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About
Hangman is a very simple pen-and-paper guessing game for two players. In this implementation,
you are always the guessing side.
Hangman Rules
- A word is randomly selected from our database
- You are given the category of the word (for example - programming, cooking, sports,
cars, etc
- A number of underscores equal to the length of the word is displayed.
- You need to choose a letter from the alphabet until
- The entire word is guessed
- You have missed six times - in this case, you lose.


 You dont seem to understand i feel im one of the Few good honest player left on the game. you dont seem to care.
and you really wonder why ppl cheat and dupe??
Its fast, easy, and you dont do anything about it.
i will be devoting myself fully to project volucris
i really hope you reply with some type of response still.
but then again i also HIGLY doubt you read all your emails.
I would Continue to play, but losing 40+ vt obliterated my will to play.
-David Devine-
EJ replies
>Then quit, I get 120 e-mails a day and I don't have time to help every single person. If
>you can't deal with it then you should for sure quite. The level of a player has
>absolutely nothing to do with whether or not they get help in the game.
I Reply
sorry if i assume too much, i sent you more than 20 emails over the course of 2 weeks after my account was accessed without my permission and all of my gear stolen. i did NOT revieve ANY TYPE of response untill i told you someone might be duping, even then you offer no help in anyway! I understand just you doing it is exhausting and time consuming... i do believe i gave more than enuf time for you to help me, aprox 1 month, and you choose to not help... or even tell me you wont help.
the level thing is only an assumption i have never heard of you helping anyone get gear back besides high level people with high level gear. If you do help mid-level people thats my bad, and i appoligize. you really should lookin into having more people answer emails and moderate things...
-David
I Reply again same day
Im jus really really upset and frustrated! i sent you SEVERAL emails the DAY i couldnt log on! when i saw my Dirus sword, along with my other gear in cons. and now because of your lack action, (what ever u wanna call it) the gear has been traded off, traded down, or lost, and I assume you Will not do anything anymore, because my gear is no longer there does that truly make it my fault somehow!?
I do wish to keep playing, but only if i get most/all my stolen gear back. this is so dumb it has come to such a bitter argument...
-David-
Ej Replies
Yes, it is your fault.
I reply
I'm done arguing over this, Im moving on to different games, best wishes to you, your family, and xen.
-David
Posted:
Wed Jun 20, 2007 9:00 am
by JSunJShineR
i know what you mean infact someone had accessed my account without permission and they stole my dls/d wand/o helm/shield/hammer/m blade/3 robes/sp leather/plate, and various lowbie items... so i emailed him around 20 times also, he replyed to me saying that he does not help people in the game at all, unless u have proof that someone got into your account. I told him that all my poo is missing and my char is deleved and my stats/skills r fucked. he siad I need a video or something like that... like I have a fuckign video of someone hacking into my account and fuckign it up... lmao ej is so fuckign retarded. Thats why PV is gonna be sweet all the faults of EJ are being recognized and being improved to make a much more liked game=)
Posted:
Wed Jun 20, 2007 11:40 am
by ziggman
yea i cant wait for a beta version of pv! EJ is just dumb straight up! he needs to understand having a bit too much gear in game does not spoil it, Ej seems more concernd with adding -dumdance features, then helping his CUSTOMERS! I work in retail and keeping a customer coming back is the TOP priority to making your buissness grow. EJ cannot wrap his mind around this concept for some reason or another. and Thus the down fall of xenimus is emeinet.
imma go take some pitcures for skinning! got some nice flowers blooming atm. i want textrues to be nicer than xeimus.

*/
So, just out of curiosity, why do you take any pride in this?
EJ is an independent game developer, and while he may not have the best product out there, he still works pretty hard on it. If you imagine that making all of the content, coding, and then handling the business of Xenimus is a trivial task, then you are sorely mistaken.
What incentive does he have to fix things when many of his community complain openly about his game? Xen probably isn't his main source of income, and I would venture that he keeps it running more as a side project to tinker with and out of a slight feeling of debt to the people who play it. He could just auction off the equipment, IP, and bandwidth, and save himself the trouble of dealing with hateful e-mails.
Think about it: if he receives a hundred or even fifty e-mails a day, all of which are complaining about his 'dumb straight up' methods, how much time is wasted? He works through them, and when the time comes to actually start coding again, do you think he's in any mood to fix things? Especially when any fixes will just spawn more of the same softheaded correspondence?
I wonder how many people enjoy Xen for each one that complains about it. Think on that. Even if it is a flawed game design, have some bloody respect for a person who spent at least four or five years making and maintaining a game instead of playing it.

 It is easy to detract, to critique another's work... what do you have to offer? What have you done that gives you the right to make fun of someone else's efforts, however flawed?
(at least I notice ziggman is making some efforts to get better textures)
I am a game developer. I keep to myself, and mostly do not comment on other's works, simply because I too am in the same position. But, when I do, I ask instead of yell, and I take inspiration and ideas, instead of spouting off smug rubbish and wasting e-mail inbox space. But, the increasingly hostile attitude taken on these boards towards Xenimus is getting out of hand. It is one thing to disagree with EJ, another quite entirely to cheerfully harass him...
...and then crow about your success here on the PV boards!
I've known the Unseen Studios team for a long time. My old account on these forums seems to have disappeared after PV was begun. I've been watching, though, and I would caution you all that Karl is at the very least as strong-willed as EJ. If you continue this sophomoric douchebaggery and believe it will influence PV upon its completion, you are sorely mistaken.
Have some respect, children.
-Chris
TL,DR version:
plz b nice 2 evry1 who r devs, j00 stup1d n00bs.
kthxbye!
Posted:
Tue Jun 26, 2007 3:23 am
by ziggman
i wouldnt have gotten upset at ej, if he would have only helped me, or even replied to my 1st email i sent! i lost 4 years worth of gear to somone and he does nothing! he did not even write a reply saying that he could not /would not help me. he just ignored me.
i understand he is busy and this game is not his life. but EJ does need to understand that he is running a business with customers, and needs to provide some type of customer service if he expects his business to grow. if u walked in to wal-mart and no one said anything to you the whole time u were there, even when u asked somone a question, how often would u go back? altho ej claims he does not play favorites im 100% sure he does. i emailed 10+ times to restore my old account... my emails went unchecked, unreplied, ignored. i talked to somone ingame i forget who he was, he emailed ej, Next day my account was back!
im sure if somone like Neorift lost all his gear ej would get it back for him NO Problems!
i Finnaly state that i am Quitting xen because he wont do anyting or even reply. this is the only time ej replied about my stolen gear!
This is the only thing i have to complain about xen.  He is retarded, he wouldnt even need to pay, id read through all his emails for a spirit leather lol.
Posted:
Wed Jun 27, 2007 6:33 am
by shadowni
I also agree. I spent 30$ on account time the other day, and now I'm looking to sell my account. My friend is doing it for me since he is the one with the secret word. (He created acct and gave to me.. but forgot to give me the sw.) So I agreed to return the account in return for about 20$ of the profit.
 If you guys need a game to play while waiting for PV, this is the one. Myself, my friend (Aluminence, Greedy Man) play that game.
Posted:
Wed Jun 27, 2007 12:08 pm
by 9sam1
hellbreath? whats that like?
Posted:
Mon Jul 02, 2007 9:12 pm
by shadowni
9sam1 wrote:hellbreath? whats that like?
It's a pretty fun game. It's kinda like Xenimus actually, but with some better graphics. =]
helbreathusa.com is the main site and the server I play on is
http://www.hbcat.pl
Posted:
Wed Aug 15, 2007 10:20 pm
by Red Tail
Wow, that is the first time I have EVER heard someone defend EJ. Not smart. You are trying to get people not to like you if you do that.
I've heard of EJ not replying so many times, I've emailed him too and he doesn't reply. Its incredibly frustrating and it seems like all he wants to do is make the game "look" better but not play better. That and make money, lots of it and not care. He does indeed need to figure out that he now needs Customer Service, the game HAS gotten that big. Xencon....mmmmmmmmmmm, good one.
Posted:
Thu Aug 23, 2007 7:47 am
by addit
I know this doesn't quite compare but I found the first diabolic mace in xenimus (i'm pretty sure it was the first diabolic item). Anyho, I was ecstatic as i'm one of those guys who have been playing for ages (was around lv30 at the time) and never found anything, EVER. Then a day later EJ reset the servers because of "duping" or something. I first wrote him one e-mail and them countless more. The thing which got to me is that he didn't even respond, not even a word. If he wanted proof he could have asked I had screenshots but I suppose he doesn't trust those. Well that and a couple of other incidents made me leave xen for good. Just my little rant...
Helbreath
Posted:
Fri Nov 09, 2007 6:06 am
by daekighn
Well i cant play this game cuz i have to pay for it so tough luck on that game
. Any1 wanna lend me a acc? If so ill play,if not dont worry bout it.. Im 13 yrs old, don't have a cc neither do my parents.
Posted:
Fri Nov 09, 2007 7:34 pm
by a_link_to_the_past
you actually don't need a credit card to purchase account time for Xenimus... You can mail EJ a check and a note describing the account you wish to place time on... I've done it a couple times when I didn't have a debit card... it may take a few for the time to get put on the account but it is the alternative to a credit card purchase.
Posted:
Sat Nov 10, 2007 11:44 pm
by ziggman
i have sent ej cash, it takes about a week.
Posted:
Sun Nov 11, 2007 2:06 am
by daekighn
Im 13 years old... i have no checks lmao!
Posted:
Mon Nov 12, 2007 7:56 am
by a_link_to_the_past
But pretty sure your parents aren't 13 years old... and I would think that your parents would write a check if you ask them nicely
@ Zigg - emm usually it depends on the day you mail off the money... it usually only took about 2 - 3 days for the money to make it to him... and it generally only took him about 1 - 2 days to put time on my account... maybe I was just lucky who knows..
I would probably still play Xenimus... its not too bad of a game if you don't mind vicious repetition.... but I work the night shift at Wal-Mart (lame) and what time isn't spend sleeping I spend it with my girlfriend.... and I would much rather spend time with her than on Xenimus.
.
Posted:
Wed Nov 14, 2007 8:34 am
by daekighn
BUT my parents have no checks or anything so i have nothing.... oh well i found a different game that i like to play. Its called Archlord
Posted:
Fri Nov 16, 2007 12:28 am
by ziggman
lmfao
Kid.... READ.... YOU CAN SEND EJ CASH (usa currency)
ALTHOUGH ITS BETTER TO DONATE TO PROJECT VOLURCIS:D


