Mailman 3 January 2018 - scikit-image

回复： shrink-wrapping filopodia
by imagepy＠sina.com Jan. 9, 2018

Jan. 9, 2018

Hi:here is a method to connect line segment:distance transform --> use the max distance you want to connect as threshold --> do watershedBut I think Canny is not a good choice to extract region(not continue and too many noise),.your image is hard, some work you can try:1. use a snake curve to fit step by step2. use ml function (which need many images to train)3. if you need not full-automatic, you can use Opencv's GrabCut.Some images attached processed by ImagePy, you can get ImagePy here:https://github.com/Image-Py/imagepyBest ----- 原始邮件 ----- 发件人：Randy Heiland <randy.heiland(a)gmail.com> 收件人：scikit-image(a)python.org 主题：[scikit-image] shrink-wrapping filopodia 日期：2018年01月05日 01点22分 Hello, and thanks for the great software! I thought I'd ask this community for suggestions on extracting the outline/shape of some images involving biological filopodia. I've attached a sample image where I manually captured one potential outline. And I've put my initial scripts, data, results here:https://github.com/rheiland/image_proc I tried to "close" (edge2.py) the noisy edges, thinking that would be a logical first step, but it didn't really do what I'd hoped. thanks, Randy _______________________________________________ scikit-image mailing list scikit-image(a)python.org https://mail.python.org/mailman/listinfo/scikit-image

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回复：Re: fill closed contour
by imagepy＠sina.com Jan. 9, 2018

Jan. 9, 2018

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.heiland(a)gmail.com> 收件人："Mailing list for scikit-image (http://scikit-image.org)" <scikit-image(a)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.soma(a)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.heiland(a)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.heiland(a)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.soma(a)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.heiland(a)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.soma(a)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.heiland(a)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 _______________________________________________ scikit-image mailing list scikit-image(a)python.org https://mail.python.org/mailman/listinfo/scikit-image _______________________________________________ scikit-image mailing list scikit-image(a)python.org https://mail.python.org/mailman/listinfo/scikit-image _______________________________________________ scikit-image mailing list scikit-image(a)python.org https://mail.python.org/mailman/listinfo/scikit-image _______________________________________________ scikit-image mailing list scikit-image(a)python.org https://mail.python.org/mailman/listinfo/scikit-image _______________________________________________ scikit-image mailing list scikit-image(a)python.org https://mail.python.org/mailman/listinfo/scikit-image _______________________________________________ scikit-image mailing list scikit-image(a)python.org https://mail.python.org/mailman/listinfo/scikit-image _______________________________________________ scikit-image mailing list scikit-image(a)python.org https://mail.python.org/mailman/listinfo/scikit-image

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回复： Convexity defects in scikit-image
by imagepy＠sina.com Jan. 9, 2018

Jan. 9, 2018

happy new year!I am not sure what you need, some tips here: for binary image:you can do a convex hull, then cut the hull by the ori image, then do a region analysis.for gray image:you can use a dot(tow gaussian filter with different sigma), which can enhance the "gray convex region". tow images attached! images are processed by imagepy, It's a ui framework based on scikit-image, you can try it here: https://github.com/Image-Py/imagepy. Best. ----- 原始邮件 ----- 发件人：Nikola Vukovic <vukovicnikola(a)gmail.com> 收件人：scikit-image(a)python.org 主题：[scikit-image] Convexity defects in scikit-image 日期：2018年01月09日 01点20分 Hi! Happy New Year! Does anyone know if it’s possible to calculate convexity defects (relative to convex hull) in scikit-image? An online tutorial or code sample would be much appreciated… Thanks! _______________________________________________ scikit-image mailing list scikit-image(a)python.org https://mail.python.org/mailman/listinfo/scikit-image

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Re: [scikit-image] fill closed contour
by Juan Nunez-Iglesias Jan. 9, 2018

