = 0

aseg_data[datab] = 1

return aseg_data

開發者ID:Deep-MI，項目名稱:FastSurfer，代碼行數:38，

示例14: grow_mask

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# 需要導入模塊: from skimage import morphology [as 別名]

# 或者: from skimage.morphology import binary_dilation [as 別名]

def grow_mask(anat, aseg, ants_segs=None, ww=7, zval=2.0, bw=4):

"""

Grow mask including pixels that have a high likelihood.

GM tissue parameters are sampled in image patches of ``ww`` size.

This is inspired on mindboggle's solution to the problem:

https://github.com/nipy/mindboggle/blob/master/mindboggle/guts/segment.py#L1660

"""

selem = sim.ball(bw)

if ants_segs is None:

ants_segs = np.zeros_like(aseg, dtype=np.uint8)

aseg[aseg == 42] = 3 # Collapse both hemispheres

gm = anat.copy()

gm[aseg != 3] = 0

refined = refine_aseg(aseg)

newrefmask = sim.binary_dilation(refined, selem) - refined

indices = np.argwhere(newrefmask > 0)

for pixel in indices:

# When ATROPOS identified the pixel as GM, set and carry on

if ants_segs[tuple(pixel)] == 2:

refined[tuple(pixel)] = 1

continue

window = gm[

pixel[0] - ww:pixel[0] + ww,

pixel[1] - ww:pixel[1] + ww,

pixel[2] - ww:pixel[2] + ww,

]

if np.any(window > 0):

mu = window[window > 0].mean()

sigma = max(window[window > 0].std(), 1.0e-5)

zstat = abs(anat[tuple(pixel)] - mu) / sigma

refined[tuple(pixel)] = int(zstat < zval)

refined = sim.binary_opening(refined, selem)

return refined

開發者ID:nipreps，項目名稱:niworkflows，代碼行數:42，

示例15: segment_active_contour

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# 需要導入模塊: from skimage import morphology [as 別名]

# 或者: from skimage.morphology import binary_dilation [as 別名]

def segment_active_contour(img, centers):

""" segmentation using acive contours

:param ndarray img: input image / segmentation

:param [[int, int]] centers: position of centres / seeds

:return (ndarray, [[int, int]]): resulting segmentation, updated centres

"""

logging.debug('segment: active_contour...')

# http://scikit-image.org/docs/dev/auto_examples/edges/plot_active_contours.html

segm = np.zeros(img.shape[:2])

img_smooth = ndimage.filters.gaussian_filter(img, 5)

center_circles, _, _ = create_circle_center(img.shape[:2], centers)

for i, snake in enumerate(center_circles):

snake = segmentation.active_contour(img_smooth, snake.astype(float),

alpha=0.015, beta=10, gamma=0.001,

w_line=0.0, w_edge=1.0,

max_px_move=1.0,

max_iterations=2500,

convergence=0.2)

seg = np.zeros(segm.shape, dtype=bool)

x, y = np.array(snake).transpose().tolist()

# rr, cc = draw.polygon(x, y)

seg[map(int, x), map(int, y)] = True

seg = morphology.binary_dilation(seg, selem=morphology.disk(3))

bb_area = int((max(x) - min(x)) * (max(y) - min(y)))

logging.debug('bounding box area: %d', bb_area)

seg = morphology.remove_small_holes(seg, min_size=bb_area)

segm[seg] = i + 1

return segm, centers, None

開發者ID:Borda，項目名稱:pyImSegm，代碼行數:31，

示例16: get_simple_eroded_dilated_mask

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# 需要導入模塊: from skimage import morphology [as 別名]

# 或者: from skimage.morphology import binary_dilation [as 別名]

def get_simple_eroded_dilated_mask(mask, erode_selem_size, dilate_selem_size, small_annotations_size):

if mask.sum() > small_annotations_size**2:

selem = rectangle(erode_selem_size, erode_selem_size)

mask_ = binary_erosion(mask, selem=selem)

else:

selem = rectangle(dilate_selem_size, dilate_selem_size)

mask_ = binary_dilation(mask, selem=selem)

return mask_

開發者ID:neptune-ai，項目名稱:open-solution-mapping-challenge，代碼行數:10，

示例17: modify_w_unet

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# 需要導入模塊: from skimage import morphology [as 別名]