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Hello, welcome.
In this short video, let us look at the derivation of the estimators of the parameters of a uniform distribution using the method of moment.
Given the uniform distribution f of x equal to one by b minus a, for random variable x between the values of a and b.
Given this uniform distribution, we want to derive estimators for the parameters, a and b using the method of moments.
To start with, we derive the first two moments based on the PDF.
Now we do that with the second moment.
Now we note the expressions for the sample moment, that is the sample first moment is xbar, that is the mean.
Equation four.
For simplicity, we use this symbol.
Using these two equations, that is, solving the systems of the two equations, we can solve our a and b.
If you look at this equation, this is basically a quadratic equation in b.
That is the sample second moment.
This should be positive.
The estimator of b is given by xbar plus...
This four can be taken outside, and it cancels with two.
We are left with three times the sample second moment minus three times the square the mean.
We have mean squared.
Therefore, this is the estimator of b.
While using this relation, that is, a equal to xbar minus b, we also have an estimator, a.
Therefore, our uniform distribution variable should be random.
The estimators of the unknown parameters a and b are given by combinations of mean and standard deviation of the sample.
Therefore, the estimators of a and b are basically linear combinations of sample mean and sample standard deviation.
Thanks for watching.


public interface TransactionHandler
| Modifier and Type | Method and Description |
|---|---|
default void |
checkpointFinish(Database db,
Object obj)
Wait for a concurrent checkpoint to finish.
|
default Object |
checkpointStart(Database db)
Called to durably write all changes.
|
void |
redo(Database db,
Transaction txn,
byte[] message)
Non-transactionally apply an idempotent redo operation.
|
void |
redo(Database db,
Transaction txn,
byte[] message,
long indexId,
byte[] key)
Non-transactionally apply an idempotent redo operation.
|
default void |
setCheckpointLock(Database db,
Lock lock)
Called once when the database is opened, providing a shared lock which is later acquired
exclusively by checkpoints.
|
void |
undo(Database db,
byte[] message)
Non-transactionally apply an idempotent undo operation.
|
void redo(Database db, Transaction txn, byte[] message) throws IOException
txn
- transaction the operation applies to; can be modifiedmessage
- message originally provided to Transaction.customRedo(byte[], long, byte[])
IOException
void redo(Database db, Transaction txn, byte[] message, long indexId, byte[] key) throws IOException
txn
- transaction the operation applies to; can be modifiedmessage
- message originally provided to Transaction.customRedo(byte[], long, byte[])
indexId
- non-zero index for lock acquisitionkey
- non-null key which has been locked exclusivelyIOException
void undo(Database db, byte[] message) throws IOException
message
- message originally provided to Transaction.customUndo(byte[])
IOException
default void setCheckpointLock(Database db, Lock lock)
checkpointStart
is called. Simply hold the lock when making transactional changes, and then no additional
locking is required when checkpointStart
is called.default Object checkpointStart(Database db) throws IOException
checkpointFinish
IOException
default void checkpointFinish(Database db, Object obj) throws IOException
obj
- optional object passed from checkpointStart
IOException
Copyright © 2011–2016 Cojen. All rights reserved.


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We concentrate on prevention, diagnosis, and treatment of adult diseases. Services are provided at our out-patient facility. We also utilize the Beaumont Hospital System and are affiliated with long-term care facilities and skilled nursing homes.
We offer nonsurgical weight loss programs utilizing FDA-approved medications, behavior modification, exercise, and changes in lifestyle for overweight and obese patients.
We take pride in offering comprehensive home-based medical care. Our focus in doing so is to maintain your health and independence, and to avoid the need for hospitalization, rehospitalization, or admission into long-term care facilities/nursing homes/skilled rehabilitation centers.


