Hi Fran�ois,
that's an excellent question, and not a troll :-). Opencv is a
very powerful library, but it focuses primarily on computer vision
(feature detection and extraction, classification, ...), as opposed to
image processing in general (with other tasks such as denoising,
segmentation, ...).
The other big difference is that skimage builds on numpy
ndarrays, and uses the full power of the numpy API (including of course
the basic facilities for processing arrays as images that come with
numpy), as well as some of scipy functions (you could have added
scipy.ndimage to your list -- a few functions in skimage are wrappers
around scipy.ndimage, that exist for the sake of completeneness). One
important consequence is that algorithms working for 3-d or even n-d
images can be easily implemented in 3-d/n-d in skimage, whereas opencv is
restricted to 2-D images (as far as I know). Thanks to the use of numpy
arrays, the API of skimage is also quite pleasant for a numpy user, more
than the API of opencv.
A related difference is that skimage is written in python and
cython, whereas opencv is a C++ library. The two libraries attract a
different crowd of developers, and a Python/Cython toolkit based on numpy
arrays is easier to develop and maintain inside the Scientific Python
ecosystem.
I'm sure that other devs/users will have things to add to this
discussion!
Cheers,
Emmanuelle
On Thu, Dec 27, 2012 at 02:06:08PM -0800, Fran�ois wrote:
> Hi users and devs,
> It came to my knowledge that another python library (based on C++ and C
> codes) for image processing exists too : opencv
> I understand that numpy intregrates some basic features and we need some
> advanced features but I have the feeling that skimages is redoundant with
> opencv in some ways.
> What's the position of skimage about that? (Don't read this question as a
> troll but like a real question).
> I mean that similar features exist in both. Would not be possible to
> reuse/integrate opencv or merge? what's the reason for keeping them apart?
> My observation is there is 4 libraries to manipulate images:
> * PIL
> * numpy
> * skimages
> * opencv
> That's a lot.
> Cheers,
Hi,
FYI, I plan to work on the detection of ellipses quite soon. Stefan
indicated to me a reference which seems fairly simple to implement.
Cheers,
François.
Greetings, we're using SciKit template_matching and getting confidence
values > 3. Does that make sense?
A simple example with test images is attached.
Thanks for the fine work!
import numpy, sys
from skimage import data
from skimage import io
from skimage.feature import match_template
def containsLikelyhood(needleLoc, haystackLoc):
haystack = data.imread(haystackLoc, as_grey=True)
needle = data.imread(needleLoc, as_grey=True)
greyLoc = match_template(haystack, needle)
ij = numpy.unravel_index(numpy.argmax(greyLoc), greyLoc.shape)
x, y = ij[::-1]
height, width = needle.shape
return greyLoc[y][x], (x, y, width, height)
if __name__ == "__main__":
print containsLikelyhood("locatedStar.PNG", "screenshot.PNG")
Thanks Stefan, I didn't know that. Appreciate it.
On Thu, Nov 28, 2013 at 9:55 AM, Stéfan van der Walt <stefan(a)sun.ac.za>wrote:
> Hi Adam
>
> On Tue, Nov 26, 2013 at 4:45 AM, Adam Hughes <hughesadam87(a)gmail.com>
> wrote:
> > I made a couple basic utilties:
> > zoom, crop, and zoomplot
>
> Thanks for sharing!
>
> A tiny IPython tip: if you end the last line of your cell with a
> semi-colon, the printing of the resulting object is disabled. Very
> useful for doing plots.
>
> Regards
> Stéfan
>
> --
> You received this message because you are subscribed to a topic in the
> Google Groups "scikit-image" group.
> To unsubscribe from this topic, visit
> https://groups.google.com/d/topic/scikit-image/Ke7_TwUew4U/unsubscribe.
> To unsubscribe from this group and all of its topics, send an email to
> scikit-image+unsubscribe(a)googlegroups.com.
> For more options, visit https://groups.google.com/groups/opt_out.
>
On Tue, Nov 26, 2013 at 5:55 AM, Juan Nunez-Iglesias <jni.soma(a)gmail.com> wrote:
> @Josh, there's two issues here: whether the import io is wasteful (which, as
> you demonstrated, it isn't), and whether it makes sense to stick imshow
> under io, which is certainly not clear to me, despite the rationalisations
> posted to this thread. =P
>
> I would argue that we rename the "viewer" package to "view" and include a
> simple imshow in there in addition to the viewer.