# 或者: from skimage.morphology import binary_dilation [as 別名]

def modify_w_unet(base_label, preds, thres=0.25):

base_label = base_label.copy()

vals = np.unique(base_label[base_label>0])

struct = generate_binary_structure(2,2)

for nb_dilation in range(3):

for val in vals:

label_mask = base_label==val

base_label[binary_dilation(label_mask,struct)&(preds[:,:,0]>thres)&(base_label==0)]=val

return base_label

開發者ID:jacobkie，項目名稱:2018DSB，代碼行數:11，

示例18: _dilate_mask

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# 需要導入模塊: from skimage import morphology [as 別名]

# 或者: from skimage.morphology import binary_dilation [as 別名]

def _dilate_mask(mask, dilation_radius=5):

"""Damages a mask for a single object by dilating it in numpy.

Args:

mask: Boolean numpy array of shape(height, width, 1).

dilation_radius: Integer, the radius of the used disk structure element.

Returns:

The dilated version of mask.

"""

disk = morphology.disk(dilation_radius, dtype=np.bool)

dilated_mask = morphology.binary_dilation(

np.squeeze(mask, axis=2), selem=disk)[..., np.newaxis]

return dilated_mask

開發者ID:tensorflow，項目名稱:models，代碼行數:16，

示例19: _apply_dilation

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# 需要導入模塊: from skimage import morphology [as 別名]

# 或者: from skimage.morphology import binary_dilation [as 別名]

def _apply_dilation(self, snow_masks):

""" Apply binary dilation for each mask in the series """

if self.dilation_size:

snow_masks = np.array([binary_dilation(mask, disk(self.dilation_size)) for mask in snow_masks])

return snow_masks

開發者ID:sentinel-hub，項目名稱:eo-learn，代碼行數:7，

示例20: overlay_skeleton_2d

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# 需要導入模塊: from skimage import morphology [as 別名]

# 或者: from skimage.morphology import binary_dilation [as 別名]

def overlay_skeleton_2d(image, skeleton, *,

image_cmap=None, color=(1, 0, 0), alpha=1,

dilate=0, axes=None):

"""Overlay the skeleton pixels on the input image.

Parameters

----------

image : array, shape (M, N[, 3])

The input image. Can be grayscale or RGB.

skeleton : array, shape (M, N)

The input 1-pixel-wide skeleton.

Other Parameters

----------------

image_cmap : matplotlib colormap name or object, optional

If the input image is grayscale, colormap it with this colormap.

The default is grayscale.

color : tuple of float in [0, 1], optional

The RGB color for the skeleton pixels.

alpha : float, optional

Blend the skeleton pixels with the given alpha.

dilate : int, optional

Dilate the skeleton by this amount. This is useful when rendering

large images where aliasing may cause some pixels of the skeleton

not to be drawn.

axes : matplotlib Axes

The Axes on which to plot the image. If None, new ones are created.

Returns

-------

axes : matplotlib Axes

The Axis on which the image is drawn.

"""

image = _normalise_image(image, image_cmap=image_cmap)

skeleton = skeleton.astype(bool)

if dilate > 0:

selem = morphology.disk(dilate)

skeleton = morphology.binary_dilation(skeleton, selem)

if axes is None:

fig, axes = plt.subplots()

image[skeleton] = alpha * np.array(color) + (1 - alpha) * image[skeleton]

axes.imshow(image)

axes.axis('off')

return axes

開發者ID:jni，項目名稱:skan，代碼行數:46，

示例21: merge_sinks

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# 需要導入模塊: from skimage import morphology [as 別名]

# 或者: from skimage.morphology import binary_dilation [as 別名]

def merge_sinks(Label, Sinks, Radius=5):

"""

Merges attraction basins obtained from gradient flow tracking using

sink locations.

Parameters

----------

Segmentation : array_like

Label image where positive values correspond to foreground pixels that

share mutual sinks.

Sinks : array_like

N x 2 array containing the (x,y) locations of the tracking sinks. Each

row is an (x,y) pair - in that order.