Module: filters
¶
Apply hysteresis thresholding to |
|
|
Filter an image with the Frangi vesselness filter. |
|
Return real and imaginary responses to Gabor filter. |
|
Return complex 2D Gabor filter kernel. |
|
Multi-dimensional Gaussian filter. |
|
Filter an image with the Hybrid Hessian filter. |
|
Apply the filter in reverse to the given data. |
|
Find the edges of an image using the Laplace operator. |
|
Return local median of an image. |
|
Filter an image with the Meijering neuriteness filter. |
|
Find the edge magnitude using the Prewitt transform. |
|
Find the horizontal edges of an image using the Prewitt transform. |
|
Find the vertical edges of an image using the Prewitt transform. |
|
Return an image of the same shape where each pixel is the index of the pixel value in the ascending order of the unique values of |
|
Find the edge magnitude using Roberts’ cross operator. |
|
Find the cross edges of an image using the Roberts’ Cross operator. |
|
Find the cross edges of an image using Roberts’ cross operator. |
|
Filter an image with the Sato tubeness filter. |
|
Find the edge magnitude using the Scharr transform. |
|
Find the horizontal edges of an image using the Scharr transform. |
|
Find the vertical edges of an image using the Scharr transform. |
|
Find the edge magnitude using the Sobel transform. |
|
Find the horizontal edges of an image using the Sobel transform. |
|
Find the vertical edges of an image using the Sobel transform. |
|
Return threshold value(s) based on ISODATA method. |
|
Compute threshold value by Li’s iterative Minimum Cross Entropy method. |
|
Compute a threshold mask image based on local pixel neighborhood. |
Return threshold value based on the mean of grayscale values. |
|
|
Return threshold value based on minimum method. |
|
Generate classes-1 threshold values to divide gray levels in image. |
|
Applies Niblack local threshold to an array. |
|
Return threshold value based on Otsu’s method. |
|
Applies Sauvola local threshold to an array. |
|
Return threshold value based on the triangle algorithm. |
|
Return threshold value based on Yen’s method. |
|
Returns a figure comparing the outputs of different thresholding methods. |
|
Unsharp masking filter. |
|
Minimum Mean Square Error (Wiener) inverse filter. |
Linear Position-Invariant Filter (2-dimensional) |
|
apply_hysteresis_threshold¶
-
skimage.filters.
apply_hysteresis_threshold
(image, low, high)[source]¶ Apply hysteresis thresholding to
image
.This algorithm finds regions where
image
is greater thanhigh
ORimage
is greater thanlow
and that region is connected to a region greater thanhigh
.- Parameters
- imagearray, shape (M,[ N, …, P])
Grayscale input image.
- lowfloat, or array of same shape as
image
Lower threshold.
- highfloat, or array of same shape as
image
Higher threshold.
- Returns
- thresholdedarray of bool, same shape as
image
Array in which
True
indicates the locations whereimage
was above the hysteresis threshold.
- thresholdedarray of bool, same shape as
References
- 1
J. Canny. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1986; vol. 8, pp.679-698. DOI:10.1109/TPAMI.1986.4767851
Examples
>>> image = np.array([1, 2, 3, 2, 1, 2, 1, 3, 2]) >>> apply_hysteresis_threshold(image, 1.5, 2.5).astype(int) array([0, 1, 1, 1, 0, 0, 0, 1, 1])
Examples using skimage.filters.apply_hysteresis_threshold
¶
frangi¶
-
skimage.filters.
frangi
(image, sigmas=range(1, 10, 2), scale_range=None, scale_step=None, beta1=None, beta2=None, alpha=0.5, beta=0.5, gamma=15, black_ridges=True)[source]¶ Filter an image with the Frangi vesselness filter.
This filter can be used to detect continuous ridges, e.g. vessels, wrinkles, rivers. It can be used to calculate the fraction of the whole image containing such objects.
Defined only for 2-D and 3-D images. Calculates the eigenvectors of the Hessian to compute the similarity of an image region to vessels, according to the method described in [1].
- Parameters
- image(N, M[, P]) ndarray
Array with input image data.
- sigmasiterable of floats, optional
Sigmas used as scales of filter, i.e., np.arange(scale_range[0], scale_range[1], scale_step)
- scale_range2-tuple of floats, optional
The range of sigmas used.
- scale_stepfloat, optional
Step size between sigmas.
- alphafloat, optional
Frangi correction constant that adjusts the filter’s sensitivity to deviation from a plate-like structure.
- beta = beta1float, optional
Frangi correction constant that adjusts the filter’s sensitivity to deviation from a blob-like structure.
- gamma = beta2float, optional
Frangi correction constant that adjusts the filter’s sensitivity to areas of high variance/texture/structure.
- black_ridgesboolean, optional
When True (the default), the filter detects black ridges; when False, it detects white ridges.
- Returns
- out(N, M[, P]) ndarray
Filtered image (maximum of pixels across all scales).
Notes
Written by Marc Schrijver, November 2001 Re-Written by D. J. Kroon, University of Twente, May 2009, [2] Adoption of 3D version from D. G. Ellis, Januar 20017, [3]
References
- 1
Frangi, A. F., Niessen, W. J., Vincken, K. L., & Viergever, M. A. (1998,). Multiscale vessel enhancement filtering. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 130-137). Springer Berlin Heidelberg. DOI:10.1007/BFb0056195
- 2
Kroon, D. J.: Hessian based Frangi vesselness filter.
- 3
Ellis, D. G.: https://github.com/ellisdg/frangi3d/tree/master/frangi
Examples using skimage.filters.frangi
¶
gabor¶
-
skimage.filters.
gabor
(image, frequency, theta=0, bandwidth=1, sigma_x=None, sigma_y=None, n_stds=3, offset=0, mode='reflect', cval=0)[source]¶ Return real and imaginary responses to Gabor filter.
The real and imaginary parts of the Gabor filter kernel are applied to the image and the response is returned as a pair of arrays.
 Frequency and orientation representations of the Gabor filter are similar to those of the human visual system. Gabor filter banks are commonly used in computer vision and image processing. They are especially suitable for edge detection and texture classification.
- Parameters
- image2-D array
Input image.
- frequencyfloat
Spatial frequency of the harmonic function. Specified in pixels.
- thetafloat, optional
Orientation in radians. If 0, the harmonic is in the x-direction.
- bandwidthfloat, optional
The bandwidth captured by the filter. For fixed bandwidth,
sigma_x
andsigma_y
will decrease with increasing frequency. This value is ignored ifsigma_x
andsigma_y
are set by the user.- sigma_x, sigma_yfloat, optional
Standard deviation in x- and y-directions. These directions apply to the kernel before rotation. If theta = pi/2, then the kernel is rotated 90 degrees so that
sigma_x
controls the vertical direction.- n_stdsscalar, optional
The linear size of the kernel is n_stds (3 by default) standard deviations.
- offsetfloat, optional
Phase offset of harmonic function in radians.
- mode{‘constant’, ‘nearest’, ‘reflect’, ‘mirror’, ‘wrap’}, optional
Mode used to convolve image with a kernel, passed to ndi.convolve
- cvalscalar, optional
Value to fill past edges of input if
mode
of convolution is ‘constant’. The parameter is passed to ndi.convolve.
- Returns
- real, imagarrays
Filtered images using the real and imaginary parts of the Gabor filter kernel. Images are of the same dimensions as the input one.
References
Examples
>>> from skimage.filters import gabor >>> from skimage import data, io >>> from matplotlib import pyplot as plt # doctest: +SKIP
>>> image = data.coins() >>> # detecting edges in a coin image >>> filt_real, filt_imag = gabor(image, frequency=0.6) >>> plt.figure() # doctest: +SKIP >>> io.imshow(filt_real) # doctest: +SKIP >>> io.show() # doctest: +SKIP
>>> # less sensitivity to finer details with the lower frequency kernel >>> filt_real, filt_imag = gabor(image, frequency=0.1) >>> plt.figure() # doctest: +SKIP >>> io.imshow(filt_real) # doctest: +SKIP >>> io.show() # doctest: +SKIP
gabor_kernel¶
-
skimage.filters.
gabor_kernel
(frequency, theta=0, bandwidth=1, sigma_x=None, sigma_y=None, n_stds=3, offset=0)[source]¶ Return complex 2D Gabor filter kernel.
Gabor kernel is a Gaussian kernel modulated by a complex harmonic function. Harmonic function consists of an imaginary sine function and a real cosine function. Spatial frequency is inversely proportional to the wavelength of the harmonic and to the standard deviation of a Gaussian kernel. The bandwidth is also inversely proportional to the standard deviation.
- Parameters
- frequencyfloat
Spatial frequency of the harmonic function. Specified in pixels.
- thetafloat, optional
Orientation in radians. If 0, the harmonic is in the x-direction.
- bandwidthfloat, optional
The bandwidth captured by the filter. For fixed bandwidth,
sigma_x
andsigma_y
will decrease with increasing frequency. This value is ignored ifsigma_x
andsigma_y
are set by the user.- sigma_x, sigma_yfloat, optional
Standard deviation in x- and y-directions. These directions apply to the kernel before rotation. If theta = pi/2, then the kernel is rotated 90 degrees so that
sigma_x
controls the vertical direction.- n_stdsscalar, optional
The linear size of the kernel is n_stds (3 by default) standard deviations
- offsetfloat, optional
Phase offset of harmonic function in radians.
- Returns
- gcomplex array
Complex filter kernel.
References
Examples
>>> from skimage.filters import gabor_kernel >>> from skimage import io >>> from matplotlib import pyplot as plt # doctest: +SKIP
>>> gk = gabor_kernel(frequency=0.2) >>> plt.figure() # doctest: +SKIP >>> io.imshow(gk.real) # doctest: +SKIP >>> io.show() # doctest: +SKIP
>>> # more ripples (equivalent to increasing the size of the >>> # Gaussian spread) >>> gk = gabor_kernel(frequency=0.2, bandwidth=0.1) >>> plt.figure() # doctest: +SKIP >>> io.imshow(gk.real) # doctest: +SKIP >>> io.show() # doctest: +SKIP
Examples using skimage.filters.gabor_kernel
¶
gaussian¶
-
skimage.filters.
gaussian
(image, sigma=1, output=None, mode='nearest', cval=0, multichannel=None, preserve_range=False, truncate=4.0)[source]¶ Multi-dimensional Gaussian filter.