This wasn't as much a design decision as evolution. Initially, we had
a module for reading, writing and displaying images, and only later
the viewer tools came along. Since then, we've started using
matplotlib almost exclusively for displaying images (e.g. in the
gallery), so I'd opt for deprecating "imshow" in favor of standard
matplotlib.
Having a viewer in the ``viewer`` package makes some sense, because it
adds additional functionality. But before we put more code in there,
it would be good to figure out how to test it--any ideas how to script
GUI tests?
Stéfan
I made a couple basic utilties:
zoom, crop, and zoomplot
They're very straightforward, but there was just enough nuance involved
that working directly with numpy and matplotlib would become a pain.
See the attached notebook and/or pdf. The PDF was generaged with
nbconvert, and the image quality is poor. Feel free to use and distribute
this; please the ignore the content claim on the title page; it defaults to
all of our pdfs that we generate with nbconvert.
On Friday, November 22, 2013 2:03:04 PM UTC-5, Adam Hughes wrote:
>
> Hi everyone,
>
> I'm used to working with big images of lots of particles. In the
> notebook, I'd like to be able to look at a full image, and then at a zoomed
> in region of interest. A few basic questions come to mind:
>
> Is there a zoom/crop function or preffered approach to basic
> manipulations of zooming and cropping, or would I have to do this at the
> numpy or matplotlib level? I saw that there's a rectangle function that
> probably would be useful here. Does anyone have any examples or personal
> code built for doing some of these common manipulations? Ideally, I want
> to take the fastest approach to:
>
> 1. Selecting a rectangular region of interest (ROI).
> 2. Cropping or zooming in on this region, and storing the ROI as its own
> array/image.
> 3. If possible, removing the ROI from the original image, and splicing
> the original image back together. If this is possible, that would be
> amazing. This would allow us to effectively cut out regions of our images
> that are obviously contaminates.
>
> Thanks.
>
>
>
Hi Juan,
I made the changes to add SLIC-zero but the code has become a bit messy and
redundant... I think it's not ready for a pull request yet but I would
welcome some feedback :)
https://github.com/romaniukm/scikit-image/tree/slico
I also updated the plot_segmentations.py example to add SLIC-zero to the
comparison. It's available here:
https://gist.github.com/romaniukm/7679402
Michal
Hey Adam, your Notebook PDF output is *sick*! ;)
Care to share how you set it up so nicely?
Michael
On Tuesday, November 26, 2013 10:30:58 AM UTC-8, Adam Hughes wrote:
>
> Sorry, last post. Here's a slightly more complete version for sake of
> archiving:
>
>
>
>
> On Mon, Nov 25, 2013 at 9:45 PM, Adam Hughes <hughes...(a)gmail.com<javascript:>
> > wrote:
>
>> I made a couple basic utilties:
>> zoom, crop, and zoomplot
>>
>> They're very straightforward, but there was just enough nuance involved
>> that working directly with numpy and matplotlib would become a pain.
>>
>> See the attached notebook and/or pdf. The PDF was generaged with
>> nbconvert, and the image quality is poor. Feel free to use and distribute
>> this; please the ignore the content claim on the title page; it defaults to
>> all of our pdfs that we generate with nbconvert.
>>
>> On Friday, November 22, 2013 2:03:04 PM UTC-5, Adam Hughes wrote:
>>>
>>> Hi everyone,
>>>
>>> I'm used to working with big images of lots of particles. In the
>>> notebook, I'd like to be able to look at a full image, and then at a zoomed
>>> in region of interest. A few basic questions come to mind:
>>>
>>> Is there a zoom/crop function or preffered approach to basic
>>> manipulations of zooming and cropping, or would I have to do this at the
>>> numpy or matplotlib level? I saw that there's a rectangle function that
>>> probably would be useful here. Does anyone have any examples or personal
>>> code built for doing some of these common manipulations? Ideally, I want
>>> to take the fastest approach to:
>>>
>>> 1. Selecting a rectangular region of interest (ROI).
>>> 2. Cropping or zooming in on this region, and storing the ROI as its
>>> own array/image.