Radius : float

Radius used to merge sinks. Sinks closer than this radius to one

another will have their regions of attraction merged.

Default value = 5.

Returns

-------

Merged : array_like

Label image where attraction regions are merged.

"""

# build seed image

SeedImage = np.zeros(Label.shape)

for i in range(Sinks.shape[0]):

SeedImage[Sinks[i, 1], Sinks[i, 0]] = i+1

# dilate sink image

Dilated = mp.binary_dilation(SeedImage, mp.disk(Radius))

# generate new labels for merged seeds, define memberships

Labels = ms.label(Dilated)

New = Labels[Sinks[:, 1].astype(np.int), Sinks[:, 0].astype(np.int)]

# get unique list of seed clusters

Unique = np.arange(1, New.max()+1)

# generate new seed list

Merged = np.zeros(Label.shape)

# get pixel list for each sink object

Props = ms.regionprops(Label.astype(np.int))

# fill in new values

for i in Unique:

Indices = np.nonzero(New == i)[0]

for j in Indices:

Coords = Props[j].coords

Merged[Coords[:, 0], Coords[:, 1]] = i

return Merged

開發者ID:DigitalSlideArchive，項目名稱:HistomicsTK，代碼行數:56，

示例22: create_shoreline_buffer

​點讚 4

​

# 需要導入模塊: from skimage import morphology [as 別名]

# 或者: from skimage.morphology import binary_dilation [as 別名]

def create_shoreline_buffer(im_shape, georef, image_epsg, pixel_size, settings):

"""

Creates a buffer around the reference shoreline. The size of the buffer is

given by settings['max_dist_ref'].

KV WRL 2018

Arguments:

-----------

im_shape: np.array

size of the image (rows,columns)

georef: np.array

vector of 6 elements [Xtr, Xscale, Xshear, Ytr, Yshear, Yscale]

image_epsg: int

spatial reference system of the image from which the contours were extracted

pixel_size: int

size of the pixel in metres (15 for Landsat, 10 for Sentinel-2)

settings: dict with the following keys

'output_epsg': int

output spatial reference system

'reference_shoreline': np.array

coordinates of the reference shoreline

'max_dist_ref': int

maximum distance from the reference shoreline in metres

Returns:

-----------

im_buffer: np.array

binary image, True where the buffer is, False otherwise

"""

# initialise the image buffer

im_buffer = np.ones(im_shape).astype(bool)

if 'reference_shoreline' in settings.keys():

# convert reference shoreline to pixel coordinates

ref_sl = settings['reference_shoreline']

ref_sl_conv = SDS_tools.convert_epsg(ref_sl, settings['output_epsg'],image_epsg)[:,:-1]

ref_sl_pix = SDS_tools.convert_world2pix(ref_sl_conv, georef)

ref_sl_pix_rounded = np.round(ref_sl_pix).astype(int)

# make sure that the pixel coordinates of the reference shoreline are inside the image

idx_row = np.logical_and(ref_sl_pix_rounded[:,0] > 0, ref_sl_pix_rounded[:,0] < im_shape[1])

idx_col = np.logical_and(ref_sl_pix_rounded[:,1] > 0, ref_sl_pix_rounded[:,1] < im_shape[0])

idx_inside = np.logical_and(idx_row, idx_col)

ref_sl_pix_rounded = ref_sl_pix_rounded[idx_inside,:]

# create binary image of the reference shoreline (1 where the shoreline is 0 otherwise)

im_binary = np.zeros(im_shape)

for j in range(len(ref_sl_pix_rounded)):

im_binary[ref_sl_pix_rounded[j,1], ref_sl_pix_rounded[j,0]] = 1

im_binary = im_binary.astype(bool)

# dilate the binary image to create a buffer around the reference shoreline

max_dist_ref_pixels = np.ceil(settings['max_dist_ref']/pixel_size)

se = morphology.disk(max_dist_ref_pixels)

im_buffer = morphology.binary_dilation(im_binary, se)

return im_buffer

開發者ID:kvos，項目名稱:CoastSat，代碼行數:62，

示例23: davis_f_measure

​點讚 4

​

# 需要導入模塊: from skimage import morphology [as 別名]

# 或者: from skimage.morphology import binary_dilation [as 別名]

def davis_f_measure(foreground_mask, gt_mask, bound_th=0.008):

"""

Compute mean,recall and decay from per-frame evaluation.