- Parameters
- imagearray-like
Input image (grayscale or color) to filter.
- sigmascalar or sequence of scalars, optional
Standard deviation for Gaussian kernel. The standard deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes.
- outputarray, optional
The
output
parameter passes an array in which to store the filter output.- mode{‘reflect’, ‘constant’, ‘nearest’, ‘mirror’, ‘wrap’}, optional
The
mode
parameter determines how the array borders are handled, wherecval
is the value when mode is equal to ‘constant’. Default is ‘nearest’.- cvalscalar, optional
Value to fill past edges of input if
mode
is ‘constant’. Default is 0.0- multichannelbool, optional (default: None)
Whether the last axis of the image is to be interpreted as multiple channels. If True, each channel is filtered separately (channels are not mixed together). Only 3 channels are supported. If
None
, the function will attempt to guess this, and raise a warning if ambiguous, when the array has shape (M, N, 3).- preserve_rangebool, optional
Whether to keep the original range of values. Otherwise, the input image is converted according to the conventions of
img_as_float
. Also see https://scikit-image.org/docs/dev/user_guide/data_types.html- truncatefloat, optional
Truncate the filter at this many standard deviations.
- Returns
- filtered_imagendarray
the filtered array
Notes
This function is a wrapper around
scipy.ndi.gaussian_filter()
.Integer arrays are converted to float.
The multi-dimensional filter is implemented as a sequence of one-dimensional convolution filters. The intermediate arrays are stored in the same data type as the output. Therefore, for output types with a limited precision, the results may be imprecise because intermediate results may be stored with insufficient precision.
Examples
>>> a = np.zeros((3, 3)) >>> a[1, 1] = 1 >>> a array([[0., 0., 0.], [0., 1., 0.], [0., 0., 0.]]) >>> gaussian(a, sigma=0.4) # mild smoothing array([[0.00163116, 0.03712502, 0.00163116], [0.03712502, 0.84496158, 0.03712502], [0.00163116, 0.03712502, 0.00163116]]) >>> gaussian(a, sigma=1) # more smoothing array([[0.05855018, 0.09653293, 0.05855018], [0.09653293, 0.15915589, 0.09653293], [0.05855018, 0.09653293, 0.05855018]]) >>> # Several modes are possible for handling boundaries >>> gaussian(a, sigma=1, mode='reflect') array([[0.08767308, 0.12075024, 0.08767308], [0.12075024, 0.16630671, 0.12075024], [0.08767308, 0.12075024, 0.08767308]]) >>> # For RGB images, each is filtered separately >>> from skimage.data import astronaut >>> image = astronaut() >>> filtered_img = gaussian(image, sigma=1, multichannel=True)
Examples using skimage.filters.gaussian
¶
hessian¶
-
skimage.filters.
hessian
(image, sigmas=range(1, 10, 2), scale_range=None, scale_step=None, beta1=None, beta2=None, alpha=0.5, beta=0.5, gamma=15, black_ridges=True)[source]¶ Filter an image with the Hybrid Hessian filter.
This filter can be used to detect continuous edges, e.g. vessels, wrinkles, rivers. It can be used to calculate the fraction of the whole image containing such objects.
Defined only for 2-D and 3-D images. Almost equal to Frangi filter, but uses alternative method of smoothing. Refer to [1] to find the differences between Frangi and Hessian filters.
- Parameters
- image(N, M[, P]) ndarray
Array with input image data.
- sigmasiterable of floats, optional
Sigmas used as scales of filter, i.e., np.arange(scale_range[0], scale_range[1], scale_step)
- scale_range2-tuple of floats, optional
The range of sigmas used.
- scale_stepfloat, optional
Step size between sigmas.
- beta = beta1float, optional
Frangi correction constant that adjusts the filter’s sensitivity to deviation from a blob-like structure.
- gamma = beta2float, optional
Frangi correction constant that adjusts the filter’s sensitivity to areas of high variance/texture/structure.
- black_ridgesboolean, optional
When True (the default), the filter detects black ridges; when False, it detects white ridges.
- Returns
- out(N, M[, P]) ndarray
Filtered image (maximum of pixels across all scales).
Notes
Written by Marc Schrijver (November 2001) Re-Written by D. J. Kroon University of Twente (May 2009) [2]
References
- 1
Ng, C. C., Yap, M. H., Costen, N., & Li, B. (2014,). Automatic wrinkle detection using hybrid Hessian filter. In Asian Conference on Computer Vision (pp. 609-622). Springer International Publishing. DOI:10.1007/978-3-319-16811-1_40
- 2
Kroon, D. J.: Hessian based Frangi vesselness filter.
Examples using skimage.filters.hessian
¶
inverse¶
-
skimage.filters.
inverse
(data, impulse_response=None, filter_params={}, max_gain=2, predefined_filter=None)[source]¶ Apply the filter in reverse to the given data.
- Parameters
- data(M,N) ndarray
Input data.
- impulse_responsecallable f(r, c, **filter_params)
Impulse response of the filter. See LPIFilter2D.__init__.
- filter_paramsdict
Additional keyword parameters to the impulse_response function.
- max_gainfloat
Limit the filter gain. Often, the filter contains zeros, which would cause the inverse filter to have infinite gain. High gain causes amplification of artefacts, so a conservative limit is recommended.
- Other Parameters
- predefined_filterLPIFilter2D
If you need to apply the same filter multiple times over different images, construct the LPIFilter2D and specify it here.
laplace¶
-
skimage.filters.
laplace
(image, ksize=3, mask=None)[source]¶ Find the edges of an image using the Laplace operator.
- Parameters
- imagendarray
Image to process.
- ksizeint, optional
Define the size of the discrete Laplacian operator such that it will have a size of (ksize,) * image.ndim.
- maskndarray, optional
An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result.
- Returns
- outputndarray
The Laplace edge map.
Notes
The Laplacian operator is generated using the function skimage.restoration.uft.laplacian().
median¶
-
skimage.filters.
median
(image, selem=None, out=None, mask=None, shift_x=False, shift_y=False, mode='nearest', cval=0.0, behavior='ndimage')[source]¶ Return local median of an image.
- Parameters
- imagearray-like
Input image.
- selemndarray, optional
If
behavior=='rank'
,selem
is a 2-D array of 1’s and 0’s. Ifbehavior=='ndimage'
,selem
is a N-D array of 1’s and 0’s with the same number of dimension thanimage
. If None,selem
will be a N-D array with 3 elements for each dimension (e.g., vector, square, cube, etc.)- outndarray, (same dtype as image), optional
If None, a new array is allocated.
- maskndarray, optional
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). Only valid when
behavior='rank'
Deprecated since version 0.16:
mask
is deprecated in 0.16 and will be removed 0.17.- shift_x, shift_yint, optional
Offset added to the structuring element center point. Shift is bounded by the structuring element sizes (center must be inside the given structuring element). Only valid when
behavior='rank'
.Deprecated since version 0.16:
shift_x
andshift_y
are deprecated in 0.16 and will be removed in 0.17.- mode{‘reflect’, ‘constant’, ‘nearest’, ‘mirror’,’‘wrap’}, optional
The mode parameter determines how the array borders are handled, where
cval
is the value when mode is equal to ‘constant’. Default is ‘nearest’.New in version 0.15:
mode
is used whenbehavior='ndimage'
.- cvalscalar, optional
Value to fill past edges of input if mode is ‘constant’. Default is 0.0
New in version 0.15:
cval
was added in 0.15 is used whenbehavior='ndimage'
.- behavior{‘ndimage’, ‘rank’}, optional
Either to use the old behavior (i.e., < 0.15) or the new behavior. The old behavior will call the
skimage.filters.rank.median()
. The new behavior will call thescipy.ndimage.median_filter()
. Default is ‘rank’.New in version 0.15:
behavior
is introduced in 0.15Changed in version 0.16: Default
behavior
has been changed from ‘rank’ to ‘ndimage’
- Returns
- out2-D array (same dtype as input image)
Output image.
See also
skimage.filters.rank.median
Rank-based implementation of the median filtering offering more flexibility with additional parameters but dedicated for unsigned integer images.
Examples
>>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters import median >>> img = data.camera() >>> med = median(img, disk(5))
meijering¶
-
skimage.filters.
meijering
(image, sigmas=range(1, 10, 2), alpha=None, black_ridges=True)[source]¶ Filter an image with the Meijering neuriteness filter.
This filter can be used to detect continuous ridges, e.g. neurites, wrinkles, rivers. It can be used to calculate the fraction of the whole image containing such objects.
Calculates the eigenvectors of the Hessian to compute the similarity of an image region to neurites, according to the method described in [1].
- Parameters
- image(N, M[, …, P]) ndarray
Array with input image data.
- sigmasiterable of floats, optional
Sigmas used as scales of filter
- alphafloat, optional
Frangi correction constant that adjusts the filter’s sensitivity to deviation from a plate-like structure.
- black_ridgesboolean, optional
When True (the default), the filter detects black ridges; when False, it detects white ridges.
- Returns
- out(N, M[, …, P]) ndarray
Filtered image (maximum of pixels across all scales).
References
- 1
Meijering, E., Jacob, M., Sarria, J. C., Steiner, P., Hirling, H., Unser, M. (2004). Design and validation of a tool for neurite tracing and analysis in fluorescence microscopy images. Cytometry Part A, 58(2), 167-176. DOI:10.1002/cyto.a.20022
Examples using skimage.filters.meijering
¶
prewitt¶
-
skimage.filters.
prewitt
(image, mask=None)[source]¶ Find the edge magnitude using the Prewitt transform.
- Parameters
- image2-D array
Image to process.
- mask2-D array, optional
An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result.
- Returns
- output2-D array
The Prewitt edge map.
Notes