>>> 3. If possible, removing the ROI from the original image, and splicing
>>> the original image back together. If this is possible, that would be
>>> amazing. This would allow us to effectively cut out regions of our images
>>> that are obviously contaminates.
>>>
>>> Thanks.
>>>
>>>
>>> --
>> You received this message because you are subscribed to a topic in the
>> Google Groups "scikit-image" group.
>> To unsubscribe from this topic, visit
>> https://groups.google.com/d/topic/scikit-image/Ke7_TwUew4U/unsubscribe.
>> To unsubscribe from this group and all of its topics, send an email to
>> scikit-image...(a)googlegroups.com <javascript:>.
>> For more options, visit https://groups.google.com/groups/opt_out.
>>
>
>
Thanks for your helpful response Juan. But I have the issue with choosing
parameter for threshold_adaptive.. Some of my images have uneven contrast,
like the lower part of image has darker background compared to the upper
part. So if the threshold is proper for the upper part, some
neighboring particles will be taken as a cluster even they are individual.
I tried different diam, but none of them leads to good threshold in terms
of the full image. Is there any other thresholding methods for those uneven
contrast images?
I also tried the edge operator *filter.sobel *and it looks good for drawing
up particles' edges(the attached image). I wonder if i can fill these
circles up to get the thresholding image? I tried *ndimage.**binary_fill_holes
*but it gives me either blank or total total black pic..
<https://lh6.googleusercontent.com/-89xSmEmy7Wc/UpL_fNubbSI/AAAAAAAAAB8/0N-N…>
On Wednesday, November 20, 2013 12:33:19 AM UTC-5, Juan Nunez-Iglesias
wrote:
>
> Hi Evelyn,
>
> I'm guessing you are applying label() directly to your image, which is not
> the right way to use it. label() connects all neighboring *nonzero*points together. Since images are rarely zero (rather than some very small
> intensity value), you are simply connecting all the pixels of your image
> together into one label.
>
> The correct way to do this is to threshold your image, eg using:
> http://scikit-image.org/docs/dev/api/skimage.filter.html#threshold-adaptive<http://www.google.com/url?q=http%3A%2F%2Fscikit-image.org%2Fdocs%2Fdev%2Fap…>
>
> So:
>
> >>> from skimage.filter import threshold_adaptive
> >>> from scipy import ndimage as nd
> >>> diam = 51
> >>> # "51" is a guess, you might have to fiddle with this parameter.
> >>> image_t = (image > threshold_adaptive(image, diam))
> >>> image_labeled = nd.label(image_t)[0]
> >>> particle_sizes = np.bincount(image_labeled.ravel())[1:]
> >>> # [1:] is to select only the foreground labels
>
> Hope this helps!
>
> Juan.
>
>
>
> On Wed, Nov 20, 2013 at 3:45 PM, Evelyn Liu <eve...(a)gmail.com<javascript:>
> > wrote:
>
>> Hi Juan,
>>
>> On Tuesday, November 19, 2013 9:39:47 PM UTC-5, Juan Nunez-Iglesias wrote:
>>>
>>> Is the goal only to count particles? In that case, I think a local
>>> thresholding (threshold_adaptive) would work on all these images. Then,
>>> just do a labelling (scipy.ndimage.label) and draw a histogram of particle
>>> sizes. You'll get a sharp peak around the true particle size, with bigger
>>> peaks for clumps
>>>
>>
>> I'd like to use scikit to plot the size distribution histogram of
>> particles in an image, which is similar with Adam's. I tried
>> scipy.ndimage.measurements.label(image), which I thought would give an
>> array about particle sizes. However, the output array is with all 1,
>> obviously nothing about size. I must get something wrong...So which
>> function should i call for the size distribution? Thanks Juan!
>>
>>
>> --
>> You received this message because you are subscribed to the Google Groups
>> "scikit-image" group.
>> To unsubscribe from this group and stop receiving emails from it, send an
>> email to scikit-image...(a)googlegroups.com <javascript:>.
>> For more options, visit https://groups.google.com/groups/opt_out.
>>
>
>
I just stumbled over the 3 lines of code on scikits' title page:
from skimage import data, io, filter
image = data.coins() # or any NumPy array!
edges = filter.sobel(image)
io.imshow(edges)
and wondered: Isn't it a bit funny to require an I/O module to show an
image that is already loaded?
My apologies if that has been discussed before.
Michael
