Re: objects with their Delaunay graphs overlaid.

first of all, I think scipy's triangulate only does 2D -- those appeared to
be 3D images.

But in any case, it only does a convex hull -- if your points are not
convex, you need constrained Delaney, which I don't know of a good
open-source code for. But triangle is a good one if you can deal with the
unclear license:

here is one wrapper:

https://github.com/drufat/triangle

I have no idea if it works.

-CHB






On Wed, Mar 14, 2018 at 6:35 PM, Franck Kalala <[email protected]>
wrote:

> It does not bring a big change....
>
> 2018-03-14 17:54 GMT+00:00 Thiago Franco Moraes <[email protected]>:
>
>> Try to invert the points:
>>
>> triangles = tri.Triangulation(points[:,1], points[:,0])
>>
>> On Wed, Mar 14, 2018 at 2:16 PM Franck Kalala Mutombo <[email protected]>
>> wrote:
>>
>>> Thank you Thiago,
>>>
>>> It is working but the triangulation goes out of the object for some
>>> points and is that I wanted to avoid compare the image I attached.
>>>
>>> Best
>>>
>>> -
>>> Franck Kalala Mutombo, (PhD. Mathematics )
>>> +243(04)844140 411 | +27(0)7646 91608
>>> skype: franckm4
>>>
>>> "*No one knows the future, however, but this does not prevent us to
>>> project** in it and to act as if we control it*"
>>>
>>>
>>> On 14 March 2018 at 16:42, Thiago Franco Moraes <[email protected]>
>>> wrote:
>>>
>>>> You have to use plt.triplot. Also, you can use
>>>> matplotlib.tri.Triangulation to triangulate using the delaunay. Something
>>>> like this:
>>>>
>>>> from matplotlib import pyplot as plt
>>>>
>>>> from skimage.io import imread
>>>> from skimage.feature import corner_harris, corner_subpix, corner_peaks,
>>>> peak_local_max
>>>> import matplotlib.tri as tri
>>>> import matplotlib.pyplot as plt
>>>>
>>>> image = imread("cup.png", as_grey='True')
>>>> points = peak_local_max(corner_harris(image), min_distance=2)
>>>> triangles = tri.Triangulation(points[:,0], points[:,1])
>>>> fig, ax = plt.subplots()
>>>> ax.imshow(image, interpolation='nearest', cmap=plt.cm.gray)
>>>> ax.triplot(triangles)
>>>> plt.show()
>>>>
>>>>
>>>>
>>>>
>>>> On Wed, Mar 14, 2018 at 1:24 PM Franck Kalala <
>>>> [email protected]> wrote:
>>>>
>>>>> Hello All
>>>>>
>>>>> I am not sure this a good place to ask this. I am just making a try.
>>>>> I have and image and I would like to reproduced the Delaunay graphs
>>>>> overlaid.
>>>>> See the attached image for an idea.
>>>>>
>>>>> I try the following code:
>>>>>
>>>>> from matplotlib import pyplot as plt
>>>>>
>>>>> from skimage.io import imread
>>>>> from skimage.feature import corner_harris, corner_subpix,
>>>>> corner_peaks, peak_local_max
>>>>> from scipy.spatial import Delaunay
>>>>> import matplotlib.pyplot as plt
>>>>>
>>>>> image = imread("cup.png", as_grey='True')
>>>>>
>>>>>
>>>>> #points = corner_peaks(corner_harris(image), min_distance=1)
>>>>> points = peak_local_max(corner_harris(image), min_distance=2)
>>>>> #coords_subpix = corner_subpix(image, coords, window_size=13)
>>>>> tri = Delaunay(points)
>>>>>
>>>>> #imgplot = plt.imshow(image,cmap='gray')
>>>>> #plt.triplot(points[:,0], points[:,1], tri.simplices.copy())
>>>>> #plt.plot(points[:,0], points[:,1], 'o')
>>>>> #plt.show()
>>>>> #print(image)
>>>>>
>>>>> coords = peak_local_max(corner_harris(image), min_distance=2)
>>>>> coords_subpix = corner_subpix(image, coords, window_size=13)
>>>>>
>>>>> fig, ax = plt.subplots()
>>>>> ax.imshow(image, interpolation='nearest', cmap=plt.cm.gray)
>>>>> ax.plot(coords[:, 1], coords[:, 0], '.b', markersize=3)
>>>>> #ax.plot(coords_subpix[:, 1], coords_subpix[:, 0], '+r', markersize=15)
>>>>> #ax.axis((0, 350, 350, 0))
>>>>> plt.show()
>>>>>
>>>>>
>>>>> this code does not help. I attach also the cup image for a try.
>>>>>
>>>>> best
>>>>>
>>>>> franck
>>>>> _______________________________________________
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>>>>>
>>>>
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