Return the square root of the sum of squares of the horizontal and vertical Prewitt transforms. The edge magnitude depends slightly on edge directions, since the approximation of the gradient operator by the Prewitt operator is not completely rotation invariant. For a better rotation invariance, the Scharr operator should be used. The Sobel operator has a better rotation invariance than the Prewitt operator, but a worse rotation invariance than the Scharr operator.
Examples
>>> from skimage import data >>> camera = data.camera() >>> from skimage import filters >>> edges = filters.prewitt(camera)
Examples using skimage.filters.prewitt
¶
prewitt_h¶
-
skimage.filters.
prewitt_h
(image, mask=None)[source]¶ Find the horizontal edges of an image using the Prewitt transform.
- Parameters
- image2-D array
Image to process.
- mask2-D array, optional
An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result.
- Returns
- output2-D array
The Prewitt edge map.
Notes
We use the following kernel:
1 1 1 0 0 0 -1 -1 -1
Examples using skimage.filters.prewitt_h
¶
prewitt_v¶
-
skimage.filters.
prewitt_v
(image, mask=None)[source]¶ Find the vertical edges of an image using the Prewitt transform.
- Parameters
- image2-D array
Image to process.
- mask2-D array, optional
An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result.
- Returns
- output2-D array
The Prewitt edge map.
Notes
We use the following kernel:
1 0 -1 1 0 -1 1 0 -1
Examples using skimage.filters.prewitt_v
¶
rank_order¶
-
skimage.filters.
rank_order
(image)[source]¶ Return an image of the same shape where each pixel is the index of the pixel value in the ascending order of the unique values of
image
, aka the rank-order value.- Parameters
- imagendarray
- Returns
- labelsndarray of type np.uint32, of shape image.shape
New array where each pixel has the rank-order value of the corresponding pixel in
image
. Pixel values are between 0 and n - 1, where n is the number of distinct unique values inimage
.- original_values1-D ndarray
Unique original values of
image
Examples
>>> a = np.array([[1, 4, 5], [4, 4, 1], [5, 1, 1]]) >>> a array([[1, 4, 5], [4, 4, 1], [5, 1, 1]]) >>> rank_order(a) (array([[0, 1, 2], [1, 1, 0], [2, 0, 0]], dtype=uint32), array([1, 4, 5])) >>> b = np.array([-1., 2.5, 3.1, 2.5]) >>> rank_order(b) (array([0, 1, 2, 1], dtype=uint32), array([-1. , 2.5, 3.1]))
roberts¶
-
skimage.filters.
roberts
(image, mask=None)[source]¶ Find the edge magnitude using Roberts’ cross operator.
- Parameters
- image2-D array
Image to process.
- mask2-D array, optional
An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result.
- Returns
- output2-D array
The Roberts’ Cross edge map.
Examples
>>> from skimage import data >>> camera = data.camera() >>> from skimage import filters >>> edges = filters.roberts(camera)
Examples using skimage.filters.roberts
¶
roberts_neg_diag¶
-
skimage.filters.
roberts_neg_diag
(image, mask=None)[source]¶ Find the cross edges of an image using the Roberts’ Cross operator.
The kernel is applied to the input image to produce separate measurements of the gradient component one orientation.
- Parameters
- image2-D array
Image to process.
- mask2-D array, optional
An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result.
- Returns
- output2-D array
The Robert’s edge map.
Notes
We use the following kernel:
0 1 -1 0
roberts_pos_diag¶
-
skimage.filters.
roberts_pos_diag
(image, mask=None)[source]¶ Find the cross edges of an image using Roberts’ cross operator.
The kernel is applied to the input image to produce separate measurements of the gradient component one orientation.
- Parameters
- image2-D array
Image to process.
- mask2-D array, optional
An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result.
- Returns
- output2-D array
The Robert’s edge map.
Notes
We use the following kernel:
1 0 0 -1
sato¶
-
skimage.filters.
sato
(image, sigmas=range(1, 10, 2), black_ridges=True)[source]¶ Filter an image with the Sato tubeness filter.
This filter can be used to detect continuous ridges, e.g. tubes, wrinkles, rivers. It can be used to calculate the fraction of the whole image containing such objects.
Defined only for 2-D and 3-D images. Calculates the eigenvectors of the Hessian to compute the similarity of an image region to tubes, according to the method described in [1].
- Parameters
- image(N, M[, P]) ndarray
Array with input image data.
- sigmasiterable of floats, optional
Sigmas used as scales of filter.
- black_ridgesboolean, optional
When True (the default), the filter detects black ridges; when False, it detects white ridges.
- Returns
- out(N, M[, P]) ndarray
Filtered image (maximum of pixels across all scales).
References
- 1
Sato, Y., Nakajima, S., Shiraga, N., Atsumi, H., Yoshida, S., Koller, T., …, Kikinis, R. (1998). Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. Medical image analysis, 2(2), 143-168. DOI:10.1016/S1361-8415(98)80009-1
Examples using skimage.filters.sato
¶
scharr¶
-
skimage.filters.
scharr
(image, mask=None)[source]¶ Find the edge magnitude using the Scharr transform.
- Parameters
- image2-D array
Image to process.
- mask2-D array, optional
An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result.
- Returns
- output2-D array
The Scharr edge map.
Notes
Take the square root of the sum of the squares of the horizontal and vertical Scharrs to get a magnitude that is somewhat insensitive to direction. The Scharr operator has a better rotation invariance than other edge filters such as the Sobel or the Prewitt operators.
References
- 1
D. Kroon, 2009, Short Paper University Twente, Numerical Optimization of Kernel Based Image Derivatives.
- 2
https://en.wikipedia.org/wiki/Sobel_operator#Alternative_operators
Examples
>>> from skimage import data >>> camera = data.camera() >>> from skimage import filters >>> edges = filters.scharr(camera)
Examples using skimage.filters.scharr
¶
scharr_h¶
-
skimage.filters.
scharr_h
(image, mask=None)[source]¶ Find the horizontal edges of an image using the Scharr transform.
- Parameters
- image2-D array
Image to process.
- mask2-D array, optional
An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result.
- Returns
- output2-D array
The Scharr edge map.
Notes
We use the following kernel:
3 10 3 0 0 0 -3 -10 -3
References
- 1
D. Kroon, 2009, Short Paper University Twente, Numerical Optimization of Kernel Based Image Derivatives.
Examples using skimage.filters.scharr_h
¶
scharr_v¶
-
skimage.filters.
scharr_v
(image, mask=None)[source]¶ Find the vertical edges of an image using the Scharr transform.
- Parameters
- image2-D array
Image to process
- mask2-D array, optional
An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result.
- Returns
- output2-D array
The Scharr edge map.
Notes
We use the following kernel:
3 0 -3 10 0 -10 3 0 -3
References
- 1
D. Kroon, 2009, Short Paper University Twente, Numerical Optimization of Kernel Based Image Derivatives.
Examples using skimage.filters.scharr_v
¶
sobel¶
-
skimage.filters.
sobel
(image, mask=None)[source]¶ Find the edge magnitude using the Sobel transform.
- Parameters
- image2-D array
Image to process.
- mask2-D array, optional
An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result.
- Returns
- output2-D array
The Sobel edge map.
Notes
Take the square root of the sum of the squares of the horizontal and vertical Sobels to get a magnitude that’s somewhat insensitive to direction.
The 3x3 convolution kernel used in the horizontal and vertical Sobels is an approximation of the gradient of the image (with some slight blurring since 9 pixels are used to compute the gradient at a given pixel). As an approximation of the gradient, the Sobel operator is not completely rotation-invariant. The Scharr operator should be used for a better rotation invariance.
Note that
scipy.ndimage.sobel
returns a directional Sobel which has to be further processed to perform edge detection.Examples
>>> from skimage import data >>> camera = data.camera() >>> from skimage import filters >>> edges = filters.sobel(camera)
sobel_h¶
-
skimage.filters.
sobel_h
(image, mask=None)[source]¶ Find the horizontal edges of an image using the Sobel transform.
- Parameters
- image2-D array
Image to process.
- mask2-D array, optional
An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result.
- Returns
- output2-D array
The Sobel edge map.
Notes
We use the following kernel:
1 2 1 0 0 0 -1 -2 -1
Examples using skimage.filters.sobel_h
¶
sobel_v¶
-
skimage.filters.
sobel_v
(image, mask=None)[source]¶ Find the vertical edges of an image using the Sobel transform.
- Parameters
- image2-D array
Image to process.
- mask2-D array, optional
An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result.
- Returns
- output2-D array
The Sobel edge map.
Notes
We use the following kernel:
1 0 -1 2 0 -2 1 0 -1
Examples using skimage.filters.sobel_v
¶
threshold_isodata¶
-
skimage.filters.
threshold_isodata
(image, nbins=256, return_all=False)[source]¶ Return threshold value(s) based on ISODATA method.
Histogram-based threshold, known as Ridler-Calvard method or inter-means. Threshold values returned satisfy the following equality:
threshold = (image[image <= threshold].mean() + image[image > threshold].mean()) / 2.0
That is, returned thresholds are intensities that separate the image into two groups of pixels, where the threshold intensity is midway between the mean intensities of these groups.
For integer images, the above equality holds to within one; for floating- point images, the equality holds to within the histogram bin-width.
- Parameters
- image(N, M) ndarray
Input image.
- nbinsint, optional
Number of bins used to calculate histogram. This value is ignored for integer arrays.
- return_all: bool, optional
If False (default), return only the lowest threshold that satisfies the above equality. If True, return all valid thresholds.
- Returns
- thresholdfloat or int or array
Threshold value(s).
References
- 1
Ridler, TW & Calvard, S (1978), “Picture thresholding using an iterative selection method” IEEE Transactions on Systems, Man and Cybernetics 8: 630-632, DOI:10.1109/TSMC.1978.4310039
- 2
