2 and you'll need to stay with OpenCV 2. 0, then you could also use libfacerec:
This is the project, that got merged into OpenCV. I made sure it works with OpenCV 2. 0 and it'll leave you with exactely the same interface as the OpenCV 2. 2 version. So once you feel like updating to OpenCV 2. 2, you'll only switch the includes. answered Jul 23 '12 at 17:18
bytefish bytefish 3, 617 1 gold badge 26 silver badges 32 bronze badges
I got the same OpenCv error, I try all help that I find here, and it still gives me an exception (exception happend on. Predict() statement). Problem was in the size of images. Size of an Images must be less then 100px (<100px) (not sure if exactly less then 100, maybe 100 would still work). I change my pictures size of 150:150 to 80:80 and its working! Hope I help someone, because this was annoying error. answered Jun 12 '14 at 18:42
I answered this question on another post but I want to make sure people searching for help with this error are sure to find the answer.
Fisher globe valve cv table
0. As the developer I admit the confusion is my fault: I didn't thoroughly check the input data passed to the training method back then, so people passing wrongly aligned data got error messages like yours. Most likely the error you see happens, because your training images don't have equal size. This is necessary for the Eigenfaces and Fisherfaces algorithm (not for the Local Binary Patterns Histograms). OpenCV 2. 0 just tries to reshape the data to a matrix and blows up with the error message you see; OpenCV 2. 2 instead checks (before training) if the input data is correctly aligned and throws a meaningful exception... with a very clear message. This post assumes it could also be due to linking the OpenCV libraries:
Getting OpenCV Error "Image step is wrong" in () method
If it's not linking the libraries it might be due to the image size. Resizing your training images, can easily be done OpenCV with cv::resize:
But you probably should consider to switch to OpenCV 2. 2, where all this is added:
This version also comes with an extensive documentation at:
However if you can't change to OpenCV 2.
fisher_exact ( table, alternative='two-sided') [source] ΒΆ
Performs a Fisher exact test on a 2x2 contingency table. Parameters: table: array_like of ints
A 2x2 contingency table. Elements should be non-negative integers. alternative: {'two-sided', 'less', 'greater'}, optional
Which alternative hypothesis to the null hypothesis the test uses. Default is 'two-sided'. Returns: oddsratio: float
This is prior odds ratio and not a posterior estimate. p_value: float
P-value, the probability of obtaining a distribution at least as
extreme as the one that was actually observed, assuming that the
null hypothesis is true. See also
chi2_contingency
Chi-square test of independence of variables in a contingency table. Notes
The calculated odds ratio is different from the one R uses. In R language,
this implementation returns the (more common) "unconditional Maximum
Likelihood Estimate", while R uses the "conditional Maximum Likelihood
Estimate". For tables with large numbers the (inexact) chi-square test implemented
in the function chi2_contingency can also be used.
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It seems unwise to do Fisher's exact test with ordinal data - you throw away so much of the information. It's possible to do exact tests by using some statistic that incorporates ordinal information. Expected cell counts less than 5 shouldn't be a problem - expecteds that are fairly similar can go down a fair bit smaller and still get an an adequate chi-square approximation (or you could simulate the distribution, or even do an exact test based on the statistic) -- but you shouldn't do a Pearson chi-square either because it also ignores the information in the ordering. A useful thing to do is choose some reasonable statistic that measures the anticipated kinds of relationship between group and the ordered categories if the alternative is true (there are a number of good ways of measuring such dependence) and build a test one that, either one based on simulating from the tables under the null (possibly with conditioning on some of the sample information), or by enumeration of the tables in the tail.