Get border pixels of labelled region
Payal Gupta
erpayal2010 at gmail.com
Wed Sep 11 04:19:35 EDT 2013
hello everyone...
i used find_contour function which returns a 2D ndarray . i can't
understand the return array so any body help me.
*program :*
image = io.imread("IMG_2.jpg")
thresh = filter.threshold_otsu(image)
binary = image > thresh
imag= color.colorconv.rgb2grey(binary)
img = array(imag)
edge_canny = canny(img)
edge_sobel = sobel(img)
contours = measure.find_contours(imag,0.9,'high')
print contours
fig, (ax0, ax1) = plt.subplots(ncols=2)
ax0.imshow(imag ,cmap=plt.cm.gray)
ax0.set_title('Roberts Edge Detection')
ax0.axis('off')
ax1.imshow(edge_canny, cmap=plt.cm.gray)
ax1.set_title('Sobel Edge Detection')
ax1.axis('off')
*returns array:*
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>>>
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On Thu, Sep 5, 2013 at 12:19 PM, Juan Nunez-Iglesias <jni.soma at gmail.com>wrote:
> Hi Payal,
>
> I think you're misunderstanding the purpose of "find_boundaries"... Not
> quite your fault since the documentation is pretty sparse there.
>
> I'll use my favourite fuzzball as an example:
>
> [image: Inline image 1]
>
> "find_boundaries" is meant to take in a segmentation, which is an image in
> which each region has a different integer label, like this one:
>
> [image: Inline image 2]
>
> Then, find_boundaries merely looks for pixels where the neighbouring
> labels are different:
>
> [image: Inline image 3]
>
> It's not doing any edge detection per se, it's just a tool for displaying
> segmentations when it is easier to look at edges than at regions (above). I
> think what you are after is edge detection? Maybe an image like this one:
>
> [image: Inline image 4]
> ?
>
> For that, you should directly use the edge detection algorithms, such as
> Sobel or Canny, without previously thresholding your image. Have a close
> look at the examples gallery to figure out if any are close to what you are
> after, and try to apply the code there to your own image:
>
> http://scikit-image.org/docs/dev/auto_examples/
>
> Some of the more pertinent ones, I think:
> http://scikit-image.org/docs/dev/auto_examples/plot_canny.html
> http://scikit-image.org/docs/dev/auto_examples/plot_edge_filter.html
> http://scikit-image.org/docs/dev/auto_examples/plot_join_segmentations.html
> http://scikit-image.org/docs/dev/auto_examples/plot_label.html
> http://scikit-image.org/docs/dev/auto_examples/plot_marked_watershed.html
>
> http://scikit-image.org/docs/dev/auto_examples/plot_random_walker_segmentation.html
> http://scikit-image.org/docs/dev/auto_examples/plot_watershed.html
>
> http://scikit-image.org/docs/dev/auto_examples/applications/plot_coins_segmentation.html
>
> Note that in the edge detection examples, Canny/Sobel is applied to the
> image *before* any thresholding — unlike the example code you provided.
>
> Juan.
>
>
> On Wed, Sep 4, 2013 at 5:31 PM, Payal Gupta <erpayal2010 at gmail.com> wrote:
>
>> hello everyone...
>> i will try this code,in this i want to do read a image then do edge
>> detection after that find the boundaries of image object. but
>> find_boundaries return me a bool array but i need a integer array. is it
>> any solution of this.
>>
>>
>> code::
>>
>> import Image
>>
>> from skimage import segmentation as seg,io,filter
>>
>> imge = io.imread("IMG_1.jpg")
>>
>> thresh = filter.threshold_otsu(imge)
>>
>> binary = imge> thresh
>>
>> edge_canny = filter.canny(binary)
>>
>> bimg =seg.find_boundaries(binary)
>>
>> print(bimg)
>>
>> fig, (ax0, ax1) = plt.subplots(ncols=2)
>>
>> ax0.imshow(bimg,cmap=plt.cm.gray)
>> ax1.imshow(binary,cmap=plt.cm.gray)
>>
>>
>> On Tue, Sep 3, 2013 at 7:42 PM, Stéfan van der Walt <stefan at sun.ac.za>wrote:
>>
>>>
>>> On 3 Sep 2013 11:45, "Payal Gupta" <erpayal2010 at gmail.com> wrote:
>>> >
>>> > thanks it working but i confused when i use function of skimage
>>> library. i want to change rgb image to a graylevel image but inbuilt
>>> function rgb2gray not convert gray level image.
>>> > reply.
>>>
>>> If you want help on this list, please provide code examples of what you
>>> are attempting.
>>>
>>> Stéfan
>>>
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