Sezgin M. and Sankur B. (2004) “Survey over Image Thresholding Techniques and Quantitative Performance Evaluation” Journal of Electronic Imaging, 13(1): 146-165, http://www.busim.ee.boun.edu.tr/~sankur/SankurFolder/Threshold_survey.pdf DOI:10.1117/1.1631315
- 3
ImageJ AutoThresholder code, http://fiji.sc/wiki/index.php/Auto_Threshold
Examples
>>> from skimage.data import coins >>> image = coins() >>> thresh = threshold_isodata(image) >>> binary = image > thresh
threshold_li¶
-
skimage.filters.
threshold_li
(image, *, tolerance=None, initial_guess=None, iter_callback=None)[source]¶ Compute threshold value by Li’s iterative Minimum Cross Entropy method.
- Parameters
- imagendarray
Input image.
- tolerancefloat, optional
Finish the computation when the change in the threshold in an iteration is less than this value. By default, this is half the smallest difference between intensity values in
image
.- initial_guessfloat or Callable[[array[float]], float], optional
Li’s iterative method uses gradient descent to find the optimal threshold. If the image intensity histogram contains more than two modes (peaks), the gradient descent could get stuck in a local optimum. An initial guess for the iteration can help the algorithm find the globally-optimal threshold. A float value defines a specific start point, while a callable should take in an array of image intensities and return a float value. Example valid callables include
numpy.mean
(default),lambda arr: numpy.quantile(arr, 0.95)
, or evenskimage.filters.threshold_otsu()
.- iter_callbackCallable[[float], Any], optional
A function that will be called on the threshold at every iteration of the algorithm.
- Returns
- thresholdfloat
Upper threshold value. All pixels with an intensity higher than this value are assumed to be foreground.
References
- 1
Li C.H. and Lee C.K. (1993) “Minimum Cross Entropy Thresholding” Pattern Recognition, 26(4): 617-625 DOI:10.1016/0031-3203(93)90115-D
- 2
Li C.H. and Tam P.K.S. (1998) “An Iterative Algorithm for Minimum Cross Entropy Thresholding” Pattern Recognition Letters, 18(8): 771-776 DOI:10.1016/S0167-8655(98)00057-9
- 3
Sezgin M. and Sankur B. (2004) “Survey over Image Thresholding Techniques and Quantitative Performance Evaluation” Journal of Electronic Imaging, 13(1): 146-165 DOI:10.1117/1.1631315
- 4
ImageJ AutoThresholder code, http://fiji.sc/wiki/index.php/Auto_Threshold
Examples
>>> from skimage.data import camera >>> image = camera() >>> thresh = threshold_li(image) >>> binary = image > thresh
Examples using skimage.filters.threshold_li
¶
threshold_local¶
-
skimage.filters.
threshold_local
(image, block_size, method='gaussian', offset=0, mode='reflect', param=None, cval=0)[source]¶ Compute a threshold mask image based on local pixel neighborhood.
Also known as adaptive or dynamic thresholding. The threshold value is the weighted mean for the local neighborhood of a pixel subtracted by a constant. Alternatively the threshold can be determined dynamically by a given function, using the ‘generic’ method.
- Parameters
- image(N, M) ndarray
Input image.
- block_sizeint
Odd size of pixel neighborhood which is used to calculate the threshold value (e.g. 3, 5, 7, …, 21, …).
- method{‘generic’, ‘gaussian’, ‘mean’, ‘median’}, optional
Method used to determine adaptive threshold for local neighbourhood in weighted mean image.
‘generic’: use custom function (see
param
parameter)‘gaussian’: apply gaussian filter (see
param
parameter for custom sigma value)‘mean’: apply arithmetic mean filter
‘median’: apply median rank filter
By default the ‘gaussian’ method is used.
- offsetfloat, optional
Constant subtracted from weighted mean of neighborhood to calculate the local threshold value. Default offset is 0.
- mode{‘reflect’, ‘constant’, ‘nearest’, ‘mirror’, ‘wrap’}, optional
The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to ‘constant’. Default is ‘reflect’.
- param{int, function}, optional
Either specify sigma for ‘gaussian’ method or function object for ‘generic’ method. This functions takes the flat array of local neighbourhood as a single argument and returns the calculated threshold for the centre pixel.
- cvalfloat, optional
Value to fill past edges of input if mode is ‘constant’.
- Returns
- threshold(N, M) ndarray
Threshold image. All pixels in the input image higher than the corresponding pixel in the threshold image are considered foreground.
References
Examples
>>> from skimage.data import camera >>> image = camera()[:50, :50] >>> binary_image1 = image > threshold_local(image, 15, 'mean') >>> func = lambda arr: arr.mean() >>> binary_image2 = image > threshold_local(image, 15, 'generic', ... param=func)
Examples using skimage.filters.threshold_local
¶
threshold_mean¶
-
skimage.filters.
threshold_mean
(image)[source]¶ Return threshold value based on the mean of grayscale values.
- Parameters
- image(N, M[, …, P]) ndarray
Grayscale input image.
- Returns
- thresholdfloat
Upper threshold value. All pixels with an intensity higher than this value are assumed to be foreground.
References
- 1
C. A. Glasbey, “An analysis of histogram-based thresholding algorithms,” CVGIP: Graphical Models and Image Processing, vol. 55, pp. 532-537, 1993. DOI:10.1006/cgip.1993.1040
Examples
>>> from skimage.data import camera >>> image = camera() >>> thresh = threshold_mean(image) >>> binary = image > thresh
Examples using skimage.filters.threshold_mean
¶
threshold_minimum¶
-
skimage.filters.
threshold_minimum
(image, nbins=256, max_iter=10000)[source]¶ Return threshold value based on minimum method.
The histogram of the input
image
is computed and smoothed until there are only two maxima. Then the minimum in between is the threshold value.- Parameters
- image(M, N) ndarray
Input image.
- nbinsint, optional
Number of bins used to calculate histogram. This value is ignored for integer arrays.
- max_iter: int, optional
Maximum number of iterations to smooth the histogram.
- Returns
- thresholdfloat
Upper threshold value. All pixels with an intensity higher than this value are assumed to be foreground.
- Raises
- RuntimeError
If unable to find two local maxima in the histogram or if the smoothing takes more than 1e4 iterations.
References
- 1
C. A. Glasbey, “An analysis of histogram-based thresholding algorithms,” CVGIP: Graphical Models and Image Processing, vol. 55, pp. 532-537, 1993.
- 2
Prewitt, JMS & Mendelsohn, ML (1966), “The analysis of cell images”, Annals of the New York Academy of Sciences 128: 1035-1053 DOI:10.1111/j.1749-6632.1965.tb11715.x
Examples
>>> from skimage.data import camera >>> image = camera() >>> thresh = threshold_minimum(image) >>> binary = image > thresh
Examples using skimage.filters.threshold_minimum
¶
threshold_multiotsu¶
-
skimage.filters.
threshold_multiotsu
(image, classes=3, nbins=256)[source]¶ Generate classes-1 threshold values to divide gray levels in image.
The threshold values are chosen to maximize the total sum of pairwise variances between the thresholded graylevel classes. See Notes and [1] for more details.
- Parameters
- image(N, M) ndarray
Grayscale input image.
- classesint, optional
Number of classes to be thresholded, i.e. the number of resulting regions.
- nbinsint, optional
Number of bins used to calculate the histogram. This value is ignored for integer arrays.
- Returns
- thresharray
Array containing the threshold values for the desired classes.
- Raises
- ValueError
If
image
contains less grayscale value then the desired number of classes.
Notes
This implementation relies on a Cython function whose complexity is \(O\left(\frac{Ch^{C-1}}{(C-1)!}\right)\), where \(h\) is the number of histogram bins and \(C\) is the number of classes desired.
The input image must be grayscale.
References
- 1
Liao, P-S., Chen, T-S. and Chung, P-C., “A fast algorithm for multilevel thresholding”, Journal of Information Science and Engineering 17 (5): 713-727, 2001. Available at: <http://ftp.iis.sinica.edu.tw/JISE/2001/200109_01.pdf> DOI:10.6688/JISE.2<IP_ADDRESS>
- 2
Tosa, Y., “Multi-Otsu Threshold”, a java plugin for ImageJ. Available at: <http://imagej.net/plugins/download/Multi_OtsuThreshold.java>
Examples
>>> from skimage.color import label2rgb >>> from skimage import data >>> image = data.camera() >>> thresholds = threshold_multiotsu(image) >>> regions = np.digitize(image, bins=thresholds) >>> regions_colorized = label2rgb(regions)
Examples using skimage.filters.threshold_multiotsu
¶
threshold_niblack¶
-
skimage.filters.
threshold_niblack
(image, window_size=15, k=0.2)[source]¶ Applies Niblack local threshold to an array.
A threshold T is calculated for every pixel in the image using the following formula:
T = m(x,y) - k * s(x,y)
where m(x,y) and s(x,y) are the mean and standard deviation of pixel (x,y) neighborhood defined by a rectangular window with size w times w centered around the pixel. k is a configurable parameter that weights the effect of standard deviation.
- Parameters
- image: ndarray
Input image.
- window_sizeint, or iterable of int, optional
Window size specified as a single odd integer (3, 5, 7, …), or an iterable of length
image.ndim
containing only odd integers (e.g.(1, 5, 5)
).- kfloat, optional
Value of parameter k in threshold formula.
- Returns
- threshold(N, M) ndarray
Threshold mask. All pixels with an intensity higher than this value are assumed to be foreground.
Notes
This algorithm is originally designed for text recognition.
The Bradley threshold is a particular case of the Niblack one, being equivalent to
>>> from skimage import data >>> image = data.page() >>> q = 1 >>> threshold_image = threshold_niblack(image, k=0) * q
for some value
q
. By default, Bradley and Roth useq=1
.References
- 1
W. Niblack, An introduction to Digital Image Processing, Prentice-Hall, 1986.
- 2
D. Bradley and G. Roth, “Adaptive thresholding using Integral Image”, Journal of Graphics Tools 12(2), pp. 13-21, 2007. DOI:10.1080/2151237X.2007.10129236
Examples
>>> from skimage import data >>> image = data.page() >>> threshold_image = threshold_niblack(image, window_size=7, k=0.1)
Examples using skimage.filters.threshold_niblack
¶
threshold_otsu¶
-
skimage.filters.
threshold_otsu