Jan. 9, 2018

You already did it. By inverting the Canny output, you labelled the holes in the image instead of the edges. To label the edges correctly, you would need to label with connectivity=2. This would give you a non-rainbowy coloring of the canny output. (Only two colors, the inner and the outer ring.) On 9 Jan 2018, 1:31 PM +1100, Randy Heiland <randy.heiland(a)gmail.com>, wrote: > 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.soma(a)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.heiland(a)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.heiland(a)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.soma(a)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.heiland(a)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.soma(a)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.heiland(a)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 > > > > > > > > > > > > _______________________________________________ > > > > > > > > > > > > scikit-image mailing list > > > > > > > > > > > > scikit-image(a)python.org > > > > > > > > > > > > https://mail.python.org/mailman/listinfo/scikit-image > > > > > > > > > > > > > > > > > > > > > > _______________________________________________ > > > > > > > > > > > scikit-image mailing list > > > > > > > > > > > scikit-image(a)python.org > > > > > > > > > > > https://mail.python.org/mailman/listinfo/scikit-image > > > > > > > > > > > > > > > > > > > > > > > > > > > > > _______________________________________________ > > > > > > > > > scikit-image mailing list > > > > > > > > > scikit-image(a)python.org > > > > > > > > > https://mail.python.org/mailman/listinfo/scikit-image > > > > > > > > > > > > > > > > _______________________________________________ > > > > > > > > scikit-image mailing list > > > > > > > > scikit-image(a)python.org > > > > > > > > https://mail.python.org/mailman/listinfo/scikit-image > > > > > > > > > > > > > > > > > > > > > > _______________________________________________ > > > > scikit-image mailing list > > > > scikit-image(a)python.org > > > > https://mail.python.org/mailman/listinfo/scikit-image > > > > > > _______________________________________________ > > > scikit-image mailing list > > > scikit-image(a)python.org > > > https://mail.python.org/mailman/listinfo/scikit-image > > > > > _______________________________________________ > scikit-image mailing list > scikit-image(a)python.org > https://mail.python.org/mailman/listinfo/scikit-image

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Re: [scikit-image] fill closed contour
by Juan Nunez-Iglesias Jan. 9, 2018

Jan. 9, 2018

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.heiland(a)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.heiland(a)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.soma(a)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.heiland(a)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.soma(a)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.heiland(a)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 > > > > > > > > > _______________________________________________ > > > > > > > > > scikit-image mailing list > > > > > > > > > scikit-image(a)python.org > > > > > > > > > https://mail.python.org/mailman/listinfo/scikit-image > > > > > > > > > > > > > > > > _______________________________________________ > > > > > > > > scikit-image mailing list > > > > > > > > scikit-image(a)python.org > > > > > > > > https://mail.python.org/mailman/listinfo/scikit-image > > > > > > > > > > > > > > > > > > > > _______________________________________________ > > > > > > scikit-image mailing list > > > > > > scikit-image(a)python.org > > > > > > https://mail.python.org/mailman/listinfo/scikit-image > > > > > > > > > > _______________________________________________ > > > > > scikit-image mailing list > > > > > scikit-image(a)python.org > > > > > https://mail.python.org/mailman/listinfo/scikit-image > > > > > > > > > > _______________________________________________ > scikit-image mailing list > scikit-image(a)python.org > https://mail.python.org/mailman/listinfo/scikit-image

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Re: [scikit-image] fill closed contour
by Randy Heiland Jan. 9, 2018

Jan. 9, 2018

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.heiland(a)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.soma(a)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.heiland(a)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.soma(a)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.heiland(a)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 >>> _______________________________________________ >>> scikit-image mailing list >>> scikit-image(a)python.org >>> https://mail.python.org/mailman/listinfo/scikit-image >>> >>> >>> _______________________________________________ >>> scikit-image mailing list >>> scikit-image(a)python.org >>> https://mail.python.org/mailman/listinfo/scikit-image >>> >>> >> _______________________________________________ >> scikit-image mailing list >> scikit-image(a)python.org >> https://mail.python.org/mailman/listinfo/scikit-image >> >> >> _______________________________________________ >> scikit-image mailing list >> scikit-image(a)python.org >> https://mail.python.org/mailman/listinfo/scikit-image >> >> >

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Re: [scikit-image] fill closed contour
by Juan Nunez-Iglesias Jan. 9, 2018

Jan. 9, 2018

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.heiland(a)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.soma(a)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.heiland(a)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 > > > > _______________________________________________ > > > > scikit-image mailing list > > > > scikit-image(a)python.org > > > > https://mail.python.org/mailman/listinfo/scikit-image > > > > > > _______________________________________________ > > > scikit-image mailing list > > > scikit-image(a)python.org > > > https://mail.python.org/mailman/listinfo/scikit-image > > > > > _______________________________________________ > scikit-image mailing list > scikit-image(a)python.org > https://mail.python.org/mailman/listinfo/scikit-image

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Convexity defects in scikit-image
by Nikola Vukovic Jan. 8, 2018

Jan. 8, 2018

Hi! Happy New Year! Does anyone know if it’s possible to calculate convexity defects (relative to convex hull) in scikit-image? An online tutorial or code sample would be much appreciated… Thanks!

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import name haar_like_feature_coord
by ahmed tabia Jan. 8, 2018

Jan. 8, 2018

hello how i can add the haar feature to anaconda because when i installed scikit-image , and when i run application with haar feature this message was affiched "cannot import name haar_like_feature_coord" thanx Ahmed

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Re: [scikit-image] fill closed contour
by Juan Nunez-Iglesias Jan. 8, 2018

Jan. 8, 2018

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.heiland(a)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 > _______________________________________________ > scikit-image mailing list > scikit-image(a)python.org > https://mail.python.org/mailman/listinfo/scikit-image

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