Hi, just some advice:I think if you did not care the edge-width, you can just
do a skeleton, then label it by 4-connect, if you care the edge-width, I think
you should trace the line, and got a vector shape(not bitmap). you can use
shapely to operate them, and use matplotlib.plot do draw. Canny is not a good
operator for region. you cannot make sure the contour closed, there may be many
noise, and the edge didnot have an accurate location.
Best----- 原始邮件 -----
发件人：Randy Heiland <randy.heil...@gmail.com>
收件人："Mailing list for scikit-image (http://scikit-image.org)"
<scikit-image@python.org>
主题：Re: [scikit-image] fill closed contour
日期：2018年01月09日 10点31分
Sorry to be dense, but could you elaborate on "labeling the holes rather than
edges"? What lines would I tweak in my script?
On Mon, Jan 8, 2018 at 9:20 PM, Juan Nunez-Iglesias <jni.s...@gmail.com> wrote:
Ah, your issue with the thickening was that label by default does not consider
diagonally-adjancent pixels as adjacent. You need to pass connectivity=2 for
this to be the case. But actually labelling the holes rather than the edges is
a rather better option. =) The final image looks very pretty and could make a
nice company logo. =P
On 9 Jan 2018, 1:03 PM +1100, Randy Heiland <randy.heil...@gmail.com>, wrote:
Argh. Nevermind... need to flip black/white on canny edges: 1-(edges*1)
On Mon, Jan 8, 2018 at 8:55 PM, Randy Heiland <randy.heil...@gmail.com> wrote:
Thanks Juan. I understand better what the ndi.measurements.label can do for me
now. I've tweaked my previous script and attached the resulting output. Does it
make sense that I need to "thicken" the contours in order to get the desired
features/regions, or is there something I'm still missing?
------------
from skimage.morphology import disk
from skimage.feature import canny
from skimage.filters import rank
from scipy import ndimage as ndi
import matplotlib.pyplot as plt
import numpy as np
image = disk(100)
for ix in range(200):
for iy in range(200):
xdel=ix-100
ydel=iy-100
if (xdel*xdel/50 + ydel*ydel/10) < 110:
image[iy,ix]=0
elif (xdel*xdel/10 + ydel*ydel/50) < 110:
image[iy,ix]=0
edges = canny(image*255.) # canny expect grayscale, i.e. 0-255 ??!
thicken = rank.gradient(edges, disk(1)) < 5
bdy = thicken.astype(np.uint8)*255
labeled_array, num_features = ndi.measurements.label(edges*1)
print("num_features (edges*1)=",num_features)
labeled_array2, num_features2 = ndi.measurements.label(bdy)
print("num_features (thick)=",num_features2)
fill = ndi.binary_fill_holes(edges)
fig, axes = plt.subplots(ncols=2, nrows=2, figsize=(6, 7))
ax = axes.ravel()
ax[0].imshow(edges*1, cmap=plt.cm.gray, interpolation='nearest')
ax[0].set_title('Canny edges')
ax[1].imshow(labeled_array, cmap=plt.cm.spectral, interpolation='nearest')
ax[1].set_title('labeled_array')
ax[2].imshow(bdy, cmap=plt.cm.gray, interpolation='nearest')
ax[2].set_title('bdy')
ax[3].imshow(labeled_array2, cmap=plt.cm.spectral, interpolation='nearest')
ax[3].set_title('labeled_array2')
plt.axis('off')
plt.show()
-->
num_features (edges*1)= 216
num_features (thick)= 6
-Randy
On Sun, Jan 7, 2018 at 11:36 PM, Juan Nunez-Iglesias <jni.s...@gmail.com> wrote:
Oh, I see what's happening. So, in your case, both void spaces are actually
holes from the perspective of the binary_fill_holes algorithm, so they both get
filled. I suggest you
a) label both contours using ndi.label
b) use binary_fill_holes on each label separately
c) subtract the filled inner hole from the filled outer hole (you can
optionally add back in the inner contour if you care about that single-pixel
precision)
This requires being able to robustly identify the inner and outer contours, but
I don't think that should be too hard? If you only have two, you can certainly
find them by finding the "larger" of the two bounding boxes. You can use
skimage.measure.regionprops for this.
I hope that helps!
Juan.
On 8 Jan 2018, 12:21 PM +1100, Randy Heiland <randy.heil...@gmail.com>, wrote:
Sure - thanks.
from skimage.morphology import disk
from skimage.feature import canny
from scipy import ndimage as ndi
import matplotlib.pyplot as plt
image = disk(100)
for ix in range(200):
for iy in range(200):
xdel=ix-100
ydel=iy-100
if (xdel*xdel/50 + ydel*ydel/10) < 110:
image[iy,ix]=0
elif (xdel*xdel/10 + ydel*ydel/50) < 110:
image[iy,ix]=0
edges = canny(image*255.) # canny expect grayscale, i.e. 0-255 ??!
fill = ndi.binary_fill_holes(edges) # I don't understand the params; can I
seed a region to fill?
fig, axes = plt.subplots(ncols=3, figsize=(9, 3))
ax = axes.ravel()
ax[0].imshow(image, cmap=plt.cm.gray, interpolation='nearest')
#ax[0].imshow(invert_img, cmap=plt.cm.gray)
#ax[0].set_title('Inverted image')
ax[0].set_title('Original image')
ax[1].imshow(edges*1, cmap=plt.cm.gray, interpolation='nearest')
ax[1].set_title('Canny edges')
ax[2].imshow(fill, cmap=plt.cm.spectral, interpolation='nearest')
ax[2].set_title('Fill')
plt.show()
On Sun, Jan 7, 2018 at 6:57 PM, Juan Nunez-Iglesias <jni.s...@gmail.com> wrote:
Hi Randy, I was going to suggest binary fill holes. Do you mind posting your
image and the code you’ve tried so we can troubleshoot?
Thanks,
Juan.
On 8 Jan 2018, 9:48 AM +1100, Randy Heiland <randy.heil...@gmail.com>, wrote:
If I have a binary image with, say, just a contour boundary (simple example: a
white background with a black circle, i.e. an "o"), how can I fill the inside
of the contour? I've played with both the watershed segmentation and the
scipy.ndimage.binary_fill_holes, without success.
thanks, Randy
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