Calculates precision/recall for boundaries between foreground_mask and

gt_mask using morphological operators to speed it up.

Arguments:

foreground_mask (ndarray): binary segmentation image.

gt_mask (ndarray): binary annotated image.

Returns:

F (float): boundaries F-measure

P (float): boundaries precision

R (float): boundaries recall

"""

assert np.atleast_3d(foreground_mask).shape[2] == 1

bound_pix = bound_th if bound_th >= 1 else \

np.ceil(bound_th * np.linalg.norm(foreground_mask.shape))

# Get the pixel boundaries of both masks

fg_boundary = seg2bmap(foreground_mask)

gt_boundary = seg2bmap(gt_mask)

fg_dil = binary_dilation(fg_boundary, disk(bound_pix))

gt_dil = binary_dilation(gt_boundary, disk(bound_pix))

# Get the intersection

gt_match = gt_boundary * fg_dil

fg_match = fg_boundary * gt_dil

# Area of the intersection

n_fg = np.sum(fg_boundary)

n_gt = np.sum(gt_boundary)

# % Compute precision and recall

if n_fg == 0 and n_gt > 0:

precision = 1

recall = 0

elif n_fg > 0 and n_gt == 0:

precision = 0

recall = 1

elif n_fg == 0 and n_gt == 0:

precision = 1

recall = 1

else:

precision = np.sum(fg_match) / float(n_fg)

recall = np.sum(gt_match) / float(n_gt)

# Compute F measure

if precision + recall == 0:

F = 0

else:

F = 2 * precision * recall / (precision + recall)

return F

開發者ID:visionml，項目名稱:pytracking，代碼行數:58，

示例24: db_eval_boundary

​點讚 4

​

# 需要導入模塊: from skimage import morphology [as 別名]

# 或者: from skimage.morphology import binary_dilation [as 別名]

def db_eval_boundary(foreground_mask, gt_mask, bound_th):

"""

Compute mean,recall and decay from per-frame evaluation.

Calculates precision/recall for boundaries between foreground_mask and

gt_mask using morphological operators to speed it up.

Arguments:

foreground_mask (ndarray): binary segmentation image.

gt_mask (ndarray): binary annotated image.

Returns:

F (float): boundaries F-measure

P (float): boundaries precision

R (float): boundaries recall

"""

assert np.atleast_3d(foreground_mask).shape[2] == 1

bound_pix = bound_th if bound_th >= 1 else \

np.ceil(bound_th * np.linalg.norm(foreground_mask.shape))

# Get the pixel boundaries of both masks

fg_boundary = seg2bmap(foreground_mask);

gt_boundary = seg2bmap(gt_mask);

fg_dil = binary_dilation(fg_boundary, disk(bound_pix))

gt_dil = binary_dilation(gt_boundary, disk(bound_pix))

# Get the intersection

gt_match = gt_boundary * fg_dil

fg_match = fg_boundary * gt_dil

# Area of the intersection

n_fg = np.sum(fg_boundary)

n_gt = np.sum(gt_boundary)

# % Compute precision and recall

if n_fg == 0 and n_gt > 0:

precision = 1

recall = 0

elif n_fg > 0 and n_gt == 0:

precision = 0

recall = 1

elif n_fg == 0 and n_gt == 0:

precision = 1

recall = 1

else:

precision = np.sum(fg_match) / float(n_fg)

recall = np.sum(gt_match) / float(n_gt)

# Compute F measure

if precision + recall == 0:

F = 0

else:

F = 2 * precision * recall / (precision + recall);

return F, precision, recall, np.sum(fg_match), n_fg, np.sum(gt_match), n_gt

開發者ID:shirgur，項目名稱:ACDRNet，代碼行數:56，

注：本文中的skimage.morphology.binary_dilation方法示例整理自Github/MSDocs等源碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。