(image, nbins=256)[source]¶ Return threshold value based on Otsu’s method.
- Parameters
- image(N, M) ndarray
Grayscale input image.
- nbinsint, optional
Number of bins used to calculate histogram. This value is ignored for integer arrays.
- Returns
- thresholdfloat
Upper threshold value. All pixels with an intensity higher than this value are assumed to be foreground.
- Raises
- ValueError
If
image
only contains a single grayscale value.
Notes
The input image must be grayscale.
References
- 1
Wikipedia, https://en.wikipedia.org/wiki/Otsu’s_Method
Examples
>>> from skimage.data import camera >>> image = camera() >>> thresh = threshold_otsu(image) >>> binary = image <= thresh
threshold_sauvola¶
-
skimage.filters.
threshold_sauvola
(image, window_size=15, k=0.2, r=None)[source]¶ Applies Sauvola local threshold to an array. Sauvola is a modification of Niblack technique.
In the original method a threshold T is calculated for every pixel in the image using the following formula:
T = m(x,y) * (1 + k * ((s(x,y) / R) - 1))
where m(x,y) and s(x,y) are the mean and standard deviation of pixel (x,y) neighborhood defined by a rectangular window with size w times w centered around the pixel. k is a configurable parameter that weights the effect of standard deviation. R is the maximum standard deviation of a greyscale image.
- Parameters
- image: ndarray
Input image.
- window_sizeint, or iterable of int, optional
Window size specified as a single odd integer (3, 5, 7, …), or an iterable of length
image.ndim
containing only odd integers (e.g.(1, 5, 5)
).- kfloat, optional
Value of the positive parameter k.
- rfloat, optional
Value of R, the dynamic range of standard deviation. If None, set to the half of the image dtype range.
- Returns
- threshold(N, M) ndarray
Threshold mask. All pixels with an intensity higher than this value are assumed to be foreground.
Notes
This algorithm is originally designed for text recognition.
References
- 1
J. Sauvola and M. Pietikainen, “Adaptive document image binarization,” Pattern Recognition 33(2), pp. 225-236, 2000. DOI:10.1016/S0031-3203(99)00055-2
Examples
>>> from skimage import data >>> image = data.page() >>> t_sauvola = threshold_sauvola(image, window_size=15, k=0.2) >>> binary_image = image > t_sauvola
Examples using skimage.filters.threshold_sauvola
¶
threshold_triangle¶
-
skimage.filters.
threshold_triangle
(image, nbins=256)[source]¶ Return threshold value based on the triangle algorithm.
- Parameters
- image(N, M[, …, P]) ndarray
Grayscale input image.
- nbinsint, optional
Number of bins used to calculate histogram. This value is ignored for integer arrays.
- Returns
- thresholdfloat
Upper threshold value. All pixels with an intensity higher than this value are assumed to be foreground.
References
- 1
Zack, G. W., Rogers, W. E. and Latt, S. A., 1977, Automatic Measurement of Sister Chromatid Exchange Frequency, Journal of Histochemistry and Cytochemistry 25 (7), pp. 741-753 DOI:10.1177/25.7.70454
- 2
ImageJ AutoThresholder code, http://fiji.sc/wiki/index.php/Auto_Threshold
Examples
>>> from skimage.data import camera >>> image = camera() >>> thresh = threshold_triangle(image) >>> binary = image > thresh
threshold_yen¶
-
skimage.filters.
threshold_yen
(image, nbins=256)[source]¶ Return threshold value based on Yen’s method.
- Parameters
- image(N, M) ndarray
Input image.
- nbinsint, optional
Number of bins used to calculate histogram. This value is ignored for integer arrays.
- Returns
- thresholdfloat
Upper threshold value. All pixels with an intensity higher than this value are assumed to be foreground.
References
- 1
Yen J.C., Chang F.J., and Chang S. (1995) “A New Criterion for Automatic Multilevel Thresholding” IEEE Trans. on Image Processing, 4(3): 370-378. DOI:10.1109/83.366472
- 2
Sezgin M. and Sankur B. (2004) “Survey over Image Thresholding Techniques and Quantitative Performance Evaluation” Journal of Electronic Imaging, 13(1): 146-165, DOI:10.1117/1.1631315 http://www.busim.ee.boun.edu.tr/~sankur/SankurFolder/Threshold_survey.pdf
- 3
ImageJ AutoThresholder code, http://fiji.sc/wiki/index.php/Auto_Threshold
Examples
>>> from skimage.data import camera >>> image = camera() >>> thresh = threshold_yen(image) >>> binary = image <= thresh
try_all_threshold¶
-
skimage.filters.
try_all_threshold
(image, figsize=(8, 5), verbose=True)[source]¶ Returns a figure comparing the outputs of different thresholding methods.
- Parameters
- image(N, M) ndarray
Input image.
- figsizetuple, optional
Figure size (in inches).
- verbosebool, optional
Print function name for each method.
- Returns
- fig, axtuple
Matplotlib figure and axes.
Notes
The following algorithms are used:
isodata
li
mean
minimum
otsu
triangle
yen
Examples
>>> from skimage.data import text >>> fig, ax = try_all_threshold(text(), figsize=(10, 6), verbose=False)
unsharp_mask¶
-
skimage.filters.
unsharp_mask
(image, radius=1.0, amount=1.0, multichannel=False, preserve_range=False)[source]¶ Unsharp masking filter.
The sharp details are identified as the difference between the original image and its blurred version. These details are then scaled, and added back to the original image.
- Parameters
- image[P, …, ]M[, N][, C] ndarray
Input image.
- radiusscalar or sequence of scalars, optional
If a scalar is given, then its value is used for all dimensions. If sequence is given, then there must be exactly one radius for each dimension except the last dimension for multichannel images. Note that 0 radius means no blurring, and negative values are not allowed.
- amountscalar, optional
The details will be amplified with this factor. The factor could be 0 or negative. Typically, it is a small positive number, e.g. 1.0.
- multichannelbool, optional
If True, the last
image
dimension is considered as a color channel, otherwise as spatial. Color channels are processed individually.- preserve_range: bool, optional
Whether to keep the original range of values. Otherwise, the input image is converted according to the conventions of
img_as_float
. Also see https://scikit-image.org/docs/dev/user_guide/data_types.html
- Returns
- output[P, …, ]M[, N][, C] ndarray of float
Image with unsharp mask applied.
Notes
Unsharp masking is an image sharpening technique. It is a linear image operation, and numerically stable, unlike deconvolution which is an ill-posed problem. Because of this stability, it is often preferred over deconvolution.
The main idea is as follows: sharp details are identified as the difference between the original image and its blurred version. These details are added back to the original image after a scaling step:
enhanced image = original + amount * (original - blurred)
When applying this filter to several color layers independently, color bleeding may occur. More visually pleasing result can be achieved by processing only the brightness/lightness/intensity channel in a suitable color space such as HSV, HSL, YUV, or YCbCr.
Unsharp masking is described in most introductory digital image processing books. This implementation is based on [1].
References
- 1
Maria Petrou, Costas Petrou “Image Processing: The Fundamentals”, (2010), ed ii., page 357, ISBN 13:<PHONE_NUMBER>398 DOI:10.1002/9781119994398
- 2
Wikipedia. Unsharp masking https://en.wikipedia.org/wiki/Unsharp_masking
Examples
>>> array = np.ones(shape=(5,5), dtype=np.uint8)*100 >>> array[2,2] = 120 >>> array array([[100, 100, 100, 100, 100], [100, 100, 100, 100, 100], [100, 100, 120, 100, 100], [100, 100, 100, 100, 100], [100, 100, 100, 100, 100]], dtype=uint8) >>> np.around(unsharp_mask(array, radius=0.5, amount=2),2) array([[0.39, 0.39, 0.39, 0.39, 0.39], [0.39, 0.39, 0.38, 0.39, 0.39], [0.39, 0.38, 0.53, 0.38, 0.39], [0.39, 0.39, 0.38, 0.39, 0.39], [0.39, 0.39, 0.39, 0.39, 0.39]])
>>> array = np.ones(shape=(5,5), dtype=np.int8)*100 >>> array[2,2] = 127 >>> np.around(unsharp_mask(array, radius=0.5, amount=2),2) array([[0.79, 0.79, 0.79, 0.79, 0.79], [0.79, 0.78, 0.75, 0.78, 0.79], [0.79, 0.75, 1. , 0.75, 0.79], [0.79, 0.78, 0.75, 0.78, 0.79], [0.79, 0.79, 0.79, 0.79, 0.79]])
>>> np.around(unsharp_mask(array, radius=0.5, amount=2, preserve_range=True), 2) array([[100. , 100. , 99.99, 100. , 100. ], [100. , 99.39, 95.48, 99.39, 100. ], [ 99.99, 95.48, 147.59, 95.48, 99.99], [100. , 99.39, 95.48, 99.39, 100. ], [100. , 100. , 99.99, 100. , 100. ]])
Examples using skimage.filters.unsharp_mask
¶
wiener¶
-
skimage.filters.
wiener
(data, impulse_response=None, filter_params={}, K=0.25, predefined_filter=None)[source]¶ Minimum Mean Square Error (Wiener) inverse filter.
- Parameters
- data(M,N) ndarray
Input data.
- Kfloat or (M,N) ndarray
Ratio between power spectrum of noise and undegraded image.
- impulse_responsecallable f(r, c, **filter_params)
Impulse response of the filter. See LPIFilter2D.__init__.
- filter_paramsdict
Additional keyword parameters to the impulse_response function.
- Other Parameters
- predefined_filterLPIFilter2D
If you need to apply the same filter multiple times over different images, construct the LPIFilter2D and specify it here.
LPIFilter2D
¶
-
class
skimage.filters.
LPIFilter2D
(impulse_response, **filter_params)[source]¶ Bases:
object
Linear Position-Invariant Filter (2-dimensional)
-
__init__
(self, impulse_response, **filter_params)[source]¶ - Parameters
- impulse_responsecallable f(r, c, **filter_params)
Function that yields the impulse response.
r
andc
are 1-dimensional vectors that represent row and column positions, in other words coordinates are (r[0],c[0]),(r[0],c[1]) etc. **filter_params are passed through.In other words,
impulse_response
would be called like this:>>> def impulse_response(r, c, **filter_params): ... pass >>> >>> r = [0,0,0,1,1,1,2,2,2] >>> c = [0,1,2,0,1,2,0,1,2] >>> filter_params = {'kw1': 1, 'kw2': 2, 'kw3': 3} >>> impulse_response(r, c, **filter_params)
Examples
Gaussian filter: Use a 1-D gaussian in each direction without normalization coefficients.
>>> def filt_func(r, c, sigma = 1): ... return np.exp(-np.hypot(r, c)/sigma) >>> filter = LPIFilter2D(filt_func)
-


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(These payment handlers have bugs in their manifests.)
(These payment handlers redirect to dangerous content.)
(This payment handler shows insecure content in the window.)


We are a revolutionary platform that
allows consumers to buy fresh and high quality produce from local farmers, which reduces the environmental damage.
What Is Our Daily Tip?
Eat at least 7 dozes of vegetables and fruits per day !
Who are our farmers ?
Pioneers,who are devoded to hard work,excellence,and doing what's best for you !


Time Series Lab
Plot Time Series.
Load data
×
Load data
Load from disk
Select CSV file
Load from URL
Load
Example Time Series
Small dataset
Electricity demand
Rainfall
Anomalies 60K
Also check
Numenta NAB
for some interesting datasets.
Show range from
to
Zoom In
Zoom Out
Previous
Next
Group by
Go
Metadata
Number of points
Start date
End date
Time resolution
Missing points


This example fetches the collections of your root Balloon directory. It uses the Balloon.collection.getChildren() method
Balloon.collection.getChildren()


--- layout: page title: Research permalink: /research/research_areas_index.html --- Projects | Research Areas


Version Control as a Development Tool
Jan 23 2016
What are the benefits of version control?
Version control is a way to track your progress while you are editing files. If you are editing multiple files, version control will track changes across all your files at once and in one place. You will keep a running history of your changes over time, and will be able to jump backward to view your project at any point in history. Also, if you have a team collaborating on a single project, version control lets each team member work on the same files simultaneously. When they finish and send their updates to the rest of the team, everyone else will be able to view their progress and understand their work.
How does git help you keep track of changes?
Git is one particular type of version control software. It structures your project into a master version, with other versions branching off. When you embark on a new venture in your project, such as adding a new feature, you can create a new branch in which to do the work. All your progress will be contained within that branch without affecting the master version fo your project. When your module has been finished, you can merge it back into the master and your new features will be included. This lets you work on several features at once without having to keep track of them together. If you have an entire team working together, everyone can work on their own features independently and merge them together to make the final version.
Why use GitHub to store your code?
GitHub is an online file repository that lets you store your code publicly, rather then on your local machine. This lets teams work from a single source across multiple computers and locations. GitHub also is fully integrated with git, so it can easily handle commits from multiple users at once and encourages team reviews by requiring someone to review each commit before merging it into the master version. GitHub also lets you publish your code generally and solicit feedback and advise from the greater GitHub community.


--- layout: post title: Command Line Basics date: '2012-07-05T14:21:12-05:00' tags: - railsgirls - command line - terminal - videos permalink: /post/26569696837/command-line-basics ---
This past weekend I was fortunate to attend Rails Girls in SF, and help introduce Rails and the associated tech to around 40 women. After answering a number of common questions many times about the command line, I decided to take the opportunity to make some videos explaining some basic concepts.
If you’re just getting started this is going to be a great resource. I cover navigating directory structure with cd
(change directory), listing files with ls
, printing out the working directory path with pwd
, and add some helpful observations and pitfalls you’ll likely to encounter. Enjoy!
This next video covers opening and editing files using the command line and a text editor. This is a critical skill when you start out programming.
After you’ve been programming for awhile it is easy to forget all of the skills needed even to get started. For many of us we use the command line without thinking, much like we don’t need to think to breathing. If you know someone that is just beginning, point them this way. Maybe after running through these videos they can get started on my Rails Course


@(channel: String, protocol: String, endpoint: String)(implicit request: RequestHeader) @main("Powered by ActiveMQ Queue and Redis", channel, protocol, endpoint) { @if(protocol == "comet"){ } }


{% extends "base.html" %} {% block body %}
The contest consists of multiple games, with participants scoring the most points, moving on to the next round.
A game consists of 10-20 questions, with a specified time limit either for the whole game or for each question. For each correct answer you get 1 point. The answer must consist of two parts:
An answer is considered correct if there is a web page containing your answer, and you provided its URL.
An answer is considered incorrect if:
The answer correctness will be checked by multiple human judges, with final arbitration reserved by the contest organizers.


| Number of Classes | Line Coverage | Mutation Coverage |
|---|---|---|
| 11 | 92% | 63% |
| Name | Line Coverage | Mutation Coverage |
|---|---|---|
| AttachImageBuilder.java | 89% |
55% |
| AttachmentHandlingBuilder.java | 93% |
59% |
| AutofillEmailBuilder.java | 100% |
81% |
| AutofillSubjectBuilder.java | 88% |
67% |
| Base64InliningBuilder.java | 100% |
20% |
| CidBuilder.java | 100% |
77% |
| CssHandlingBuilder.java | 89% |
55% |
| CssInliningBuilder.java | 82% |
56% |
| EmailBuilder.java | 95% |
69% |
| ImageHandlingBuilder.java | 89% |
55% |
| ImageInliningBuilder.java | 85% |
52% |


validus¶
- A dead simple Python data validation library. :target: https://validus.readthedocs.io/en/latest/?badge=latest
alt: Documentation Status It supports Python 3 only.
>>> import validus
>>><EMAIL_ADDRESS>True
Resources¶
Credits¶
This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.